AMPPS

AMPPS is a solution stack of Apache, MySQL, MongoDB, PHP, Perl and Python for Windows NT, Linux and macOS. It comes with 419 PHP web applications, over 1000 PHP classes and 6 versions of PHP. AMPPS is created by Softaculous Ltd. a company founded in 2009 which makes the Softaculous Auto installer. AMPPS is used to develop on PHP, MySQL applications like WordPress, Joomla, and Drupal.
Software list.
The software has 419 PHP applications.


Laser machine control

Laser machine control is an electronic system for automatic operation of land scrapers or excavators. Advanced systems with GPS replaced these laser-based systems in some countries, but it is still used in some countries like India.
System Components.
There are different types of systems patented by different inventors or companies. But the generalised system has four essential parts: rotating laser transmitter, laser receiver, control unit, solenoid valves.
Rotating laser transmitter.
The laser transmitter emits a narrow laser beam which rotates horizontally. this creates a Horizontal reference plane of laser beam. The beam can be spread horizontally but should be sharp and narrow in vertical plane. The transmitter may use infrared or red laser but for safety measure all manufacturers limits the power to 5 milliwatts. The transmitter uses semiconductor laser diodes as laser source which is powered by batteries.
The transmitter corrects its position to get perfect horizontal plane with acceleration sensor/ gyro sensors / bubble based level sensor and a microprocessor based servo mechanism.
Laser receiver.
The laser receiver detects the height of laser plane with an array of photo diodes, generally a series of photo diodes in 9" strip with a gap between diodes equal to the width of the beam. the receiver reads the height with the sensors and convert to a usable analog or digital signal and sends to the machine control unit. Modern systems use CAN protocol for the output signals where old systems use a 4/5/6 channel analog current signal.
Control unit.
The control unit is the processing unit which decides how to react with the incoming signals get a desired land level. It adjusts the height of the scraper by activating or deactivating the electro-hydraulic actuators (solenoid valves). Generally these are ON OFF type solenoid valves but some modern systems use proportional valves . On the dash board of control unit there are some keys and indicators are provided as user interface through which user can monitor the operation or manually operate the machine when needed.
Solenoid valves.
Solenoid valves are act like an interface between the electrical systems and the hydraulic system, these can be on-off type or proportional depending on the control unit.


Nozzle and flapper

The nozzle and flapper mechanism is a displacement type detector which converts mechanical movement into a pressure signal by covering the opening of a nozzle with a flat plate called the flapper. This restricts fluid flow through the nozzle and generates a pressure signal.
It is a widely used mechanical means of creating a high gain fluid amplifier. In industrial control systems, they played an important part in the development of pneumatic PID controllers and are still widely used today in pneumatic and hydraulic control and instrumentation systems.
Operating principle.
The operating principle makes use of the high gain effect when a flapper plate is placed a small distance from a small pressurized nozzle emitting a fluid.
The example shown is pneumatic. At sub-millimeter distances, a small movement of the flapper plate results in a large change in flow. The nozzle is fed from a chamber which is in turn fed by a restriction, so changes of flow result in changes of chamber pressure. The nozzle diameter must be larger than the restriction orifice in order to work. The high gain of the open loop mechanism can be made linear using a pressure feedback bellows on the flapper to create a force balance system with a linear output. The "live" zero of 0.2 bar or 3 psi is set by the bias spring which ensures that the device is working in its linear region.
The industry standard ranges of either 3-15 psi (USA), or 0.2 - 1.0 bar (metric), is normally used in pneumatic PID controllers, valve positioning servomechanisms and force balance transducers.
Application.
The nozzle and flapper in pneumatic controls is a simple low maintenance device which operates well in a harsh industrial environment, and does not present an explosion risk in hazardous atmospheres. They were the industry controller amplifier for many decades until the advent of practical and reliable electronic high gain amplifiers. However they are still used extensively for field devices such as control valve positioners, and I to P and P to I converters.
A proportional controller schematic is shown here.
The set point is transmitted through the flapper plate via the fulcrum to close the orifice and increase the chamber pressure. The feedback bellows resists and the output signal goes to the control valve which opens with increasing actuator pressure. As the flow increases, the process value bellows counteracts the set point bellows until equilibrium is reached. This will be a value below the set point, as there must always be an error to generate an output. The addition of an integral or "reset" bellows would remove this error.
The principle is also used in hydraulic systems controls.


Factory Bot (Rails Testing)

Factory Bot, originally known as Factory Girl, is a software library for the Ruby programming language that provides factory methods to create test fixtures for automated software testing. The fixture objects can be created on the fly; they may be plain Ruby objects with a predefined state, ORM objects with existing database records or mock objects.
Factory Bot is often used in testing Ruby on Rails applications; where it replaces Rails' built-in fixture mechanism. Rails' default setup uses a pre-populated database as test fixtures, which are global for the complete test suite. Factory Bot, on the other hand, allows developers to define a different setup for each test and thus helps to avoid dependencies within the test suite.
Factories.
Defining Factories.
A factory is defined by a name and its set of attributes. The class of the test object is either determined through the name of the factory or set explicitly.
FactoryBot.define do
 # Determine class automatically
 factory :user do
 end
 # Specify class explicitly
 factory :superhero, class: User do
 end
end
Features.
Traits.
Traits allow grouping of attributes which can be applied to any factory.
factory :status do
 trait :international do
 end
 trait :resident do
 end
 trait :comp_sci do
 end
 trait :electrical do
 end
 factory :comp_sci_international_student, traits: [:international, :comp_sci]
 factory :electrical_resident_student, traits: [:resident, :electrical]
end
Alias.
Factory Bot allows creating aliases for existing factories so that the factories can be reused.
factory :user, aliases: [:student, :teacher] do
end
factory :notice do
 teacher
 # Alias used "teacher" for "user"
 end
factory :notification do
 student
 #Alias used student for user
end
Sequences.
Factory Bot allows creating unique values for a test attribute in a given format.
FactoryBot.define do
 factory :title do
 # Title 1, Title 2 and so on...
 end
end
Inheritance.
Factories can be inherited while creating a factory for a class. This allows the user to reuse common attributes from parent factories and avoid writing duplicate code for duplicate attributes. Factories can be written in a nested fashion to leverage inheritance.
factory :user do
 factory :admin do
 admin_rights true
 end
end
admin_user = create(:admin)
admin_user.name # Micheal
admin_user.admin_rights # true
Parent factories can also be specified explicitly.
factory :user do
end
factory :admin, parent: :user do
end
Callback.
Factory Bot allows custom code to be injected at four different stages:


Distribution-free control chart

Distribution-free (nonparametric) control charts are one of the most important tools of statistical process monitoring and control. Implementation techniques of distribution-free control charts do not require any knowledge about the underlying process distribution or its parameters. The main advantage of distribution-free control charts is its in-control robustness, in the sense that, irrespective of the nature of the underlying process distributions, the properties of these control charts remain the same when the process is smoothly operating without presence of any assignable cause.
Early research on nonparametric control charts may be found in 1981 when P.K. Bhattacharya and D. Frierson introduced a nonparametric control chart for detecting small disorders. However, major growth of nonparametric control charting schemes has taken place only in the recent years.
Popular distribution-free control charts.
There are distribution-free control charts for both Phase-I analysis and Phase-II monitoring.
One of the most notable distribution-free control charts for Phase-I analysis is RS/P chart proposed by G. Capizzi and G. Masaratto. RS/P charts separately monitor location and scale parameters of a univariate process using two separate charts. In 2019, Chenglong Li, Amitava Mukherjee and Qin Su proposed a single distribution-free control chart for Phase-I analysis using multisample Lepage statistic.
Some popular Phase-II distribution-free control charts for univariate continuous processes includes:


Variable structure system

A variable structure system, or VSS, is a discontinuous nonlinear system of the form
where formula_2 is the state vector, formula_3 is the time variable, and formula_4 is a "piecewise continuous" function. Due to the "piecewise" continuity of these systems, they behave like different continuous nonlinear systems in different regions of their state space. At the boundaries of these regions, their dynamics switch abruptly. Hence, their "structure" "varies" over different parts of their state space.
The development of variable structure control depends upon methods of analyzing variable structure systems, which are special cases of hybrid dynamical systems.
References.
2. Emelyanov, S.V., ed. (1967). Variable Structure Control Systems. Moscow: Nauka.
3. Emelyanov S, Utkin V, Tarin V, Kostyleva N, Shubladze A, Ezerov V, Dubrovsky E. 1970. Theory of Variable Structure Control Systems (in Russian). Moscow: Nauka.
4. Variable Structure Systems: From Principles to Implementation. A. Sabanovic, L. Fridman and S. Spurgeon (eds.), IEE, London, 2004, ISBN 0863413501.
5. Advances in Variable Structure Systems and Sliding Mode Control—Theory and Applications. Li, S., Yu, X., Fridman, L., Man, Z., Wang, X.(Eds.), Studies in Systems, Decision and Control,v.115, Springer, 2017, ISBN 978-3-319-62895-0
6.Variable-Structure Systems and Sliding-Mode Control. M. Steinberger, M. Horn, L. Fridman.(eds.), Studies in Systems, Decision and Control, v.271, Springer International Publishing, Cham, 2020, ISBN 978-3-030-36620-9.
Further reading.
Y. Shtessel, C. Edwards, L. Fridman, A. Levant. Sliding Mode Control and Observation, Series: Control Engineering, Birkhauser: Basel, 2014, ISBN 978-0-81764-8923


System Center Mobile Device Manager

System Center Mobile Device Manager is a Mobile device management (MDM) solution providing over-the-air (OTA) management of Windows Mobile Smartphone security, applications and settings. System Center Mobile Device Manager supports devices running the Windows Mobile 6.1 and above operating system. Earlier, functions of this product provided by System Center Configuration Manager.
Important: Mainstream support for System Center Mobile Device Manager 2008 ended on July 9, 2013, and extended support ended on July 10, 2018.
Features.
Through ActiveDirectory-based policies, the product provides the following functions:
Client.
System Center Mobile Device Manager client is located in ROM. All device management activities are centrally managed from the server side.
Server.
System Center Mobile Device Manager server components are deployed on multiple server computers, including a Mobile VPN server, a Windows Update server, and an ActiveDirectory domain controller.


List of sensors

This is a list of sensors sorted by sensor type.
Speed sensor.
Speed sensors are machines used to detect the speed of an object, usually a transport vehicle. They include:


Monitorix

Monitorix is a computer network monitoring tool that periodically collects system data and uses the web interface to show the information as graphs. Monitorix allows monitoring of overall system performance, and can help detect bottlenecks, failures, unusually long response times and other anomalies.
One part of the tool is a collector, called "monitorix." This Perl daemon is started automatically like any other system service. The second program of Monitorix is a CGI script ("monitorix.cgi)." Since version 3.0 Monitorix has its own HTTP server included, what makes installing an own web server unnecessary.
Monitorix is free software licensed under the terms of the GNU General Public License version 2 (GPLv2) as published by the Free Software Foundation. It uses the RRDtool (written by Tobi Oetiker) and is written in Perl.


Markov chain approximation method

In numerical methods for stochastic differential equations, the Markov chain approximation method (MCAM) belongs to the several numerical (schemes) approaches used in stochastic control theory. Regrettably the simple adaptation of the deterministic schemes for matching up to stochastic models such as the Runge–Kutta method does not work at all.
It is a powerful and widely usable set of ideas, due to the current infancy of stochastic control it might be even said 'insights.' for numerical and other approximations problems in stochastic processes. They represent counterparts from deterministic control theory such as optimal control theory.
The basic idea of the MCAM is to approximate the original controlled process by a chosen controlled markov process on a finite state space. In case of need, one must as well approximate the cost function for one that matches up the Markov chain chosen to approximate the original stochastic process.


Discrete event dynamic system

In control engineering, a discrete-event dynamic system (DEDS) is a discrete-state, event-driven system of which the state evolution depends entirely on the occurrence of asynchronous discrete events over time. Although similar to continuous-variable dynamic systems (CVDS), DEDS consists solely of discrete state spaces and event-driven state transition mechanisms.
Topics in DEDS include:


Hyperstability

In stability theory, hyperstability is a property of a system that requires the state vector to remain bounded if the inputs are restricted to belonging to a subset of the set of all possible inputs.
Definition: A system is hyperstable if there are two constants formula_1 such that any state trajectory of the system satisfies the inequality:


IBM 1132

The IBM 1132 line printer was the normal printer for the IBM 1130 computer system. It printed 120 character lines at 80 lines per minute. The character set consisted of numbers, upper-case letters and some special characters.
The 1965-introduced 1132 was built around a stripped down IBM 407 printing mechanism. The 407 was IBM's top-of-the-line accounting machine from the 1950s. The 1130 had 120 power transistors, each wired to the print magnet for one printer column. The magnet released a lever that engaged a cam with a spinning clutch shaft. The engaged cam then made one revolution, pushing its print wheel toward the ribbon and paper, thereby printing one character.
As the set of 120 print wheels spun, the 1130 received an interrupt as each of the possible 48 characters was about to move into position. The printing driver software had to quickly output a 120 bit vector designating which transistors were to fire so as to drive the print wheel against the ribbon and paper. This put a big performance burden on the CPU, but resulted in an inexpensive (for the time) printer.
Sometimes a printer output line transistor would fail, resulting in a blank print position. If you knew your way around inside the 1130, it was possible to swap circuit cards so as to move the bad print position to near the right end of the printed line. This kept the 1130 usable until the repair person showed up.
The 1132 came in two models with the following characteristics:


Cryptix General License

The Cryptix General License is in use by the Cryptix project, well known for their Java Cryptography Extension. It is a modified version of the BSD license, with similarly liberal terms. The Free Software Foundation states that it is a permissive free software license compatible with the GNU General Public License.


Collaborative diffusion

Collaborative Diffusion is a type of pathfinding algorithm which uses the concept of "antiobjects", objects within a computer program that function opposite to what would be conventionally expected. Collaborative Diffusion is typically used in video games, when multiple agents must path towards a single target agent. For example, the ghosts in Pac-Man. In this case, the background tiles serve as antiobjects, carrying out the necessary calculations for creating a path and having the foreground objects react accordingly, whereas having foreground objects be responsible for their own pathing would be conventionally expected.
Collaborative Diffusion is favored for its efficiency over other pathfinding algorithms, such as A*, when handling multiple agents. Also, this method allows elements of competition and teamwork to easily be incorporated between tracking agents. Notably, the time taken to calculate paths remains constant as the number of agents increases.


Micro injection molding

Micro injection molding is a molding process for the manufacture of plastics components for shot weights of 1 to 0.1 grams with tolerances in the range of 10 to 100 microns. This molding process permits the manufacture of complicated small geometries with maximum possible accuracy and precision.
Basic concept.
The basic concept of the micro injection molding process is quite similar to the regular injection molding process. In this process, a micro injection unit is integrated in the injection molding machine. When it comes to the production of micro components the machine and process technology mainly depend on the below points:
Critical factors.
Parting line issue.
A parting line (PL) is the line of separation on the part where the two halves of the mold meet. The parting line matching for micro parts is a big issue. The interlocking features of mold cavity and core for precise mating are used to reduce such issues.
Degating issue.
Another major critical factor of micro injection technology is that the smaller part size causes problems with degating (gate removal).
Sprue and runner size.
Runner and sprue diameters are another concern. The total volume of the feed system (sprue, runners and gates) can exceed the volume of the parts by a factor of 100 or more.
Materials and applications for micro injection molding.
The most common polymers used in micro injection molding are reported in the table below:
Machine used for micro injection molding.
In the 1980s, micro injection molding techniques utilized traditional injection molding, but no dedicated machines were available until the mid-1990s. Currently, commercial micro molding systems are produced from Milacron, Arburg, and Sumitomo Demag as micro injection units for regular machines. At the same time, Wittmann Battenfeld, Babyplast and Desma are manufacturers of dedicated micro injection molding machines.
Milacron developed two types of micro injection units:
Arburg developed a micro injection molding machine with an 8 mm injection to ensure high degree of dosing precision. This type of machine is combined with a second screw, which is responsible for melting and homogenous mixing of the material.
Sumitomo Demag developed a customized micro molding injection unit suitable for micro parts weighing of 5 g to 0.1 g.
Applications.
Micro injection molding is widely applied for parts and devices in the medical, pharmaceutical, electronics, automotive, optical and other industries. In general, the medical micro injection molding market is the leading one, due to an increase in the usage of sophisticated micro components for endoscopic surgery, minimally invasive treatments, point-of-care testing and other advanced technology developments. Applications in other fields include parts for electric motors, micron-tolerance door components, thing-wall containers, etc.
Market prospects.
The miniaturization of automotive, medical, electronics, telecommunications devices is driving the need for micro molding of smaller components. The global polymer and thermoplastic micro molding market covering medical, automotive, electronics and telecommunications was valued at $308m in 2012. The micro injection molding plastic market was valued at $1,145.85 million in 2022 and is anticipated to reach $2,640 million by 2030, growing at a compound annual growth rate (CAGR) of 11.0% from 2023 to 2030.


