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string testing

  • 1 контроль обсадных колонн

    Универсальный русско-английский словарь > контроль обсадных колонн

  • 2 испытательная колонна

    Универсальный русско-английский словарь > испытательная колонна

  • 3 Испытания на взаимодействие

    Универсальный русско-английский словарь > Испытания на взаимодействие

  • 4 испытания на взаимодействие

    Универсальный русско-английский словарь > испытания на взаимодействие

  • 5 контроль обсадных колонн

    Русско-английский словарь по нефти и газу > контроль обсадных колонн

  • 6 тестирование цепочек

    ( программ) string testing

    Русско-английский словарь по вычислительной технике и программированию > тестирование цепочек

  • 7 пробная струна

    Русско-английский физический словарь > пробная струна

  • 8 натурные испытания

    2) Economy: (полевые) actual-service test
    3) Telecommunications: military tests, military test
    4) Astronautics: verification test in situ
    5) Metrology: field tests
    6) Sakhalin energy glossary: full-scale testing
    8) Makarov: actual testing
    9) Electrochemistry: field trial, operational trials
    10) oil&gas: string test (натурные испытания компрессоров в комплексной испытательной системе, моделирующей условия реальной работы, compressor string test)
    11) Research and development: verification test in site

    Универсальный русско-английский словарь > натурные испытания

  • 9 пробная струна

    Универсальный русско-английский словарь > пробная струна

  • 10 разрыв трубы

    2) Military: pipe breakage
    4) Drilling: string burst

    Универсальный русско-английский словарь > разрыв трубы

  • 11 труба

    conduit, duct, pipe, tube
    * * *
    труба́ ж.
    (для транспортировки и т. п.) pipe; ( собирательно) piping; (в различных системах, в теплообменниках и котлах, в машиностроении) tube; ( собирательно) tubing
    гнуть трубу́ — bend a pipe [a tube]
    плю́щить трубу́ — flatten a pipe [a tube]
    разда́ть трубу́ — expand a pipe [a tube]
    сва́ривать тру́бы — weld pipes [tubes]
    труба́ смина́ется — a pipe [a tube] collapses
    соединя́ть тру́бы — join pipes [tubes]
    тяну́ть тру́бы — draw pipes [tubes]
    фланжи́ровать [фланцева́ть] трубу́ — flange the ends of a pipe [a tube]
    асбоцеме́нтная труба́ — asbestos-cement pipe
    аэродинами́ческая труба́ — wind [aerodynamic] tunnel, (air) tunnel
    продува́ть в аэродинами́ческой трубе́ — test smth. in a wind tunnel, subject smth. to wind-tunnel testing
    безнапо́рная труба́ — nonpressure pipe
    труба́ без наре́зки — bare pipe
    бесшо́вная труба́ — seamless pipe
    бури́льная труба́ — drill pipe
    опуска́ть бури́льную трубу́ ( в скважину) — run a drill pipe in (a borehole)
    поднима́ть бури́льную трубу́ ( из скважины) — pull a drill pipe out (of a borehole)
    вентиляцио́нная труба́ — vent tube, ventilation [air] duct, air conduit
    труба́ Венту́ри — Venturi tube
    вестова́я труба́ — vent pipe
    вихрева́я труба́ тепл. — vortex tube
    водонапо́рная труба́ — pressure-water pipe
    водоопускна́я труба́ — downcomer
    водоотво́дная труба́ — drain-pipe
    водоперепускна́я труба́ — water circulator
    водопрово́дная труба́ — water-supply pipe
    водопропускна́я труба́ — pipe culvert
    водосто́чная труба́ — drain-pipe
    воздуходу́вная труба́ — blast pipe
    возду́шная, кольцева́я труба́ ( доменной печи) — bustle pipe
    волни́стая труба́ — corrugated pipe
    волочё́ная труба́ — drawn pipe
    впускна́я труба́ — intake [inlet, admission] pipe
    вса́сывающая труба́ — suction pipe
    выпускна́я труба́ — exhaust [outlet] pipe, exhaust stack
    выхлопна́я труба́ — exhaust [outlet] pipe, exhaust stack
    выхлопна́я труба́ реакти́вного дви́гателя — jetpipe
    га́зовая труба́ — gas pipe
    гельмпорто́вая труба́ — rudder tube, rudder well
    гидродинами́ческая труба́ — water tunnel
    глазуро́ванная труба́ — glazed pipe
    труба́ глуши́теля — muffler pipe
    горячека́таная труба́ — hot-rolled pipe
    горячетя́нутая труба́ — hot-drawn pipe
    дейдву́дная труба́ мор. — stern tube
    труба́ для подво́дного бетони́рования — tremie pipe
    дрена́жная труба́ — drain-pipe
    дымова́я труба́ — chimney, chimney [smoke] stack; ( на судне) (smoke) funnel, smoke stack
    дымова́я труба́ воздухонагрева́теля — stove chimney
    дымога́рная труба́ — fire [smoke] tube
    жарова́я труба́ ( парового котла) — flue [furnace] tube; flame tube
    железобето́нная труба́ — reinforced-concrete pipe
    забо́рная труба́ — intake pipe
    загру́зочная труба́ — charging [feeding] pipe
    зри́тельная труба́ (в геодезических и др. инструментах) — telescope
    зри́тельная труба́ наведена́ на ве́ху А — the telescope sights stake A
    наводи́ть зри́тельную трубу́ на ве́ху С — sight the telescope to stake C
    зри́тельная, веду́щая труба́ — guiding telescope
    зри́тельная, перекладна́я труба́ — reversible telescope
    труба́ ка́бельной канализа́ции — cable duct
    труба́ ка́бельной канализа́ции, бло́чная — multiple (cable) duct
    труба́ ка́бельной канализа́ции, одино́чная — single (cable) duct
