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mathematical tools

  • 1 mathematical tools

    Универсальный англо-русский словарь > mathematical tools

  • 2 mathematical tools technique

    Универсальный англо-русский словарь > mathematical tools technique

  • 3 Since we have neither the mathematical tools nor the intellectual capacity to model the complete behavior of large discrete systems, we must be content with acceptable level of confidence regarding their correctness

    Универсальный англо-русский словарь > Since we have neither the mathematical tools nor the intellectual capacity to model the complete behavior of large discrete systems, we must be content with acceptable level of confidence regarding their correctness

  • 4 the study of ... requires powerful mathematical tools ...

      • изучение... требует мощных математических инструментов...

    English-Russian dictionary of phrases and cliches for a specialist researcher > the study of ... requires powerful mathematical tools ...

  • 5 матэматычнае забеспячэнне

    mathematical tools

    Беларуска-ангельскі слоўнік матэматычных тэрмінаў і тэрміналагічных словазлучэнняў > матэматычнае забеспячэнне

  • 6 математический аппарат

    mathematical apparatus, mathematical tools, mathematical techniques

    Russian-english psychology dictionary > математический аппарат

  • 7 математический аппарат

    1) Engineering: body of mathematics
    2) Mathematics: mathematical apparatus, s
    3) Information technology: mathematical tool

    Универсальный русско-английский словарь > математический аппарат

  • 8 Поскольку у нас нет ни математиче

    General subject: Since we have neither the mathematical tools nor the intellectual capacity to model the complete behavior of large discrete systems, we must be content with acceptable level of confidence regarding their correctness

    Универсальный русско-английский словарь > Поскольку у нас нет ни математиче

  • 9 tool

    1) инструмент || обрабатывать инструментом
    2) резец; режущий инструмент; штихель; ручное устройство
    4) приспособление; оснастка || оснащать
    5) станок || обрабатывать на станке; подвергать механической обработке
    7) полигр. ручное тиснение || тиснить вручную
    8) геол. зонд
    •
    - hand crimp tool - metal-cutting machine tool - metal-removal machine tool

    English-Russian scientific dictionary > tool

  • 10 Babbage, Charles

    [br]
    b. 26 December 1791 Walworth, Surrey, England
    d. 18 October 1871 London, England
    [br]
    English mathematician who invented the forerunner of the modern computer.
    [br]
    Charles Babbage was the son of a banker, Benjamin Babbage, and was a sickly child who had a rather haphazard education at private schools near Exeter and later at Enfield. Even as a child, he was inordinately fond of algebra, which he taught himself. He was conversant with several advanced mathematical texts, so by the time he entered Trinity College, Cambridge, in 1811, he was ahead of his tutors. In his third year he moved to Peterhouse, whence he graduated in 1814, taking his MA in 1817. He first contributed to the Philosophical Transactions of the Royal Society in 1815, and was elected a fellow of that body in 1816. He was one of the founders of the Astronomical Society in 1820 and served in high office in it.
    While he was still at Cambridge, in 1812, he had the first idea of calculating numerical tables by machinery. This was his first difference engine, which worked on the principle of repeatedly adding a common difference. He built a small model of an engine working on this principle between 1820 and 1822, and in July of the latter year he read an enthusiastically received note about it to the Astronomical Society. The following year he was awarded the Society's first gold medal. He submitted details of his invention to Sir Humphry Davy, President of the Royal Society; the Society reported favourably and the Government became interested, and following a meeting with the Chancellor of the Exchequer Babbage was awarded a grant of £1,500. Work proceeded and was carried on for four years under the direction of Joseph Clement.
    In 1827 Babbage went abroad for a year on medical advice. There he studied foreign workshops and factories, and in 1832 he published his observations in On the Economy of Machinery and Manufactures. While abroad, he received the news that he had been appointed Lucasian Professor of Mathematics at Cambridge University. He held the Chair until 1839, although he neither resided in College nor gave any lectures. For this he was paid between £80 and £90 a year! Differences arose between Babbage and Clement. Manufacture was moved from Clement's works in Lambeth, London, to new, fireproof buildings specially erected by the Government near Babbage's house in Dorset Square, London. Clement made a large claim for compensation and, when it was refused, withdrew his workers as well as all the special tools he had made up for the job. No work was possible for the next fifteen months, during which Babbage conceived the idea of his "analytical engine". He approached the Government with this, but it was not until eight years later, in 1842, that he received the reply that the expense was considered too great for further backing and that the Government was abandoning the project. This was in spite of the demonstration and perfectly satisfactory operation of a small section of the analytical engine at the International Exhibition of 1862. It is said that the demands made on manufacture in the production of his engines had an appreciable influence in improving the standard of machine tools, whilst similar benefits accrued from his development of a system of notation for the movements of machine elements. His opposition to street organ-grinders was a notable eccentricity; he estimated that a quarter of his mental effort was wasted by the effect of noise on his concentration.
    [br]
    Principal Honours and Distinctions
    FRS 1816. Astronomical Society Gold Medal 1823.
    Bibliography
    Babbage wrote eighty works, including: 1864, Passages from the Life of a Philosopher.
    July 1822, Letter to Sir Humphry Davy, PRS, on the Application of Machinery to the purpose of calculating and printing Mathematical Tables.
    Further Reading
    1961, Charles Babbage and His Calculating Engines: Selected Writings by Charles Babbage and Others, eds Philip and Emily Morrison, New York: Dover Publications.
    IMcN