Monique Chyba

Monique Chyba (born 1969) is a control theorist who works as a professor of mathematics at the University of Hawaiʻi at Mānoa. Her work on control theory has involved the theory of singular trajectories, and applications in the control of autonomous underwater vehicles. More recently, she has also applied control theory to the prediction and modeling of the spread of COVID-19 in Hawaii.
Education and career.
Chyba's parents Mirek and Jana Chyba were Czech, but settled in Geneva, in Switzerland. Chyba earned a Ph.D. through the University of Burgundy in Dijon, France, in 1997, while working as a teaching assistant at the University of Geneva. Her dissertation, "Le Cas Martinet en Geometrie Sous-Riemannienne [the Martinet case in sub-Riemannian geometry]", was supervised by Bernard Bonnard.
After postdoctoral research at Pierre and Marie Curie University, Harvard University, INRIA Sophia Antipolis, Princeton University, and the University of California, Santa Cruz, she joined the University of Hawaiʻi faculty in 2002. and was promoted to full professor in 2012.
Book.
Chyba is an author of the book "Singular Trajectories and their Role in Control Theory" (with Bernard Bonnard, Springer, 2003).
Recognition.
In 2014, Chiba University in Japan gave Chyba their Science and Lectureship Award.


FIR transfer function

Transfer function filter utilizes the transfer function and the Convolution theorem to produce a filter. In this article, an example of such a filter using finite impulse response is discussed and an application of the filter into real world data is shown.
FIR (Finite Impulse Response) Linear filters.
In digital processing, an FIR filter is a time-continuous filter that is invariant with time. This means that the filter does not depend on the specific point of time, but rather depends on the time duration. The specification of this filter uses a transfer function which has a frequency response which will only pass the desired frequencies of the input. This type of filter is non-recursive, which means that the output can be completely derived at from a combination of the input without any recursive values of the output. This means that there is no feedback loop that feeds the new output the values of previous outputs. This is an advantage over recursive filters such as IIR filter (Infinite Impulse Response) in the applications that require a linear phase response because it will pass the input without a phase distortion.
Mathematical model.
Let the output function be formula_1 and the input is formula_2. The convolution of the input with a transfer function formula_3 provides a filtered output. The mathematical model of this type of filter is:
h(formula_5) is a transfer function of an impulse response to the input. The convolution allows the filter to only be activated when the input recorded a signal at the same time value. This filter returns the input values (x(t)) if k falls into the support region of function h. This is the reason why this filter is called finite response. If k is outside of the support region, the impulse response is zero which makes output zero. The central idea of this h(formula_5) function can be thought of as a quotient of two functions.
According to Huang (1981) Using this mathematical model, there are four methods of designing non-recursive linear filters with various concurrent filter designs:
Single-sided Linear Filter.
Input function.
Define the input signal:
formula_8 adds a random number from 1 to 200 to the sinusoidal function which serves to distort the data.
Single-sided filter.
Use an exponential function as the impulse response for the support region of positive values.
The frequency response of this filter resembles a low-pass filter as in the lower frequency.
Double-sided filter.
Let the input signal to be the same as the single-sided function.
Use an exponential function as the impulse response for the support region of positive values as before. In this double-sided filter, also implement another exponential function. The opposite in signs of the powers of the exponent is to maintain the non-infinite results when computing the exponential functions.
formula_10
Examine this filter in its frequency domain, we see that the magnitude response is the same trend as the single sided filter. However, the frequencies that can be passed are smaller than those of the single-sided filter. This resulted in a smoother output. The significant of this consequence is that the double-sided filters types of linear filters are better being a filter.
FIR Transfer function Linear filter Application.
Linear filter performs better when it is a double-sided filter. This requires the data to be known in advance which makes it a challenge for these filters to function well in situations where signals cannot be known ahead of time such as radio signal processing. However, this means that linear filters are extremely useful in filtering pre-loaded data. In addition, because of its non-recursive nature which preserves the phase angles of the input, linear filters are usually used in image processing, video processing, data processing or pattern detection. Some examples are image enhancement, restoration and pre-whitening for spectral analysis. Additionally, linear non-recursive filters are always stable and usually produce a purely real output which makes them more favorable. They are also computationally easy which usually creates a big advantage for using this FIR linear filter.


Unscented optimal control

In mathematics, unscented optimal control combines the notion of the unscented transform with deterministic optimal control to address a class of uncertain optimal control problems. It is a specific application of Riemmann-Stieltjes optimal control theory, a concept introduced by Ross and his coworkers.
Mathematical description.
Suppose that the initial state formula_1 of a dynamical system,
formula_2
is an uncertain quantity. Let formula_3 be the sigma points. Then sigma-copies of the dynamical system are given by,
formula_4
Applying standard deterministic optimal control principles to this ensemble generates an unscented optimal control. Unscented optimal control is a special case of tychastic optimal control theory. According to Aubin and Ross, tychastic processes differ from stochastic processes in that a tychastic process is conditionally deterministic.
Applications.
Unscented optimal control theory has been applied to UAV guidance, spacecraft attitude control, air-traffic control and low-thrust trajectory optimization


Supervisory program

A supervisory program or supervisor is a computer program, usually part of an operating system, that controls the execution of other routines and regulates work scheduling, input/output operations, error actions, and similar functions and regulates the flow of work in a data processing system.
It can also refer to a program that allocates computer component space and schedules computer events by task queuing and system interrupts. Control of the system is returned to the supervisory program frequently enough to ensure that demands on the system are met.
Historically, this term was essentially associated with IBM's line of mainframe operating systems starting with OS/360. In other operating systems, the supervisor is generally called the kernel.
In the 1970s, IBM further abstracted the supervisor state from the hardware, resulting in a hypervisor that enabled full virtualization, i.e. the capacity to run multiple operating systems on the same machine totally independently from each other. Hence the first such system was called "Virtual Machine" or "VM".


Impact extrusion

Impact extrusion is a manufacturing process similar to extrusion and drawing by which products are made with a metal slug. The slug is pressed at a high velocity with extreme force into a die or mold by a punch.
Process.
The punch is attached to a mechanical or hydraulic press. These machines reciprocate in a cycle 20 to 60 times per minute. A cold slug is placed below the punch and over the die. The punch makes contact with the slug forcing it around the circumference of the punch and into the die. The metal slug deforms to fit the punch on the inside and the die on the outside. Lubricants are added to aid the machine for an easier punch-out. It only takes one impact for the finished shape to form from the slug. Once the slug has been contoured to the desired shape, a counter-punch ejector removes the work piece from within the die.
Some Characteristics of the Process.
The wall thickness of the work piece is directly correlated with the clearance between the punch and die.
The thinner the wall of the work piece the tighter its tolerances are.
The end product has a better surface finish than the starting piece and the grain of the material is reformed to its new shape. This adds strength to the new form compared to cutting into the grain like in a machining process.
Effects on Work Material Properties.
After going through this process the properties of the material used are altered. Its hardness and yield strength are increased, cross-sectional area is decreased, some residual surface stresses will be present and micro cracks may appear. Physical and chemical properties are only influenced slightly.
Die Style.
Four major types of dies (tools) can be used. They are: forward, backward/reverse, combined, and hydrostatic extrusion. Forward extrusion pushes the slug into the die. Backward/reverse extrusion pushes the slug around the punch. Combined extrusion forces the slug both into the die and around the punch. Hydrostatic extrusion is used on brittle materials (i.e. molybdenum, beryllium, and tungsten) by applying pressure gradually to force the brittle material through the die. This is generally accomplished by the same method as forward extrusion.
Typical Workpiece Materials.
Typical materials for this process are: aluminium, brass, tin, mild steel, stainless steel, magnesium, titanium, and zinc.
Tool Materials.
Typical tool steels used in extruding aluminum:
Tool Geometry.
When using the technique of backward impact extrusion, putting an angle on the punch in the press is used to decrease the amount of pressure applied to the punch. This decreases the chance of creating a dead zone, which is an area of no pressure. On the opposite end of things, forward impact extrusion uses a radius on punch to keep the course in the workpiece material moving.


