basic+number

1. область; зона; диапазон

2. граница < области>

3. оболочка; обшивка; кожух

4. огибающая <поверхность, кривая>

5. баллон

9-g envelope

airspeed-altitude envelope

airstart envelope

altitude envelope

altitude-Mach number envelope

angle-of-attack/Mach number envelope

angle-of-attack envelope

AOA envelope

asymmetric sweep envelope

authorized flight envelope

basic flight envelope

boom refuelling envelope

buffet-free flight envelope

BVR envelope

center-of-gravity envelope

clean configuration envelope

cleared flight envelope

cruising envelope

divergence envelope

engagement envelope

environmental envelope

extensive flight envelope

ferry flight envelope

firing envelope

flight envelope

flight path angle envelope

G-loc envelope

gun-firing envelope

heavy-weight envelope

helicopter envelope

helium-filled envelope

high-speed envelope

icing envelope

lethal envelope

level-flight envelope

low-speed envelope

Mach-altitude envelope

maneuver envelope

maneuvering envelope

minimum drag envelope

mission envelope

normal performance envelope

off-boresight envelope

operating flight envelope

operational flight envelope

peripheral flight envelope

permissible flight envelope

recommended flight envelope

refueling envelope

restrictive flight envelope

rotating envelope

safe-flight envelope

seat envelope

seat performance envelope

separation envelope

service flight envelope

slow-speed envelope

speed and altitude envelope

structural envelope

terrain following envelope

test envelope

threat envelope

torque/temperature envelope

transonic envelope

variable camber envelope

weapon envelope

weapon release envelope

weight versus center of gravity envelope

SUBJECT AREA: Photography, film and optics

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The son of a tax collector, Ducos du Hauron began researches into colour photography soon after the publication of Clerk Maxwell's experiment in 1861. In a communication sent in 1862 for presentation at the Académie des Sciences, but which was never read, he outlined a number of methods for photography of colours. Subsequently, in his book Les Couleurs en photographie, published in 1869, he outlined most of the principles of additive and subtractive colour photography that were later actually used. He covered additive processes, developed from Clerk Maxwell's demonstrations, and subtractive processes which could yield prints. At the time, the photographic materials available prevented the processes from being employed effectively. The design of his Chromoscope, in which transparent reflectors could be used to superimpose three additive images, was sound, however, and formed the basis of a number of later devices. He also proposed an additive system based on the use of a screen of fine red, yellow and blue lines, through which the photograph was taken and viewed. The lines blended additively when seen from a certain distance. Many years later, in 1907, Ducos du Hauron was to use this principle in an early commercial screen-plate process, Omnicolore. With his brother Alcide, he published a further work in 1878, Photographie des Couleurs, which described some more-practical subtractive processes. A few prints made at this time still survive and they are remarkably good for the period. In a French patent of 1895 he described yet another method for colour photography. His "polyfolium chromodialytique" involved a multiple-layer package of separate red-, green-and blue-sensitive materials and filters, which with a single exposure would analyse the scene in terms of the three primary colours. The individual layers would be separated for subsequent processing and printing. In a refined form, this is the principle behind modern colour films. In 1891 he patented and demonstrated the anaglyph method of stereoscopy, using superimposed red and green left and right eye images viewed through green and red filters. Ducos du Hauron's remarkable achievement was to propose theories of virtually all the basic methods of colour photography at a time when photographic materials were not adequate for the purpose of proving them correct. For his work on colour photography he was awarded the Progress Medal of the Royal Photographic Society in 1900, but despite his major contributions to colour photography he remained in poverty for much of his later life.

