Active learning (machine learning)

Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called "teacher" or "oracle".
There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, incremental learning policies in the field of online machine learning.
Large-scale active learning projects may benefit from crowdsourcing frameworks such as Amazon Mechanical Turk that include many humans in the active learning loop.
Definitions.
Let be the total set of all data under consideration. For example, in a protein engineering problem, would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity.
During each iteration, is broken up into three subsets
Most of the current research in active learning involves the best method to choose the data points for .
Query strategies.
Algorithms for determining which data points should be labeled can be organized into a number of different categories, based upon their purpose:
A wide variety of algorithms have been studied that fall into these categories.
Minimum marginal hyperplane.
Some active learning algorithms are built upon support-vector machines (SVMs) and exploit the structure of the SVM to determine which data points to label. Such methods usually calculate the margin, of each unlabeled datum in and treat as an -dimensional distance from that datum to the separating hyperplane.
Minimum Marginal Hyperplane methods assume that the data with the smallest are those that the SVM is most uncertain about and therefore should be placed in to be labeled. Other similar methods, such as Maximum Marginal Hyperplane, choose data with the largest . Tradeoff methods choose a mix of the smallest and largest s.


Fractional job scheduling

Fractional job scheduling is a variant of optimal job scheduling in which it is allowed to break jobs into parts and process each part separately on the same or a different machine. Breaking jobs into parts may allow for improving the overall performance, for example, decreasing the makespan. Moreover, the computational problem of finding an optimal schedule may become easier, as some of the optimization variables become continuous. On the other hand, breaking jobs apart might be costly.
Variants.
There are several variants of job scheduling problems in which it is allowed to break jobs apart. They can be broadly classified into preemption and splitting.
Job scheduling with preemption.
Various problems have been studied in job scheduling with preemption. One of them is generalized multiprocessor scheduling (GMS). It has two variants.
In both variants, the goal is to find a schedule that minimizes the makespan subject to the preemption constraints.
For identical machines, Shchepin and Vakhania prove that with at most formula_3 total preemptions, the problem is NP-hard, whereas McNaughton shows a linear-time algorithm with formula_4 preemptions.
For uniform machines, a polynomial algorithm by Gonzalez and Sahni yields at most formula_5 preemptions. Shachnai, Tamir, and Woeginger proved NP-hardness for the case where the number of preemption is strictly less than formula_5. They also presented a PTAS for GMS with a global preemption bound, and another PTAS for GMS with job-wise preemption bound when the number of machines is a fixed constant.
Soper and Strusevitch study the special case in which at most one preemption is allowed. They show that makespan minimization is polynomial for two machines.
Many papers study other variants of preemptive scheduling. For example, Liu and Cheng consider single-machine scheduling with job release and delivery dates, where there is no firm bound on the number of preemptions, but each preemption requires spending time on "job setup".
Some works like Blazewicz "et al." or Deng "et al." study preemptive scheduling for jobs with parallelism where jobs must be processed simultaneously on several processors.
Job scheduling with splitting.
Various objectives have been studied. There are many variants including different setup times. In machine scheduling, the "setup time" refers to the time required to prepare a machine for a specific job or task. Sequence-dependent setup time is a situation where the setup time required for a job depends on the job that came before it, rather than being constant for all jobs (independent job setup time).
Serafini assumes unbounded splittings and preemptions and gives polynomial-time algorithms that minimize the maximum tardiness and the maximum weighted tardiness, for uniform and unrelated machines.
Xing and Zhang allow unbounded splittings, and give polynomial algorithms for many optimality criteria (such as makespan, lateness, tardiness, and more), with identical, uniform, and unrelated machines. For the case with independent job setup time, they give a formula_7 approximation algorithm.
Son et al. study makespan minimization on a single machine with a machine-availability constraint with a lower bound on the length of each part of a job that is split.
For identical machines, Shim and Kim suggest a branch and bound algorithm with the objective to minimize total tardiness with independent job setup time. Yalaoui and Chu propose a heuristic to the same problem, with the objective to minimize the makespan. Kim et al. suggest a two-phase heuristic algorithm with the objective of minimizing total tardiness. With the objective to minimize the makespan, Kim studies another variant with family setup time in which no setup is required when parts from the same job are produced consecutively. And, Wang et al. include a learning property that improves the processing time of a job according to the learning effect. The learning has to be restarted if one job is split and processed by a different machine.
For uniform machines, Kim and Lee study a variant with dedicated machines (there are some dedicated machines for each job), sequence-dependent setup times, and limited set-up resources (jobs require setup operators that are limited) with the objective to minimize the makespan. Krysta, Sanders, and Vöcking study makespan minimization using the k-splittable variant, a variant where each job is allowed to be split into most formula_8 different machines. They show that this variant and another more general variant where each job has its own splitability parameter, are NP-hard. They give some approximation algorithms, but their main result is a polynomial-time algorithm for both problems, for a fixed number of machines. They show that allowing a bounded number of splittings reduces the complexity of scheduling.
In all these works, there is no global bound on the number of splitting jobs.


Lyapunov function

In the theory of ordinary differential equations (ODEs), Lyapunov functions, named after Aleksandr Lyapunov, are scalar functions that may be used to prove the stability of an equilibrium of an ODE. Lyapunov functions (also called Lyapunov’s second method for stability) are important to stability theory of dynamical systems and control theory. A similar concept appears in the theory of general state space Markov chains, usually under the name Foster–Lyapunov functions.
For certain classes of ODEs, the existence of Lyapunov functions is a necessary and sufficient condition for stability. There is no general technique for constructing Lyapunov functions for ODEs, however, depending on formulation type, a systematic method to construct Lyapunov functions for ordinary differential equations using their most general form in autonomous cases was given by Prof. Cem Civelek. Though, in many specific cases the construction of Lyapunov functions is known. For instance, according to a lot of applied mathematicians, for a dissipative gyroscopic system a Lyapunov function could not be constructed. However, using the method expressed in the publication above, even for such a system a Lyapunov function could be constructed as per related article by C. Civelek and Ö. Cihanbegendi. In addition, quadratic functions suffice for systems with one state; the solution of a particular linear matrix inequality provides Lyapunov functions for linear systems, and conservation laws can often be used to construct Lyapunov functions for physical systems.
Definition.
A Lyapunov function for an autonomous dynamical system
with an equilibrium point at formula_2 is a scalar function formula_3 that is continuous, has continuous first derivatives, is strictly positive for formula_4, and for which the time derivative formula_5 is non positive (these conditions are required on some region containing the origin). The (stronger) condition that formula_6 is strictly positive for formula_4 is sometimes stated as formula_6 is "locally positive definite", or formula_9 is "locally negative definite".
Further discussion of the terms arising in the definition.
Lyapunov functions arise in the study of equilibrium points of dynamical systems. In formula_10 an arbitrary autonomous dynamical system can be written as
for some smooth formula_12
An equilibrium point is a point formula_13 such that formula_14 Given an equilibrium point, formula_15 there always exists a coordinate transformation formula_16 such that:
Thus, in studying equilibrium points, it is sufficient to assume the equilibrium point occurs at formula_18.
By the chain rule, for any function, formula_19 the time derivative of the function evaluated along a solution of the dynamical system is
A function formula_21 is defined to be locally positive-definite function (in the sense of dynamical systems) if both formula_22 and there is a neighborhood of the origin, formula_23, such that:
Basic Lyapunov theorems for autonomous systems.
Let formula_25 be an equilibrium of the autonomous system
and use the notation formula_27 to denote the time derivative of the Lyapunov-candidate-function formula_28:
Locally asymptotically stable equilibrium.
If the equilibrium is isolated, the Lyapunov-candidate-function formula_28 is locally positive definite, and the time derivative of the Lyapunov-candidate-function is locally negative definite:
for some neighborhood formula_23 of origin then the equilibrium is proven to be locally asymptotically stable.
Stable equilibrium.
If formula_28 is a Lyapunov function, then the equilibrium is Lyapunov stable. The converse is also true, and was proved by J. L. Massera.
Globally asymptotically stable equilibrium.
If the Lyapunov-candidate-function formula_28 is globally positive definite, radially unbounded, the equilibrium isolated and the time derivative of the Lyapunov-candidate-function is globally negative definite:
then the equilibrium is proven to be globally asymptotically stable.
The Lyapunov-candidate function formula_36 is radially unbounded if
Example.
Consider the following differential equation on formula_38:
Considering that formula_40 is always positive around the origin it is a natural candidate to be a Lyapunov function to help us study formula_41. So let formula_42 on formula_43. Then,
This correctly shows that the above differential equation, formula_45 is asymptotically stable about the origin. Note that using the same Lyapunov candidate one can show that the equilibrium is also globally asymptotically stable.


Shape coding

Shape coding is a method of design of a control that allows the control's function to be signified by the shape of the control. It was used successfully by Alphonse Chapanis on airplane controls to improve aviation safety.