PDE-constrained optimization

PDE-constrained optimization is a subset of mathematical optimization where at least one of the constraints may be expressed as a partial differential equation. Typical domains where these problems arise include aerodynamics, computational fluid dynamics, image segmentation, and inverse problems. A standard formulation of PDE-constrained optimization encountered in a number of disciplines is given by:formula_1where formula_2 is the control variable and formula_3 is the squared Euclidean norm and is not a norm itself. Closed-form solutions are generally unavailable for PDE-constrained optimization problems, necessitating the development of numerical methods.
Applications.
Optimal control of bacterial chemotaxis system.
The following example comes from p. 20-21 of Pearson. Chemotaxis is the movement of an organism in response to an external chemical stimulus. One problem of particular interest is in managing the spatial dynamics of bacteria that are subject to chemotaxis to achieve some desired result. For a cell density formula_4 and concentration density formula_5 of a chemoattractant, it is possible to formulate a boundary control problem:formula_6where formula_7 is the ideal cell density, formula_8 is the ideal concentration density, and formula_2 is the control variable. This objective function is subject to the dynamics:formula_10where formula_11 is the Laplace operator.


BIBO stability

In signal processing, specifically control theory, bounded-input, bounded-output (BIBO) stability is a form of stability for signals and systems that take inputs. If a system is BIBO stable, then the output will be bounded for every input to the system that is bounded.
A signal is bounded if there is a finite value formula_1 such that the signal magnitude never exceeds formula_2, that is
Time-domain condition for linear time-invariant systems.
Continuous-time necessary and sufficient condition.
For a continuous time linear time-invariant (LTI) system, the condition for BIBO stability is that the impulse response, formula_5 , be absolutely integrable, i.e., its L1 norm exists.
Discrete-time sufficient condition.
For a discrete time LTI system, the condition for BIBO stability is that the impulse response be absolutely summable, i.e., its formula_7 norm exists.
Proof of sufficiency.
Given a discrete time LTI system with impulse response formula_9 the relationship between the input formula_10 and the output formula_11 is
where formula_13 denotes convolution. Then it follows by the definition of convolution
Let formula_15 be the maximum value of formula_16, i.e., the formula_17-norm.
If formula_21 is absolutely summable, then formula_22 and
So if formula_21 is absolutely summable and formula_25 is bounded, then formula_26 is bounded as well because formula_27.
The proof for continuous-time follows the same arguments.
Frequency-domain condition for linear time-invariant systems.
Continuous-time signals.
For a rational and continuous-time system, the condition for stability is that the region of convergence (ROC) of the Laplace transform includes the imaginary axis. When the system is causal, the ROC is the open region to the right of a vertical line whose abscissa is the real part of the "largest pole", or the pole that has the greatest real part of any pole in the system. The real part of the largest pole defining the ROC is called the abscissa of convergence. Therefore, all poles of the system must be in the strict left half of the s-plane for BIBO stability.
This stability condition can be derived from the above time-domain condition as follows:
where formula_29 and formula_30
The region of convergence must therefore include the imaginary axis.
Discrete-time signals.
For a rational and discrete time system, the condition for stability is that the region of convergence (ROC) of the z-transform includes the unit circle. When the system is causal, the ROC is the open region outside a circle whose radius is the magnitude of the pole with largest magnitude. Therefore, all poles of the system must be inside the unit circle in the z-plane for BIBO stability.
This stability condition can be derived in a similar fashion to the continuous-time derivation:
where formula_32 and formula_33.
The region of convergence must therefore include the unit circle.


Ross' π lemma

Ross' lemma, named after I. Michael Ross, is a result in computational optimal control. Based on generating Carathéodory- solutions for feedback control, Ross' -lemma states that there is fundamental time constant within which a control solution must be computed for controllability and stability. This time constant, known as Ross' time constant, is proportional to the inverse of the Lipschitz constant of the vector field that governs the dynamics of a nonlinear control system.
Theoretical implications.
The proportionality factor in the definition of Ross' time constant is dependent upon the magnitude of the disturbance on the plant and the specifications for feedback control. When there are no disturbances, Ross' -lemma shows that the open-loop optimal solution is the same as the closed-loop one. In the presence of disturbances, the proportionality factor can be written in terms of the Lambert W-function.
Practical applications.
In practical applications, Ross' time constant can be found by numerical experimentation using DIDO. Ross "et al" showed that this time constant is connected to the practical implementation of a Caratheodory- solution. That is, Ross "et al" showed that if feedback solutions are obtained by zero-order holds only, then a significantly faster sampling rate is needed to achieve controllability and stability. On the other hand, if a feedback solution is implemented by way of a Caratheodory- technique, then a larger sampling rate can be accommodated. This implies that the computational burden on generating feedback solutions is significantly less than the standard implementations. These concepts have been used to generate collision-avoidance maneuvers in robotics in the presence of uncertain and incomplete information of the static and dynamic obstacles.


IBM Z System Automation

IBM Z System Automation (SA z/OS) is a policy-based automation solution to ensure the availability of applications and system resources. It runs within IBM Z NetView, and uses its capabilities to interact with z/OS.
Functionality.
The primary objective of this software is to keep Resources on the z/OS systems in a desired (or goal) state of Available or Unavailable. Each resource can be individually controlled via commands to set the goal state by means of Requests (or Votes). This goal state is stored, so should a resource fail or the system be shut down, it can be brought back to its previous Desired state quickly and easily. A Resource is any Application or System Resource which can be monitored and controlled. Resources have relationships defined between them, which ensures they are started and stopped in the correct order.
The resources are defined in a policy database (PDB), which enables all the definitions for an entire enterprise to be defined just once. A Build process is used to extract the relevant resources for each systems into Automation Control Files (ACF). As a minimum, a resource definition contains: jobname, start commands, stop commands, status messages, relationships.
SA z/OS is IBM Parallel Sysplex compliant, and can manage your business applications whether they run within a monoplex, between multiple sysplexes, or in distributed Linux, UNIX or Windows platforms. The Service Management Unite Automation dashboard provides a single point of control to monitor and operate in your environment.
SA z/OS is required for Geographically Dispersed Parallel Sysplex (GDPS).
Supported Operating Systems.
This software is available on following Operating Systems:


Protocol analyzer

A protocol analyzer is a tool (hardware or software) used to capture and analyze signals and data traffic over a communication channel. Such a channel varies from a local computer bus to a satellite link, that provides a means of communication using a standard communication protocol (networked or point-to-point). Each type of communication protocol has a different tool to collect and analyze signals and data.
Specific types of protocol analyzers include:


FreeNATS

FreeNATS (the Free Network Automatic Testing System) is an open-source network monitoring software application developed by David Cutting under the banner of PurplePixie Systems.
FreeNATS is free software licensed under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.


A. Stephen Morse

A. Stephen Morse (born June 18, 1939) is the Dudley Professor of distributed control and adaptive control in electrical engineering at Yale University.
Early life and education.
Morse was born in Mt. Vernon, New York. He received his B.S. from Cornell University, his M.S. from the University of Arizona, and his Ph.D. from Purdue University.
Awards.
Morse received the IEEE Control Systems Award and the Richard E. Bellman Control Heritage Award in 1999 and 2013, respectively. Morse was elected a member of the National Academy of Engineering in 2002 for contributions to geometric control theory, adaptive control, and the stability of hybrid systems.