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SUBJECT AREA: Chemical technology, Mining and extraction technology, Weapons and armour

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The son of a barber-surgeon who deserted his family to serve in the Swedish army, Nobel showed little interest in academic pursuits as a child and was sent to sea at the age of 16, but jumped ship in Egypt and was eventually employed as an architect by the pasha. Returning to Sweden, he won a scholarship to the Stockholm School of Architecture, where he studied from 1821 to 1825 and was awarded a number of prizes. His interest then leaned towards mechanical matters and he transferred to the Stockholm School of Engineering. Designs for linen-finishing machines won him a prize there, and he also patented a means of transforming rotary into reciprocating movement. He then entered the real-estate business and was successful until a fire in 1833 destroyed his house and everything he owned. By this time he had married and had two sons, with a third, Alfred (of Nobel Prize fame; see Alfred Nobel), on the way. Moving to more modest quarters on the outskirts of Stockholm, Immanuel resumed his inventions, concentrating largely on India rubber, which he applied to surgical instruments and military equipment, including a rubber knapsack.

It was talk of plans to construct a canal at Suez that first excited his interest in explosives. He saw them as a means of making mining more efficient and began to experiment in his backyard. However, this made him unpopular with his neighbours, and the city authorities ordered him to cease his investigations. By this time he was deeply in debt and in 1837 moved to Finland, leaving his family in Stockholm. He hoped to interest the Russians in land and sea mines and, after some four years, succeeded in obtaining financial backing from the Ministry of War, enabling him to set up a foundry and arms factory in St Petersburg and to bring his family over. By 1850 he was clear of debt in Sweden and had begun to acquire a high reputation as an inventor and industrialist. His invention of the horned contact mine was to be the basic pattern of the sea mine for almost the next 100 years, but he also created and manufactured a central-heating system based on hot-water pipes. His three sons, Ludwig, Robert and Alfred, had now joined him in his business, but even so the outbreak of war with Britain and France in the Crimea placed severe pressures on him. The Russians looked to him to convert their navy from sail to steam, even though he had no experience in naval propulsion, but the aftermath of the Crimean War brought financial ruin once more to Immanuel. Amongst the reforms brought in by Tsar Alexander II was a reliance on imports to equip the armed forces, so all domestic arms contracts were abruptly cancelled, including those being undertaken by Nobel. Unable to raise money from the banks, Immanuel was forced to declare himself bankrupt and leave Russia for his native Sweden. Nobel then reverted to his study of explosives, particularly of how to adapt the then highly unstable nitroglycerine, which had first been developed by Ascanio Sobrero in 1847, for blasting and mining. Nobel believed that this could be done by mixing it with gunpowder, but could not establish the right proportions. His son Alfred pursued the matter semi-independently and eventually evolved the principle of the primary charge (and through it created the blasting cap), having taken out a patent for a nitroglycerine product in his own name; the eventual result of this was called dynamite. Father and son eventually fell out over Alfred's independent line, but worse was to follow. In September 1864 Immanuel's youngest son, Oscar, then studying chemistry at Uppsala University, was killed in an explosion in Alfred's laboratory: Immanuel suffered a stroke, but this only temporarily incapacitated him, and he continued to put forward new ideas. These included making timber a more flexible material through gluing crossed veneers under pressure and bending waste timber under steam, a concept which eventually came to fruition in the form of plywood.

In 1868 Immanuel and Alfred were jointly awarded the prestigious Letterstedt Prize for their work on explosives, but Alfred never for-gave his father for retaining the medal without offering it to him.

