technological+machine

 

технологическая машина
Машина, предназначенная для преобразования обрабатываемого предмета, состоящего в изменении его размеров, формы, свойств или состояния.
[Сборник рекомендуемых терминов. Выпуск 99. Теория механизмов и машин. Академия наук СССР. Комитет научно-технической терминологии. 1984 г.]

Тематики

• теория механизмов и машин

Обобщающие термины

• основы теории машин

EN

• technological machine

FR

• machine technologique

SUBJECT AREA: Paper and printing

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Fourdrinier's father was a paper manufacturer and stationer of London, from a family of French Protestant origin. Henry took up the same trade and, with his brother Sealy (d. 1847), devoted many years to developing the papermaking machine. Their first patent was taken out in 1801, but success was still far off. A machine for making paper had been invented a few years previously by Nicolas Robert at the Didot's mill at Essonnes, south of Paris. Robert quarrelled with the Didots, who then contacted their brother-in-law in England, John Gamble, in an attempt to raise capital for a larger machine. Gamble and the Fourdriniers called in the engineer Bryan Donkin, and between them they patented a much improved machine in 1807. In the new machine, the paper pulp flowed on to a moving continuous woven wire screen and was then squeezed between rollers to remove much of the water. The paper thus formed was transferred to a felt blanket and passed through a second press to remove more water, before being wound while still wet on to a drum. For the first time, a continuous sheet of paper could be made. Other inventors soon made further improvements: in 1817 John Dickinson obtained a patent for sizing baths to improve the surface of the paper; while in 1820 Thomas Crompton patented a steam-heated drum round which the paper was passed to speed up the drying process. The development cost of £60,000 bankrupted the brothers. Although Parliament extended the patent for fourteen years, and the machine was widely adopted, they never reaped much profit from it. Tsar Alexander of Russia became interested in the papermaking machine while on a visit to England in 1814 and promised Henry Fourdrinier £700 per year for ten years for super-intending the erection of two machines in Russia; Henry carried out the work, but he received no payment. At the age of 72 he travelled to St Petersburg to seek recompense from the Tsar's successor Nicholas I, but to no avail. Eventually, on a motion in the House of Commons, the British Government awarded Fourdrinier a payment of £7,000. The paper trade, sensing the inadequacy of this sum, augmented it with a further sum which they subscribed so that an annuity could be purchased for Henry, then the only surviving brother, and his two daughters, to enable them to live in modest comfort. From its invention in ancient China (see Cai Lun), its appearance in the Middle Ages in Europe and through the first three and a half centuries of printing, every sheet of paper had to made by hand. The daily output of a hand-made paper mill was only 60–100 lb (27–45 kg), whereas the new machine increased that tenfold. Even higher speeds were achieved, with corresponding reductions in cost; the old mills could not possibly have kept pace with the new mechanical printing presses. The Fourdrinier machine was thus an essential element in the technological developments that brought about the revolution in the production of reading matter of all kinds during the nineteenth century. The high-speed, giant paper-making machines of the late twentieth century work on the same principle as the Fourdrinier of 1807.

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   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)

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4) Техника: management advisory team, memory address test, memory address translator

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6) Метеорология: Moist Acidic Tundra

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8) Автомобильный термин: manifold air temperature

9) Биржевой термин: Moving Average Treasury

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13) Иммунология: microagglutination technique

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1) Компьютерная техника: Modular Administration Tool, mean acquisition time

2) Американизм: Minimum Alternate Tax

3) Военный термин: Medium Assault Transport, Minimum Activity Tour, mechanical aptitude test, military advisory team, military air transport, military aircraft types, mine assembly team, missile acceptance team, missile acceptance tests, missile adapter tester, missile, antitank, mobile aerial target, mobile arming tower, mobile assistance team, multiple address telegram

4) Техника: management advisory team, memory address test, memory address translator

