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Epaulette-Germany-Until World War I, officers of the Imperial German Army generally wore silver epaulettes as a distinguishing feature of their full-dress uniforms. For ranks up to and including captain these were "scale" epaulettes without fringes, for majors and colonels with fine fringes and for generals with a heav... | milkshake721/2.1M-wiki-STEM |
Epaulette-Haiti-Gold epaulettes in Haiti, were frequently worn throughout the 18th and 19th centuries in full dress. During the Haitian Revolution, Gen. Charles Leclerc of the French Army wrote a letter to Napoleon Bonaparte saying, "We must destroy half of those in the plains and must not leave a single colored person... | milkshake721/2.1M-wiki-STEM |
Epaulette-Ottoman Empire-During the Tanzimat period in the Ottoman Empire, western style uniforms and court dresses were adopted. Gold epaulettes were worn in full dress. | milkshake721/2.1M-wiki-STEM |
Epaulette-Russian Empire-Both the Imperial Russian Army and the Imperial Russian Navy sported different forms of epaulettes for its officers and senior NCOs. Today the current Kremlin Regiment continues the epaulette tradition.
Types of epaulette of the Russian Empire 1. Infantry 1a. Subaltern-officer, here: poruchik o... | milkshake721/2.1M-wiki-STEM |
Epaulette-Sweden-Epaulettes first appeared on Swedish uniforms in the second half of the 18th century. The epaulette was officially incorporated into Swedish uniform regulations in 1792, although foreign recruited regiments had had them earlier. Senior officers were to wear golden crowns to distinguish their rank from ... | milkshake721/2.1M-wiki-STEM |
Epaulette-United Kingdom-Epaulettes first appeared on British uniforms in the second half of the 18th century. The epaulette was officially incorporated into Royal Navy uniform regulations in 1795, although some officers wore them before this date. Under this system, flag officers wore silver stars on their epaulettes ... | milkshake721/2.1M-wiki-STEM |
Epaulette-United Kingdom-British cavalry on active service in the Sudan (1898) and during the Boer War (1899–1902) sometimes wore epaulettes made of chainmail to protect against sword blows landing on the shoulder. The blue "Number 1 dress" uniforms of some British cavalry regiments and yeomanry units still retain this... | milkshake721/2.1M-wiki-STEM |
Epaulette-United Kingdom-In modern times, epaulettes are frequently worn by professionals within the ambulance service to signify clinical grade for easy identification. These are typically green in colour with gold writing and may contain one to three pips to signify higher managerial ranks. | milkshake721/2.1M-wiki-STEM |
Epaulette-United States-Epaulettes were authorized for the United States Navy in the first official uniform regulations, Uniform of the Navy of the United States, 1797. Captains wore an epaulette on each shoulder, lieutenants wore only one, on the right shoulder. By 1802, lieutenants wore their epaulette on the left sh... | milkshake721/2.1M-wiki-STEM |
Epaulette-United States-In 1851, the epaulettes became universally gold. Both majors and second lieutenants had no specific insignia. A major would have been recognizable as he would have worn a senior field officer's more elaborate epaulette fringes. The rank insignia was silver for senior officers and gold for the ba... | milkshake721/2.1M-wiki-STEM |
Epaulette-United States-Shoulder straps were adopted to replace epaulettes for field duty in 1836. | milkshake721/2.1M-wiki-STEM |
Epaulette-United States-Licensed officers of the U.S. Merchant Marine may wear shoulder marks and sleeve stripes appropriate to their rank and branch of service. Deck officers wear a foul anchor above the stripes on their shoulder marks, and engineering officers wear a three-bladed propeller. In the U.S. Merchant Marin... | milkshake721/2.1M-wiki-STEM |
Epaulette-In popular culture-In literature, film and political satire, dictators, particularly of unstable Third World nations, are often depicted in military dress with oversized gold epaulettes.The eponymous character of Revolutionary Girl Utena along with the rest of the duelists have stylised epaulettes on their un... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Artificial neural network-Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Artificial neural network-Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times. | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Training-Neural networks learn (or are trained) by processing examples, each of which contains a known "input" and "result", forming probability-weighted associations between the two, which are stored within the data structure of the net itself. The training of a neural network from a given ex... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Training-Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-The simplest kind of feedforward neural network (FNN) is a linear network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. The sum of the products of the weights and the inputs is calculated in each node. The mean sq... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-The first deep learning MLP was published by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965, as the Group Method of Data Handling. The first deep learning MLP trained by stochastic gradient descent was published in 1967 by Shun'ichi Amari. | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-In computer experiments conducted by Amari's student Saito, a five layer MLP with two modifiable layers learned useful internal representations to classify non-linearily separable pattern classes.Self-organizing maps (SOMs) were described by Teuvo Kohonen in 1982. SOMs are neurophysiol... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-The backpropagation algorithm is an efficient application of the Leibniz chain rule (1673) to networks of differentiable nodes. It is also known as the reverse mode of automatic differentiation or reverse accumulation, due to Seppo Linnainmaa (1970). The term "back-propagating errors"... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-From 1988 onward, the use of neural networks transformed the field of protein structure prediction, in particular when the first cascading networks were trained on profiles (matrices) produced by multiple sequence alignments.In the 1980s, backpropagation did not work well for deep FNNs... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-The modern Transformer was introduced by Ashish Vaswani et al. in their 2017 paper "Attention Is All You Need." It combines this with a softmax operator and a projection matrix. | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-Transformers have increasingly become the model of choice for natural language processing. Many modern large language models such as ChatGPT, GPT-4, and BERT use it. Transformers are also increasingly being used in computer vision.In 1991, Juergen Schmidhuber also published adversarial... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-Excellent image quality is achieved by Nvidia's StyleGAN (2018) based on the Progressive GAN by Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Here the GAN generator is grown from small to large scale in a pyramidal fashion. | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-Sepp Hochreiter's diploma thesis (1991) was called "one of the most important documents in the history of machine learning" by his supervisor Juergen Schmidhuber. Hochreiter identified and analyzed the vanishing gradient problem and proposed recurrent residual connections to solve it. ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-History-In 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used the LSTM principle to create the Highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. 7 months later, Kaiming He, Xiangyu Zhang; Shaoqing Ren, and Jian Sun won the ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. They soon reoriented towards improving empirical results, abandoning attempts to remain true to their biological precursors. ANNs have the abili... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Artificial neurons ANNs are composed of artificial neurons which are conceptually derived from biological neurons. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons. The inputs can be the feature values of a sample of external dat... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-To find the output of the neuron we take the weighted sum of all the inputs, weighted by the weights of the connections from the inputs to the neuron. We add a bias term to this sum. This weighted sum is sometimes called the activation. This weighted sum is then passed through a (usuall... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Organization The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produc... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Hyperparameter A hyperparameter is a constant parameter whose value is set before the learning process begins. The values of parameters are derived via learning. Examples of hyperparameters include learning rate, the number of hidden layers and batch size. The values of some hyperparame... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Learning Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights (and optional thresholds) of the network to improve the accuracy of the result. This is done by minimizing the observed errors. Learning... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Learning rate The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation. A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Cost function While it is possible to define a cost function ad hoc, frequently the choice is determined by the function's desirable properties (such as convexity) or because it arises from the model (e.g. in a probabilistic model the model's posterior probability can be used as an inve... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Backpropagation Backpropagation is a method used to adjust the connection weights to compensate for each error found during learning. The error amount is effectively divided among the connections. Technically, backprop calculates the gradient (the derivative) of the cost function associ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Learning paradigms Machine learning is commonly separated into three main learning paradigms, supervised learning, unsupervised learning and reinforcement learning. Each corresponds to a particular learning task. | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Supervised learning Supervised learning uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case, the cost function is related to eliminating incorrect deductions. A commonly used cost is the mean-squared error, whi... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Unsupervised learning In unsupervised learning, input data is given along with the cost function, some function of the data x and the network's output. The cost function is dependent on the task (the model domain) and any a priori assumptions (the implicit properties of the model, its ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Reinforcement learning In applications such as playing video games, an actor takes a string of actions, receiving a generally unpredictable response from the environment after each one. The goal is to win the game, i.e., generate the most positive (lowest cost) responses. In reinforceme... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Formally the environment is modeled as a Markov decision process (MDP) with states s1,...,sn∈S and actions a1,...,am∈A . Because the state transitions are not known, probability distributions are used instead: the instantaneous cost distribution P(ct|st) , the observation distributio... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-ANNs serve as the learning component in such applications. Dynamic programming coupled with ANNs (giving neurodynamic programming) has been applied to problems such as those involved in vehicle routing, video games, natural resource management and medicine because of ANNs ability to mit... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Self-learning Self-learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice in... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, where from it initially and only once receives initial emotions a... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Neuroevolution Neuroevolution can create neural network topologies and weights using evolutionary computation. With modern enhancements, neuroevolution is competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caug... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Stochastic neural network Stochastic neural networks originating from Sherrington–Kirkpatrick models are a type of artificial neural network built by introducing random variations into the network, either by giving the network's artificial neurons stochastic transfer functions, or by gi... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Other In a Bayesian framework, a distribution over the set of allowed models is chosen to minimize the cost. Evolutionary methods, gene expression programming, simulated annealing, expectation-maximization, non-parametric methods and particle swarm optimization are other learning algori... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Models-Modes Two modes of learning are available: stochastic and batch. In stochastic learning, each input creates a weight adjustment. In batch learning weights are adjusted based on a batch of inputs, accumulating errors over the batch. Stochastic learning introduces "noise" into the process... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Types-ANNs have evolved into a broad family of techniques that have advanced the state of the art across multiple domains. The simplest types have one or more static components, including number of units, number of layers, unit weights and topology. Dynamic types allow one or more of these to ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Types-Some of the main breakthroughs include: convolutional neural networks that have proven particularly successful in processing visual and other two-dimensional data; long short-term memory avoid the vanishing gradient problem and can handle signals that have a mix of low and high frequency... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Network design-Neural architecture search (NAS) uses machine learning to automate ANN design. Various approaches to NAS have designed networks that compare well with hand-designed systems. The basic search algorithm is to propose a candidate model, evaluate it against a dataset, and use the re... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Network design-Design issues include deciding the number, type, and connectedness of network layers, as well as the size of each and the connection type (full, pooling, etc. ). | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Network design-Hyperparameters must also be defined as part of the design (they are not learned), governing matters such as how many neurons are in each layer, learning rate, step, stride, depth, receptive field and padding (for CNNs), etc. The Python code snippet provides an overview of the t... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Use-Using artificial neural networks requires an understanding of their characteristics.
Choice of model: This depends on the data representation and the application. Overly complex models are slow learning.
Learning algorithm: Numerous trade-offs exist between learning algorithms. Almost any ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Applications-Because of their ability to reproduce and model nonlinear processes, artificial neural networks have found applications in many disciplines. Application areas include system identification and control (vehicle control, trajectory prediction, process control, natural resource manag... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Applications-ANNs have been proposed as a tool to solve partial differential equations in physics and simulate the properties of many-body open quantum systems. In brain research ANNs have studied short-term behavior of individual neurons, the dynamics of neural circuitry arise from interactio... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-Computational power The multilayer perceptron is a universal function approximator, as proven by the universal approximation theorem. However, the proof is not constructive regarding the number of neurons required, the network topology, the weights and the learning param... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-Two notions of capacity are known by the community. The information capacity and the VC Dimension. The information capacity of a perceptron is intensively discussed in Sir David MacKay's book which summarizes work by Thomas Cover. The capacity of a network of standard ne... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-Convergence Models may not consistently converge on a single solution, firstly because local minima may exist, depending on the cost function and the model. Secondly, the optimization method used might not guarantee to converge when it begins far from any local minimum. ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-The convergence behavior of certain types of ANN architectures are more understood than others. When the width of network approaches to infinity, the ANN is well described by its first order Taylor expansion throughout training, and so inherits the convergence behavior o... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-Generalization and statistics Applications whose goal is to create a system that generalizes well to unseen examples, face the possibility of over-training. This arises in convoluted or over-specified systems when the network capacity significantly exceeds the needed fre... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-The second is to use some form of regularization. This concept emerges in a probabilistic (Bayesian) framework, where regularization can be performed by selecting a larger prior probability over simpler models; but also in statistical learning theory, where the goal is t... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-Supervised neural networks that use a mean squared error (MSE) cost function can use formal statistical methods to determine the confidence of the trained model. The MSE on a validation set can be used as an estimate for variance. This value can then be used to calculate... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Theoretical properties-By assigning a softmax activation function, a generalization of the logistic function, on the output layer of the neural network (or a softmax component in a component-based network) for categorical target variables, the outputs can be interpreted as posterior probabilit... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Criticism-Training A common criticism of neural networks, particularly in robotics, is that they require too much training for real-world operation. Potential solutions include randomly shuffling training examples, by using a numerical optimization algorithm that does not take too large steps ... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Criticism-Theory A central claim of ANNs is that they embody new and powerful general principles for processing information. These principles are ill-defined. It is often claimed that they are emergent from the network itself. This allows simple statistical association (the basic function of a... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Criticism-Technology writer Roger Bridgman commented: Neural networks, for instance, are in the dock not only because they have been hyped to high heaven, (what hasn't?) but also because you could create a successful net without understanding how it worked: the bunch of numbers that captures i... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Criticism-In spite of his emphatic declaration that science is not technology, Dewdney seems here to pillory neural nets as bad science when most of those devising them are just trying to be good engineers. An unreadable table that a useful machine could read would still be well worth having.
... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Criticism-Hardware Large and effective neural networks require considerable computing resources. While the brain has hardware tailored to the task of processing signals through a graph of neurons, simulating even a simplified neuron on von Neumann architecture may consume vast amounts of memor... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Criticism-Schmidhuber noted that the resurgence of neural networks in the twenty-first century is largely attributable to advances in hardware: from 1991 to 2015, computing power, especially as delivered by GPGPUs (on GPUs), has increased around a million-fold, making the standard backpropagat... | milkshake721/2.1M-wiki-STEM |
Artificial neural network-Criticism-Practical counterexamples Analyzing what has been learned by an ANN is much easier than analyzing what has been learned by a biological neural network. Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering general principles t... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Turbine engine failure-A turbine engine failure occurs when a turbine engine unexpectedly stops producing power due to a malfunction other than fuel exhaustion. It often applies for aircraft, but other turbine engines can fail, like ground-based turbines used in power plants or combined diesel an... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Reliability-Turbine engines in use on today's turbine-powered aircraft are very reliable. Engines operate efficiently with regularly scheduled inspections and maintenance. These units can have lives ranging in the tens of thousands of hours of operation. However, engine malfunctions or failures o... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Reliability-The Federal Aviation Administration (FAA) was quoted as stating turbine engines have a failure rate of one per 375,000 flight hours, compared to of one every 3,200 flight hours for aircraft piston engines. | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Reliability-Due to "gross under-reporting" of general aviation piston engines in-flight shutdowns (IFSD), the FAA has no reliable data and assessed the rate "between 1 per 1,000 and 1 per 10,000 flight hours".Continental Motors reports the FAA states general aviation engines experience one failur... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Reliability-The Pratt & Whitney Canada PT6 is known for its reliability with an in-flight shutdown rate of one per 333,333 hours from 1963 to 2016, lowering to one per 651,126 hours over 12 months in 2016. | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Reliability-Emergency landing Following an engine shutdown, a precautionary landing is usually performed with airport fire and rescue equipment positioned near the runway. The prompt landing is a precaution against the risk that another engine will fail later in the flight or that the engine fail... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Reliability-Rotorcraft Turboprop-powered aircraft and turboshaft-powered helicopters are also powered by turbine engines and are subject to engine failures for many similar reasons as jet-powered aircraft. In the case of an engine failure in a helicopter, it is often possible for the pilot to ent... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Shutdowns that are not engine failures-Most in-flight shutdowns are harmless and likely to go unnoticed by passengers. For example, it may be prudent for the flight crew to shut down an engine and perform a precautionary landing in the event of a low oil pressure or high oil temperature warning i... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Possible causes-Engine failures can be caused by mechanical problems in the engine itself, such as damage to portions of the turbine or oil leaks, as well as damage outside the engine such as fuel pump problems or fuel contamination. A turbine engine failure can also be caused by entirely externa... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Failures during takeoff-A turbine-powered aircraft's takeoff procedure is designed around ensuring that an engine failure will not endanger the flight. This is done by planning the takeoff around three critical V speeds, V1, VR and V2. V1 is the critical engine failure recognition speed, the spee... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Failure during extended operations-In order to allow twin-engined aircraft to fly longer routes that are over an hour from a suitable diversion airport, a set of rules known as ETOPS (Extended Twin-engine Operational Performance Standards) is used to ensure a twin turbine engine powered aircraft ... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Engine failures may be classified as either as "contained" or "uncontained".