    Biographical history of technology > Babbage, Charles

  • 11 Ramsden, Jesse

    [br]
    b. 6 October 1735 (?) Halifax, Yorkshire, England
    d. 5 November 1800 Brighton, Sussex, England
    [br]
    English instrument-maker who developed machines for accurately measuring angular and linear scales.
    [br]
    Jesse Ramsden was the son of an innkeeper but received a good general education: after attending the free school at Halifax, he was sent at the age of 12 to his uncle for further study, particularly in mathematics. At the age of 16 he was apprenticed to a cloth-worker in Halifax and on completion of the apprenticeship in 1755 he moved to London to work as a clerk in a cloth warehouse. In 1758 he became an apprentice in the workshop of a London mathematical instrument-maker named Burton. He quickly gained the skill, particularly in engraving, and by 1762 he was able to set up on his own account. He married in 1765 or 1766 the youngest daughter of the optician John Dollond FRS (1706– 61) and received a share of Dollond's patent for making achromatic lenses.
    Ramsden's experience and reputation increased rapidly and he was generally regarded as the leading instrument-maker of his time. He opened a shop in the Haymarket and transferred to Piccadilly in 1775. His staff increased to about sixty workers and apprentices, and by 1789 he had constructed nearly 1,000 sextants as well as theodolites, micrometers, balances, barometers, quadrants and other instruments.
    One of Ramsden's most important contributions to precision measurement was his development of machines for obtaining accurate division of angular and linear scales. For this work he received a premium from the Commissioners of the Board of Longitude, who published his descriptions of the machines. For the trigonometrical survey of Great Britain, initiated by General William Roy FRS (1726–90) and continued by the Board of Ordnance, Ramsden supplied a 3 ft (91 cm) theodolite and steel measuring chains, and was also engaged to check the glass tubes used to measure the fundamental base line.
    [br]
    Principal Honours and Distinctions
    FRS 1786; Royal Society Copley Medal 1795. Member, Imperial Academy of St Petersburg 1794. Member, Smeatonian Society of Civil Engineers 1793.
    Bibliography
    Instruments, London.
    1779, "Description of two new micrometers", Philosophical Transactions of the Royal Society 69:419–31.
    1782, "A new construction of eyeglasses for such telescopes as may be applied to mathematical instruments", Philosophical Transactions of the Royal Society 73:94–99.
    Further Reading
    R.S.Woodbury, 1961, History of the Lathe to 1850, Cleveland, Ohio; W.Steeds, 1969, A History of Machine Tools 1700–1910, Oxford (both provide a brief description of Ramsden's dividing machines).
    RTS

