basic+string

1) код; система кодирования || кодировать

2) программа, текст программы, код || программировать, писать программу

3) технические условия

4) нормы и правила; правила эксплуатации

1) назначение

2) обозначение; наименование; маркировка

3) рлк целеуказание; подсвет цели

1) длина; расстояние; протяжённость

2) участок; отрезок; кусок

3) продолжительность, длительность

4) прокатанная заготовка лесн. сортимент

1. объем, величина; вместимость

2. производительность; выработка; ( производственная) мощность; нагрузка

3. пропускная способность;

4. способность

5. электрическая ёмкость

— absorption capacity

— actual capacity

— adhesive capacity

— adsorptive capacity

— available capacity

— basic capacity

— battery capacity

— bearing capacity

— boiler capacity

— calculated capacity

— capacity of drum

— capillary capacity

— carrying capacity

— cation exchange capacity

— compensation hook capacity

— cutting-carrying capacity

— daily capacity

— damping capacity

— delivery capacity

— deschargeable capacity

— drum capacity

— engine capacity

— fracture flow capacity

— gross capacity

— hauling capacity

— heat absorption capacity

— heat capacity

— high capacity

— high resolution capacity

— hoisting capacity

— holding capacity

— idle capacity

— injection capacity

— intake capacity

— jacking capacity

— leak off capacity

— lifting capacity

— liquid capacity

— load bearing capacity

— load-carrying capacity

— methylene blue capacity

— nominal capacity

— overload capacity

— pipe capacity

— pipeline transmission capacity

— piston load capacity

— plant capacity

— producing capacity

— production capacity

— pump capacity

— pumping capacity

— rated capacity

— refrigerating capacity

— relative capacity

— reservoir capacity

— safe working capacity

— sand capacity

— saturation capacity

— short-time capacity

— solids-carrying capacity

— specific capacity

— tank capacity

— tested capacity

— thermal capacity

— throughput capacity

— torsional capacity

— total capacity

— ultimate capacity

— useful capacity

— water capacity

— wind load capacity

— work capacity

— working capacity

1. способность

2. пропускная способность

— casualty-producing capacity

— design capacity

— field producing capacity

— fuel capacity

— gross column capacity

— guaranteed capacity

— hook load capacity

— lift capacity

— open flow capacity

— overload capacity

— pipe capacity

— productive capacity

— racking capacity

— setback capacity

— spare capacity

1) способность

2) пропускная способность

3) производственная мощность ( бурового станка)

4) производительность ( компрессора)

5) пропускная способность

6) грузоподъёмность

•

- capacity of drilling mud to hold cuttings in suspension

* * *

• величина

• дебит

• мощность оборудования или технологического комплекса

• мощность производственная мощность

• подача

• производительность

• производственная мощность

1) команда (см. тж command, order)