A contained engine failure is one in which all internal rotating components remain within or embedded in the engine's case (including any containment wrapping that is part of the engine... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-An uncontained engine event occurs when an engine failure results in fragments of rotating engine parts penetrating and escaping through the engine case.The very specific technical distinction between a contained and uncontained engine failure derives from regul... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-The containment of failed rotating parts is a complex process which involves high energy, high speed interactions of numerous locally and remotely located engine components (e.g., failed blade, other blades, containment structure, adjacent cases, bearings, beari... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Notable uncontained engine failure accidents National Airlines Flight 27: a McDonnell Douglas DC-10 flying from Miami to San Francisco in 1973 had an overspeed failure of a General Electric CF6-6, resulting in one fatality. | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Two LOT Polish Airlines flights, both Ilyushin Il-62s, suffered catastrophic uncontained engine failures in the 1980s. The first was in 1980 on LOT Polish Airlines Flight 7 where flight controls were destroyed, killing all 87 on board. In 1987, on LOT Polish Air... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-The Tu-154 crash near Krasnoyarsk was a major aircraft crash that occurred on Sunday, December 23, 1984, in the vicinity of Krasnoyarsk. The Tu-154B-2 airliner of the 1st Krasnoyarsk united aviation unit (Aeroflot) performed passenger flight SU-3519 on the Krasn... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Cameroon Airlines Flight 786: a Boeing 737 flying between Douala and Garoua, Cameroon in 1984 had a failure of a Pratt & Whitney JT8D-15 engine. Two people died. | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-British Airtours Flight 28M: a Boeing 737 flying from Manchester to Corfu in 1985 suffered an uncontained engine failure and fire on takeoff. The takeoff was aborted and the plane turned onto a taxiway and began evacuating. Fifty-five passengers and crew were un... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-United Airlines Flight 232: a McDonnell Douglas DC-10 flying from Denver to Chicago in 1989. The failure of the rear General Electric CF6-6 engine caused the loss of all hydraulics, forcing the pilots to attempt a landing using differential thrust. There were 11... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Baikal Airlines Flight 130: a starter of engine No. 2 on a Tu-154 heading from Irkutsk to Domodedovo, Moscow in 1994, failed to stop after engine startup and continued to operate at over 40,000 rpm with open bleed valves from engines, which caused an uncontained... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-ValuJet 597: A DC-9-32 taking off from Hartsfield Jackson Atlanta International Airport on June 8, 1995, suffered an uncontained engine failure of the 7th stage high pressure compressor disk due to inadequate inspection of the corroded disk. The resulting ruptur... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Delta Air Lines Flight 1288: a McDonnell Douglas MD-88 flying from Pensacola, Florida to Atlanta in 1996 had a cracked compressor rotor hub failure on one of its Pratt & Whitney JT8D-219 engines. Two died. | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-TAM Flight 9755: a Fokker 100, departing Recife/Guararapes–Gilberto Freyre International Airport for São Paulo/Guarulhos International Airport on 15 September 2001, suffered an uncontained engine failure (Rolls-Royce RB.183 Tay) in which fragments of the engine ... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Qantas Flight 32: an Airbus A380 flying from London Heathrow to Sydney (via Singapore) in 2010 had an uncontained failure in a Rolls-Royce Trent 900 engine. The failure was found to have been caused by a misaligned counter bore within a stub oil pipe leading to ... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-British Airways Flight 2276: a Boeing 777-200ER flying from Las Vegas to London in 2015 suffered an uncontained engine failure on its #1 GE90 engine during takeoff, resulting in a large fire on its port side. The aircraft successfully aborted takeoff and the pla... | milkshake721/2.1M-wiki-STEM |
Turbine engine failure-Contained and uncontained failures-Air France Flight 66: an Airbus A380, registration F-HPJE performing flight from Paris, France, to Los Angeles, United States, was en route about 200 nautical miles (230 mi; 370 km) southeast of Nuuk, Greenland, when it suffered a catastrophic engine failure in ... | milkshake721/2.1M-wiki-STEM |
Nuclear magnetic resonance-Nuclear magnetic resonance-Nuclear magnetic resonance (NMR) is a physical phenomenon in which nuclei in a strong constant magnetic field are perturbed by a weak oscillating magnetic field (in the near field) and respond by producing an electromagnetic signal with a frequency characteristic of... | milkshake721/2.1M-wiki-STEM |
Nuclear magnetic resonance-Nuclear magnetic resonance-The most commonly used nuclei are 1H and 13C, although isotopes of many other elements, such as 19F, 31P, and33S, can be studied by high-field NMR spectroscopy as well. In order to interact with the magnetic field in the spectrometer, the nucleus must have an intrin... | milkshake721/2.1M-wiki-STEM |
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