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1 development simulation model
Англо-русский словарь нормативно-технической терминологии > development simulation model
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2 development simulation model
Большой англо-русский и русско-английский словарь > development simulation model
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3 development simulation model
Англо-русский словарь нефтегазовой промышленности > development simulation model
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4 development simulation model
1) Нефть: модель разработки2) Нефть и газ: модель разработки месторожденияУниверсальный англо-русский словарь > development simulation model
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5 development simulation model
Англо-русский словарь по машиностроению > development simulation model
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6 model
модель, макет; образец; шаблон; копия || моделировать
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модель; макет; образец
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модель (для исследования физических явлений; может быть теоретической, физической или математической)
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модель; макет; образец- availability model
- average velocity model
- breakdown model
- bulk-freezing model
- catastrophic failure model
- chain model
- constant hazard model
- constant velocity model
- convolutional model
- corrosion model
- critical-failure model
- depth model
- development simulation model
- discrete model
- electrical model
- exploding reflector model
- failure model
- failure development model
- failure diagnosis model
- failure rate model
- failure tendency model
- fatigue model
- fatigue life model
- fault model
- fault-effect model
- fault-handling model
- fault-testing model
- field model
- formation model
- frozen state model
- gas pool model
- gas reservoir model
- geological model
- geological structural model
- hazard-rate model
- homogeneous equilibrium model
- layered model
- life model
- magnetotelluric model
- maintainability model
- maintenance model
- network model
- oil spill risk assessment model
- plane model
- preproduction model
- preventive model
- product performance model
- proportional hazards model
- reliability growth model
- reliability operational model
- reliability prediction model
- reliability simulation model
- reliability structure model
- relief model
- replacement model
- reservoir model
- safety model
- salt-dome model
- scaled model
- seismic model
- service model
- slip model
- static reliability model
- stratified model
- stress-strength reliability model
- system reliability model
- two-fluid model
- two-layer model
- vertically stratified model
- vulnerability model* * * -
7 development
разработка; развитие; эволюция; отладка (механизма); усовершенствование; улучшение; строительство; сооружение; мелиорация; выведение сорта (напр. растения); конструктивное улучшение; сюрфасография; развёртка (поверхности); вывод (формулы); доводка; образование (кристалла); проявление (фото)- development of aggregates - development of failure - development of gas - development of heat - development of method - development of new technologies - development road - development settings - development simulation model - development test - development trials - development tools - development type - oil development -
8 model
1. модель; макет; образец; эталон/ модельный/ моделировать2. модель; вариант; типactuator modelactuator disk modeladvanced development modelaerodynamic modelaeroelastic modelaeroelastically scaled modelair combat modelairplane modelairplane-like modelapproach modelarrow-wing modelatmospheric modelautopilot modelautorotation modelautothrottle modelBaldwin-Lomax modelbasic modelbeam modelbeam/lumped mass modelbiomorphic modelblowing modelbody alone modelbreadboard modelcable-mounted modelcargo load modelCFD modelcombustion modelcombustion-flow modelcommand modelcompensatory modelcomposite modelcompressibility modified modelcomputer modelcone-cylinder modelcone-finned modelconical-flow modelconsistent modelconstant amplitude fatigue modelconstitutive modelcontinuous-mass modelcontinuum modelcontroller modelcorrelation modelcounter-rotation modelcrack modelcrack growth modelcrack growth retardation modelcrossover modelcumulative damage modeldamage accumulation modeldamper modeldatabase modeldeterministic modeldevelopment-type modeldifferential-game modeldiscrete modeldistributed lift modeldisturbance modeldowndraft modeldrop modelDryden modelDugdale modeldynamically scaled modeldynamics modelenergy-conservation modelengagement modelengine modelengineering development modelerror modelfailure modelfatigue modelfilament modelfine grid modelfinite element modelfixed-base modelfixed-wing modelflow modelflutter modelflutter-suppression modelfour-input/four-output