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absolute code вчт. машинный код access code вчт. код доступа address code вчт. код адреса alphabetic code вчт. буквенный код alphameric code вчт. буквенно-цифровой код alphanumeric code вчт. алфавитно-цифровая система индексов alphanumeric code вчт. алфавитно-цифровой код alphanumeric code вчт. буквенно-цифровой индекс alphanumeric code вчт. буквенно-цифровой код area code трехзначный междугородный телефонный код assembler code вчт. программа на ассемблере attribute-control code вчт. код управления признаком authentification code вчт. код аутентификации bar code штриховой код baseline code вчт. основное тело программы basic order code вчт. код основной команды BCD code вчт. двоично-десятичный код binary code вчт. двоичный код binary-coded decimal code вчт. двоично-десятичный код biquinary code вчт. двоично-пятеричный код block code вчт. блочный код brevity code вчт. сокращенный код bug-arresting code вчт. программа со стопором ошибок building code строительные нормы и правила card code вчт. код перфокарты chain code вчт. цепной код character code вчт. код символа code юр. кодекс, свод законов; civil code гражданский кодекс; criminal code уголовный кодекс civil code гражданский кодекс code законы code законы чести, морали; моральные нормы; code of conduct нормы поведения code индекс code код; Morse code азбука (или код) Морзе code вчт. код code код code вчт. код code юр. кодекс, свод законов; civil code гражданский кодекс; criminal code уголовный кодекс code кодекс code вчт. кодировать code кодировать code вчт. кодировать code кодифицировать code маркировать code маркировка code вчт. машинная программа code вчт. машинное слово code нормы code правила code принципы code присваивать шифр code вчт. программировать code проставлять шифр code свод законов code вчт. система кодирования code торг. система кодирования code стандарт code шифр code шифровать по коду, кодировать code of accounts план отчета code of civil procedure гражданский процессуальный кодекс code законы чести, морали; моральные нормы; code of conduct нормы поведения code of conduct кодекс поведения code of ethics этический кодекс ethics: code этика; a code of ethics моральный кодекс code of fair information practice правила честной информационной практики code of liberalization of capital movements правила снятия ограничений на движение капитала comma-free code вчт. код без запятой commercial code коммерческие правила commercial code свод законов о торговле compiled code вчт. объектный код completion code вчт. код завершения computer code вчт. система команд condition code вчт. код условия conditional code вчт. код условия conversion code вчт. код преобразования code юр. кодекс, свод законов; civil code гражданский кодекс; criminal code уголовный кодекс criminal code уголовный кодекс currency code валютный код cycle code вчт. циклический код data code вчт. кодовый набор data link code вчт. код передачи данных decimal code вчт. десятичный код destination code вчт. адрес назначения destination code вчт. код абонента device code вчт. адрес устройства dialling code код набора digital code вчт. цифровой код dot-and-dash code вчт. код морзе dot-and-dash: dot-and-dash: code code азбука Морзе drive code вчт. управляющий код error code вчт. код ошибки error-checking code вчт. код с контролем ошибок error-control code вчт. код с обнаружением ошибок error-correcting code вчт. код с исправлением ошибок error-detecting code вчт. код с обнаружением ошибок escape code вчт. код смены алфавита escape code вчт. управляющий код executable code вчт. рабочая программа exit code вчт. код завершения exponent code вчт. код порядка false code вчт. запрещенный код field control code вчт. код контроля поля fragile code вчт. недолговечная программа function code вчт. код режима работы highway code правила дорожного движения ID codeidentification code идентификационный код identity code личный код illegal code вчт. запрещенный код illegal code вчт. нелегальная программа input code вчт. входной код instruction code вчт. код команды instruction code вчт. набор команд instruction code вчт. система команд instruction code вчт. состав команд interlock code вчт. код блокировки internal code вчт. внутренний код interpretive code вчт. интерпретируемый код interrupt code вчт. код прерывания inverse code вчт. обратный код line-feed code вчт. код протяжки lock code вчт. замок lock code вчт. код защиты lock codes вчт. замки machine code вчт. машинный код machine-instruction