State observer

In control theory, a state observer or state estimator is a system that provides an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and provides the basis of many practical applications.
Knowing the system state is necessary to solve many control theory problems; for example, stabilizing a system using state feedback. In most practical cases, the physical state of the system cannot be determined by direct observation. Instead, indirect effects of the internal state are observed by way of the system outputs. A simple example is that of vehicles in a tunnel: the rates and velocities at which vehicles enter and leave the tunnel can be observed directly, but the exact state inside the tunnel can only be estimated. If a system is observable, it is possible to fully reconstruct the system state from its output measurements using the state observer.
Typical observer model.
Linear, delayed, sliding mode, high gain, Tau, homogeneity-based, extended and cubic observers are among several observer structures used for state estimation of linear and nonlinear systems. A linear observer structure is described in the following sections.
Discrete-time case.
The state of a linear, time-invariant discrete-time system is assumed to satisfy
where, at time formula_3, formula_4 is the plant's state; formula_5 is its inputs; and formula_6 is its outputs. These equations simply say that the plant's current outputs and its future state are both determined solely by its current states and the current inputs. (Although these equations are expressed in terms of discrete time steps, very similar equations hold for continuous systems). If this system is observable then the output of the plant, formula_6, can be used to steer the state of the state observer.
The observer model of the physical system is then typically derived from the above equations. Additional terms may be included in order to ensure that, on receiving successive measured values of the plant's inputs and outputs, the model's state converges to that of the plant. In particular, the output of the observer may be subtracted from the output of the plant and then multiplied by a matrix formula_8; this is then added to the equations for the state of the observer to produce a so-called "Luenberger observer", defined by the equations below. Note that the variables of a state observer are commonly denoted by a "hat": formula_9 and formula_10 to distinguish them from the variables of the equations satisfied by the physical system.
The observer is called asymptotically stable if the observer error formula_13 converges to zero when formula_14. For a Luenberger observer, the observer error satisfies formula_15. The Luenberger observer for this discrete-time system is therefore asymptotically stable when the matrix formula_16 has all the eigenvalues inside the unit circle.
For control purposes the output of the observer system is fed back to the input of both the observer and the plant through the gains matrix formula_17.
The observer equations then become:
or, more simply,
Due to the separation principle we know that we can choose formula_17 and formula_8 independently without harm to the overall stability of the systems. As a rule of thumb, the poles of the observer formula_25 are usually chosen to converge 10 times faster than the poles of the system formula_26.
Continuous-time case.
The previous example was for an observer implemented in a discrete-time LTI system. However, the process is similar for the continuous-time case; the observer gains formula_8 are chosen to make the continuous-time error dynamics converge to zero asymptotically (i.e., when formula_25 is a Hurwitz matrix).
For a continuous-time linear system
where formula_31, the observer looks similar to discrete-time case described above:
The observer error formula_34 satisfies the equation
The eigenvalues of the matrix formula_25 can be chosen arbitrarily by appropriate choice of the observer gain formula_8 when the pair formula_38 is observable, i.e. observability condition holds. In particular, it can be made Hurwitz, so the observer error formula_39 when formula_40.
Peaking and other observer methods.
When the observer gain formula_8 is high, the linear Luenberger observer converges to the system states very quickly. However, high observer gain leads to a peaking phenomenon in which initial estimator error can be prohibitively large (i.e., impractical or unsafe to use). As a consequence, nonlinear high-gain observer methods are available that converge quickly without the peaking phenomenon. For example, sliding mode control can be used to design an observer that brings one estimated state's error to zero in finite time even in the presence of measurement error; the other states have error that behaves similarly to the error in a Luenberger observer after peaking has subsided. Sliding mode observers also have attractive noise resilience properties that are similar to a Kalman filter.
Another approach is to apply multi observer, that significantly improves transients and reduces observer overshoot. Multi-observer can be adapted to every system where high-gain observer is applicable.
State observers for nonlinear systems.
High gain, sliding mode and extended observers are the most common observers for nonlinear systems.
To illustrate the application of sliding mode observers for nonlinear systems, first consider the no-input non-linear system:
where formula_43. Also assume that there is a measurable output formula_44 given by
There are several non-approximate approaches for designing an observer. The two observers given below also apply to the case when the system has an input. That is,
Linearizable error dynamics.
One suggestion by Krener and Isidori and Krener and Respondek can be applied in a situation when there exists a linearizing transformation (i.e., a diffeomorphism, like the one used in feedback linearization) formula_48 such that in new variables the system equations read
The Luenberger observer is then designed as
The observer error for the transformed variable formula_52 satisfies the same equation as in classical linear case.
As shown by Gauthier, Hammouri, and Othman
and Hammouri and Kinnaert, if there exists transformation formula_48 such that the system can be transformed into the form
then the observer is designed as
where formula_58 is a time-varying observer gain.
Ciccarella, Dalla Mora, and Germani obtained more advanced and general results, removing the need for a nonlinear transform and proving global asymptotic convergence of the estimated state to the true state using only simple assumptions on regularity.
Switched observers.
As discussed for the linear case above, the peaking phenomenon present in Luenberger observers justifies the use of switched observers. A switched observer encompasses a relay or binary switch that acts upon detecting minute changes in the measured output. Some common types of switched observers include the sliding mode observer, nonlinear extended state observer, fixed time observer, switched high gain observer and uniting observer. The sliding mode observer uses non-linear high-gain feedback to drive estimated states to a hypersurface where there is no difference between the estimated output and the measured output. The non-linear gain used in the observer is typically implemented with a scaled switching function, like the signum (i.e., sgn) of the estimated – measured output error. Hence, due to this high-gain feedback, the vector field of the observer has a crease in it so that observer trajectories "slide along" a curve where the estimated output matches the measured output exactly. So, if the system is observable from its output, the observer states will all be driven to the actual system states. Additionally, by using the sign of the error to drive the sliding mode observer, the observer trajectories become insensitive to many forms of noise. Hence, some sliding mode observers have attractive properties similar to the Kalman filter but with simpler implementation.
As suggested by Drakunov, a sliding mode observer can also be designed for a class of non-linear systems. Such an observer can be written in terms of original variable estimate formula_59 and has the form
where:
The idea can be briefly explained as follows. According to the theory of sliding modes, in order to describe the system behavior, once sliding mode starts, the function formula_85 should be replaced by equivalent values (see "equivalent control" in the theory of sliding modes). In practice, it switches (chatters) with high frequency with slow component being equal to the equivalent value. Applying appropriate lowpass filter to get rid of the high frequency component on can obtain the value of the equivalent control, which contains more information about the state of the estimated system. The observer described above uses this method several times to obtain the state of the nonlinear system ideally in finite time.
The modified observation error can be written in the transformed states formula_86. In particular,
and so
So:
So, for sufficiently large formula_106 gains, all observer estimated states reach the actual states in finite time. In fact, increasing formula_106 allows for convergence in any desired finite time so long as each formula_108 function can be bounded with certainty. Hence, the requirement that the map formula_109 is a diffeomorphism (i.e., that its Jacobian linearization is invertible) asserts that convergence of the estimated output implies convergence of the estimated state. That is, the requirement is an observability condition.
In the case of the sliding mode observer for the system with the input, additional conditions are needed for the observation error to be independent of the input. For example, that
does not depend on time. The observer is then
Multi-observer.
Multi-observer extends the high-gain observer structure from single to multi observer, with many models working simultaneously. This has two layers: the first consists of multiple high-gain observers with different estimation states, and the second determines the importance weights of the first layer observers. The algorithm is simple to implement and does not contain any risky operations like differentiation. The idea of multiple models was previously applied to obtain information in adaptive control.
Assuming that the number of high-gain observers equals formula_112,
where formula_115 is the observer index. The first layer observers consists of the same gain formula_116 but they differ with the initial state formula_117. In the second layer all formula_118 from formula_119 observers are combined into one to obtain single state vector estimation
where formula_121 are weight factors. These factors are changed to provide the estimation in the second layer and to improve the observation process.
Let assume that
and
where formula_124 is some vector that depends on formula_125 observer error formula_126.
Some transformation yields to linear regression problem
This formula gives possibility to estimate formula_128. To construct manifold we need mapping formula_129 between formula_130 and ensurance that formula_131 is calculable relying on measurable signals.
First thing is to eliminate parking phenomenon for formula_132 from observer error
Calculate formula_134 times derivative on formula_135 to find mapping m lead to formula_136 defined as
1 & 0 & 0 & \cdots & 0 \\
CL & 1 & 0 & \cdots & 0 \\
CAL & CL & 1 & \cdots & 0 \\
CA^{2}L & CAL & CL & \cdots & 0 \\
\vdots & \vdots & \vdots & \ddots \\
CA^{n-2}L & CA^{n-3}L & CA^{n-4}L & \cdots & 1
\end{bmatrix}
\begin{bmatrix}
\int\limits^t_{t-t_d}