modelfractional derivative modelfracture modelfree to roll modelfree-flight modelfree-flying modelfree-spinning modelfreely flying modelfrequency-domain modelfull modelfull-order modelfull-span modelfull-span wing modelgame modelgeneric modelgeometric modelgeometrically scaled modelgravity modelgravity anomaly modelgust modelhalf-plane modelhalf-wing modelhigh-fineness-ratio modelhuman operator modelidentified modelilluminated modelinfinite-blade modelinput modelinstrumented modelinverse modelisolated wing modelk-e modelk-W modelkinematic modelkinetic modellarge-scale modellead-lag pilot modellead-only pilot modellinear modellongitudinal modellower-order modellumped parameter modellumped-mass modelMach-scaled modelmagnetically suspended modelmass-and-spring modelmass-spring modelMaxwell modelmembrane and rod modelmeteorological modelmicromechanical modelMiner-Palmgren damage modelminimum phase modelmissile modelmodal modelmomentum-conserved modelmoving modelmultiaxis modelmultidegree of freedom modelmultiloop modelmultiscale modelneuromuscular modelobservation modelover-parameterized modelparabolized Navier-Stokes modelpendulation modelperformance modelperturbation modelphenomenological modelphysical modelpilot-aircraft modelpilot-vehicle modelpiston modelpitch modelpitch-plunge modelpitch-lateral-directional modelplant modelplastic modelpneumodynamic modelpowered modelpowered-lift modelprecision modelprediction modelpreview modelproduction modelproperly parameterized modelpropfan modelpropulsion modelpure gain pilot modelquantized modelquasi-static modelR&M modelradar modelradial spring modelradio control modelradio controlled modelreal-world modelreduced order modelreference modelreflectivity modelreingestion modelreplica modelreplica-type modelrocket-propelled modelroll modelrotor-body modelrotorcraft modelscale modelscaled modelscattering modelself-consistent modelsemiempirical modelsemispan modelsemispan wing modelsensitivity modelsimulation modelsingle-axis modelsingle-body modelsingle-rotation modelspectrum fatigue modelspray modelstall modelstate modelstate space modelstatistical modelstiffness modelstochastic modelstress modelstructural modelstudy modelsupersonic cruise modeltask modelterrain modelthin-jet modelthree-degree-of-a-freedom modelthree-state modelthrust modeltire modeltransfer-function modeltransparent modeltruth modeltunnel-supported modelturbulence modeltwin-body modeltwo layer turbulence modeltwo-control modeltwo-degrees-of-freedom modeltwo-equation turbulence modelunquantized modeluntuned modelusage modelV/STOL modelvaporization modelvehicle stability modelvertical dynamic modelvestibular modelviscous/inviscid modelvisual cueing modelwake modelwake/wing modelwater tunnel modelWheeler retardation modelwind-tunnel modelwindshear modelwing-canard modelwing-rotor modelwireframe modelyaw model -
9 model
модель; образец; моделировать0,16-scale model — модель в 0,16 натуральной величины
combat aircrew rescue simulation model — модель процесса спасения членов экипажа при повреждении ЛА в боевой обстановке
fixed-wing supersonic transport model — модель сверхзвукового транспортного самолёта с крылом неизменяемой геометрии
have... hours in model — иметь налёт... часов на данном типе (ЛА)
space shuttle booster model — модель [макет] разгонной ступени [ускорителя] челночного КЛА
space shuttle orbiter model — модель [макет] орбитальной ступени челночного КЛА
sting(-mounted, -supported) model — модель (установленная) на державке
v.g. model — модель [вариант] с изменяемой геометрией
— C model -
10 model
1) макет; модель || моделировать2) образец4) модель, тип ( изделия)5) шаблон•- countably saturated model - countably uniform model - coupled channels model - finite state model - finitely generated model - game-theory model - random trial increment model - random walk model - sampling model -
11 model
1) модель; макет; образец; эталон || моделировать; изготавливать по образцу или эталону || образцовый; эталонный2) форма || придавать форму3) фасонного сечения (напр. о металле)•- 3-D model
- 3-D wireframe model
- algorithm model
- analog model
- animation model
- as-machined model
- autoregressive model
- behavioral model
- brand-new model
- CAD model
- CAD solid model
- cammed model
- causal model
- CGS model
- client-server model
- CN model
- component connection model
- computational model
- computer model
- conceptual model
- control model
- data model
- development models
- dexel model
- diagnosis model
- diagnostic model
- die model
- discrete parts manufacturing model
- disturbance model
- ER model
- error model of a machine
- error model of a single axis
- experimental model
- feature-based CAD model
- feature-based model
- finite-dimensional model
- flexible manufacturing model
- FMS model
- force deflection model
- freeform computer model
- full-scale model
- generalized model
- generic action model
- generic activity model
- generic model