code вчт. система команд machine-operation code вчт. система команд machine-readable code вчт. машинночитаемый код magnetic tape code вчт. код магнитной ленты maritime code кодекс торгового мореплавания maritime code морской кодекс message code вчт. код сообщения micro code вчт. микрокоманда micro code вчт. микропрограмма minimum-access code вчт. программирование с минимизацией задержки mnemonic code вчт. мнемокод modular code вчт. модульная программа modulation code вчт. модулирующий код code код; Morse code азбука (или код) Морзе Morse: Morse разг. см. Morse code, Morse telegraph Morse: Morse: code code, code alphabet азбука Морзе; Morse telegraph телеграф Морзе multiple-address code вчт. код многоадресной команды name code вчт. именной код natural binary code вчт. обычный двоичный код noise combating code вчт. помехоустойчивый код nonexistence code вчт. контроль запрещенных комбинаций nonexistent code вчт. запрещенный код nonexistent code вчт. непредусмотренный код nonexistent code вчт. несуществующий код nonreproducing code вчт. непечатаемый код number address code вчт. код адреса числа number code вчт. код числа numeric code вчт. цифровой код numeric code вчт. числовой код object code вчт. выходная программа object code вчт. объектный код one-address code вчт. код одноадресной команды one-level code вчт. абсолютный код operand code вчт. код операнда operation code вчт. код операции optimized code вчт. оптимизированная программа output code вчт. выходной код own code вчт. собственная подпрограмма paired-disparity code вчт. попарно-сбалансировый код parity-check code вчт. код с контролем четности penal code уголовный кодекс pointer-threaded code вчт. шитый код polynomial code вчт. полиномиальный код position code вчт. позиционный код position-independent code вчт. непозиционный код positional code вчт. позиционный код post code почтовый индекс postal code почтовый индекс prefix code вчт. префиксный код print restore code вчт. код возобновления печати procedural code процессуальный кодекс product code вчт. композиционный код pulse code вчт. импульсный код punched tape code вчт. код перфоленты pure code вчт. чистый код recurrent code вчт. циклический код redundant code вчт. избыточный код reenterable code вчт. повторно входимая программа reentrant code вчт. повторно входимая программа reflected code вчт. циклический код relative code вчт. программа в относительных адресах relocatable code вчт. перемещаемая программа repertory code вчт. набор команд reserved code вчт. зарезервированная команда retrieval code вчт. код поиска retrieval code вчт. поисковый ключ return code вчт. код возврата routing code вчт. код маршрута row-binary code вчт. построчный двоичный код safety code вчт. безопасный код sectoral grouping code код распределения населения по социально-экономическому положению self-checking code вчт. код с обнаружением ошибок self-correcting code вчт. само-корректирующийся код serial code вчт. последовательный код seven bit code вчт. семиразрядный код severity code вчт. код серьезности ошибки short code вчт. сокращенный код sign code вчт. код знака single-address code вчт. код одноадресной команды skeletal code вчт. план программы skip code вчт. код пропуска source code вчт. исходный код source code comp. исходный код source code вчт. исходный текст space code вчт. код интервала space code вчт. код пробела spaghetti code вчт. неструктурная программа specific code вчт. абсолютный код state code вчт. код состояния status code вчт. код состояния status code comp. код состояния stop code вчт. код останова straight-line code вчт. программа без циклов strait binary code вчт. обычный двоичный код strip code вчт. штриховой код symbol code вчт. код символа symbolic code вчт. псевдокод tape code вчт. код ленты task code вчт. код задачи telecommunication code вчт. код для телесвязи termination code вчт. код завершения ternary code вчт. троичный код threaded code вчт. шитый код throw-away code вчт. технологическая программа trace back code вчт. код обратного пути transaction code вчт. код транзакции transaction codes вчт. коды транзакции transmission code вчт. код передачи transmitter-start code вчт. стартовый код трансмиттера unit-distance code вчт. код с одиночным расстоянием unitary code вчт. унитарный код universal product code универсальный товарный код unused code вчт. запрещенный код unused code вчт. неиспользуемый код user identification code вчт. код пользователя zip code почтовый индекс zip: code code амер. почтовый индекс zone code вчт. код зоны