    Biographical history of technology > Ramsden, Jesse

  • 12 Artificial Intelligence

       In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)
       Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)
       Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....
       When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)
       4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, Eventually
       Just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)
       Many problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)
       What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)
       [AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)
       The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)
       9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract Form
       The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)
       There are many different kinds of reasoning one might imagine:
        Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."
        Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)
       Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)
       Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)
       The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)
        14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory Formation
       It is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)
       We might distinguish among four kinds of AI.
       Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.
       Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.
    ... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)
       Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)
        16) Determination of Relevance of Rules in Particular Contexts
       Even if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)
       Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)
        18) The Assumption That the Mind Is a Formal System
       Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)
        19) A Statement of the Primary and Secondary Purposes of Artificial Intelligence
       The primary goal of Artificial Intelligence is to make machines smarter.
       The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)
       The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....
       AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)
        21) Perceptual Structures Can Be Represented as Lists of Elementary Propositions
       In artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)
       Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)
       Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)
       The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)

    Historical dictionary of quotations in cognitive science > Artificial Intelligence

  • 13 narzędzi|e

    n (G pl narzędzi) 1. (proste) tool; (precyzyjne) instrument
    - narzędzia stolarskie carpentry tools
    - narzędzia chirurgiczne surgical instruments
    - narzędzia tortur instruments of torture
    - narzędzia rolnicze agricultural implements
    2. (środek, metoda) tool, device
    - narzędzia matematyczne/statystyczne mathematical/statistical tools a. devices
    - ankiety są narzędziami w socjologii (opinion) surveys are one of the tools used in sociology
    - być narzędziem w czyimś ręku a. w czyichś rękach przen. to be a tool in sb’s hands
    - stał się posłusznym narzędziem w ręku mafii he became a pliant tool in the hands of the mafia

    The New English-Polish, Polish-English Kościuszko foundation dictionary > narzędzi|e

  • 14 Poncelet, Jean Victor

    [br]
    b. 1 July 1788 Metz, France
    d. 22 December 1867 Paris, France
    [br]
    French mathematician and military and hydraulic engineer.
    [br]
    Poncelet studied mathematics at the Ecole Polytechnique in Paris from 1807 to 1810. He joined the Army, gaining admission to the Corps of Engineers. He worked on the fortifications on the Isle of Walcheren in Holland, and in 1812 he found himself on the Russian front, engulfed in the disastrous defeat of the French at Krasnoi. Poncelet was left for dead on the field, but he was found by the Russians and taken to Saratov, where he was imprisoned for two years. He had ample opportunity there to ponder mathematical problems, a mental process from which stemmed his pioneering advances in projective geometry.
    After his release he returned to this native city of Metz, where he undertook routine military engineering and teaching tasks. These left him time to pursue his mathematical studies in projective geometry. This bore fruit in a series of publications, most notably the first volume of his Traité des propriétés projectives des figures (1822, Paris), the first book to be devoted to the new discipline of projective geometry. With his election to the Académie des Sciences in 1834, Poncelet moved to Paris and devoted much of his time to developing courses in applied mechanics in the Faculty of Science, resulting in a number of books, especially the Introduction à la mécanique industrielle, physique ou expérimentale (1841, Paris: Metz). In 1848 he had attained the rank of general and was made Commandant of the Ecole Polytechnique, a post he held for two years. After his retirement in 1850 he was deeply involved in the industrial machines and tools division at both the Great Exhibition in London in 1851 and the similar exhibition in Paris in 1855.
    Most of Poncelet's work in applied mechanics and technology was conceived during the period 1825–40. His technological innovations were centred on hydraulic engineering, and in 1826 he invented an inward-flow turbine. At the same time he directed his attention to the vertical undershot water-wheel, with wooden blades set radially and substituted curved metal blades: he used tight-fitting masonry and floors in the wheel pits so that all the water would be swept into the spaces between the blades. In addition, he ensured that the water flowing from the blades fell clear of the wheel and did not run in tail water. This greatly improved the efficiency of the water-wheel.
    [br]
    Bibliography
    H.Tribout, 1936, Un Grand Savant: le général Jean-Victor Poncelet, Paris, pp. 204–20 (the most complete list of his published works).
    Further Reading
    I.Didion, 1870, "Notice sur la vie et les ouvrages du général J.-V.Poncelet", Mémoires de l'Académie de Metz 50:101–59.
    M.Daumas (ed), 1968, Histoire des techniques, Vol. 3, Paris (briefly describes his technological work).
    LRD