5) Математика: Multi Auto Transformation

6) Метеорология: Moist Acidic Tundra

7) Юридический термин: Marijuana Action Team

8) Автомобильный термин: manifold air temperature

9) Биржевой термин: Moving Average Treasury

10) Сокращение: Marksman Advanced Trainer, Master of Arts in Teaching, Measurement of Atmosphere Turbulence, Modular Advanced Test, Multi-Arms Trainer, multi-focal atrial tachycardia, M1 Abrams Tank, MAC Address Translation, Machine-Assisted Translation, Maintenance Access Terminal, Maintenance Augmentation Team, Maintenance Tape, Maintenance and Administration Terminal, Major Area Team, Male Annihilation Technique ( fruit flies control), Man About Town, Management Aptitude Test, Management Assistance Team, Management, Administrative, and Technological, Manual Angle Tracking, Map Administration Tool, Maryland Accessible Telecommunications Program, Master Automotive Technician, Masters in the Art of Teaching, Material/Materiel, Material/Mine Assembly Team, Materials Management, Mathematics, Matte, Maximum Attainable Throughput, Mean Annual Temperature, Media Access Time, Medical Analysis Tool, Medical Association of Thailand, Medication Administration Technician, Memory Acceleration Technology, Meridian Administration Tool, Metropolitan Achievement Test, Metropolitan Area Transit, Metropolitan Area Trunks, Michigan Association of Timbermen, Microdyne Automated Terminal, Microscopic Agglutination Test, Military Air Traffic, Miller Analogies Test, Miller Analysis Test, Minimal Aggregate Traffic, Minimum Activity Tour (US Navy), Mission Applications Tool, Mission Area Team, Mixed Air Temperature (HVAC systems), Mobile Access Terminal, Mobile Advisory Team, Mobile Aeronautical Telemetry, Mobile Agent Technology, Mobilization Assistance Team, Modular Alarm Transmitter, Module Acceptance Test, Monitoring, Analysis and Testing, Monthly Average Treasury, Morehead Area Transit (Kentucky, USA), Moscow Art Theater, Moscow Arts Theatre, Movement Area Training, Moving Annual Total, Moving Treasuries Average, Mu Alpha Theta (national high school and junior college mathematics club for honors students), Multi Axis Trainer, Multifocal Atrial Tachycardia (type of supra ventricular arrhythmia), Multiple Access Transponder, Municipal Association of Tasmania, Muscle Activation Technique, Myoskeletal Alignment Techniques

11) Физиология: Maternal, Medication Administration Training

12) Вычислительная техника: machine-aided translation, microalloy transistor

13) Иммунология: microagglutination technique

14) Транспорт: Marine Aviation Transport

15) Экология: Measurement of Atmospheric Turbulence

16) Деловая лексика: Machine Automation Tools, Model Action Talk, Most Appreciated Team

17) Программирование: Multiply And Add To

18) Автоматика: manufacturing automation technology

19) Расширение файла: Micro Alloy Transistor, Table shortcut (MS Access), Matrix data file (Matlab)

20) Аэропорты: Matadi, Congo

21) Программное обеспечение: Multimedia Authoring Tool

1) Компьютерная техника: Modular Administration Tool, mean acquisition time

2) Американизм: Minimum Alternate Tax

3) Военный термин: Medium Assault Transport, Minimum Activity Tour, mechanical aptitude test, military advisory team, military air transport, military aircraft types, mine assembly team, missile acceptance team, missile acceptance tests, missile adapter tester, missile, antitank, mobile aerial target, mobile arming tower, mobile assistance team, multiple address telegram

4) Техника: management advisory team, memory address test, memory address translator