[ˈɪndeks]

adjusted index скорректированный индекс adjustment index индекс выравнивания aggregate liability index совокупный показатель риска aggregative index вчт. составной индекс array index вчт. индекс массива average weighted index средневзвешенный индекс basic index основной показатель bond price index курсовой индекс облигаций building cost index индекс стоимости строительных работ capacity index индекс пропускной способности card index картотека catchword index док. указатель с описаниями под характерным словом chain index вчт. цепной индекс classified index систематизированный указатель clusterization index вчт. индекс кластеризации code line index вчт. кодовый индекс кадра comparative index сравнительный показатель confidence index показатель достоверности construction cost index индекс стоимости строительства consumer confidence index индекс уверенности потребителя consumer price index индекс розничных цен correction index поправочный коэффициент cost-of-living index индекс прожиточного минимума cost-of-living index индекс стоимости жизни cross-weighted index индекс с двойным взвешиванием cycle index вчт. параметр цикла index: downward change in index изменение индекса в сторону понижения downward change in index снижение индекса exchange rate index индекс валютного курса expectation index полит.эк. вероятный индекс floor space index процент застройки fog index индекс непонятности geometric index геометрический индекс geometrical index геометрический индекс gross index вчт. главный индекс harmonic index гармонический индекс harmonical index гармонический индекс help index вчт. справочный указатель hourly wage index индекс почасовой заработной платы index алфавитный указатель, каталог index алфавитный указатель index индекс, указатель index вчт. индекс index индекс index индексировать index вчт. индексный index каталог index оглавление index показатель index предметный указатель index снабжать указателем index составлять указатель index список index вчт. указатель index указатель index числовой показатель, коэффициент Index: Index: Dow Jones index индекс Доу-Джонса index: index: downward change in index изменение индекса в сторону понижения index of average values индекс средних значений index of commodity prices индекс цен на товары index of correlation коэффициент корреляции index of dispersion показатель рассеяния index of economic activity показатель экономической активности index of names указатель названий index of net retail prices индекс розничных нетто-цен index of quotations индекс котировок index of share prices индекс курсов акций index of subject heading док. рубрика предметного указателя index of wages индекс заработной платы index of wholesale prices индекс оптовых цен index to building records указатель строительного регистра keyword-in-context index вчт. указатель ключевых слов median index медианный индекс monthly index месячный индекс monthly price index месячный индекс цен moving base index вчт. индекс с переменной базой multipurpose index индекс используемый для различных целей national pension index национальный пенсионный индекс net price index индекс чистой цены output index индекс объема продукции overall index общий показатель present value index индекс текущей стоимости price index индекс цен production index индекс объема производства quality index показатель качества quantitative index количественный показатель quantity index количественный показатель rectified index number сглаженный индекс reduced price index индекс сниженных цен reliability index показатель надежности retail price index (RPI) индекс розничных цен reverse index обратный индекс risk index кумуляционная карта secondary index вторичный индекс share index фондовый индекс share price index индекс курса акций stock index фондовый индекс subject index doc. предметный указатель systematic index doc. систематический указатель track index вчт. индекс дорожки tree index древовидный индекс trend-adjusted index индекс скорректированный с учетом тренда unit value index индекс средней цены единицы продукции unweighted index невзвешенный индекс wage-regulating price index индекс цен, регулирующий заработную плату weighted index взвешенный индекс weighted index number взвешенный индекс wholesale price index индекс оптовых цен

   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)

   1) A Comparison of the Digital Computer and the Brain

   The brain has been compared to a digital computer because the neuron, like a switch or valve, either does or does not complete a circuit. But at that point the similarity ends. The switch in the digital computer is constant in its effect, and its effect is large in proportion to the total output of the machine. The effect produced by the neuron varies with its recovery from [the] refractory phase and with its metabolic state. The number of neurons involved in any action runs into millions so that the influence of any one is negligible.... Any cell in the system can be dispensed with.... The brain is an analogical machine, not digital. Analysis of the integrative activities will probably have to be in statistical terms. (Lashley, quoted in Beach, Hebb, Morgan & Nissen, 1960, p. 539)

   2) Computers Do Not Crunch Numbers, They Manipulate Symbols

   It is essential to realize that a computer is not a mere "number cruncher," or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols.... Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines....