    труба́ ка́бельной канализа́ции, паке́тная — multiway (cable) duct
    кавитацио́нная труба́ — cavitation tunnel
    канализацио́нная труба́ — sewer pipe
    ка́таная труба́ — rolled pipe
    керами́ческая труба́ — earthenware [stone-ware] duct, clay conduit
    кипяти́льная труба́ — steam-generating [steaming] tube
    клё́паная труба́ — rivet(t)ed pipe
    колле́кторная труба́ — manifold pipe
    коло́нковая труба́ — core barrel
    компенсацио́нная труба́ — expansion pipe
    контро́льная труба́ — tell-tale pipe
    лита́я труба́ — cast pipe
    машинострои́тельная труба́ — mechanical tubing
    металли́ческая труба́ — metal pipe
    напо́рная труба́ — pressure pipe
    насо́сно-компре́ссорная труба́ ( для скважин) — tubing string
    обводна́я труба́ — by-pass pipe
    обра́тная труба́ — return pipe
    отводна́я труба́ ( внутренней канализации) — soil [waste] stack
    отводя́щая труба́ — outlet [discharge] tube
    отса́сывающая труба́ ( гидротурбины) — брит. draught tube; амер. draft tube
    отса́сывающая, изо́гнутая труба́ — elbow-type draught tube
    парова́я труба́ — steam pipe
    перебро́сная труба́ — crossover pipe, upflow tube
    перегово́рная труба́ — speaking [voice] tube
    переливна́я труба́ — overflow pipe
    перехо́дная труба́ — reducing pipe
    пла́менная труба́ — flame tube
    погру́женная труба́ — immersion [drowned] pipe
    подзо́рная труба́ — spy-glass
    прито́чная труба́ — influent pipe
    проду́вочная труба́ — blow-off tube
    пролё́тная труба́ ( клистрона) — drift tube
    промывна́я труба́ — wash [flushing] pipe
    пылеотводна́я труба́ — dust trunk
    разводна́я труба́ — circulation [distribution] pipe
    растру́бная труба́ — inserted-joint pipe
    ребри́стая труба́ — finned tube
    сварна́я труба́ — welded pipe
    сифо́нная труба́ — siphon pipe
    труба́ сква́жины, обса́дная — casing
    смывна́я труба́ — flushing pipe
    труба́ со швом — seam pipe
    спускна́я труба́ — discharge [outlet] pipe; blow-off pipe
    труба́ с резьбо́й — threaded pipe
    стальна́я труба́ — steel pipe
    толстосте́нная труба́ — thick-walled tube
    тонкосте́нная труба́ — thin-walled tube
    транспорти́рующая труба́ — conveying pipe
    фа́новая труба́ — sewage pipe
    фасо́нные тру́бы — shaped tubing, shaped [non-circular section] tubes
    фла́нцевая труба́ — flanged pipe
    холоди́льная труба́ — refrigeration pipe
    холоднока́таная труба́ — cold-rolled pipe
    холоднотя́нутая труба́ — cold-drawn pipe
    цельноко́ваная труба́ — forged pipe
    цепна́я труба́ — chain (locker) pipe
    циркуляцио́нная труба́ — circulating pipe; ( в выпарном аппарате) downcomer
    чугу́нная труба́ — cast-iron pipe
    шла́мовая труба́ — wash pipe
    экра́нная труба́ — water-wall tube
    я́корная труба́ — hawse pipe

    Русско-английский политехнический словарь > труба

  • 12 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, Eventually
       Just 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)
       Many 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 Form
       The 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 Formation
       It 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 Contexts
       Even 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)
        18) The Assumption That the Mind Is a Formal System
       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 Intelligence
       The 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 Propositions
       In 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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  • Mutation testing — For the biological term, see: Gene mutation analysis. Software Testing portal Mutation testing (or Mutation analysis or Program mutation) is a method of software testing, which involves modifying programs source code or byte code in small ways …   Wikipedia

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  • Magic string — A magic string is an input that a programmer believes will never come externally and which activates otherwise hidden functionality. A user of this program would likely provide input that gives an expected response in most situations. However, if …   Wikipedia

  • Self-testing code — is software which incorporates built in tests (see test first development).In Java, to execute a unit test from the command line, a class can have methods like the following.// Executing main runs the unit test. public static void main(String []… …   Wikipedia

  • Dependency injection — (DI) is a design pattern in object oriented computer programming whose purpose is to improve testability of, and simplify deployment of components in very large software systems. The Dependency Injection pattern involves at least three elements:… …   Wikipedia

  • performing arts — arts or skills that require public performance, as acting, singing, or dancing. [1945 50] * * * ▪ 2009 Introduction Music Classical.       The last vestiges of the Cold War seemed to thaw for a moment on Feb. 26, 2008, when the unfamiliar strains …   Universalium

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