5) Математика: Multi Auto Transformation

6) Метеорология: Moist Acidic Tundra

7) Юридический термин: Marijuana Action Team

8) Автомобильный термин: manifold air temperature

9) Биржевой термин: Moving Average Treasury

10) Сокращение: Marksman Advanced Trainer, Master of Arts in Teaching, Measurement of Atmosphere Turbulence, Modular Advanced Test, Multi-Arms Trainer, multi-focal atrial tachycardia, M1 Abrams Tank, MAC Address Translation, Machine-Assisted Translation, Maintenance Access Terminal, Maintenance Augmentation Team, Maintenance Tape, Maintenance and Administration Terminal, Major Area Team, Male Annihilation Technique ( fruit flies control), Man About Town, Management Aptitude Test, Management Assistance Team, Management, Administrative, and Technological, Manual Angle Tracking, Map Administration Tool, Maryland Accessible Telecommunications Program, Master Automotive Technician, Masters in the Art of Teaching, Material/Materiel, Material/Mine Assembly Team, Materials Management, Mathematics, Matte, Maximum Attainable Throughput, Mean Annual Temperature, Media Access Time, Medical Analysis Tool, Medical Association of Thailand, Medication Administration Technician, Memory Acceleration Technology, Meridian Administration Tool, Metropolitan Achievement Test, Metropolitan Area Transit, Metropolitan Area Trunks, Michigan Association of Timbermen, Microdyne Automated Terminal, Microscopic Agglutination Test, Military Air Traffic, Miller Analogies Test, Miller Analysis Test, Minimal Aggregate Traffic, Minimum Activity Tour (US Navy), Mission Applications Tool, Mission Area Team, Mixed Air Temperature (HVAC systems), Mobile Access Terminal, Mobile Advisory Team, Mobile Aeronautical Telemetry, Mobile Agent Technology, Mobilization Assistance Team, Modular Alarm Transmitter, Module Acceptance Test, Monitoring, Analysis and Testing, Monthly Average Treasury, Morehead Area Transit (Kentucky, USA), Moscow Art Theater, Moscow Arts Theatre, Movement Area Training, Moving Annual Total, Moving Treasuries Average, Mu Alpha Theta (national high school and junior college mathematics club for honors students), Multi Axis Trainer, Multifocal Atrial Tachycardia (type of supra ventricular arrhythmia), Multiple Access Transponder, Municipal Association of Tasmania, Muscle Activation Technique, Myoskeletal Alignment Techniques

11) Физиология: Maternal, Medication Administration Training

12) Вычислительная техника: machine-aided translation, microalloy transistor

13) Иммунология: microagglutination technique

14) Транспорт: Marine Aviation Transport

15) Экология: Measurement of Atmospheric Turbulence

16) Деловая лексика: Machine Automation Tools, Model Action Talk, Most Appreciated Team

17) Программирование: Multiply And Add To

18) Автоматика: manufacturing automation technology

19) Расширение файла: Micro Alloy Transistor, Table shortcut (MS Access), Matrix data file (Matlab)

20) Аэропорты: Matadi, Congo

21) Программное обеспечение: Multimedia Authoring Tool

1) неисправность; повреждение; разрушение

2) выход из строя; отказ; сбой

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- adhesion failure

SUBJECT AREA: Paper and printing

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The son of a master weaver, he originally wished to become an engineer, but while remaining in the parental home he had to follow his father's trade in the textile industry, becoming a master weaver himself in 1839 at Hainichen. He was a good observer and a keen model maker. It was at this stage, in the early 1840s, that he began experimenting with a new material for papermaking. Until then the raw material had been waste rag from the textile industry, but the ever-increasing demands of the mechanical printing presses, especially those producing newspapers, were beginning to outstrip supply. Keller tried using pine wood ground with a wet grindstone. The mass of fibres that resulted was then heated with water to form a thick brew which he then strained through a cloth. By this means Keller obtained a pulp that could be used for papermaking. He constructed a simple grinding machine that could disintegrate the wood without splinters; this was used to make paper in the Altchemnitzer paper mill, and the newspaper Frankenberger Intelligenz-und Wochenblatt was the first to be printed on wood-pulp paper. Keller could not secure state funds to promote his invention, so he approached an expert in papermaking, Heinrich Voelter, Technical Director of the Vereinigten Bautzener Papierfabrik. Voelter put up 700 thaler, and in August 1845 the state of Saxony granted a patent in both their names. In 1848 the first practical machine for grinding wood was produced, but four years later the patent expired. Unfortunately Keller could not afford the renewal fee, and it was Voelter who developed the process of wood-pulp papermaking under his own name, leaving Keller behind. Without this invention, the output of paper from the mills could not have kept pace with the demands of the printing industry, and the mass readership that these technological developments made possible could not have been served. It is no fault of Keller's that wood-pulp paper contains within itself the seeds of its own deterioration and ultimate destruction, presenting librarians of today with an intractable problem of preservation. Keller's part in this technical breakthrough is established in his "ideas" notebook covering the years 1841 and 1842, preserved in the museum at Hainichen.