    Biographical history of technology > Poncelet, Jean Victor

  • 15 необходимый

    necessary, needed, required, essential, of necessity
    • В некоторых случаях может оказаться необходимым (предварительно преобразовать и т. п.)... - It may be necessary in some cases to...
    • Введя необходимые объяснения (= сведения), мы теперь продолжим (изучение и т. п.)... - Having provided this background, we now proceed with...
    • Далее выведем необходимое условие существования... - Let us next deduce a necessary condition for the existence of...
    • Данное условие является необходимым. - The condition is necessary.
    • Действительно, с этой точки зрения не является необходимым... - In fact, from this point of view it is not necessary to...
    • Для обратного (утверждения) необходимы более глубокие рассуждения. - A deeper argument is required for the converse.
    • Замечательным является то, что эти необходимые условия одновременно являются и достаточными. - The remarkable fact is that these necessary conditions are also sufficient.
    • К счастью это не является необходимым. - Fortunately this is not necessary.
    • Легко видеть, что это условие является необходимым. - It is easy to see that this condition is necessary.
    • Могло бы показаться, что... (рассматривать, изучать и т. п.) не является необходимым. - It may seem unnecessary to...
    • Можно показать, что они являются как достаточными, так и необходимыми. - It may be shown that they are sufficient as well as necessary.
    • Мы все еще не разработали математический аппарат, необходимый для... - We have not yet developed the mathematical apparatus needed to...
    • Мы заключаем (наше рассмотрение), делая несколько весьма очевидных, но необходимых замечаний (относительно)... - We conclude by making some rather obvious but necessary remarks on...
    • Мы отмечаем, что (вычислять и то. га.)... не является необходимым. - We remark that it is not necessary to...
    • Мы уже подготовили все инструменты, необходимые для... - We have now assembled the tools we need for...
    • Нам необходим критерий для определения, действительно ли... - We need a criterion for determining whether...
    • Не является необходимым... - It is not necessary to...
    • Некоторое знание... необходимо для понимания... - Some knowledge of... is necessary to an understanding of...
    • Некоторые изменения кажутся необходимыми. - Some revisions seem imperative.
    • Необходим опыт для... - It takes experience to...
    • Необходимое дополнительное условие доставляется (чем-л). - The required additional condition is provided by...
    • Необходимы новые аналитические средства, чтобы изучать... - New analytical tools are needed to study...
    • Никогда не должно быть необходимым... - It should never be necessary to...
    • Остается необходимым лишь... - То this end it is only necessary to...
    • Позднее нам будут необходимы некоторые факты относительно... - Later on we shall need certain facts about...
    • Сейчас нам необходимы следующие два факта... - We need in this instance the two results...
    • Следовательно это необходимое условие для... - This is therefore a necessary condition for...
    • Следовательно, мы имеем необходимое и достаточное условие для... - Thus we have a necessary and sufficient condition for... '
    • Следовательно, необходимое решение принимает вид... - The required solution is therefore...
    • Следовательно, эти условия необходимы для равновесия. - Hence these conditions are necessary for equilibrium.
    • Сначала нам необходимы несколько дополнительных определений. - A few more definitions are required first.
    • Теперь дадим необходимое и достаточное условие для того, чтобы... - We now give a necessary and sufficient condition for...
    • Чтобы получить необходимый результат, мы... - То obtain the required result, let...
    • Эти результаты часто бывают необходимы. - These results are needed frequently.
    • Это даст нам необходимую характеристику (чего-л). - This will give us the required characterization of...
    • Это необходимое следствие того, что... - This is a necessary consequence of the fact that...
    • Это необходимые условия для... - These are the necessary conditions for...
    • Это последнее условие не является необходимым, если... - This last proviso is not needed when...
    • Этот пример показывает, что может быть необходимым... - This example shows that it may be necessary to...