Injection molding machine

An injection molding machine (also spelled as injection moulding machine in BrE), also known as an injection press, is a machine for manufacturing plastic products by the injection molding process. It consists of two main parts, an "injection unit" and a "clamping unit".
Operation.
Injection molding machine molds can be fastened in either a horizontal or vertical position. Most machines are horizontally oriented, but vertical machines are used in some niche applications such as insert molding, allowing the machine to take advantage of gravity. Some vertical machines also do not require the mold to be fastened. There are many ways to fasten the tools to the platens, the most common are manual clamps (both halves are bolted to the platens); however, hydraulic clamps (chocks are used to hold the tool in place) and magnetic clamps are also used. The magnetic and hydraulic clamps are used where fast tool changes are required.
The person designing the mold chooses whether the mold uses a cold runner system or a hot runner system to carry the plastic and fillers from the injection unit to the cavities.
A cold runner is a simple channel carved into the mold.
The plastic that fills the cold runner cools as the part cools and is then ejected with the part as a sprue.
A hot runner system is more complicated, often using cartridge heaters to keep the plastic in the runners hot as the part cools.
After the part is ejected, the plastic remaining in a hot runner is injected into the next part.
Types of injection molding machines.
Machines are classified primarily by the type of driving systems they use: hydraulic, mechanical, electrical, or hybrid
Hydraulic.
Hydraulic machines have historically been the only option available to molders until Nissei Plastic Industrial introduced the first all-electric injection molding machine in 1983. Hydraulic machines, although not nearly as precise, are the predominant type in most of the world, with the exception of Japan.
Mechanical.
Mechanical type machines use the toggle system for building up tonnage on the clamps of the machine. Tonnage is required on all machines so that the clamps of the machine do not open due to the injection pressure. If the mold partially opens up, it will create flashing in the plastic product.
Electric.
The electric press, also known as Electric Machine Technology (EMT), reduces operation costs by cutting energy consumption and also addresses some of the environmental concerns surrounding the hydraulic press. Electric presses have been shown to be quieter, faster, and have a higher accuracy, however the machines are more expensive.
Hybrid injection (sometimes referred to as "Servo-Hydraulic") molding machines claim to take advantage of the best features of both hydraulic and electric systems, but in actuality use almost the same amount of electricity to operate as an electric injection molding machine depending on the manufacturer.
A robotic arm is often used to remove the molded components; either by side or top entry, but it is more common for parts to drop out of the mold, through a chute and into a container.
Main components of injection molding machine.
Injection unit.
Consists of three main components:
Clamping unit.
Consists of three main components:


Tech Safe Systems

Tech Safe Systems was a Norfolk-based company that are specialists in the design, engineering and manufacturing of launch and recovery systems (LARS), control cabins, workshops for ROVs, and electric and hydraulic winches, most commonly in the deep water industries. It was acquired by Outreach Ltd in 2014.
History.
Tech Safe began in 1996 when a gap in the market was identified to provide a complete package of LARS, winches, cabins and workshops, integrated and tested with the customer’s ROV as a complete system. This ensured all the equipment worked together prior to going offshore. Outreach Ltd acquired Tech Safe Systems on 31 July 2014.
Sectors.
Tech Safe Systems operated both nationally and, for some of its products, internationally.
Products.
Tech Safe Systems operated across the United Kingdom and, for many of its products, across the world and are specialists in the design, engineering and manufacturing of:
most commonly in the deep water industries. These products begin life with Autodesk Product Design Suite.
Projects.
OWF Project in Wikinger offshore wind farm in the Baltic Sea. Tech Safe developed a Lloyd's Register Certified solution for large diameter drilling (LDD). This included a package of three winches and two control cabins to facilitate the command and control package for their pre-piling template and bubble curtain system.


Die (manufacturing)

A die is a specialized machine tool used in manufacturing industries to cut and/or form material to a desired shape or profile. Stamping dies are used with a press, as opposed to drawing dies (used in the manufacture of wire) and casting dies (used in molding) which are not. Like molds, dies are generally customized to the item they are used to create.
Products made with dies range from simple paper clips to complex pieces used in advanced technology. Continuous-feed laser cutting may displace the analogous die-based process in the automotive industry, among others.
Die stamping.
Blanking and piercing are two die cutting operations, and bending is an example of a die forming operation.
Die forming.
Forming operations work by deforming materials like sheet metal or plastic using force (compression, tension, or both) and rely on the material's mechanical properties. Forming dies are typically made by tool and die makers and put into production after mounting into a press.
Differences between materials.
For the vacuum forming of plastic sheet only a single form is used, typically to form transparent plastic containers (called blister packs) for merchandise. Vacuum forming is considered a simple molding thermoforming process but uses the same principles as die forming.
For the forming of sheet metal, such as automobile body parts, two parts may be used: one, called the "punch", performs the stretching, bending, and/or blanking operation, while another part that is called the "die block" securely clamps the workpiece and provides similar stretching, bending, and/or blanking operation. The workpiece may pass through several stages using different tools or operations to obtain the final form. In the case of an automotive component, there will usually be a shearing operation after the main forming is done. Additional crimping or rolling operations may be performed to ensure that all sharp edges are hidden and/or to add rigidity to the panel.
Die components.
The main components of a die set (including press mounting) are as follows. Note that because nomenclature varies between sources, alternate names are in parenthesis:
Steel-rule die.
"Steel-rule" die, also known as "cookie cutter" dies, are used for cutting sheet metal and softer materials, such as plastics, wood, cork, felt, fabrics, and paperboard. The cutting surface of the die is the edge of hardened steel strips, known as "steel rule". These steel rules are usually located using saw or laser-cut grooves in plywood. The mating die can be a flat piece of hardwood or steel, a male shape that matches the workpiece profile, or it can have a matching groove that allows the rule to nest into. Rubber strips are wedged in with the steel rule to act as the stripper plate; the rubber compresses on the down-stroke and on the up-stroke it pushes the workpiece out of the die. The main advantage of steel-rule dies is the low cost to make them, as compared to solid dies; however, they are not as robust as solid dies, so they are usually only used for short production runs.
Rotary die.
In the broadest sense, a "rotary die" is a cylindrical shaped die that may be used in any manufacturing field. However, it most commonly refers to cylindrical shaped dies used to process soft materials, such as paper or cardboard. Two rules are used, cutting and creasing rules. This is for corrugated boards whose thickness is more than 2 mm. Rotary dies are faster than flat dies.
The term also refers to dies used in the roll forming process.
Wire pulling.
Wire-making dies have a hole through the middle of them. A wire or rod of steel, copper, other metals, or alloy enters into one side and is lubricated and reduced in size. The leading tip of the wire is usually pointed in the process. The tip of the wire is then guided into the die and rolled onto a block on the opposite side. The block provides the power to pull the wire through the die.
The die is divided into several different sections. First is an entrance angle that guides the wire into the die. Next is the approach angle, which brings the wire to the nib, which facilitates the reduction. Next is the bearing and the back relief. Lubrication is added at the entrance angle. The lube can be in powdered soap form. If the lubricant is soap, the friction of the drawing of wire heats the soap to liquid form and coats the wire. The wire should never actually come in contact with the die. A thin coat of lubricant should prevent the metal to metal contact.
For pulling a substantial rod down to a fine wire a series of several dies is used to obtain progressive reduction of diameter in stages.
Standard wire gauges used to refer to the number of dies through which the wire had been pulled. Thus, a higher-numbered wire gauge meant a thinner wire. Typical telephone wires were 22-gauge, while main power cables might be 3- or 4-gauge.


Flatness (systems theory)

Flatness in systems theory is a system property that extends the notion of controllability from linear systems to nonlinear dynamical systems. A system that has the flatness property is called a "flat system". Flat systems have a (fictitious) "flat output", which can be used to explicitly express all states and inputs in terms of the flat output and a finite number of its derivatives.
Definition.
A nonlinear system
formula_1
is flat, if there exists an output
formula_2
that satisfies the following conditions:
If these conditions are satisfied at least locally, then the (possibly fictitious) output is called "flat output", and the system is "flat".
Relation to controllability of linear systems.
A linear system
formula_14
with the same signal dimensions for formula_15 as the nonlinear system is flat, if and only if it is controllable. For linear systems both properties are equivalent, hence exchangeable.
Significance.
The flatness property is useful for both the analysis of and controller synthesis for nonlinear dynamical systems. It is particularly advantageous for solving trajectory planning problems and asymptotical setpoint following control.


Material removal rate

Material removal rate (MRR) is the amount of material removed per time unit (usually per minute) when performing machining operations such as using a lathe or milling machine. The more material removed per minute, the higher the material removal rate. The MRR is a single number that enables you to do this. It is a direct indicator of how efficiently you are cutting, and how profitable you are. MRR is the volume of material removed per minute. The higher your cutting parameters, the higher the MRR.
Phrased in another way, the MRR is equal to the volume of residue formed as a direct result of the removal from the workpiece per unit of time during a cutting operation.
The material removal rate in a work process can be calculated as the depth of the cut, times the width of the cut, times the feed rate. The material removal rate is typically measured in cubic centimeters per minute (cm3/min).