2) инструкция; программа действий

3) обучение

•

- absolute instruction

- search for an item

- fuzzy search

- global search

- basic set commands

- delay string command

- flash no step command

- functional command

- position commands

- spacing commands

1) граф

2) график || строить график

3) диаграмма || чертить диаграмму

4) одномерный комплекс

•

- abstract graph

- across terminal graph

- adjacency graph

- afunctional graph

- algebraic graph

- almost complete graph graph

- alternative graph

- ambiguous graph

- antibalanced graph

- antipodal graph

- antisymmetric graph

- arbitrarily traceable graph

- arbitrary graph

- arc-weighted graph

- area probability graph

- associated graph

- asymmetric graph

- augmented graph

- automaton graph

- automorphic graph

- auxiliary graph

- basic graph

- basin of graph

- biased graph

- bichromatic graph

- bicoherent graph

- bicolorable graph

- bicomplete graph

- bicubic graph

- bidegree graph

- bidirected graph

- bidirectional graph

- biplanar graph

- block graph

- bond graph

- bridge graph

- bridgeless graph

- broken-line graph

- cascade graph

- catenary graph

- cause-effect graph

- circle graph

- circuit closed graph

- circuit graph

- circuit symmetric graph

- circuit-free graph

- circuitless graph

- circulant graph

- circular-arc graph

- clique graph

- clusterable graph

- coderived graph

- coinitial graph

- column graph

- communication graph

- compact graph

- comparability graph

- comparison graph

- competition graph

- completely regular graph

- composite graph

- computer graph

- condensed graph

- conjugate graph

- contractible graph

- contrafunctional graph

- contriangulated graph

- convergent graph

- converse graph

- convex graph

- correction graph

- countable graph

- covering graph

- critical graph

- critically connected graph

- current graph

- curved graph

- cycle-balanced graph

- decomposable graph

- degenerate graph

- dense graph

- denumerable graph

- derivative graph

- descendance graph

- diametrically critical graph

- directed graph

- disconnected graph

- disjunctive graph

- divalent graph

- dotted graph

- double graph

- double logarithmic graph

- dual graph

- dually regular graph

- duobalanced graph

- edge graph

- edge-colorable graph

- edge-connected graph

- edge-critical graph

- edge-transitive graph

- edge-weighted graph

- embeddable graph

- empty graph

- entire graph

- enumerated graph

- equipathic graph

- error graph

- essential graph

- even graph

- exact graph

- expandable graph

- extended graph

- factorizable graph

- factorization of graph

- faithful graph

- feedback graph

- five-colorable graph

- five-colored graph

- fixed-point-free graph

- flexible graph

- four-colorable graph

- full line graph

- function graph

- fuzzy graph

- general graph

- generalized graph

- genetic graph

- genus of graph

- geometrical graph

- graph of distribution

- graph of equation

- graph of errors

- graph of function

- graph of game

- graph of inequality

- graph of manifold

- graph of mapping

- graph of operator

- heteromorphic graph

- hexavelent graph

- hierarchical graph

- high-low graph

- homeomorphic graph

- homeomorphically irreducible graph

- homogeneous graph

- hypercritical graph

- hyperoctahedral graph

- hyperprimitive graph

- hypoouterplanar graph

- identity graph

- implication graph

- incidence graph

- incomplete graph

- incremental graph

- indecomposable graph

- independence graph

- integrable graph

- interaction graph

- interchange graph

- intersection graph

- interval graph

- invariant graph

- inverse graph

- inverted graph

- irreducible graph

- irreflexive graph

- isolated graph

- isometric graph

- iterated graph

- knot graph

- ladder graph

- language graph

- lattice graph

- leftcentral graph

- leftcentralizing graph

- linearization graph

- line-rooted graph

- line-symmetric graph

- lobe graph

- local graph

- locally balanced graph

- locally convex graph

- logarithmic graph

- logarithmic probability graph

- loop graph

- loop-free graph

- magic graph

- marked graph

- matrix graph

- maximal graph

- metrically regular graph

- minimal graph

- minimally connected graph

- mixed graph

- multiple bar graph

- multistage graph

- multivalent graph

- mutation graph

- mutually connected graph

- n-chromatic graph

- n-colorable graph

- n-colored graph

- n-connected graph

- n-cyclable graph

- nearly regular graph

- nonbiplanar graph

- noncomplete graph

- noncritical graph

- nondirected graph

- nonfactorable graph

- nonoriented graph

- nonplanar graph

- nonseparable graph

- nonseparating graph

- nontoroidal graph

- nontransitive graph

- nontrivial graph

- normal graph

- normalized graph

- n-partite graph

- n-point graph

- nullity graph

- odd graph

- one-point graph

- operator graph

- order graph

- orderable graph

- ordinary graph

- orientable graph

- outer-embeddable graph

- overlap graph

- pair graph

- pancyclic graph

- parallax-correction graph

- partial graph

- partial order graph

- partition of graph

- partitionable graph

- path-balanced graph

- path-symmetrical graph

- pentagonal graph

- perfect graph

- permutation graph

- point-symmetric graph

- point-to-point graph

- polygonal graph

- polyhedral graph

- polyhedron graph

- population graph

- position graph

- predication graph

- preference graph

- primary graph

- probability graph

- problem-solving graph