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1) общ. окружающая среда

2) общ. окружающая обстановка [среда\], окружение (условия, в которых существует кто-л. или что-л.)

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1. ком. коефіцієнт корисної дії (ККД); коефіцієнт використання; 2. вир. продуктивність; ефективність; 3. кадр. виконання норм виробітку

1. показник, що встановлює відношення кількості використаної робочої сили, матеріалу тощо до обсягу виробництва; ♦ під показником ефективності розуміють здатність підприємства, організації і т. д. найефективніше використовувати свої ресурси (resources), щоб одержати максимальний дохід (return); до показників ефективності (efficiency ratios) належать, напр.: середній період сплати дебіторської заборгованості (average collection period); оборотність запасів (inventory turnover); коефіцієнт оборотності дебіторської заборгованості (debtors turnover ratio); 2. виробництво продукції (output) з найменшими витратами (cost); 3. здатність менеджменту (management²), робітників (employee) та іншого персоналу (personnel) виконати задовільну норму виробітку

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allocative efficiency ефективність розподілу ресурсів; average efficiency середня продуктивність • середній коефіцієнт корисної дії; commercial efficiency промисловий коефіцієнт корисної дії; consumption efficiency ефективність споживання; cost efficiency ефективність витрат; dynamic efficiency динамічна ефективність; economic efficiency економічна ефективність; energy efficiency ефективність використання енергії; enterprise efficiency ефективність підприємства; exact efficiency точне значення коефіцієнта корисної дії; fuel efficiency ефективність використання пального; high efficiency високий коефіцієнт корисної дії • висока продуктивність; increased efficiency підвищена продуктивність; industrial efficiency економічна ефективність промислового виробництва • промисловий коефіцієнт корисної дії; intertemporal efficiency міжчасова ефективність; labour efficiency продуктивність робочої сили; low efficiency низький коефіцієнт корисної дії • низька продуктивність; machine efficiency ефективність механізму; manufacturing efficiency ефективність виробництва; market efficiency ефективність ринку; maximum efficiency максимальна продуктивність; mean efficiency середній коефіцієнт корисної дії • середня продуктивність; net efficiency чистий коефіцієнт корисної дії; operational efficiency операційна ефективність; optimum efficiency оптимальна ефективність; peak efficiency максимальна ефективність; performance efficiency рівень продуктивності; preference efficiency ефективність переваги; production efficiency ефективність виробництва; purchasing efficiency ефективність закупівлі; resource use efficiency ефективність виробництва • ефективність використання ресурсів; staff efficiency ефективність персоналу; statistical efficiency статистична ефективність; technical efficiency технічна ефективність; technological efficiency технологічна ефективність; working efficiency продуктивність • потужність