Kernel methods for vector output

Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate the properties of functions in a computationally efficient way and allow algorithms to easily swap functions of varying complexity.
In typical machine learning algorithms, these functions produce a scalar output. Recent development of kernel methods for functions with vector-valued output is due, at least in part, to interest in simultaneously solving related problems. Kernels which capture the relationship between the problems allow them to "borrow strength" from each other. Algorithms of this type include multi-task learning (also called multi-output learning or vector-valued learning), transfer learning, and co-kriging. Multi-label classification can be interpreted as mapping inputs to (binary) coding vectors with length equal to the number of classes.
In Gaussian processes, kernels are called covariance functions. Multiple-output functions correspond to considering multiple processes. See Bayesian interpretation of regularization for the connection between the two perspectives.
History.
The history of learning vector-valued functions is closely linked to transfer learning- storing knowledge gained while solving one problem and applying it to a different but related problem. The fundamental motivation for transfer learning in the field of machine learning was discussed in a NIPS-95 workshop on “Learning to Learn,” which focused on the need for lifelong machine learning methods that retain and reuse previously learned knowledge. Research on transfer learning has attracted much attention since 1995 in different names: learning to learn, lifelong learning, knowledge transfer, inductive transfer, multitask learning, knowledge consolidation, context-sensitive learning, knowledge-based inductive bias, metalearning, and incremental/cumulative learning. Interest in learning vector-valued functions was particularly sparked by multitask learning, a framework which tries to learn multiple, possibly different tasks simultaneously.
Much of the initial research in multitask learning in the machine learning community was algorithmic in nature, and applied to methods such as neural networks, decision trees and -nearest neighbors in the 1990s. The use of probabilistic models and Gaussian processes was pioneered and largely developed in the context of geostatistics, where prediction over vector-valued output data is known as cokriging. Geostatistical approaches to multivariate modeling are mostly formulated around the linear model of coregionalization (LMC), a generative approach for developing valid covariance functions that has been used for multivariate regression and in statistics for computer emulation of expensive multivariate computer codes. The regularization and kernel theory literature for vector-valued functions followed in the 2000s. While the Bayesian and regularization perspectives were developed independently, they are in fact closely related.
Notation.
In this context, the supervised learning problem is to learn the function formula_1 which best predicts vector-valued outputs formula_2 given inputs (data) formula_3.
In general, each component of (formula_2), could have different input data (formula_10) with different cardinality (formula_11) and even different input spaces (formula_12).
Geostatistics literature calls this case "heterotopic", and uses "isotopic" to indicate that the each component of the output vector has the same set of inputs.
Here, for simplicity in the notation, we assume the number and sample space of the data for each output are the same.
Regularization perspective.
From the regularization perspective, the problem is to learn formula_13 belonging to a reproducing kernel Hilbert space of vector-valued functions (formula_14). This is similar to the scalar case of Tikhonov regularization, with some extra care in the notation.
formula_15It is possible, though non-trivial, to show that a representer theorem also holds for Tikhonov regularization in the vector-valued setting.
Note, the matrix-valued kernel formula_16 can also be defined by a scalar kernel formula_17 on the space formula_18. An isometry exists between the Hilbert spaces associated with these two kernels:
Gaussian process perspective.
The estimator of the vector-valued regularization framework can also be derived from a Bayesian viewpoint using Gaussian process methods in the case of a finite dimensional Reproducing kernel Hilbert space. The derivation is similar to the scalar-valued case Bayesian interpretation of regularization. The vector-valued function formula_20, consisting of formula_21 outputs formula_22, is assumed to follow a Gaussian process:
where formula_24 is now a vector of the mean functions formula_25 for the outputs and formula_26 is a positive definite matrix-valued function with entry formula_27 corresponding to the covariance between the outputs formula_28 and formula_29.
For a set of inputs formula_30, the prior distribution over the vector formula_31 is given by formula_32, where formula_33 is a vector that concatenates the mean vectors associated to the outputs and formula_34 is a block-partitioned matrix. The distribution of the outputs is taken to be Gaussian:
where formula_36 is a diagonal matrix with elements formula_37 specifying the noise for each output. Using this form for the likelihood, the predictive distribution for a new vector formula_38 is:
where formula_40 is the training data, and formula_41 is a set of hyperparameters for formula_42 and formula_43.
Equations for formula_44 and formula_45 can then be obtained:
where formula_48 has entries formula_49 for formula_50 and formula_51. Note that the predictor formula_52 is identical to the predictor derived in the regularization framework. For non-Gaussian likelihoods different methods such as Laplace approximation and variational methods are needed to approximate the estimators.
Example kernels.
Separable.
A simple, but broadly applicable, class of multi-output kernels can be separated into the product of a kernel on the input-space and a kernel representing the correlations among the outputs:
In matrix form: formula_58
where formula_59 is a formula_60 symmetric and positive semi-definite matrix. Note, setting formula_59 to the identity matrix treats the outputs as unrelated and is equivalent to solving the scalar-output problems separately.
For a slightly more general form, adding several of these kernels yields sum of separable kernels (SoS kernels).
From regularization literature.
Derived from regularizer.
One way of obtaining formula_56 is to specify a regularizer which limits the complexity of formula_1 in a desirable way, and then derive the corresponding kernel. For certain regularizers, this kernel will turn out to be separable.
"Mixed-effect regularizer"
where:
where formula_69 matrix with all entries equal to 1.
This regularizer is a combination of limiting the complexity of each component of the estimator (formula_70) and forcing each component of the estimator to be close to the mean of all the components. Setting formula_71 treats all the components as independent and is the same as solving the scalar problems separately. Setting formula_72 assumes all the components are explained by the same function.
"Cluster-based regularizer"
where:
where formula_85
This regularizer divides the components into formula_86 clusters and forces the components in each cluster to be similar.
"Graph regularizer"
where formula_88 matrix of weights encoding the similarities between the components
where formula_90,   formula_91
Note, formula_92 is the graph laplacian. See also: graph kernel.
Learned from data.
Several approaches to learning formula_59 from data have been proposed. These include: performing a preliminary inference step to estimate formula_59 from the training data, a proposal to learn formula_59 and formula_96 together based on the cluster regularizer, and sparsity-based approaches which assume only a few of the features are needed.
From Bayesian literature.
Linear model of coregionalization (LMC).
In LMC, outputs are expressed as linear combinations of independent random functions such that the resulting covariance function (over all inputs and outputs) is a valid positive semidefinite function. Assuming formula_21 outputs formula_98 with formula_99, each formula_100 is expressed as:
where formula_102 are scalar coefficients and the independent functions formula_103 have zero mean and covariance covformula_104 if formula_105 and 0 otherwise. The cross covariance between any two functions formula_28 and formula_107 can then be written as:
where the functions formula_109, with formula_110 and formula_111 have zero mean and covariance covformula_112 if formula_113 and formula_105. But formula_115 is given by formula_27. Thus the kernel formula_42 can now be expressed as
where each formula_119 is known as a coregionalization matrix. Therefore, the kernel derived from LMC is a sum of the products of two covariance functions, one that models the dependence between the outputs, independently of the input vector formula_120 (the coregionalization matrix formula_121), and one that models the input dependence, independently of formula_98(the covariance function formula_123).
Intrinsic coregionalization model (ICM).
The ICM is a simplified version of the LMC, with formula_124. ICM assumes that the elements formula_125 of the coregionalization matrix formula_126 can be written as formula_127, for some suitable coefficients formula_128. With this form for formula_125:
where
In this case, the coefficients
and the kernel matrix for multiple outputs becomes formula_133. ICM is much more restrictive than the LMC since it assumes that each basic covariance formula_134 contributes equally to the construction of the autocovariances and cross covariances for the outputs. However, the computations required for the inference are greatly simplified.
Semiparametric latent factor model (SLFM).
Another simplified version of the LMC is the semiparametric latent factor model (SLFM), which corresponds to setting formula_135 (instead of formula_136 as in ICM). Thus each latent function formula_137 has its own covariance.
Non-separable.
While simple, the structure of separable kernels can be too limiting for some problems.
Notable examples of non-separable kernels in the regularization literature include:
In the Bayesian perspective, LMC produces a separable kernel because the output functions evaluated at a point formula_120 only depend on the values of the latent functions at formula_120. A non-trivial way to mix the latent functions is by convolving a base process with a smoothing kernel. If the base process is a Gaussian process, the convolved process is Gaussian as well. We can therefore exploit convolutions to construct covariance functions. This method of producing non-separable kernels is known as process convolution. Process convolutions were introduced for multiple outputs in the machine learning community as "dependent Gaussian processes".
Implementation.
When implementing an algorithm using any of the kernels above, practical considerations of tuning the parameters and ensuring reasonable computation time must be considered.
Regularization perspective.
Approached from the regularization perspective, parameter tuning is similar to the scalar-valued case and can generally be accomplished with cross validation. Solving the required linear system is typically expensive in memory and time. If the kernel is separable, a coordinate transform can convert formula_140 to a block-diagonal matrix, greatly reducing the computational burden by solving D independent subproblems (plus the eigendecomposition of formula_59). In particular, for a least squares loss function (Tikhonov regularization), there exists a closed form solution for formula_142:
Bayesian perspective.
There are many works related to parameter estimation for Gaussian processes. Some methods such as maximization of the marginal likelihood (also known as evidence approximation, type II maximum likelihood, empirical Bayes), and least squares give point estimates of the parameter vector formula_41. There are also works employing a full Bayesian inference by assigning priors to formula_41 and computing the posterior distribution through a sampling procedure. For non-Gaussian likelihoods, there is no closed form solution for the posterior distribution or for the marginal likelihood. However, the marginal likelihood can be approximated under a Laplace, variational Bayes or expectation propagation (EP) approximation frameworks for multiple output classification and used to find estimates for the hyperparameters.
The main computational problem in the Bayesian viewpoint is the same as the one appearing in regularization theory of inverting the matrix
This step is necessary for computing the marginal likelihood and the predictive distribution. For most proposed approximation methods to reduce computation, the computational efficiency gained is independent of the particular method employed (e.g. LMC, process convolution) used to compute the multi-output covariance matrix. A summary of different methods for reducing computational complexity in multi-output Gaussian processes is presented in.