- progressively bounded graph

- proof graph

- pruned graph

- pseudogeometric graph

- pseudonet graph

- pseudosymmetric graph

- pulse graph

- pure graph

- quadrangular graph

- quadratic lattice graph

- quadrupole graph

- quasiordered graph

- quasiseparable graph

- quotient graph

- radial graph

- radius of graph

- random graph

- rank graph

- ratio graph

- r-chromatic graph

- reconstructible graph

- rectangular graph

- red-blue graph

- reduced graph

- refutation graph

- regression graph

- regressively bounded graph

- relational graph

- representation graph

- reproduction graph

- residual graph

- resolution graph

- retractible graph

- reverse graph

- rightcentral graph

- rightcentralizing graph

- rigid cycle graph

- rigid graph

- rotated graph

- row graph

- ruled graph

- sample graph

- scattering graph

- schedule graph

- self-complementary graph

- self-conjugate graph

- self-converse graph

- self-dual graph

- self-negational graph

- semicomplete graph

- semilogarithmic graph

- seminormal graph

- semioptimal graph

- semiregular graph

- sequence graph

- series-parallel graph

- sesquivalent graph

- shift graph

- signal graph

- signed graph

- simple graph

- simply rooted graph

- single graph

- skeleton graph

- skew graph

- smoothed graph

- solenoidal graph

- solid graph

- sparse graph

- special graph

- split graph

- star graph

- statistical graph

- stochastic graph

- straight-line graph

- strength of graph

- strictly unilateral graph

- string graph

- strong graph

- strongly isomorphic graphs

- subdivision graph

- superperfect graph

- superposed graph

- symmetrized graph

- system graph

- tensor composite graph

- terminal graph

- tetrahedral graph

- thickness-minimal graph

- threshold graph

- through terminal graph

- time graph

- topological graph

- totally disconnected graph

- tough graph

- transformation graph

- transmission graph

- traversable graph

- triangle graph

- triangle-free graph

- triangular graph

- triangularly embeddable graph

- tricolorable graph

- tripartite graph

- triply connected graph

- trivalent graph

- trivially perfect graph

- two-partite graph

- two-sided graph

- two-terminal graph

- underlying graph

- unicursal graph

- unicyclic graph

- uniform graph

- unilateral graph

- unilaterally connected graph

- unimodular graph

- unipathic graph

- uniquely colorable graph

- unistor graph

- unit interval graph

- unitary graph

- unitizing graph

- universal graph

- unlabeled graph

- unoriented graph

- upper embeddable graph

- utility graph

- vectorial graph

- vertex-bichromatic graph

- vertex-chromatic graph

- vertex-colorable graph

- vertex-connected graph

- vertex-critical graph

- vertex-transitive graph

- void graph

- vortex graph

- weak graph

- weakly connected graph

- whole graph

- zero-symmetric graph

[ˈkɔnstənt]

address constant вчт. адресная константа all other things constant при прочих равных условиях basic real constant вчт. базисная вещественная константа block constant вчт. признак группы c-type constant вчт. символьная константа case constant вчт. константа варианта character constant вчт. символьная константа compile-time constant вчт. статическая константа complex constant вчт. комплексная константа constant верный constant константа constant неизменный, неослабный constant неизменный constant непрерывный constant физ., мат. постоянная (величина), константа; constant of friction коэффициент трения constant постоянная constant постоянная величина constant постоянный; in terms of constant prizes в неизменных ценах constant постоянный constant твердый; верный (идее и т. п.) constant устойчивый constant физ., мат. постоянная (величина), константа; constant of friction коэффициент трения contiguous constant вчт. зависимая константа decay constant вчт. постоянная затухания deferred constant вчт. задержанная постоянная deferred constant вчт. константа времени выполнения diffusion constant вчт. коэффициент диффузии figurative constant вчт. данные файла figurative constant вчт. фигуральная константа floating constant вчт. плавающая константа function constant вчт. функциональная константа group constant вчт. групповая константа grouped constant вчт. табличная константа hexadecimal constant вчт. шестнадцатеричная константа constant постоянный; in terms of constant prizes в неизменных ценах integer constant вчт. целочисленная константа layout constant вчт. макет расположения layout constant вчт. шаблон manifest constant вчт. именованная константа manifest constant вчт. переименованная константа multiplicative constant вчт. постоянный множитель named constant вчт. именованная константа nonconfiguous constant вчт. независимая константа notation constant вчт. нотационная константа numeric constant вчт. числовая константа octal constant вчт. восьмеричная константа predicate constant вчт. предикатная константа proportionality constant вчт. коэффициент пропорциональности real constant вчт. вещественная константа row constant вчт. ограничение по строкам run-time constant вчт. константа времени выполнения sign constant вчт. знаковая константа smoothing constant вчт. сглаживающая постоянная static constant вчт. статическая константа string constant вчт. строковая константа structured constant вчт. структурная константа system constant вчт. константа операционной системы transfer constant вчт. коэффициент передачи type constant вчт. типизованная константа typed constant вчт. типированная постоянная zero constant вчт. константа нуль