    Русско-английский словарь научного общения > необходимый

  • 16 precision

    noun, no pl.
    Genauigkeit, die; attrib.
    * * *
    [-'siʒən]
    noun (exactness; accuracy: He spoke with great precision; ( also adjective) precision tools (=tools used for obtaining very accurate results).) die Genauigkeit; Präzisions-...
    * * *
    pre·ci·sion
    [prɪˈsɪʒən]
    I. n no pl
    1. (accuracy) Genauigkeit f, Präzision f, Exaktheit f
    with absolute/mathematical \precision mit absoluter/mathematischer Genauigkeit
    2. ( approv: meticulous care) Sorgfalt f
    II. adj attr, inv exakt, präzise
    \precision drilling exakte Bohrung
    \precision timing präzise Zeitplanung, genaues Timing
    * * *
    [prI'sIZən]
    n
    Genauigkeit f; (of work, movement also) Präzision f
    * * *
    precision [prıˈsıʒn]
    A s Präzision f, Genauigkeit f, TECH auch Genauigkeitsgrad m:
    express sth with precision etwas präzis(e) ausdrücken;
    arms of precision MIL Präzisionswaffen
    B adj TECH Präzisions…, Fein…:
    a) Feineinstellung f,
    b) Artillerie: genaues Einschießen;
    precision balance Präzisions-, Feinwaage f;
    precision bombing gezielter Bombenwurf, Punktzielbombenwurf m;
    precision instrument Präzisionsinstrument n;
    precision mechanic Feinmechaniker(in);
    precision mechanics pl (als sg konstruiert) Feinmechanik f;
    precision tool Präzisionswerkzeug n
    * * *
    noun, no pl.
    Genauigkeit, die; attrib.
    * * *
    n.
    Exaktheit f.
    Genauigkeit f.
    Präzision f.

    English-german dictionary > precision

  • 17 средство для

    Средство для
     Mathematical modeling and simulation are useful tools for analyzing performance and control problems in complex systems.
     The model has become a useful tool in studying the less well-understood parts of the actual process.

    Русско-английский научно-технический словарь переводчика > средство для

  • 18 Средство инструментальное

    Elliptic partial differential equations are important tools for mathematical modelers in a wide variety of fields

    Русско-английский словарь по прикладной математике и механике > Средство инструментальное