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efficiency in consumption ефективність у споживанні; efficiency in distribution ефективність у розподілі; efficiency in production ефективність у виробництві; efficiency of investments ефективність капіталовкладення; efficiency of labour продуктивність робочої сили; efficiency of payments ефективність здійснення платежів; efficiency of production ефективність виробництва; marginal efficiency of capital гранична ефективність капіталу; marginal efficiency of investment гранична ефективність капіталовкладення; to improve efficiency поліпшувати/ поліпшити продуктивність • поліпшувати/поліпшити ефективність; to increase efficiency збільшувати/збільшити продуктивність • збільшувати/збільшити ефективність; to obtain maximum efficiency досягати/досягнути максимальної продуктивності; to raise efficiency підвищувати/підвищити продуктивність; to reduce efficiency зменшувати/зменшити продуктивність • зменшувати/зменшити ефективність

efficiency¹:: efficiency ratios; efficiency¹ ‡ efficiency ratios (384)

пор. effectiveness

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1) база; основание; станина; фундамент

2) база; базовая поверхность, установочная поверхность

3) пята; подошва

4) опорная плоскость

5) базовая деталь

6) цоколь ( лампы)

7) базовый, основной

•

- assembly base

1) модель; макет; образец; эталон || моделировать; изготавливать по образцу или эталону || образцовый; эталонный

2) форма || придавать форму

3) фасонного сечения (напр. о металле)