- hierarchic data model
- hierarchical data model
- hierarchical model
- hierarchically structured model
- horizontal model
- infinite-dimensional model
- information-logical model
- kinetic laser anneal model
- language model
- large-scale model
- learning model
- life-size model
- life-sized model
- log normal model
- master model
- mathematical surface model
- meaning $ text model
- network model
- observation model
- orthogonal flute model
- OSI model
- parallel computational model
- parameter-oriented model
- Petri model
- PN model
- polyhedral model
- principal model
- process-message model
- product model
- profile model
- qualitative model
- quantitative model
- queueing model
- R and D model
- reference model
- relation model
- relational model
- repair model
- representative model
- reverse engineer model
- scale model
- scaled-down model
- scaled-up model
- sculptured surface model
- semantic model
- shop floor model
- shop floor production model
- signal model
- simulated model
- simulation model
- software model
- solid model
- solids model
- stochastic model
- structural model
- surface model
- surfaced CAD model
- symbolic model
- task-specific model
- technological model
- test model
- time-series model
- tool animation model
- top-of-the-line model
- topological model
- tracking model
- transaction model of AGVs
- true-volume model
- underconstrained model
- undimensioned model
- unifying model
- vertical model
- vibration model
- volumetric error model
- wholistic model
- wire-frame model
- working model
- world modelEnglish-Russian dictionary of mechanical engineering and automation > model
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12 model
1) модель (напр. экономики)2) тип, марка конструкции, модель (напр. автомобиля) -
13 simulation
Gen Mgtthe construction of a mathematical model to imitate the behavior of a real-world situation or system in order to test the outcomes of alternative courses of action. Simulation was used in a military context by the Chinese as many as 5,000 years ago and has applications in the fields of science, research and development, economics, and business systems. The use of simulation has become more widespread since the development of computers in the 1950s, which facilitated the manipulation of large quantities of data and made it possible to model more complex systems. Simulation techniques are used in situations where reallife experimentation would be impossible, costly, or dangerous, and for training purposes. -
14 модель разработки
Большой англо-русский и русско-английский словарь > модель разработки
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15 test
испытание, проба, исследование, см. тж. testing, trials; испытывать, пробовать; исследоватьacceptable environmental range test — испытание для определения диапазона допустимых изменений условий окружающей среды
jolt and jumble test — разг. испытание на удар и вибрацию
partial climb flight tests — лётные испытания «на зубцы»
single engine stall tests — испытания на срыв [сваливание] с одним работающим двигателем
supercharged CFR engine test — оценка детонационной стойкости (авиационных бензинов) на одноцилиндровой установке CFR
water(-flow, -impingement) test — холодная проливка (ракетного двигателя)
— air test— bed test— hot test— jet test -
16 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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17 test
1. испытание, испытания; проверка; контроль; тестирование; опробование;см. тж. testing/ испытывать; проверять; контролировать; тестировать; опробовать2. тест; проба3. критерий; признакaccelerate-stop testsaccelerated mission testacceleration-deceleration testaerobatic flight testsaeroelastic testsagility testair resonance testair-to-air testair-to-air combat testairframe testairspeed calibration testall-attitude flight testasymmetric loads testsasymmetric wing sweep flight testsauto-guidance testsbefore-flight-rated testsbench testbiaxial fatigue testbird impact testbird ingestion testbird strike testbird strike testsburn-in testburst pressure testcatapult testscentrifugal load testcentrifuge testsclean configuration testclosed-loop testcockpit workload testcombined-systems testscompatibility testcomponent testscompression testcomputer-aided testconstant amplitude testcontinued takeoff testscontrol testconvergence testcooling testcrack-detection testscrash testcreep testcrush testscyclic testsdamage tolerance testdamage resistance testdeparture testdepressed-trajectory testdestruction testdevelopment testsdivergence testsdual-frequency testdurability testdynamometer testselectromagnetic interference testselectromagnetic-vulnerability testemergency survival testEMI testsendurance testengine reingestion testenvironmental testexploratory testsfatigue testflammability testflaps up landing testsflexure testflight clearance testflight simulation testsflight-by-flight fatigue testflight simulation