   The terms "computer" and "computation" are themselves unfortunate, in view of their misleading arithmetical connotations. The definition of artificial intelligence previously cited-"the study of intelligence as computation"-does not imply that intelligence is really counting. Intelligence may be defined as the ability creatively to manipulate symbols, or process information, given the requirements of the task in hand. (Boden, 1981, pp. 15, 16-17)

   3) Getting Computers to Explain Things to Themselves

   The task is to get computers to explain things to themselves, to ask questions about their experiences so as to cause those explanations to be forthcoming, and to be creative in coming up with explanations that have not been previously available. (Schank, 1986, p. 19)

   4) Some Limits of Artificial Intelligence

   In What Computers Can't Do, written in 1969 (2nd edition, 1972), the main objection to AI was the impossibility of using rules to select only those facts about the real world that were relevant in a given situation. The "Introduction" to the paperback edition of the book, published by Harper & Row in 1979, pointed out further that no one had the slightest idea how to represent the common sense understanding possessed even by a four-year-old. (Dreyfus & Dreyfus, 1986, p. 102)

   5) The Computer as a Humanizing Influence

   A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness.

   In fact, the computer, early in its career, was not an instrument of the philistines, but a humanizing influence. It helped to revive an idea that had fallen into disrepute: the idea that the mind is real, that it has an inner structure and a complex organization, and can be understood in scientific terms. For some three decades, until the 1940s, American psychology had lain in the grip of the ice age of behaviorism, which was antimental through and through. During these years, extreme behaviorists banished the study of thought from their agenda. Mind and consciousness, thinking, imagining, planning, solving problems, were dismissed as worthless for anything except speculation. Only the external aspects of behavior, the surface manifestations, were grist for the scientist's mill, because only they could be observed and measured....

   It is one of the surprising gifts of the computer in the history of ideas that it played a part in giving back to psychology what it had lost, which was nothing less than the mind itself. In particular, there was a revival of interest in how the mind represents the world internally to itself, by means of knowledge structures such as ideas, symbols, images, and inner narratives, all of which had been consigned to the realm of mysticism. (Campbell, 1989, p. 10)

   6) The Intentionality of Computers Is Essentially Borrowed, Hence Derivative

   [Our artifacts] only have meaning because we give it to them; their intentionality, like that of smoke signals and writing, is essentially borrowed, hence derivative. To put it bluntly: computers themselves don't mean anything by their tokens (any more than books do)-they only mean what we say they do. Genuine understanding, on the other hand, is intentional "in its own right" and not derivatively from something else. (Haugeland, 1981a, pp. 32-33)

   7) The Possibility of Computer Thought

   he debate over the possibility of computer thought will never be won or lost; it will simply cease to be of interest, like the previous debate over man as a clockwork mechanism. (Bolter, 1984, p. 190)

   8) We Are Getting Better at Building Even Better Computers, an Ever- Escalating Upward Spiral

   t takes us a long time to emotionally digest a new idea. The computer is too big a step, and too recently made, for us to quickly recover our balance and gauge its potential. It's an enormous accelerator, perhaps the greatest one since the plow, twelve thousand years ago. As an intelligence amplifier, it speeds up everything-including itself-and it continually improves because its heart is information or, more plainly, ideas. We can no more calculate its consequences than Babbage could have foreseen antibiotics, the Pill, or space stations.

   According to weak AI, the principle value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion than before. But according to strong AI the computer is not merely a tool in the study of the mind; rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states. And according to strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. (Searle, 1981b, p. 353)

    10) Why People Are Smarter Than Computers

   What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.

   What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is "software." If we only had the right computer program, the argument goes, we might be able to capture the fluidity and adaptability of human information processing. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. However, we do not think that software is the whole story.