Kernel adaptive filter

In signal processing, a kernel adaptive filter is a type of nonlinear adaptive filter. An adaptive filter is a filter that adapts its transfer function to changes in signal properties over time by minimizing an error or loss function that characterizes how far the filter deviates from ideal behavior. The adaptation process is based on learning from a sequence of signal samples and is thus an online algorithm. A nonlinear adaptive filter is one in which the transfer function is nonlinear.
Kernel adaptive filters implement a nonlinear transfer function using kernel methods. In these methods, the signal is mapped to a high-dimensional linear feature space and a nonlinear function is approximated as a sum over kernels, whose domain is the feature space. If this is done in a reproducing kernel Hilbert space, a kernel method can be a universal approximator for a nonlinear function. Kernel methods have the advantage of having convex loss functions, with no local minima, and of being only moderately complex to implement.
Because high-dimensional feature space is linear, kernel adaptive filters can be thought of as a generalization of linear adaptive filters. As with linear adaptive filters, there are two general approaches to adapting a filter: the least mean squares filter (LMS) and the recursive least squares filter (RLS).
Self organising kernel adaptive filters that use iteration to achieve convex LMS error minimisation address some of the statistical and practical issues of non-linear models that do not arise in the linear case. Regularisation is particularly important feature for non-linear models and also often used in linear adaptive filters to reduce statistical uncertainties. However because nonlinear filters typically have a much higher potential structural complexity (or higher dimensional feature space) compared to the subspace actually required, regularisation of some kind must deal with the under-determined model. Though some specific forms of parameter regularisation such as prescribed by Vapink's SRM & SVM address the dimensionality problem statistically to some extent, there remain further statistical and practical issues for truly adaptive non-linear filters. Adaptive filters are often used for tracking the behaviour of a time-varying system or systems which cannot be fully modelled from the data and structure available, hence the models may not only need to adapt parameters, but structure too.
Where structural parameters of kernels are derived directly from data being processed (as in the above "Support Vector" approach) there are convenient opportunities for analytically robust methods of self organisation of the kernels available to the filter. The linearised feature space induced by kernels allows linear projection of new samples on to the current structure of the model where novelty in new data can be easily differentiated from noise-born errors which should not result in a change to model structure. Analytical metrics for structure analysis can be used to parsimoniously grow model complexity when required or optimally prune the existing structure when processor resource limits are reached. Structure updates are also relevant when system variation is detected and the long-term memory of the model should be updated as for the Kalman Filter case in linear filters.
Iterative gradient descent that is typically used in adaptive filters has also gained popularity in offline batch-mode support vector based machine learning because of its computational efficiency for large data set processing. Both time series and batch data processing performance is reported to be able to easily handle over 100,000 training examples using as little as 10kB RAM. Data sizes this large are challenging to the original formulations of support vector machines and other kernel methods, which for example relied on constrained optimisation using linear or quadratic programming techniques.


State Threads

The State Threads library is a small application library which provides a foundation for writing fast and highly scalable Internet applications (such as web servers, proxy servers, mail transfer agents, or any network-data-driven application) on Unix-like platforms.
This library combines the simplicity of the multithreaded programming paradigm, in which one thread supports each simultaneous connection, with the performance and scalability of an event-driven state machine architecture. In other words, this library offers a threading API for structuring an Internet application as a state machine.
The State Threads library is a derivative of the Netscape Portable Runtime library (NSPR) and therefore is distributed under the Mozilla Public License (MPL) version 1.1 or the GNU General Public License (GPL) version 2 or later.


Okuma Corporation

 is a machine tool builder based in Ōguchi, Aichi Prefecture, Japan. It has global market share in CNC machine tools such as CNC lathes, machining centers, and turn-mill machining centers. The company also offers FA (factory automation) products and servomotors.
It is listed on the Tokyo Stock Exchange and is a component of the Nikkei 225 stock index.
History.
The company was founded in 1898, as the Okuma Noodle Machine Co., to manufacture and sell noodle-making machines. Eiichi Okuma, the founder of the original company, was working on how to make udon more effectively. He was using lathe to make "sticks", that has an important role in cutting the udon noodle. But the lathes used in those days in Japan were of poor precision. This was one of big reasons which convinced Okuma to start making machine tools. In 1918 Eiichi established Okuma Machinery Works Ltd. and started selling the OS lathe.
Okuma is a machine tool builder with a history of more than 100 years. Lathes were the main product category in the early days of company. The line now includes many CNC machine tools, including lathes, machining centers (mills), multitasking (turn-mill) machines, and grinding machines. Okuma's Double-Column Machining Center has a large market share in Japan.
Technological development.
Most machine tool builders source their CNC controls from partners such as Fanuc, Mitsubishi Electric, Siemens, and Heidenhain. Several builders have developed their own CNC controls over the years (including Mazak, Okuma, Haas, Dalian Kede and others), but Okuma is unusual among machine tool builders for the degree to which it designs and builds all of its own hardware, software, and machine components. This is the company's "Single Source" philosophy.
Okuma's CNC control is called the "OSP" series. It offers closed-loop positioning via its absolute position feedback system. The "OSP" name began as an abbreviation for "Okuma Sampling Pathcontrol".
In an industry that pushes hard for continual technological innovation, Okuma has often been an innovative leader. For example, it has been among the leaders of development for thermal compensation and collision avoidance. Thermal compensation is designing the machine elements and control to minimize the dimensional distortion that results from the heat generated by machining. This is done both by preventing heat buildup (for example, flowing coolant through machine elements formerly not cooled) and by detecting and compensating for temperature rises when they occur (for example, monitoring temperature with a sensor and using the sensor's output signal as input to the control logic). Collision avoidance is designing the machine to predict and prevent interference, for example, having the machine "know" the form and location of all fixturing so that it can foresee a crash and stop its own movement before crashing. Recent innovation includes technology to avoid chatter, both by predicting and preventing it and by early automatic detection and correction (via dynamic changes of speeds and feeds) when it does occur.


Feeder line (manufacturing)

A feeder line is a secondary assembly line which provides parts for use in a primary assembly line. Researchers assert that the traditional level scheduling methodology of assembly line planning is not effective unless feeder lines provide parts to the primary assembly line.