fatigue testsflow field testsflow visualization testsflutter testflutter-proof testsflyover testsforce testsforced oscillation testsforeign object damage testfree oscillation testsfree spinning facility testsfree-flight testfree-to-roll testsfrequency response testsfrequency-sweep testfuel runout testfull-scale testground effect testground resonance testhandling qualities testshard-ride testheat testsheavyweight testhigh-angle-of-attack testshigh-alpha testhover testshover in-ground-effect testhover-in-ground testhovering testshumidity testsicing testimpact testin-plant testsinput-to-output testsiron bird testslanding testlanding flap testslife roll testlimit cycle testslimited-envelope flight testload-deflection testlogic testlow-observability testmaneuvering testsmanual flight testsmodal testmodal survey testmode interaction testsmodel tests of airfoilsmoire interferometry testsnoise testnondestructive testnormal takeoff testsNyquist stability testopen-loop testsoperability testoscillatory testsoverland testsoxygen testsperformance testpilot-in-loop testsplenum-chamber burning testspost-flight testpreflight testpressure testpressurization testproof-lood testproof-of-concept testsradar cross-section testradiographic testrain testramp testsrate of climb testrejected takeoff testreliability testremote-site testrepair testresidual strength testresonance testsreverse-thrust testsrig testrobustness testroll-on-rim testrolling testrotary-balance testsrough ground profile testshake testshakedown testshear testsideslip testssimulation verification testsimulator testsmall-scale testsmoke testspin testsspray ingestion teststability teststall testsstatic testsstatic strength teststeady rolling testssteady state teststealth teststiffness teststore compatibility teststrength testsstructural testssupercritical testsystem integration testtail on/off teststakeoff testtaxy testtensile testtension testtethered testtowing testtransfer function testsvalidation testsvectored thrust testvibration testvulnerability testwater-tank testwaveoff testswheel testwhirl testwind blast testwind tunnel testwing-fatigue testwingborne mode flight testyaw oscillation testszero-speed-zero-altitude testzero-zero test -
18 environment
1) окружающая среда; условия окружающей среды; обстановка2) вчт окружениеб) область памяти, резервируемая для переменных, используемых приложением (в операционной системе MS DOS)в) процедура, окаймлённая операторами начала и окончания процедуры (в системе LATEX)•- database environment
- dead acoustic environment
- design environment
- distributed computing environment
- electromagnetic environment
- electromagnetic pulse environment
- electronic environment
- embedded environment - event-driven environment - hardware environment
- induced environment
- integrated development environment - multicarrier environment
- multipath environment
- multi-user simulation environment
- nested environment - operational environment
- programming environment
- radiation environment
- real-life environment
- reentry environment
- rugged environment - severe environment - software environment
- space environment - use environment
- virtual machine environment
- windowing environment
- windows environment -
19 environment
1) окружающая среда; условия окружающей среды; обстановка2) вчт. окружениеб) область памяти, резервируемая для переменных, используемых приложением (в операционной системе MS DOS)в) процедура, окаймлённая операторами начала и окончания процедуры (в системе LATEX)•- application program support environment
- artificial environment
- client/server open development environment
- common open software environment
- cross-platform environment
- database environment
- dead acoustic environment
- design environment
- distributed computing environment
- electromagnetic environment
- electromagnetic pulse environment
- electronic environment
- embedded advanced sampling environment
- embedded environment
- EMP environment
- event-driven environment
- GNU network object model environment
- graphical user environment
- ground environment
- hardware environment
- induced environment
- integrated development and debugging environment
- integrated development environment
- interactive environment
- ISO development environment
- Java runtime environment
- luminous environment
- multicarrier environment
- multipath environment
- multi-user simulation environment
- nested environment
- normal input/output control environment
- nuclear environment
- open collaboration environment
- operating environment
- operational environment
- programming environment
- radiation environment
- real-life environment
- reentry environment
- rugged environment
- semiautomatic ground environment
- service environment
- severe environment
- simple communications programming environment
- simulated environment
- software environment
- space environment
- structured and open environment
- test environment