Fluid power

Fluid power is the use of fluids under pressure to generate, control, and transmit power. Fluid power is conventionally subdivided into hydraulics (using a liquid such as mineral oil or water) and pneumatics (using a gas such as compressed air or other gases). Although steam is also a fluid, steam power is usually classified separately from fluid power (implying hydraulics or pneumatics). Compressed-air and water-pressure systems were once used to transmit power from a central source to industrial users over extended geographic areas; fluid power systems today are usually within a single building or mobile machine.
Fluid power systems perform work by a pressurized fluid bearing directly on a piston in a cylinder or in a fluid motor. A fluid cylinder produces a force resulting in linear motion, whereas a fluid motor produces torque resulting in rotary motion. Within a fluid power system, cylinders and motors (also called actuators) do the desired work. Control components such as valves regulate the system.
Elements.
A fluid power system has a pump driven by a prime mover (such as an electric motor or internal combustion engine) that converts mechanical energy into fluid energy, Pressurized fluid is controlled and directed by valves into an actuator device such as a hydraulic cylinder or pneumatic cylinder, to provide linear motion, or a hydraulic motor or pneumatic motor, to provide rotary motion or torque. Rotary motion may be continuous or confined to less than one revolution.
Hydraulic pumps.
Dynamic (non positive displacement) pumps
This type is generally used for low-pressure, high volume flow applications. Since they are not capable of withstanding high pressures, there is little use in the fluid power field. Their maximum pressure is limited to 250-300 psi (1.7 - 2.0 MPa). This type of pump is primarily used for transporting fluids from one location to another. Centrifugal and axial flow propeller pumps are the two most common types of dynamic pumps.
Positive displacement pumps
This type is universally used for fluid power systems. With this pump, a fixed amount of fluid is ejected into the hydraulic system per revolution of pump shaft rotation. These pumps are capable of overcoming the pressure resulting from the mechanical loads on the system as well as the resistance to flow due to friction. These two features are highly desirable in fluid power pumps. These pumps also have the following advantages over non positive displacement pumps:
Characteristics.
Fluid power systems can produce high power and high forces in small volumes, compared with electrically-driven systems. The forces that are exerted can be easily monitored within a system by gauges and meters. In comparison to systems that provide force through electricity or fuel, fluid power systems are known to have long service lives if maintained properly. The working fluid passing through a fluid motor inherently provides cooling of the motor, which must be separately arranged for an electric motor. Fluid motors normally produce no sparks, which are a source of ignition or explosions in hazardous areas containing flammable gases or vapors.
Fluid power systems are susceptible to pressure and flow losses within pipes and control devices. Fluid power systems are equipped with filters and other measures to preserve the cleanliness of the working fluid. Any dirt in the system can cause wear of seals and leakage, or can obstruct control valves and cause erratic operation. The hydraulic fluid itself is sensitive to temperature and pressure along with being somewhat compressible. These can cause systems to not run properly. If not run properly, cavitation and aeration can occur.
Application.
Mobile applications of fluid power are widespread. Nearly every self-propelled wheeled vehicle has either hydraulically-operated or pneumatically-operated brakes. Earthmoving equipment such as bulldozers, backhoes and others use powerful hydraulic systems for digging and also for propulsion. A very compact fluid power system is the automatic transmission found in many vehicles, which includes a hydraulic torque converter.
Fluid power is also used in automated systems, where tools or work pieces are moved or held using fluid power. Variable-flow control valves and position sensors may be included in a servomechanism system for precision machine tools. Below is a more detailed list of applications and categories that fluid power is used for:
Common hydraulic circuit application.
Synchronizing.
This circuit works off of synchronization. As a cylinder reaches a certain point another will be activated, either by a hydraulic limit switch valve or by the build-up of pressure in the cylinder. These circuits are used in manufacturing. An example of this would be on an assembly line. As a hydraulic arm is activated to grab an object. It then will reach a point of extension or retraction, where the other cylinder is activated to screw a cap or top onto the object. Hence the term "synchronizing".
Regenerative.
In a regenerative circuit, a double acting cylinder is used. This cylinder has a pump that has a fixed output. The use of a regenerative circuit permits use of a smaller size pump for any given application. This works by re-routing the fluid to the cap instead of back to the tank. For example, in a drilling process a regenerative circuit will allow drilling at a consistent speed, and retraction at a much faster speed. This gives the operator faster and more precise production.
Electrical control.
Combinations of electrical control of fluid power elements are widespread in automated systems. A wide variety of measuring, sensing, or control elements are available in electrical form. These can be used to operate solenoid valves or servo valves that control the fluid power element. Electrical control may be used to allow, for example, remote control of a fluid power system without running long control lines to a remotely located manual control valve.


IEC 62351

IEC 62351 is a standard developed by WG15 of IEC TC57. This is developed for handling the security of TC 57 series of protocols including IEC 60870-5 series, IEC 60870-6 series, IEC 61850 series, IEC 61970 series & IEC 61968 series. The different security objectives include authentication of data transfer through digital signatures, ensuring only authenticated access, prevention of eavesdropping, prevention of playback and spoofing, and intrusion detection.


Capacitive displacement sensor

Capacitive displacement sensors "are non-contact devices capable of high-resolution measurement of the position and/or change of position of any conductive target". They are also able to measure the thickness or density of non-conductive materials. Capacitive displacement sensors are used in a wide variety of applications including semiconductor processing, assembly of precision equipment such as disk drives, precision thickness measurements, machine tool metrology and assembly line testing. These types of sensors can be found in machining and manufacturing facilities around the world.
Basic capacitive theory.
Capacitance is an electrical property which is created by applying an electrical charge to two conductive objects with a gap between them. A simple demonstration is two parallel conductive plates of the same profile with a gap between them and a charge applied to them. In this situation, the Capacitance can be expressed by the equation:
Where "C" is the capacitance, ε0 is the permittivity of free space constant, "K" is the dielectric constant of the material in the gap, "A" is the area of the plates, and "d" is the distance between the plates.
There are two general types of capacitive displacement sensing systems. One type is used to measure thicknesses of conductive materials. The other type measures thicknesses of non conductive materials or the level of a fluid.
A capacitive sensing system for conductive materials uses a model similar to the one described above, but in place of one of the conductive plates, is the sensor, and in place of the other, is the conductive target to be measured. Since the area of the probe and target remain constant, and the dielectric of the material in the gap (usually air) also remains constant, "any change in capacitance is a result of a change in the distance between the probe and the target." Therefore, the equation above can be simplified to:
Where α indicates a proportional relationship.
Due to this proportional relationship, a capacitive sensing system is able to measure changes in capacitance and translate these changes in distance measurements.
The operation of the sensor for measuring thickness of non-conductive materials can be thought of as two capacitors in series, with each having a different dielectric (and dielectric constant). The sum of the thicknesses of the two dielectric materials remains constant but the thickness of each can vary. The thickness of the material to be measured displaces the other dielectric. The gap is often an air gap, (dielectric constant = 1) and the material has a higher dielectric. As the material gets thicker, the capacitance increases and is sensed by the system.
A sensor for measuring fluid levels works as two capacitors in parallel with constant total area. Again the difference in the dielectric constant of the fluid and the dielectric constant of air results in detectable changes in the capacitance between the conductive probes or plates.
Applications.
Precision positioning.
One of the more common applications of capacitive sensors is for precision positioning. Capacitive displacement sensors can be used to measure the position of objects down to the nanometer level. This type of precise positioning is used in the semiconductor industry where silicon wafers need to be positioned for exposure. Capacitive sensors are also used to pre-focus the electron microscopes used in testing and examining the wafers.
Disc drive industry.
In the disc drive industry, capacitive displacement sensors are used to measure the runout (a measure of how much the axis of rotation deviates from an ideal fixed line) of disc drive spindles. By knowing the exact runout of these spindles, disc drive manufacturers are able to determine the maximum amount of data that can be placed onto the drives. Capacitive sensors are also used to ensure that disc drive platters are orthogonal to the spindle before data is written to them.
Precision thickness measurements.
Capacitive displacement sensors can be used to make very precise thickness measurements. Capacitive displacement sensors operate by measuring changes in position. If the position of a reference part of known thickness is measured, other parts can be subsequently measured and the differences in position can be used to determine the thickness of these parts. In order for this to be effective using a single probe, the parts must be completely flat and measured on a perfectly flat surface. If the part to be measured has any curvature or deformity, or simply does not rest firmly against the flat surface, the distance between the part to be measured and the surface it is placed upon will be erroneously included in the thickness measurement. This error can be eliminated by using two capacitive sensors to measure a single part. Capacitive sensors are placed on either side of the part to be measured. By measuring the parts from both sides, curvature and deformities are taken into account in the measurement and their effects are not included in the thickness readings.
The thickness of plastic materials can be measured with the material placed between two electrodes a set distance apart. These form a type of capacitor. The plastic when placed between the electrodes acts as a dielectric and displaces air (which has dielectric constant of 1, different from the plastic). Consequently, the capacitance between the electrodes changes. The capacitance changes can then be measured and correlated with the material's thickness.
Capacitive sensors circuits can be constructed that are able to detect changes in capacitance on the order of a 10−5 picofarads (10 attofarads).
Non-conductive targets.
While capacitive displacement sensors are most often used to sense changes in position of conductive targets, they can also be used to sense the thickness and/or density of non-conductive targets as well. A non-conductive object placed in between the probe and conductive target will have a different dielectric constant than the air in the gap and will therefore change the Capacitance between probe and target. (See the first equation above) By analyzing this change in capacitance, the thickness and density of the non-conductor can be determined.
Machine tool metrology.
Capacitive displacement sensors are often used in metrology applications. In many cases, sensors are used “to measure shape errors in the part being produced. But they also can measure the errors arising in the equipment used to manufacture the part, a practice known as machine tool metrology”. In many cases, the sensors are used to analyze and optimize the rotation of spindles in various machine tools, examples include surface grinders, lathes, milling machines, and air bearing spindles. By measuring errors in the machines themselves, rather than simply measuring errors in the final products, problems can be dealt with and fixed earlier in the manufacturing process.
Assembly line testing.
Capacitive displacement sensors are often used in assembly line testing. Sometimes they are used to test assembled parts for uniformity, thickness or other design features. At other times, they are used to simply look for the presence or absence of a certain component, such as glue. Using capacitive sensors to test assembly line parts can help to prevent quality concerns further along in the production process.
Comparison to eddy current displacement sensors.
Capacitive displacement sensors share many similarities to eddy current (or inductive) displacement sensors; however capacitive sensors use an electric field as opposed to the magnetic field used by eddy current sensors This leads to a variety of differences between the two sensing technologies, with the most notable differences being that capacitive sensors are generally capable of higher resolution measurements, and eddy current sensors work in dirty environments while capacitive sensors do not.