Sum-of-squares optimization

A sum-of-squares optimization program is an optimization problem with a linear cost function and a particular type of constraint on the decision variables. These constraints are of the form that when the decision variables are used as coefficients in certain polynomials, those polynomials should have the polynomial SOS property. When fixing the maximum degree of the polynomials involved, sum-of-squares optimization is also known as the Lasserre hierarchy of relaxations in semidefinite programming.
Sum-of-squares optimization techniques have been applied across a variety of areas, including control theory (in particular, for searching for polynomial Lyapunov functions for dynamical systems described by polynomial vector fields), statistics, finance and machine learning.
Optimization problem.
Given a vector formula_1 and polynomials formula_2 for formula_3, formula_4, a sum-of-squares optimization problem is written as
formula_5
Here "SOS" represents the class of sum-of-squares (SOS) polynomials.
The quantities formula_6 are the decision variables. SOS programs can be converted to semidefinite programs (SDPs) using the duality of the SOS polynomial program and a relaxation for constrained polynomial optimization using positive-semidefinite matrices, see the following section.
Dual problem: constrained polynomial optimization.
Suppose we have an formula_7-variate polynomial formula_8 , and suppose that we would like to minimize this polynomial over a subset formula_9. Suppose furthermore that the constraints on the subset formula_10 can be encoded using formula_11 polynomial equalities of degree at most formula_12, each of the form formula_13 where formula_14 is a polynomial of degree at most formula_12. A natural, though generally non-convex program for this optimization problem is the following:
formula_16
subject to:
formula_17
where formula_18 is the formula_19-dimensional vector with one entry for every monomial in formula_20 of degree at most formula_21, so that for each multiset formula_22 formula_23, formula_24 is a matrix of coefficients of the polynomial formula_25 that we want to minimize, and formula_26 is a matrix of coefficients of the polynomial formula_27 encoding the formula_28-th constraint on the subset formula_29. The additional, fixed constant index in our search space, formula_30, is added for the convenience of writing the polynomials formula_25 and formula_32 in a matrix representation.
This program is generally non-convex, because the constraints  are not convex. One possible convex relaxation for this minimization problem uses semidefinite programming to replace the rank-one matrix of variables formula_33 with a positive-semidefinite matrix formula_34: we index each monomial of size at most formula_12 by a multiset formula_36 of at most formula_12 indices, formula_38. For each such monomial, we create a variable formula_39 in the program, and we arrange the variables formula_39 to form the matrix formula_41, where formula_42is the set of real matrices whose rows and columns are identified with multisets of elements from formula_7 of size at most formula_21. We then write the following semidefinite program in the variables formula_39:
formula_46
subject to:
formula_47
formula_48
formula_49
formula_50
where again formula_24 is the matrix of coefficients of the polynomial formula_25 that we want to minimize, and formula_26 is the matrix of coefficients of the polynomial formula_27 encoding the formula_28-th constraint on the subset formula_29.
The third constraint ensures that the value of a monomial that appears several times within the matrix is equal throughout the matrix, and is added to make formula_34 respect the symmetries present in the quadratic form formula_58.
Duality.
One can take the dual of the above semidefinite program and obtain the following program:
formula_59
subject to:
formula_60
We have a variable formula_61 corresponding to the constraint formula_62 (where formula_63 is the matrix with all entries zero save for the entry indexed by formula_64), a real variable formula_65for each polynomial constraint formula_66 and for each group of multisets formula_67, we have a dual variable formula_68for the symmetry constraint formula_69. The positive-semidefiniteness constraint ensures that formula_70 is a sum-of-squares of polynomials over formula_29: by a characterization of positive-semidefinite matrices, for any positive-semidefinite matrix formula_72, we can write formula_73 for vectors formula_74. Thus for any formula_75,
formula_76
where we have identified the vectors formula_77 with the coefficients of a polynomial of degree at most formula_21. This gives a sum-of-squares proof that the value formula_79 over formula_80.
The above can also be extended to regions formula_81 defined by polynomial inequalities.
Sum-of-squares hierarchy.
The sum-of-squares hierarchy (SOS hierarchy), also known as the Lasserre hierarchy, is a hierarchy of convex relaxations of increasing power and increasing computational cost. For each natural number formula_82 the corresponding convex relaxation is known as the "formula_83th level" or "formula_84-th round of the SOS hierarchy." The formula_85st round, when formula_86, corresponds to a basic semidefinite program, or to sum-of-squares optimization over polynomials of degree at most formula_87. To augment the basic convex program at the formula_85st level of the hierarchy to formula_83-th level, additional variables and constraints are added to the program to have the program consider polynomials of degree at most formula_90.
The SOS hierarchy derives its name from the fact that the value of the objective function at the formula_83-th level is bounded with a sum-of-squares proof using polynomials of degree at most formula_92 via the dual (see "Duality" above). Consequently, any sum-of-squares proof that uses polynomials of degree at most formula_92 can be used to bound the objective value, allowing one to prove guarantees on the tightness of the relaxation.
In conjunction with a theorem of Berg, this further implies that given sufficiently many rounds, the relaxation becomes arbitrarily tight on any fixed interval. Berg's result states that every non-negative real polynomial within a bounded interval can be approximated within accuracy formula_94 on that interval with a sum-of-squares of real polynomials of sufficiently high degree, and thus if formula_95 is the polynomial objective value as a function of the point formula_96, if the inequality formula_97 holds for all formula_96 in the region of interest, then there must be a sum-of-squares proof of this fact. Choosing formula_99 to be the minimum of the objective function over the feasible region, we have the result.
Computational cost.
When optimizing over a function in formula_100 variables, the formula_83-th level of the hierarchy can be written as a semidefinite program over formula_102 variables, and can be solved in time formula_102 using the ellipsoid method.
Sum-of-squares background.
A polynomial formula_104 is a "sum of squares" ("SOS") if there exist polynomials formula_105 such that formula_106. For example,
formula_107
is a sum of squares since
formula_108
where
formula_109
Note that if formula_104 is a sum of squares then formula_111 for all formula_112. Detailed descriptions of polynomial SOS are available.
Quadratic forms can be expressed as formula_113 where formula_114 is a symmetric matrix. Similarly, polynomials of degree ≤ 2"d" can be expressed as
formula_115
where the vector formula_116 contains all monomials of degree formula_117. This is known as the Gram matrix form. An important fact is that formula_104 is SOS if and only if there exists a symmetric and positive-semidefinite matrix formula_114 such that formula_120.
This provides a connection between SOS polynomials and positive-semidefinite matrices.


Structured support vector machine

The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels.
As an example, a sample instance might be a natural language sentence, and the output label is an annotated parse tree. Training a classifier consists of showing pairs of correct sample and output label pairs. After training, the structured SVM model allows one to predict for new sample instances the corresponding output label; that is, given a natural language sentence, the classifier can produce the most likely parse tree.
Training.
For a set of formula_1 training instances formula_2, formula_3 from a sample space formula_4 and label space formula_5, the structured SVM minimizes the following regularized risk function.
The function is convex in formula_7 because the maximum of a set of affine functions is convex. The function formula_8 measures a distance in label space and is an arbitrary function (not necessarily a metric) satisfying formula_9 and formula_10. The function formula_11 is a feature function, extracting some feature vector from a given sample and label. The design of this function depends very much on the application.
Because the regularized risk function above is non-differentiable, it is often reformulated in terms of a quadratic program by introducing one slack variable formula_12 for each sample, each representing the value of the maximum. The standard structured SVM primal formulation is given as follows.
Inference.
At test time, only a sample formula_14 is known, and a prediction function formula_15 maps it to a predicted label from the label space formula_5. For structured SVMs, given the vector formula_7 obtained from training, the prediction function is the following.
Therefore, the maximizer over the label space is the predicted label. Solving for this maximizer is the so-called inference problem and similar to making a maximum a-posteriori (MAP) prediction in probabilistic models. Depending on the structure of the function formula_19, solving for the maximizer can be a hard problem.
Separation.
The above quadratic program involves a very large, possibly infinite number of linear inequality constraints. In general, the number of inequalities is too large to be optimized over explicitly. Instead the problem is solved by using delayed constraint generation where only a finite and small subset of the constraints is used. Optimizing over a subset of the constraints enlarges the feasible set and will yield a solution that provides a lower bound on the objective. To test whether the solution formula_7 violates constraints of the complete set inequalities, a separation problem needs to be solved. As the inequalities decompose over the samples, for each sample formula_21 the following problem needs to be solved.
The right hand side objective to be maximized is composed of the constant formula_23 and a term dependent on the variables optimized over, namely formula_24. If the achieved right hand side objective is smaller or equal to zero, no violated constraints for this sample exist. If it is strictly larger than zero, the most violated constraint with respect to this sample has been identified. The problem is enlarged by this constraint and resolved. The process continues until no violated inequalities can be identified.
If the constants are dropped from the above problem, we obtain the following problem to be solved.
This problem looks very similar to the inference problem. The only difference is the addition of the term formula_26. Most often, it is chosen such that it has a natural decomposition in label space. In that case, the influence of formula_27 can be encoded into the inference problem and solving for the most violating constraint is equivalent to solving the inference problem.