•

- 2 degree-of-freedom mathematical model

- Association for Payment Clearing Services

- Association of British Chambers of Commerce

- Association of British Factors

- Association of Certified and Corporate Accountants

- Association of International Bond Dealers

- Association of Management Consultants

- Association of Primary Dealers in Government Securities

- Association of Scientific, Technical and Managerial Staff

- Association of South East Asian Nations

- Agricultural Cooperative Association

- Aluminium Stockholders Association

- American Association of Advertising Agencies

- American Association of Equipment Lessors

- American Arbitration Association

- American Banker's Association

- American Economic Association

- American Management Association

- American Social Science Association

- American Sociological Association

- American Standards Association

- American Statistical Association

- American Trucking Association

- British Bankers' Association

- British Engineering Standards Association

- British Insurance Association

- British Merchant Banking and Securities Association

- British Overseas Banks Association

- Building Society Association

- Canadian Engineering Standards Association

- Central Landowners Association

- Civil Service Clerical Association

- Consumers' Association

- Engineers' and Managers' Association

- European Association for Animal Production

- European Free Trade Association

- Family Planning Association

- Federal National Mortgage Association

- Federal Savings and Loan Association

- Finance Houses Association

- Foreign Credit Insurance Association

- Government National Mortgage Association

- Health Insurance Association of America

- Independent Bankers' Association

- Independent Natural Gas Association of America

- Inter-American Association of Industrial Property

- International Association for Quality Research on Food Plants

- International Association for Research in Income and Wealth

- International Association for the Protection of Industrial Property

- International Association of Agricultural Economists

- International Association of Food Distribution

- International Association of Horticultural Producers

- International Association of Soil Science

- International Development Association

- International Patent and Trademark Association

- International Sociological Association

- International Standards Association

- Investment Bankers' Association

- Issuing Houses Association

- Latin-American Free Trade Association

- Life Insurance Association of America

- Life Office Management Association

- London Discount Market Association

- National Association for the Advancement of Colored People

- National Association of Accountants

- National Association of Bank Auditors and Controllers

- National Association of Business Economists

- National Association of Cost Accountants

- National Association of County Agricultural Agents

- National Association of Electric Companies

- National Association of Home-Builders

- National Association of Insurance Brokers

- National Association of Investment Clubs

- National Association of Investment Companies

- National Association of Investors' Brokers

- National Association of Manufacturers

- National Association of Purchasing Management

- National Association of Realtors

- National Association of Regulatory Utility Commissioners

- National Association of Securities Dealers and Investment Managers

- National and Local Government Officers' Association

- National Birth Control Association

- National Farm Loan Association

- National Futures Association

- National Machine Accountants Association

- National Pest Control Association

- National Planning Association

- National Security Industrial Association

- New York Patent Law Association

- Pacific Industrial Property Association

- Population Association of America

- Production Credit Association

- Production Engineering Research Association

- Railway Accounting Officers' Association

- Scandinavian Agricultural Research Workers' Association

- Securities Association

- Transport Salaried Staff's Association

- World Association for the Struggle Against Hunger

- World Association of Industrial and Technological Research Organizations

- World Association of Travel Agencies

SUBJECT AREA: Telecommunications

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Bell's grandfather was a professor of elocution in London and his father an authority on the physiology of the voice and on elocution; Bell was to follow in their footsteps. He was educated in Edinburgh, leaving school at 13. In 1863 he went to Elgin, Morayshire, as a pupil teacher in elocution, with a year's break to study at Edinburgh University; it was in 1865, while still in Elgin, that he first conceived the idea of the electrical transmission of speech. He went as a master to Somersetshire College, Bath (now in Avon), and in 1867 he moved to London to assist his father, who had taken up the grandfather's work in elocution. In the same year, he matriculated at London University, studying anatomy and physiology, and also began teaching the deaf. He continued to pursue the studies that were to lead to the invention of the telephone. At this time he read Helmholtz's The Sensations of Tone, an important work on the theory of sound that was to exert a considerable influence on him.

In 1870 he accompanied his parents when they emigrated to Canada. His work for the deaf gained fame in both Canada and the USA, and in 1873 he was apponted professor of vocal physiology and the mechanics of speech at Boston University, Massachusetts. There, he continued to work on his theory that sound wave vibrations could be converted into a fluctuating electric current, be sent along a wire and then be converted back into sound waves by means of a receiver. He approached the problem from the background of the theory of sound and voice production rather than from that of electrical science, and by 1875 he had succeeded in constructing a rough model. On 7 March 1876 Bell spoke the famous command to his assistant, "Mr Watson, come here, I want you": this was the first time a human voice had been transmitted along a wire. Only three days earlier, Bell's first patent for the telephone had been granted. Almost simultaneously, but quite independently, Elisha Gray had achieved a similar result. After a period of litigation, the US Supreme Court awarded Bell priority, although Gray's device was technically superior.

In 1877, three years after becoming a naturalized US citizen, Bell married the deaf daughter of his first backer. In August of that year, they travelled to Europe to combine a honeymoon with promotion of the telephone. Bell's patent was possibly the most valuable ever issued, for it gave birth to what later became the world's largest private service organization, the Bell Telephone Company.

Bell had other scientific and technological interests: he made improvements in telegraphy and in Edison's gramophone, and he also developed a keen interest in aeronautics, working on Curtiss's flying machine. Bell founded the celebrated periodical Science.

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SUBJECT AREA: Mining and extraction technology

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After attending the School of Mines at Bochum, from 1862 he worked as an overseer in the coal-mining district of Ibbenbüren until he joined a mining company in the Ruhr area. There, as head of the machine shop, he was mainly concerned with sinking new shafts. In 1873 he became the Technical Director of the Hannover mine, near Bochum, which belonged to Krupp. When the shaft hoisting was to be extended to a lower level Koepe conceived the idea of applying a friction winder to the hoist instead of a drum, in order to save weight and costs. His method involved the use of an endless rope to which the cages were fixed without a safety catch. The rope passed over pulleys instead of coiling and uncoiling on a drum, and he consequently proposed to have the motor erected on top of the shaft rather than beside it, as had been the practice until then.

Koepe's innovation turned out to be highly effective for hoisting heavy loads from deep shafts and was still popular in many countries in the 1990s, although the Krupp company did not accept it for a long time. He had severe personal problems with the company, and as Krupp refused to have his system patented he had to take it out in his own name in 1877. However, Krupp did not pay for the extension of the patent, nor did they pass the dossiers over to him, so the patent expired two years later. It was not until 1888 that a hoisting engine equipped with a friction winder was erected for the first time in a head gear, above the new Hannover II shaft. The following year Koepe left the Krupp company and settled as a freelance consulting engineer in Bochum; he was successful in having his system introduced by other mining companies. Ironi-cally, in 1948 the world's first four-rope winding, based on his system, was installed at the Hannover mine.

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