- use environment
- virtual machine environment
- windowing environment
- windows environmentThe New English-Russian Dictionary of Radio-electronics > environment
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20 test
1) испытания || испытывать2) проверка; контроль || проверять; контролировать3) тест; тестирование || тестировать4) критерий; условие; признак•- test of independence
- accelerated life test
- acceleration test
- acceptance test
- actual test
- aging test
- alpha test
- asymptotic test
- audible test
- augmented test
- augmented Dickey-Fuller test
- autocorrelation test
- Bayes test
- bed of nails test
- bench test
- best unbiased test
- beta test
- biased test
- Box-Pierce test
- breakdown test
- breaking test
- break-point test
- Breush-Pagan test
- built-in test
- built-in error rate test
- burn-in reliability test
- built-in self-test
- busy test
- calibration test
- camera linearity test
- captive test
- Charpy test
- check test
- chi-square test
- chi-square test for goodness-of-fit
- chi-square test for homogeneity
- Chow test
- clock-rate test
- closed-loop test
- cointegration test
- combined environmental reliability test
- common factor test
- comparative listening test
- comparison test
- computer-aided test
- conditional moment test
- connectivity test
- conservative test
- consistent test
- constant acceleration test
- constant-load amplitude test
- continuity test
- cumulative sum test
- cumulative sum of squares test
- degradation rate test
- destructive test
- development test
- diagnostic test
- diagnostic function test
- Dickey-Fuller test
- dielectric breakdown test
- differencing test
- distribution-free test
- drive fitness test
- dummy test
- Durbin's h-test
- Durbin-Watson test
- dynamic test
- efficient test
- electrostatic discharge test
- engaged test
- engineering test
- environmental test
- ESD test
- exact test
- exhaustive test
- extensive test
- extreme test
- F-test
- failure-rate test
- field test
- Fisher's test
- Fisher's exact test
- flash test
- forced-failure test
- Friedman's test
- functional test
- gamma test
- Gleiser test
- Godfrey test
- Goldfeld-Quandt test
- go/no-go confidence test
- goodness-of-fit test
- goodness-of-fit chi-square test
- Granger causality test
- Hausman test
- high-potential test
- homogeneity test
- hot-weather test
- hypothesis test
- impact test
- in-circuit test
- independence chi-square test
- indoor test
- information matrix test
- integrated test
- intelligence test
- in-use life test
- invariant test
- J-test
- Kolmogorov-Smirnov test
- Kruskal-Wallis test
- Lagrange multiplier test
- leak test
- leakage test
- life test
- likelihood ratio test
- Ljung-Box test
- local loopback test
- logical test
- log-rank test
- longevity test
- long-term test
- long-time test
- loopback test
- lot-by-lot test
- mandrel test
- Mann-Whitney rank sum test
- Mantel-Cox test
- marginal test
- matrix life test
- memory address test
- misspecification test
- mock-up test
- model test
- modem loopback test
- moisture resistance test
- most powerful test
- multiple-comparison test
- nested test
- non-Bayes test
- nondestructive test
- non-linearity test
- non-nested test
- non-parametric test
- normal-theory based test
- off-line test
- omitted variables test
- on-demand test
- one-sample test
- one-sided test
- on-line test
- on-off test
- open-loop test
- operating-life test
- operational readiness and reliability test
- outer product of gradient test
- over-identifying restrictions test
- parameter constancy test
- parameter-free test
- parametric test
- Pearson's test
- percentage test
- performance test
- power-on self test
- predictive failure test
- preliminary test
- premodel test
- progressive stress test
- proof test
- prototype test
- qualification test
- randomization test
- randomized-step test
- rank test
- reliability test
- remote loopback test
- rig test
- ringing test
- robust statistical test
- routine test
- runs test
- semidestructive test
- sequential test
- sequential probability ratio test
- service test
- shakedown test
- shake-table test
- shelf-life test
- shock test
- short-term test
- short-time test
- significance test
- simulated test
- simulation test
- sing test
- space test
- specification test
- SS test
- static test
- statistical test
- step-stress test
- strength test
- structural test
- studentized test
- Student's test
- subjective test
- system test
- systems test
- terminal strength test
- thermal test
- thermal-fatigue test
- thermal-shock test
- tropical test
- truth-table test
- tuning-fork test
- Turing test
- two-sided test
- ultrasonic test
- unbiased test
- uniformly most powerful test
- unit root test
- variable addition test
- variable deletion test
- vertical-interval test
- vibration test
- vitality test
- voltage-breakdown test
- Wald test
- wear test
- White test
- Wilcoxon signed rank test
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