Jakob Stoustrup

Jakob Stoustrup is a Danish researcher employed at Aalborg University, where he serves as professor of control theory at the Department of Electronic Systems.
Education.
Jakob Stoustrup received the M.Sc. degree in Electrical Engineering in 1987, and the Ph.D. degree in Applied Mathematics in 1991, both from the Technical University of Denmark.
Background, career, and scientific contributions.
After a first position as teaching assistant at the Technical University of Denmark, and visiting researcher at Eindhoven University of Technology, The Netherlands, 1988, he became Senior Researcher sponsored by Danish Technical Research Council, 1991. Assistant Professor 1991–1995, and Associate Professor 1995–1996, both at the Department of Mathematics, Technical University of Denmark. He was visiting professor at the University of Strathclyde, Glasgow, U.K., in 1996, and later visiting professor at the Mittag-Leffler Institute, Stockholm, Sweden, 2003. From 1997–2013 and since 2016 (full) Professor at Automation & Control, Aalborg University, and from 2006–2013, he acted as Head of Research for the Department of Electronic Systems. From 2014 to 2016 he acted as Chief Scientist with the Pacific Northwest National Laboratory, where he led the Control of Complex Systems Initiative. In 2017, Jakob Stoustrup was appointed as pro-dean for the TECH Faculty at Aalborg University.
Stoustrup has been a Member of the Swedish Research Council (Signals and Systems), of the Norwegian Research Council, of the European Research Council and of The Danish Research Council for Technology and Production Sciences. He has acted as associate editor, guest editor, and editorial board member of international journals. At several occasions, Jakob Stoustrup has acted as plenary speaker at international conferences, and he has also acted as a General Chair for such events. Jakob Stoustrup has been appointed by the Institute of Electrical and Electronics Engineers, as Chairman of a Control Systems Society/Robotics & Automation Society Joint Chapter. In 2008, Jakob Stoustrup was elected as Chairman for a Technical Committee of the International Federation of Automatic Control, TC6.4. In 2011, he was appointed as a member of the Technical Board of the International Federation of Automatic Control. Jakob Stoustrup has extensive industrial cooperation, and has been CEO for two technological start-up companies. He has led numerous major research projects based on a high number of research grants and contracts.
The main contributions of Jakob Stoustrup have been to robust control theory and to the theory of fault tolerant control systems. In these two areas he has published approximately 300 peer-reviewed scientific papers.
In 2009, Jakob Stoustrup proposed a novel research direction in the area of control theory, called plug-and-play control.
As an unusual accomplishment, his work spans the whole range from the development of new theoretical methods to practical industrial applications.
In the area of robust control theory, Jakob Stoustrup has in particular contributed to the development of loop transfer recovery methods for the design of H∞ controllers and to the development of robust design methods for systems having parametric uncertainty descriptions. Loop transfer recovery methods have been among the most popular model based design methods used in industry for decades, due to their intuitive relationships between full state feedback designs and observer based designs. Loop transfer recovery methods were originally designed as an extension to the LQG design methodology, but Jakob Stoustrup and his co-workers advanced the recovery methods to the area of H∞ control, thereby admitting robustness aspects to be included directly in the design paradigm.
Jakob Stoustrup's contributions to the design of robust controllers for systems with parametric uncertainty descriptions have mainly been focusing on establishing methods based on convex optimization. Whereas parametric uncertainty descriptions are often natural candidates for systems with first principle models, because they reflect the variation of physical parameters, the underlying optimization problems often turns out to be non-convex, meaning that they do not readily admit efficient on-line solutions with guaranteed performance. However, in the work of Stoustrup and co-workers, it was described how a class of such problems could be turned into convex optimization problems, and explicit algorithms for efficient solutions were suggested.
One theoretical result from Stoustrup in the area of robust control states that for a fairly general class of systems, the order of a decentralized H∞ controller tends to infinity as the performance approaches its optimal value, and in fact that not even an infinite-dimensional (causal) controller exists in that case.
In the area of fault tolerant control systems, the main contribution of Jakob Stoustrup has been to introduce a number of optimization based methods in order to solve fault diagnosis and fault tolerant control problems.
The results include explicit methods for time-varying, non-linear, and uncertain systems for the design of fault diagnosis and fault tolerant control systems. Inspired by his previous work in the area of robust control theory, Jakob Stoustrup and his co-workers has proposed a general architecture for the modeling and design of fault diagnosis and fault tolerant control systems, handling the above-mentioned challenges.
One theoretical result by Jakob Stoustrup in the area of fault tolerant control systems provides a positive answer to a previously open problem. By a constructive proof it is established that under mild conditions, a feedback controller for a system with two or more sensors always exists such that the system remains stable if the signal from any one of the sensors disappear. It is also shown, however, that the smallest order of such a controller can be unboundedly large.
Besides from the theoretical achievements mentioned above, Jakob Stoustrup has accomplished to bring a significant number among the theoretical results to actual industrial practice. Jakob Stoustrup and his group have worked with a significant number of industries in a wide range of industrial sectors. Examples of industrial applications from his group include:
These industrial applications have been carried out by Jakob Stoustrup in cooperation with more than 50 industrial companies in several countries.


Unimat

The Unimat was a series of combination machines sold for light hobbyist engineering, such as model engineering. They were distinctive as the same major components could be re-arranged into either a lathe or milling machine.
It covers a range of commercially sold machines intended for machining and metalworking for model making hobbyists manufactured by the Emco company. The machines enable the user to have a drill press, lathe and milling machine. Most of the Unimat range is no longer in production, but the smallest Unimat 1 and its variants is now produced by the Cool Tool Gmbh.
Models.
"TheCoolTool" company.
This model of the Unimat 1 has many plastic parts. It is capable of working mostly wood and plastics.
This model of the Unimat 1 has many plastic parts and plastic machining cross slides. It is capable of working wood, plastic and soft metals i.e. aluminum and brass. It is the same machine as the basic with additional capabilities such as wood turning, sawing, drilling, milling and metal turning.
This is a collection of enhancements for any of the Unimat 1 tools. It includes options for more power, table saw and router table.
This model of the Unimat 1 has been upgraded to metal parts and cross slides that give the unit a higher level of accuracy. It is capable of working any of the materials from the Basic or Classic versions plus soft steel.


Industrial civilization

Industrial civilization refers to the state of civilization following the Industrial Revolution, characterised by widespread use of powered machines. The transition of an individual region from pre-industrial society into an industrial society is referred to as the process of industrialisation, which may occur in different regions of the world at different times. Individual regions may specialise further as the civilisation continues to advance, resulting in some regions transitioning to a service economy, or information society, or post-industrial society (these are still dependent on industry, but allow individuals to move out of manufacturing jobs). The present era is sometimes referred to as the Information Age . De-industrialization of a region may occur for a range of reasons.
Industrial civilization has allowed a significant growth both in world population, thanks to mechanised agriculture and advances in modern medicine, and in the standard of living.
Such a civilization is mostly dependent on fossil fuel, with efforts underway to find alternatives for energy production. Some areas have exhibited de-industrialization as certain industries go into decline, or are superseded.
Contrast with other terms.
Contrast with industrial society.
"Industrial civilization" refers to the broader state of civilization, which spans multiple societies; industrial society just to specific segments (within the civilization) dependent on manufacturing jobs, whilst industrial civilisation as a whole involves many regions interdependent (via international trade) specialized in different ways, including information society and service economy. Note that these societies are still dependent on industrial civilization for their goods, and food imports coming from mechanised agriculture.
Contrast with industrial revolution.
The "industrial revolution" is the historical event that ushered in industrial civilization. The modern world has evolved further following development in mass production and information technology (allowing service economy, and information society).
Contrast with industrialisation.
"Industrialisation" is the process of any individual area being transformed. Industrial civilisation as a whole may have regions that still benefit from industrial societies, without being industrialised themselves, or having specialised in other ways (e.g. service economies).


Visual sensor network

A visual sensor network or smart camera network or intelligent camera network is a network of spatially distributed smart camera devices capable of processing, exchanging data and fusing images of a scene from a variety of viewpoints into some form more useful than the individual images. A visual sensor network may be a type of wireless sensor network, and much of the theory and application of the latter applies to the former. The network generally consists of the cameras themselves, which have some local image processing, communication and storage capabilities, and possibly one or more central computers, where image data from multiple cameras is further processed and fused (this processing may, however, simply take place in a distributed fashion across the cameras and their local controllers). Visual sensor networks also provide some high-level services to the user so that the large amount of data can be distilled into information of interest using specific queries.
The primary difference between visual sensor networks and other types of sensor networks is the nature and volume of information the individual sensors acquire: unlike most sensors, cameras are directional in their field of view, and they capture a large amount of visual information which may be partially processed independently of data from other cameras in the network. Alternatively, one may say that while most sensors measure some value such as temperature or pressure, visual sensors measure "patterns". In light of this, communication in visual sensor networks differs substantially from traditional sensor networks.
Applications.
Visual sensor networks are most useful in applications involving area surveillance, tracking, and environmental monitoring. Of particular use in surveillance applications is the ability to perform a dense 3D reconstruction of a scene and storing data over a period of time, so that operators can view events as they unfold over any period of time (including the current moment) from any arbitrary viewpoint in the covered area, even allowing them to "fly" around the scene in real time. High-level analysis using object recognition and other techniques can intelligently track objects (such as people or cars) through a scene, and even determine what they are doing so that certain activities could be automatically brought to the operator's attention. Another possibility is the use of visual sensor networks in telecommunications, where the network would automatically select the "best" view (perhaps even an arbitrarily generated one) of a live event.


SmartCAM

SmartCAM is a suite of Computer-Aided Manufacturing (CAM) and CAD/CAM software applications that uses toolpath modeling to assist CNC machinists in creating computer-numerically controlled (CNC) programs that direct CNC machine tools.
The SmartCAM family of applications include systems in support of CNC milling, turning, mill/turning, Wire EDM and fabrication.
One of the pioneers of "stand-alone" CAM systems available for the personal computer, SmartCAM was initially developed in 1984 by Point Control Company in Eugene, Oregon, and after a series of corporate acquisitions from 1994 to 2001, in 2003 ownership and development has been conducted by SmartCAMcnc in Springfield, Oregon.