[ˈvɛərɪəbl]

access variable вчт. переменная доступа anonymous variable вчт. анонимная переменная apparent variable вчт. связанная переменная array variable вчт. массив attitude variable показатель отношения потребителей automatic variable вчт. динамическая локальная переменная autonomous variable вчт. независимая переменная auxiliary variable вчт. вспомогательная переменная background variable основная переменная based variable вчт. базированная переменная basic variable вчт. базисная переменная boolean variable вчт. логическая переменная bound variable вчт. связанная переменная bounded variable вчт. ограниченная переменная chance variable случайная величина chance variable вчт. случайная переменная character variable вчт. знаковая переменная class variable вчт. переменная класса compile-time variable вчт. переменная периода трансляции complemented variable вчт. переменная с отрицанием continuous variable вчт. непрерывная переменная control variable вчт. управляющая переменная correlated variables коррелированные переменные critical variable стат. критический фактор discontinuous variable вчт. разрывная переменная discrete variable вчт. дискретная переменная dual variable вчт. двойственная переменная dummy variable вчт. несущественная переменная dummy variable вчт. фиктивная переменная environment variable вчт. переменная режима file variable вчт. файловая переменная fixed variable вчт. заданная переменная free variable вчт. свободная переменная global variable вчт. глобальная переменная hidden variable вчт. замаскированная переменная independent variable вчт. независимая переменная independent variable независимая переменная independent variable стат. независимая переменная instance variable вчт. экземплярная переменная integer variable вчт. целая переменная integer variable вчт. целочисленная переменная jointly dependent variables вчт. совместно зависимые переменные label variable вчт. переменная типа метки latent variable вчт. ненаблюдаемая переменная latent variable вчт. скрытая переменная leading variable вчт. ведущая переменная legitimate variable вчт. истинная переменная linguistic variable вчт. лингвистическая переменная loop variable вчт. параметр из цикла master variable вчт. главная переменная memory variable вчт. переменная памяти metalinguistic variable вчт. металингвистическая переменная missing variable вчт. недостающая переменная monetary variable денежная переменная величина monetary variable денежный фактор morphic variable вчт. морфическая переменная multiple random variable многомерная случайная величина nonbasic variable вчт. небазисная переменная nonrandom variable вчт. неслучайная переменная normalized variable вчт. нормированная переменная numerical variable вчт. числовая переменная operation variable вчт. операторная переменная outgoing variable вчт. выводимая переменная output variable вчт. выходная переменная predetermined variable вчт. заранее определенная переменная primal variable переменная прямой задачи primal variables переменные прямой задачи queue-size variable вчт. длина очереди random variable случайная величина real variable вчт. вещественная переменная register variable вчт. регистровая переменная service-time variable вчт. время обслуживания slack variable вчт. свободная переменная slave variable вчт. зависимая переменная state variable вчт. переменная состояния state variable вчт. фазовая переменная status variable вчт. параметр состояния stochastic variable вчт. случайная величина stochastic variables случайные величины stochastical variable вчт. случайная величина stochastical variables случайные величины string variable вчт. строковая переменная subscript variable вчт. индексированная переменная syntactic variable вчт. синтаксическая переменная system variable вчт. системная переменная task variable вчт. переменная задачи temporary variable вчт. временная переменная unbound variable вчт. несвязанная переменная uncomplemented variable вчт. переменная без отрицания undeclared variable вчт. необъявленная переменная uninitialized variable вчт. неинициализированная переменная unrestricted variable вчт. не имеющая ограничений переменная unrestricted variable вчт. неограниченная переменная variable биол. аберрантный; изменчивый variable изменчивый, непостоянный; variable weather неустойчивая погода variable изменчивый variable изменяемый variable количественный признак variable меняющийся variable непостоянный variable мор. неровный ветер variable неустойчивый variable вчт. переменная variable мат. переменная (величина) variable мат. переменная величина variable переменный (тж. мат.) variable переменный variable регулируемый variable мор. районы океана, где нет постоянного ветра variable изменчивый, непостоянный; variable weather неустойчивая погода word variable вчт. переменная типа слово