  • 19 Brown, Joseph Rogers

    [br]
    b. 26 January 1810 Warren, Rhode Island, USA
    d. 23 July 1876 Isles of Shoals, New Hampshire, USA
    [br]
    American machine-tool builder and co-founder of Brown \& Sharpe.
    [br]
    Joseph Rogers Brown was the eldest son of David Brown, who was modestly established as a maker of and dealer in clocks and watches. Joseph assisted his father during school vacations and at the age of 17 left to obtain training as a machinist. In 1829 he joined his father in the manufacture of tower clocks at Pawtucket, Rhode Island, and two years later went into business for himself in Pawtucket making lathes and small tools. In 1833 he rejoined his father in Providence, Rhode Island, as a partner in the manufacture of docks, watches and surveying and mathematical instruments. David Brown retired in 1841.
    J.R.Brown invented and built in 1850 a linear dividing engine which was the first automatic machine for graduating rules in the United States. In 1851 he brought out the vernier calliper, the first application of a vernier scale in a workshop measuring tool. Lucian Sharpe was taken into partnership in 1853 and the firm became J.R.Brown \& Sharpe; in 1868 the firm was incorporated as the Brown \& Sharpe Manufacturing Company.
    In 1855 Brown invented a precision gear-cutting machine to make clock gears. The firm obtained in 1861 a contract to make Wilcox \& Gibbs sewing machines and gave up the manufacture of clocks. At about this time F.W. Howe of the Providence Tool Company arranged for Brown \& Sharpe to make a turret lathe required for the manufacture of muskets. This was basically Howe's design, but Brown added a few features, and it was the first machine tool built for sale by the Brown \& Sharpe Company. It was followed in 1862 by the universal milling machine invented by Brown initially for making twist drills. Particularly for cutting gear teeth, Brown invented in 1864 a formed milling cutter which could be sharpened without changing its profile. In 1867 the need for an instrument for checking the thickness of sheet material became apparent, and in August of that year J.R.Brown and L.Sharpe visited the Paris Exhibition and saw a micrometer calliper invented by Jean Laurent Palmer in 1848. They recognized its possibilities and with a few developments marketed it as a convenient, hand-held measuring instrument. Grinding lathes were made by Brown \& Sharpe in the early 1860s, and from 1868 a universal grinding machine was developed, with the first one being completed in 1876. The patent for this machine was granted after Brown's sudden death while on holiday.
    [br]
    Further Reading
    J.W.Roe, 1916, English and American Tool Builders, New Haven: Yale University Press; repub. 1926, New York and 1987, Bradley, Ill.: Lindsay Publications Inc. (further details of Brown \& Sharpe Company and their products).
    R.S.Woodbury, 1958, History of the Gear-Cutting Machine, Cambridge, Mass.: MIT Press ——, 1959, History of the Grinding Machine, Cambridge, Mass.: MIT Press.
    ——, 1960, History of the Milling Machine, Cambridge, Mass.: MIT Press.
    RTS

    Biographical history of technology > Brown, Joseph Rogers

  • 20 Slater, Samuel

    SUBJECT AREA: Textiles
    [br]
    b. 9 June 1768 Belper, Derbyshire, England
    d. 21 April 1835 USA
    [br]
    Anglo-American manufacturer who established the first American mill to use Arkwright's spinning system.
    [br]
    Samuel's father, William, was a respected independent farmer who died when his son was aged 14; the young Slater was apprenticed to his father's friend, Jedediah Strutt for six and a half years at the beginning of 1783. He showed mathematical ability and quickly acquainted himself thoroughly with cotton-spinning machinery made by Arkwright, Hargreaves and Crompton. After completing his apprenticeship, he remained for a time with the Strutts to act as Supervisor for a new mill.
    At that time it was forbidden to export any textile machinery or even drawings or data from England. The emigration of textile workers was forbidden too, but in September 1789 Slater left for the United States in disguise, having committed the details of the construction of the cotton-spinning machinery to memory. He reached New York and was employed by the New York Manufacturing Company.
    In January 1790 he met Moses Brown in Providence, Rhode Island, and on 5 April 1790 he signed a contract to construct Arkwright's spinning machinery for Almy \& Brown. It took Slater more than a year to get the machinery operational because of the lack of skilled mechanics and tools, but by 1793 the mill was running under the name of Almy, Brown \& Slater. In October 1791 Slater had married Hannah Wilkinson, and in 1798 he set up his own mill in partnership with his father-in-law, Orziel Wilkinson. This mill was built in Pawtucket, near the first mill, but other mills soon followed in Smithville, Rhode Island, and elsewhere. Slater was the Incorporator, and for the first fifteen years was also President of the Manufacturer's Bank in Pawtucket. It was in his business role and as New England's first industrial capitalist that Slater made his most important contributions to the emergence of the American textile industry.
    [br]
    Further Reading
    G.S.White, 1836, Memoirs of Samuel Philadelphia (theearliestaccountofhislife). Dictionary of American Biography, Vol. XVII. Scientific American 63. P.E.Rivard, 1974, Samuel Slater, Father of American Manufactures, Slater Mill. D.J.Jeremy, 1981, Transatlantic Industrial Revolution. The Diffusion of Textile
    Technologies Between Britain and America, 1790–1830s, Oxford (covers Slater's activities in the USA very fully).
    RLH

    Biographical history of technology > Slater, Samuel

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