Control system

A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial control systems which are used for controlling processes or machines. The control systems are designed via control engineering process.
For continuously modulated control, a feedback controller is used to automatically control a process or operation. The control system compares the value or status of the process variable (PV) being controlled with the desired value or setpoint (SP), and applies the difference as a control signal to bring the process variable output of the plant to the same value as the setpoint.
For sequential and combinational logic, software logic, such as in a programmable logic controller, is used.
Logic control.
Logic control systems for industrial and commercial machinery were historically implemented by interconnected electrical relays and cam timers using ladder logic. Today, most such systems are constructed with microcontrollers or more specialized programmable logic controllers (PLCs). The notation of ladder logic is still in use as a programming method for PLCs.
Logic controllers may respond to switches and sensors and can cause the machinery to start and stop various operations through the use of actuators. Logic controllers are used to sequence mechanical operations in many applications. Examples include elevators, washing machines and other systems with interrelated operations. An automatic sequential control system may trigger a series of mechanical actuators in the correct sequence to perform a task. For example, various electric and pneumatic transducers may fold and glue a cardboard box, fill it with the product and then seal it in an automatic packaging machine.
PLC software can be written in many different ways – ladder diagrams, SFC (sequential function charts) or statement lists.
On–off control.
On–off control uses a feedback controller that switches abruptly between two states. A simple bi-metallic domestic thermostat can be described as an on-off controller. When the temperature in the room (PV) goes below the user setting (SP), the heater is switched on. Another example is a pressure switch on an air compressor. When the pressure (PV) drops below the setpoint (SP) the compressor is powered. Refrigerators and vacuum pumps contain similar mechanisms. Simple on–off control systems like these can be cheap and effective.
Fuzzy logic.
Fuzzy logic is an attempt to apply the easy design of logic controllers to the control of complex continuously varying systems. Basically, a measurement in a fuzzy logic system can be partly true.
The rules of the system are written in natural language and translated into fuzzy logic. For example, the design for a furnace would start with: "If the temperature is too high, reduce the fuel to the furnace. If the temperature is too low, increase the fuel to the furnace."
Measurements from the real world (such as the temperature of a furnace) are "fuzzified" and logic is calculated arithmetic, as opposed to Boolean logic, and the outputs are "de-fuzzified" to control equipment.
When a robust fuzzy design is reduced to a single, quick calculation, it begins to resemble a conventional feedback loop solution and it might appear that the fuzzy design was unnecessary. However, the fuzzy logic paradigm may provide scalability for large control systems where conventional methods become unwieldy or costly to derive.
Fuzzy electronics is an electronic technology that uses fuzzy logic instead of the two-value logic more commonly used in digital electronics.
Physical implementation.
The range of control system implementation is from compact controllers often with dedicated software for a particular machine or device, to distributed control systems for industrial process control for a large physical plant.
Logic systems and feedback controllers are usually implemented with programmable logic controllers.


Polygonal turning

Polygonal turning (or polygon turning) is a machining process which allows non-circular forms (polygons) to be machine turned without interrupting the rotation of the raw material.
Technical details.
Polygonally turned parts may have several points, teeth, or other forms at the ends or along their circumference. The technique requires synchronisation of the movement of the polygonal turning mill and the part being machined. Polygonal turning allows rapid production and clean machining of advanced geometries.
The polygon turning unit has a multitude of inserts, and is synchronized so that when an insert cuts the turning bar stock, it cuts the bar at the same radial position each time the workpiece rotates. This enables geometries such as hexes, squares and flats to be machined at faster speeds than by milling.
Historical notes.
The Polygonal Turning Corporation of Marquette, Michigan manufactured shaped joinery products for domestic use during the 1890s.


Spring Back Compensation

Due to the plastic-elastic characteristic of a metal, it is typical that any deformation of sheet metal at room temperature will have both elastic and plastic deformation. After the metal workpiece is removed from the tool or deformation implement, the elastic deformation will be released and only the plastic deformation will remain. When a metal forming tool is planned and designed to deform a workpiece, the shape imparted by the tool will be a combination of elastic and plastic deformation. The release of the elastic deformation is the spring back often observed at the end of a metal forming process.
The spring back has to be compensated to achieve an accurate result.
Usually, that is realized by overbending the material corresponding to the magnitude of the spring back. That means for the practical side of the bending process; the bending former, enters deeper into the bending prism.
For other sheet metal forming operations like drawing, it entails deforming the sheet metal past the planned net shape of the part, so that when the spring back is released from the part, the plastic deformation in that part delivers the desired shape of the part. In the case of complex tools, the spring back has to be already considered in the engineering and construction phases. Therefore, complex software simulations are used. Frequently this is not enough to deliver the desired results. In such cases practical experiments are done, using the trial-and-error plus experience method to correct the tool. However, the results (work pieces) are only stable, if all influencing factors are the same.
This mainly includes:
The list of factors may be continued. Spring back assessment of final formed products is a difficult problem and is affected by the complexity of the formed shape. The NUMISHEET 93 conference benchmark problem involves the draw bending of a U-channel using three measured parameters. Parameter less approaches have been proposed for more complex geometries but need validation.
Practical example: electronic bending tools with spring-back compensation.
Manufactures of electrical assembly's produce components that are flat, using copper and aluminum. The mechanical properties of copper and aluminum are very different and require different programmable inputs in order to achieve the same dimensional characteristics. This variation in inputs is due to spring-back compensation.
Bending technology for flat material which measures each bend angle and provides spring back compensation is required. This gives the bend angle of flat materials true accuracy. This is attained by using bending prisms with electronic angular measurement technology. While bending two flat bolds supporting the material turn around. The bolds are directly connected to the angular sensors. A computer or rather the machine control then calculates the required final stroke. The spring back of every bend is compensated regardless of material type.
If the measuring accuracy is 0.1º, a high angle accuracy of +/- 0.2º is achieved instantly with the first workpiece without any rework. Because no adjustments are required, material waste amounts and setup times drop considerably. Even inconsistencies within a single piece of material are automatically adjusted.


Relaxed intersection

The relaxed intersection of "m" sets corresponds to the classical
intersection between sets except that it is allowed to relax few sets in order to avoid an empty intersection.
This notion can be used to solve constraints satisfaction problems
that are inconsistent by relaxing a small number of constraints.
When a bounded-error approach is considered for parameter estimation,
the relaxed intersection makes it possible to be robust with respect
to some outliers.
Definition.
The "q"-relaxed intersection of the "m" subsets
formula_1
of formula_2,
denoted by
formula_3
is the set of all
formula_4
which belong to all
formula_5
's, except
formula_6
at most.
This definition is illustrated by Figure 1.
Define
formula_7
We have
formula_8
Characterizing the q-relaxed intersection is a thus a set inversion problem.
Example.
Consider 8 intervals:
formula_9
formula_10
formula_11
formula_12
formula_13
formula_14
We have
formula_15
formula_16
formula_17
formula_18
formula_19
formula_20
formula_21
Relaxed intersection of intervals.
The relaxed intersection of intervals is not necessary an interval. We thus take
the interval hull of the result. If formula_22's are intervals, the relaxed
intersection can be computed with a complexity of "m".log("m") by using the
Marzullo's algorithm. It suffices to
sort all lower and upper bounds of the "m" intervals to represent the
function formula_23. Then, we easily get the set
formula_24
which corresponds to a union of intervals.
We then return the
smallest interval which contains this union.
Figure 2 shows the function
formula_25
associated to the previous example.
Relaxed intersection of boxes.
To compute the "q"-relaxed intersection of "m" boxes of
formula_26, we project all "m" boxes with respect to the "n" axes.
For each of the "n" groups of "m" intervals, we compute the "q"-relaxed intersection.
We return Cartesian product of the "n" resulting intervals.
Figure 3 provides an
illustration of the 4-relaxed intersection of 6 boxes. Each point of the
red box belongs to 4 of the 6 boxes.
Relaxed union.
The "q"-relaxed union of formula_27 is defined by
formula_28
Note that when "q"=0, the relaxed union/intersection corresponds to
the classical union/intersection. More precisely, we have
formula_29
and
formula_30
De Morgan's law.
If formula_31 denotes the complementary set of formula_22, we have
formula_33
formula_34
As a consequence
formula_35
Relaxation of contractors.
Let formula_36 be "m" contractors for the sets formula_37,
then
formula_38
is a contractor for formula_39
and
formula_40
is a contractor for formula_41, where
formula_42
are contractors for
formula_43
Combined with a branch-and-bound algorithm such as SIVIA (Set Inversion Via Interval Analysis), the "q"-relaxed
intersection of "m" subsets of formula_2 can be computed.
Application to bounded-error estimation.
The "q"-relaxed intersection can be used for robust localization
or for tracking.
Robust observers can also be implemented using the relaxed intersections to be robust with respect to outliers.
We propose here a simple example
to illustrate the method.
Consider a model the "i"th model output of which is given by
formula_45
where formula_46. Assume that we have
formula_47
where formula_48 and formula_49 are given by the following list
formula_50
The sets formula_51 for different formula_6 are depicted on
Figure 4.


Linear octree

A linear octree is an octree that is represented by a linear array instead of a tree data structure.
To simplify implementation, a linear octree is usually complete (that is, every internal node has exactly 8 child nodes) and where the maximum permissible depth is fixed a priori (making it sufficient to store the complete list of leaf nodes). That is, all the nodes of the octree can be generated from the list of its leaf nodes. Space filling curves are often used to represent linear octrees.


EnergyCS

EnergyCS is a Monrovia, California-based company specializing in integration and controls for high-energy, large format batteries. The company provides battery management systems for lithium-ion batteries and other advance energy storage technologies and is active in the electric vehicle and stationary energy storage space.
EnergyCS is also a pioneer in the area of PHEV (Plug-in hybrid Electric Vehicles). The company produced the first lithium-ion powered plug-in Prius.