IEC/IEEE 61850-9-3

IEC/IEEE 61850-9-3 (Power Utility Profile) or PUP is an international standard for precise time distribution and clock synchronization in electrical grids with an accuracy of 1 μs.
It supports precise time stamping of voltage and current measurement for differential protection, wide area monitoring and protection, busbar protection and event recording.
It can be used to ensure deterministic operation of critical functions in the automation system.
It belongs to the IEC 61850 standard suite for communication networks and systems for power utility automation.
IEC/IEEE 61850-9-3 is a profile (subset) of IEEE Std 1588 Precision Time Protocol (PTP) when clocks are singly attached.
IEC/IEEE 61850-9-3 provides seamless fault tolerance by attaching clocks to duplicated networks paths and by support of simultaneously active redundant master clocks.
For this case, the extensions to PTP defined in IEC 62439-3 Annex A apply.
Main features.
IEC/IEEE 61850-9-3 uses the following IEEE Std 1588 options:
Performance.
IEC/IEEE 61850-9-3 aims at an accuracy of better than 1 μs after crossing 15 bridges with transparent clocks.
It assumes that all network elements (bridges, routers, media converters, links) support PTP with a given performance:
By relying on these guaranteed values, the network engineer can calculate the time inaccuracy at different nodes of the network and place the clocks, especially the grandmaster clocks suitably.
IEC TR 61850-90-4 (Network engineering guidelines) gives advice on the use of IEC/IEEE 61850-9-3.
IEEE 1588 settings.
IEC/IEEE 61850-9-3 restricts the parameters of IEEE Std 1588 to the following values:
Local time distribution.
For applications that do not use the corresponding function in IEC 61850, the grandmaster may distribute local time (e.g. for human display) using the ALTERNATE_TIME_OFFSET_INDICATOR TLV as specified in IEEE Std 1588 §16.3.
Standard owners.
This protocol has been developed 2012-2014 by the IEC SC65C WG15 in the framework of IEC 62439-3, which applies to all IEC industrial networks, as PTP profile L2P2P (Layer2, peer-to-peer).
To avoid parallel standards in IEC and IEEE in the field of grid automation, this work has been placed under the umbrella of the IEC&IEEE Joint Development 61850-9-3.
Technical responsibility rests with IEC SC65C WG15, which is committed to keep the IEC 62439-3 profile L2P2P and IEC/IEEE 61850-9-3 aligned.


Intelligent agent

In artificial intelligence, an intelligent agent (IA) is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.
Leading AI textbooks define "artificial intelligence" as the "study and design of intelligent agents", a definition that considers goal-directed behavior to be the essence of intelligence. Goal-directed agents are also described using a term borrowed from economics, "rational agent".
An agent has an "objective function" that encapsulates all the IA's goals. Such an agent is designed to create and execute whatever plan will, upon completion, maximize the expected value of the objective function. For example, a reinforcement learning agent has a "reward function" that allows the programmers to shape the IA's desired behavior, and an evolutionary algorithm's behavior is shaped by a "fitness function".
Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.
Intelligent agents are often described schematically as an abstract functional system similar to a computer program. Abstract descriptions of intelligent agents are called abstract intelligent agents (AIA) to distinguish them from their real-world implementations. An autonomous intelligent agent is designed to function in the absence of human intervention. Intelligent agents are also closely related to software agents (an autonomous computer program that carries out tasks on behalf of users).
As a definition of artificial intelligence.
"" defines an "agent" as
Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators,
defines a "rational agent" as:
An agent that acts so as to maximize the expected value of a performance measure based on past experience and knowledge.
and defines the field of "artificial intelligence" research as:
The study and design of rational agents
Padgham & Winikoff (2005) agree that an intelligent agent is situated in an environment and responds in a timely (though not necessarily real-time) manner to environment changes. However, intelligent agents must also proactively pursue goals in a flexible and robust way. Optional desiderata include that the agent be rational, and that the agent be capable of belief-desire-intention analysis.
Kaplan and Haenlein define artificial intelligence as "A system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation." This definition is closely related to that of an intelligent agent.
Advantages.
Philosophically, this definition of artificial intelligence avoids several lines of criticism. Unlike the Turing test, it does not refer to human intelligence in any way. Thus, there is no need to discuss if it is "real" vs "simulated" intelligence (i.e., "synthetic" vs "artificial" intelligence) and does not indicate that such a machine has a mind, consciousness or true understanding (i.e., it does not imply John Searle's "strong AI hypothesis"). It also doesn't attempt to draw a sharp dividing line between behaviors that are "intelligent" and behaviors that are "unintelligent"—programs need only be measured in terms of their objective function.
More importantly, it has a number of practical advantages that have helped move AI research forward. It provides a reliable and scientific way to test programs; researchers can directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". It also gives them a common language to communicate with other fields—such as mathematical optimization (which is defined in terms of "goals") or economics (which uses the same definition of a "rational agent").
Objective function.
An agent that is assigned an explicit "goal function" is considered more intelligent if it consistently takes actions that successfully maximize its programmed goal function. The goal can be simple ("1 if the IA wins a game of Go, 0 otherwise") or complex ("Perform actions mathematically similar to ones that succeeded in the past"). The "goal function" encapsulates all of the goals the agent is driven to act on; in the case of rational agents, the function also encapsulates the acceptable trade-offs between accomplishing conflicting goals. (Terminology varies; for example, some agents seek to maximize or minimize a "utility function", "objective function", or "loss function".)
Goals can be explicitly defined or induced. If the AI is programmed for "reinforcement learning", it has a "reward function" that encourages some types of behavior and punishes others. Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similar to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to accomplish its narrow classification task.
Systems that are not traditionally considered agents, such as knowledge-representation systems, are sometimes subsumed into the paradigm by framing them as agents that have a goal of (for example) answering questions as accurately as possible; the concept of an "action" is here extended to encompass the "act" of giving an answer to a question. As an additional extension, mimicry-driven systems can be framed as agents who are optimizing a "goal function" based on how closely the IA succeeds in mimicking the desired behavior. In the generative adversarial networks of the 2010s, an "encoder"/"generator" component attempts to mimic and improvise human text composition. The generator is attempting to maximize a function encapsulating how well it can fool an antagonistic "predictor"/"discriminator" component.
While symbolic AI systems often accept an explicit goal function, the paradigm can also be applied to neural networks and to evolutionary computing. Reinforcement learning can generate intelligent agents that appear to act in ways intended to maximize a "reward function". Sometimes, rather than setting the reward function to be directly equal to the desired benchmark evaluation function, machine learning programmers will use reward shaping to initially give the machine rewards for incremental progress in learning. Yann LeCun stated in 2018 that "Most of the learning algorithms that people have come up with essentially consist of minimizing some objective function." AlphaZero chess had a simple objective function; each win counted as +1 point, and each loss counted as -1 point. An objective function for a self-driving car would have to be more complicated. Evolutionary computing can evolve intelligent agents that appear to act in ways intended to maximize a "fitness function" that influences how many descendants each agent is allowed to leave.
The theoretical and uncomputable AIXI design is a maximally intelligent agent in this paradigm; however, in the real world, the IA is constrained by finite time and hardware resources, and scientists compete to produce algorithms that can achieve progressively higher scores on benchmark tests with real-world hardware.
Classes of intelligent agents.
Russel and Norvig's classification.
 group agents into five classes based on their degree of perceived intelligence and capability:
Simple reflex agents.
Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on the "condition-action rule": "if condition, then action".
This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.
Infinite loops are often unavoidable for simple reflex agents operating in partially observable environments. If the agent can randomize its actions, it may be possible to escape from infinite loops.
Model-based reflex agents.
A model-based agent can handle partially observable environments. Its current state is stored inside the agent maintaining some kind of structure that describes the part of the world which cannot be seen. This knowledge about "how the world works" is called a model of the world, hence the name "model-based agent".
A model-based reflex agent should maintain some sort of internal model that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state. Percept history and impact of action on the environment can be determined by using the internal model. It then chooses an action in the same way as reflex agent.
An agent may also use models to describe and predict the behaviors of other agents in the environment.
Goal-based agents.
Goal-based agents further expand on the capabilities of the model-based agents, by using "goal" information. Goal information describes situations that are desirable. This provides the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state. Search and planning are the subfields of artificial intelligence devoted to finding action sequences that achieve the agent's goals.
Utility-based agents.
Goal-based agents only distinguish between goal states and non-goal states. It is also possible to define a measure of how desirable a particular state is. This measure can be obtained through the use of a "utility function" which maps a state to a measure of the utility of the state. A more general performance measure should allow a comparison of different world states according to how well they satisfied the agent's goals. The term utility can be used to describe how "happy" the agent is.
A rational utility-based agent chooses the action that maximizes the expected utility of the action outcomes - that is, what the agent expects to derive, on average, given the probabilities and utilities of each outcome. A utility-based agent has to model and keep track of its environment, tasks that have involved a great deal of research on perception, representation, reasoning, and learning.
Learning agents.
Learning has the advantage that it allows the agents to initially operate in unknown environments and to become more competent than its initial knowledge alone might allow. The most important distinction is between the "learning element", which is responsible for making improvements, and the "performance element", which is responsible for selecting external actions.
The learning element uses feedback from the "critic" on how the agent is doing and determines how the performance element, or "actor", should be modified to do better in the future. The performance element is what we have previously considered to be the entire agent: it takes in percepts and decides on actions.
The last component of the learning agent is the "problem generator". It is responsible for suggesting actions that will lead to new and informative experiences.
Weiss's classification.
 defines four classes of agents:
Other.
In 2013, Alexander Wissner-Gross published a theory pertaining to Freedom and Intelligence for intelligent agents.
Hierarchies of agents.
To actively perform their functions, Intelligent Agents today are normally gathered in a hierarchical structure containing many “sub-agents”. Intelligent sub-agents process and perform lower-level functions. Taken together, the intelligent agent and sub-agents create a complete system that can accomplish difficult tasks or goals with behaviors and responses that display a form of intelligence.
Generally, an agent can be constructed by separating the body into the sensors and actuators, and so that it operates with a complex perception system that takes the description of the world as input for a controller and outputs commands to the actuator. However, a hierarchy of controller layers is often necessary to balance the immediate reaction desired for low-level tasks and the slow reasoning about complex, high-level goals.
Agent function.
A simple agent program can be defined mathematically as a function f (called the "agent function") which maps every possible percepts sequence to a possible action the agent can perform or to a coefficient, feedback element, function or constant that affects eventual actions:
Agent function is an abstract concept as it could incorporate various principles of decision making like calculation of utility of individual options, deduction over logic rules, fuzzy logic, etc.
The program agent, instead, maps every possible percept to an action.
We use the term percept to refer to the agent's perceptional inputs at any given instant. In the following figures, an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Applications.
Intelligent agents are applied as automated online assistants, where they function to perceive the needs of customers in order to perform individualized customer service. Such an agent may basically consist of a dialog system, an avatar, as well an expert system to provide specific expertise to the user. They can also be used to optimize coordination of human groups online. Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents. Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars. It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior. The basic idea of using agent-based modeling to understand self-driving cars was discussed as early as 2003.
Alternative definitions and uses.
"Intelligent agent" is also often used as a vague marketing term, sometimes synonymous with "virtual personal assistant". Some 20th-century definitions characterize an agent as a program that aids a user or that acts on behalf of a user. These examples are known as software agents, and sometimes an "intelligent software agent" (that is, a software agent with intelligence) is referred to as an "intelligent agent".
According to Nikola Kasabov, IA systems should exhibit the following characteristics:


Data Discovery and Query Builder

Data Discovery and Query Builder (DDQB) is a data abstraction technology, developed by IBM, that allows users to retrieve information from a data warehouse, in terms of the user's specific area of expertise instead of SQL.
DDQB serves the user through a web based graphical user interface and configurable data abstraction model (DAM), which contains both an understanding of the user knowledge domain and the database below it.
DDQB uses a set of Eclipse-based customization tooling and can be deployed as a set of Web Services.


Arbor milling

Arbor milling is a cutting process which removes material via a multi-toothed cutter. An arbor mill is a type of milling machine characterized by its ability to rapidly remove material from a variety of materials. This milling process is not only rapid but also versatile.
Process Schematic.
This process progressively makes a surface to the user's specifications as the material is moved against the milling tool or the workpiece stays stationary while the arbor milling cutter moves across it to provide the desired shape. There are two types of milling that involve the directional movement of the workpiece, conventional and climb. If the workpiece is moving the opposite direction of the tool rotation this is called conventional milling. If the workpiece is moving the same direction as the tool rotation, this is called climb milling.

Setup and Equipment.
Arbor milling is commonly performed on a horizontal milling machine. The tool is mounted on an arbor/mandrel (like an axle) that is suspended between the spindle and arbor support. This type of machine allows the tool to be placed in numerous positions in relation to the workpiece.
Workpiece Materials.
The workpiece involved in arbor milling can be a flat material or a shaped material: either one can be worked with desirable results. The hardness of the materials milled should be no harder than Rockwell C25(Rockwell scale), but workpieces harder than this can be successfully milled. Materials with good or excellent machinability include aluminum, brass, mild steel, cast iron, and thermoset plastics. Though initially ductile, stainless steel tends to work harden and thus has only a fair compatibility with this milling process (though it is in the feasible range).
Tooling Materials.
Although high speed tool steel has been used in the past it is quickly being replaced by carbide, ceramic, or diamond tooling. Because carbide inserts are long lasting and easily replaced, they lend themselves to high production. Ceramic tools are brittle but can withstand high temperatures. This makes high speed machining possible. Diamond tools are used to achieve a superior surface finish (though they can only be used on non-ferrous materials).
Tolerances and Surface Finish.
In most applications, tolerances can be held within ±0.005 in. For precision application, tolerances can be held within ±0.001 in. It is possible to have a surface finish range of 32 to 500 microinches, but typically the range is 63 to 200 microinches. Finish cuts will generate surfaces near 32 to 63 microinches, roughing cuts near 200 microinches.
Tool Styles and Possibilities.
The most common tool styles used in arbor milling are: double angle, form relived, plane, and staggered tooth Among many other tool styles. The double angle milling cutter can make a wide variety of V shaped cuts with straight surfaces in the material. A form relieved milling cutter can produce U shaped cuts with curved surfaces, unlike the double angle cutter, into the material. A plane milling cutter can produce surfaces similar to a planer but can make varying contours across the material. A staggered tooth milling cutter can produce a rectangular groove in the material at varying depths and widths. The cutters can be stacked to mill combined profiles. The typical width of cuts made by arbor milling range from 0.25 in to 6 in, and the typical depths range from 0.02 in to 0.05 in.
Effects on Work Material Properties.
Mechanical properties of the workpiece may be affected with a built-up edge or dull tool. Arbor Milling can create an untempered martensitic layer on the surface of heat-treated alloy steels, about 0.001 in thick. Other materials are affected very little by arbor milling.
Process Conditions.
Shown are the suggested ranges for cutting speeds and feed rates using high speed tool steel under dry cutting conditions at a 0.015 in depth of cut. Generally cutting speeds are lower for hard materials, higher for soft materials. Both cutting speeds and feed rates can be substantially increased when coolants are used and carbide tooling is substituted for steel tooling.
Typical Speeds and Feeds
Lubrication and Cooling.
Due to high cutting speeds a cutting fluid is required to lubricate and cool the tool and workpiece. The fluids can increase tool life, cutting speeds, and the quality of the finished surface. There are three common cutting fluids: mineral, synthetic, and water-soluble oils. These fluids can be applied by spraying, misting, or flooding the workpiece.