   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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)

    17) Form and Content Are Not Fundamentally Different

   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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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)

 

фотоэлектрическая станция
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EN

• photovoltaic facility
• photovoltaic plant
• photovoltaic power plant

 

фотоэлектрическая установка
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[Интент]

Параллельные тексты EN-RU

Photovoltaic plants

A photovoltaic plant permits to convert the energy associated with solar irradiation into electrical energy of direct type; these plants are constituted by panels of semiconducting material, which can generate electrical power once exposed to the rays of the sun.

Photovoltaic plants can be grid-connected or supply a single load (stand alone plant).

In this last case an accumulator battery shall be present to provide power supply in case of lack of solar radiation.

The basic element of a photovoltaic plant is the photovoltaic cell constituted by semiconducting material (amorphous silicon or monocrystalline silicon); this cell, exposed to the rays of the sun, is able to supply a maximum current Impp at a maximum voltage Vmpp, which a maximum power called Wp corresponds to.

More photovoltaic cells are connected in series to form a string to raise the voltage level; by connecting more strings in parallel, the current level is increased.

For example, if a single cell can provide 5A at 35.5 Vd.c., in order to reach the level of 100A at 500 Vd.c., it is necessary to connect 20 strings in parallel, each of them constituted by 15 cells.

Generally speaking, a stand alone photovoltaic plant is constituted by the following devices:
- photovoltaic array: constituted by the photovoltaic cells suitably interconnected and used for the conversion of sunlight energy into electrical energy;
- charge regulator: it is an electronic device able to regulate charging and discharging of accumulators;
- accumulator batteries: they can provide power supply in case of lack of solar radiation;
- DC/AC inverter: it has the function of turning direct current into alternating current by controlling it and stabilizing its frequency and waveform.

The following figure shows the block diagram of a stand alone photovoltaic plant..
[ABB]

Фотоэлектрические установки

Фотоэлектрические установки осуществляют прямое преобразование солнечной энергии в электрическую. Такие установки состоят из панелей полупроводникового материала, вырабатывающего электрическую энергию под воздействием солнечного излучения.

Фотоэлектрические установки являться частью общей энергосистемы или снабжать электроэнергией отдельную нагрузку (автономную установку).

В последнем случае в состав системы должна входить аккумуляторная батарея, обеспечивающая бесперебойную подачу электроэнергии в случае недостаточного солнечного излучения.

Основной частью фотоэлектрической установки являются фотоэлементы, изготовленные из полупроводникового материала (аморфного или монокристаллического кремния). При облучении солнечными лучами фотоэлемент генерирует максимальный ток Iмакс. при максимальном напряжении Uмакс, что соответствует максимальной мощности Wмакс..

Для увеличения выходного напряжения несколько фотоэлементов соединяют последовательно в ряд. Для увеличения тока несколько рядов соединяют параллельно.

Так, например, если один фотоэлемент может произвести ток 5 А при напряжении 35,5 В пост. тока, то для получения источника электроэнергии с током 100 А при напряжении 500 В пост. тока требуется соединить параллельно 20 рядов по 15 фотоэлементов в каждом.

Обычно автономная фотоэлектрическая установка состоит из следующих устройств:
- фотоэлектрическая батарея: состоит из соединенных между собой фотоэлементов, преобразующих солнечную энергию в электрическую;
- регулятор заряда: электронное устройство, предназначенное для регулирования заряда и разряда аккумуляторов;
- аккумуляторная батарея: обеспечивает электропитание нагрузки при недостаточном солнечном излучении.
- инвертор: преобразует постоянный ток в переменный ток стабильной частоты и формы.

На следующем рисунке показана функциональная схема автономной фотоэлектрической установки
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