Conditional short-circuit current

Conditional short-circuit current is the value of the alternating current component of a prospective current, which a switch without integral short-circuit protection, but protected by a suitable short circuit protective device (SCPD) in series, can withstand for the operating time of the current under specified test conditions. It may be understood to be the RMS value of the maximum permissible current over a specified time interval (t0,t1) and operating conditions.
The IEC definition is critiqued to be open to interpretation.
formula_1


Loop performance

Loop performance in control engineering indicates the performance of control loops, such as a regulatory PID loop. Performance refers to the accuracy of a control system's ability to track (output) the desired signals to regulate the plant process variables in the most beneficial and optimised way, without delay or overshoot.
Importance.
Regulatory control loops are critical in automated manufacturing and utility industries like refining, paper and chemicals manufacturing, power generation, among others. They are used to control a particular parameter within a process. The parameter that is being controlled could be temperature, pressure, flow or level of some process. For example, temperature controllers are used in boilers which are used in production of gasoline.
Software.
There are many software applications that help in measuring and analysing the performance of control loops in industrial plants. Benchmarking the loop performance and identifying opportunities for improvement are key drivers for improving plant reliability, production throughput and safe operation.


Torque screwdriver

A torque screwdriver is a screwdriver with components that ensure tightening to a specified torque, ensuring tightening which is sufficient, but not excessive. An insufficiently tightened screw connection may loosen in operation, and excessive tightening can damage parts; for example, if the nuts holding the wheel of a car in place are too loose, or damaged by overtightening, a wheel may come off at speed. Torque screwdrivers are used in mechanical production, manufacturing, and maintenance; their use is part of quality assurance.
Most torque screwdrivers allow the torque to be set to any value within a range. All have a torque-limiting clutch that disengages once the preset torque has been reached.
Torque screwdrivers can exert torques from 0.04 N⋅m to at least 27 N⋅m. Although no single tool covers the entire range, low-, mid-, and high-torque ranges are available.
Torque screwdrivers and torque wrenches have similar purposes and mechanisms.
Torque-limiting clutch.
The clutch is the component that defines a torque screwdriver. This is achieved with steel balls rolling between indented plates, compressed by a spring at one end, and the other side driving a screw or fastener. The torque limiting clutch is the part of the tool that limits the amount of torque being applied to the fastener at the receiving end of the tool. On simpler tools the clutch settings may be marked with arbitrary numbers (e.g., from 1 for the lowest available torque to 20 for the highest, without necessarily having a linear relationship with actual torque) rather than torque values.
Torque screwdrivers are available with several types of clutch, including “cam-over”, “cushion clutch”, and “auto shutoff”. Most of these clutch types are used in electric screwdrivers, air screwdrivers, impulse screwdrivers, manual torque screwdrivers, and cordless torque screwdrivers. Each type has the ability to preset a specified torque value. In some cases a tool may need to be certified in a calibration lab to verify its torque output; a certificate may be issued by an organisation such as NIST in the United States.
Cam over.
A Cam-over clutch is usually found in a manual torque screwdriver where the clutch simply “fags“ or “cams-over”, meaning that it signals the maximum torque has been achieved.
Cushion clutch.
Cushion clutch or “slip clutch” styles are found in both electric screwdrivers and air screwdrivers. This clutch style is similar to the “cam-over” once the final torque is reached because the clutch continues to cam over and slip. It will continue to run until the operator releases the throttle.
Auto shutoff.
An auto-shutoff clutch switches off the tool once the maximum torque is reached. Auto shutoff tools are designed for critical applications. They provide precision torque control and reduce energy consumption by eliminating idling.
Drive source.
The torque may be provided manually (by the operator's wrist), by an electric motor, or by a pneumatic drive.
Manual torque screwdriver.
Manual torque screwdrivers are made in straight and pistol-grip models. Manual torque screwdrivers can have a range of 0.04 N⋅m (6 in oz) to 20 N⋅m (170 in lb).
Electric torque screwdriver.
Screwdriving requires torque to be applied by a rotary motion. Drilling responds to the same description; general-purpose power tools ("drill/drivers") are designed for both screwdriving and drilling, with a slipping clutch and low speed added to the drilling functionality. In an industrial environment dedicated tools optimised for their particular function are more often used.
Corded.
Corded electric torque screwdrivers are commonly made in three different designs: pistol grip, angle and inline. This type is the one mostly used for industrial assembly applications such as electronic assembly and small parts assembly. Brushed electric motors and more efficient brushless motors are used. The torque ranges typically from 0.02 N⋅m to at least 27 N⋅m, with speeds of up to 2,000 revolutions per minute (rpm).
Electric screwdrivers with transducers can be categorized into three groups according to their physical features:
Cordless.
Cordless torque screwdriver are powered by batteries, usually rechargeable batteries with voltages from 3.6 to 18 volts. Dedicated screwdrivers for domestic use tend to operate off 3.6 to 4.8 volts and have relatively low maximum torque; drill/drivers operate off higher voltages and can deliver higher maximum torque. Cordless torque screwdrivers are used for the same applications as cordless screwdrivers without torque control.
Pneumatic torque screwdriver.
The pneumatic torque screwdriver is widely used for assembly requiring higher levels of torque. These tools are commonly used in automotive, aerospace and marine manufacturing. Pneumatic tools require a constant pressurized air source. Torque for this type of torque screwdriver ranges from 0.17 N⋅m (1.5 in lb) to 30 N⋅m (265 in lb), and speeds range from 800 to 2600 rpm. These tools must be near their compressed air source, not a problem in manufacturing but less suitable for general maintenance. Torque may not be controlled as accurately as by electrically powered tools.


Mod parrot

mod_parrot is an optional module for the Apache web server. It embeds a Parrot virtual machine interpreter into the Apache server and provides access to the Apache API to allow handlers to be written in Parrot assembly language, or any high-level language targeted to Parrot.


Active-set method

In mathematical optimization, the active-set method is an algorithm used to identify the active constraints in a set of inequality constraints. The active constraints are then expressed as equality constraints, thereby transforming an inequality-constrained problem into a simpler equality-constrained subproblem.
An optimization problem is defined using an objective function to minimize or maximize, and a set of constraints
that define the feasible region, that is, the set of all "x" to search for the optimal solution. Given a point formula_2 in the feasible region, a constraint
is called active at formula_4 if formula_5, and inactive at formula_2 if formula_7 Equality constraints are always active. The active set at formula_4 is made up of those constraints formula_9 that are active at the current point .
The active set is particularly important in optimization theory, as it determines which constraints will influence the final result of optimization. For example, in solving the linear programming problem, the active set gives the hyperplanes that intersect at the solution point. In quadratic programming, as the solution is not necessarily on one of the edges of the bounding polygon, an estimation of the active set gives us a subset of inequalities to watch while searching the solution, which reduces the complexity of the search.
Active-set methods.
In general an active-set algorithm has the following structure:
Methods that can be described as active-set methods include:


Angeliki Pantazi

Angeliki Pantazi is a Greek researcher in neuromorphic computing and in applications of control theory to computer data storage systems, for IBM Research in Zurich.
Education and career.
Pantazi studied electrical engineering and computer technology at the University of Patras, where she earned a diploma in 1996 and a Ph.D. in 2005. She has been affiliated with IBM Research in Zurich since 2002, and became a permanent member of the research staff in 2006.
Recognition.
Pantazi is a Fellow of the International Federation of Automatic Control. She was named as an IBM Master Inventor in 2014.
She was part of a group of IBM researchers who in 2009 won both the Control Systems Technology Award and the Transactions on Control Systems Technology Outstanding Paper Award of the IEEE Control Systems Society, for their work on nanopositioning in microelectromechanical systems. She was the 2017 winner of the Control Systems Society Transition to Practice Award, for "the development of advanced control technologies for magnetic tape data storage and nanopositioning applications".


Control logic

Control logic is a key part of a software program that controls the operations of the program. The control logic responds to commands from the user, and it also acts on its own to perform automated tasks that have been structured into the program.
Control logic can be modeled using a state diagram, which is a form of hierarchical state machine. These state diagrams can also be combined with flow charts to provide a set of computational semantics for describing complex control logic. This mix of state diagrams and flow charts is illustrated in the figure on the right, which shows the control logic for a simple stopwatch. The control logic takes in commands from the user, as represented by the event named “START”, but also has automatic recurring sample time events, as represented by the event named “TIC”.


Servo Robot Group

SERVO-ROBOT Group is a company that develops and creates intelligent sensing and digital vision systems to simplify manufacturing process automation such as welding. Therefore, the main activity is to build intelligent sensing systems based on precision measurement with laser beams and other intelligent sensing devices applicable to various industries such as automotive, railroad, pipe and tube, aerospace, shipbuilding, fabricated structures, windmill towers manufacturing, etc.
Founded in 1983, SERVO-ROBOT has established its world headquarters, production plant and research and development center in the St-Bruno Industrial Park, south of Montreal, Quebec, Canada. More than 95% of SERVO-ROBOT's products are exported outside of Canada every year.
Applications.
Innovations developed in patents mentioned in the above section resulted in concrete solutions easily applicable to markets ranging from automotive to aerospace which has helped many companies and factories to become more productive and reach their Six Sigma constant improvements goals.
External links.
Robotic Industry Association
Manufacturing Talk