   In our view, people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at.... hese tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can play a potentially decisive role in determining the outcome of processing. (McClelland, Rumelhart & Hinton, 1986, pp. 3-4)

 

двойная изоляция
Система изоляции, состоящая как из основной, так и дополнительной изоляции.
[ ГОСТ Р 52161. 1-2004 ( МЭК 60335-1: 2001)]

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двойная изоляция
Изоляция, включающая в себя как основную, так и дополнительную изоляцию.
[ ГОСТ Р 52319-2005( МЭК 61010-1: 2001)]

---

двойная изоляция
Изоляция, включающая в себя основную и дополнительную изоляцию.
[ ГОСТ Р МЭК 60050-195-2005]
[ ГОСТ Р МЭК 60050-826-2009]

---

двойная изоляция
изоляция, содержащая как основную, так и дополнительную изоляции.
[ ГОСТ 6570-96]

EN

double insulation
insulation comprising both basic insulation and supplementary insulation
Source: 826-03-19
[IEV number 195-06-08]
[IEC 60335-1, ed. 4.0 (2001-05)]

FR

double isolation
isolation comprenant à la fois une isolation principale et une isolation supplémentaire
Source: 826-03-19
[IEV number 195-06-08]
[IEC 60335-1, ed. 4.0 (2001-05)]

Тематики

• электробезопасность
• электроустановки

EN

• double insulation

DE

• doppelte Isolierung

FR

• double isolation

3.4.3 двойная изоляция (double insulation): Изоляция, включающая в себя как основную, так и дополнительную изоляцию.

Источник: ГОСТ Р МЭК 60745-1-2005: Машины ручные электрические. Безопасность и методы испытаний. Часть 1. Общие требования оригинал документа

1.2.9.4 двойная изоляция (double insulation): Изоляция, состоящая из основной и дополнительной изоляции.

Источник: ГОСТ Р МЭК 60950-1-2009: Оборудование информационных технологий. Требования безопасности. Часть 1. Общие требования оригинал документа

1.2.9.4 двойная изоляция (double insulation): Изоляция, состоящая из основной и дополнительной изоляций.

Источник: ГОСТ Р МЭК 60950-1-2005: Оборудование информационных технологий. Требования безопасности. Часть 1. Общие требования оригинал документа

3.12 двойная изоляция (double insulation): Изоляция, состоящая из основной и дополнительной изоляции.

Источник: ГОСТ Р 54111.3-2011: Дорожные транспортные средства на топливных элементах. Требования техники безопасности. Часть 3. Защита людей от поражения электрическим током оригинал документа

3.15 двойная изоляция (double insulation): Система изоляции, включающая основную изоляцию и дополнительную изоляцию.

Источник: ГОСТ Р МЭК 60745-1-2009: Машины ручные электрические. Безопасность и методы испытаний. Часть 1. Общие требования оригинал документа

3.4.3 двойная изоляция (double insulation): Изоляция, включающая в себя как основную, так и дополнительную изоляцию.

Источник: ГОСТ IEC 60745-1-2011: Машины ручные электрические. Безопасность и методы испытаний. Часть 1. Общие требования

3.3.3 двойная изоляция (double insulation): Система изоляции, состоящая как из основной, так и дополнительной изоляции.

Источник: ГОСТ Р 52161.1-2004: Безопасность бытовых и аналогичных электрических приборов. Часть 1. Общие требования оригинал документа

1.2.18 двойная изоляция (double insulation): Изоляция, состоящая из основной и дополнительной изоляции.

Источник: ГОСТ Р МЭК 60598-1-2011: Светильники. Часть 1. Общие требования и методы испытаний оригинал документа

3.5.3 ДВОЙНАЯ ИЗОЛЯЦИЯ (DOUBLE INSULATION): Изоляция, включающая в себя ОСНОВНУЮ ИЗОЛЯЦИЮ и ДОПОЛНИТЕЛЬНУЮ ИЗОЛЯЦИЮ.

Источник: ГОСТ IEC 61010-031-2011: Безопасность электрических контрольно-измерительных приборов и лабораторного оборудования. Часть 031. Требования безопасности к щупам электрическим ручным для электрических измерений и испытаний