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processing of measured data

  • 1 обработка данных измерений

    Banks. Exchanges. Accounting. (Russian-English) > обработка данных измерений

  • 2 Messwertverarbeitung

    f < msr> (allg.) ■ measured-value processing; processing of measured data
    f < msr> (Sensor) ■ signal processing; signal-processing

    German-english technical dictionary > Messwertverarbeitung

  • 3 обработка данных измерений

    Универсальный русско-английский словарь > обработка данных измерений

  • 4 оказывать влияние

    Оказывать влияние - to have an effect, to have an influence, to exert an effect, to exert an influence, to bear influence, to be influential, to produce an impact; to be a contribution (как констатация факта)
     Processing procedures which may be influential in the film composition include the substrate finishing and the film curing.
     The presence of the extractive probe produced an impact on the optically measured data rate that ranged from a small to a substantial effect depending on the operating conditions.
     The improvement in overall performance cannot be attributed entirely to the novel tip section shape, but it is thought to be a contribution.

    Русско-английский научно-технический словарь переводчика > оказывать влияние

  • 5 данные

    данные сущ
    data
    автоматическая обработка данных
    automatic data processing
    акустические данные
    acoustic data
    аналоговый регистратор полетных данных
    analogue data recorder
    аэронавигационные данные
    aeronautical data
    бортовая комплексная система регистрации данных
    aircraft integrated data system
    бортовая система обработки данных
    air-interpreted system
    ввод данных о полете
    flight data input
    весовые данные
    weight data
    весовые и центровочные данные
    weight and balance data
    географическое положение на данный момент
    current geographical position
    данные воздушных перевозок
    traffic summary
    данные в узлах координатной сетки
    grid-point data
    данные для опознавания
    identification data
    данные измеренного шума
    measured noise data
    данные наблюдений
    observation data
    данные об условиях полета
    flight environment data
    данные о магнитном склонении
    magnetic variation data
    данные о результатах испытаний воздушного судна
    aircraft test data
    данные о результатах испытания в воздухе
    air data
    данные, полученные от наземных служб
    ground-derived data
    данные, полученные с борта
    air-derived data
    загрузочные данные
    loading data
    индикатор навигационных данных
    navigation display
    индикатор результатов обработки данных
    data processing display
    информативные данные
    citing data
    исходные данные
    basic data
    канал передачи данных
    1. data channel
    2. data link канал передачи данных в полете
    flight data link
    коммутационная система передачи данных
    data switching system
    летные данные
    flight data
    метеорологические данные
    meteorological data
    необработанные данные
    raw data
    оборудование автоматической передачи данных
    automatic data transfer equipment
    обработка данных
    data reduction
    основные данные
    main data
    основные технические данные воздушного судна
    aircraft basic specifications
    осреднение полетных данных
    flight data averaging
    отгрузочные данные
    shipping data
    перечень летно-технических данных
    data sheet
    покидать данное воздушное пространство
    leave the airspace
    полетное время, продолжительность полета в данный день
    flying time today
    полет по приборам, обязательный для данной зоны
    compulsory IFR flight
    представление данных
    data presentation
    представление статистических данных
    filing of statistical data
    расчетные данные
    design data
    регистратор данных
    data recorder
    сводка погоды по данным радиолокационного наблюдения
    radar weather report
    Сектор обработки данных
    Data Processing Unit
    сеть передачи данных с пакетной коммутацией
    packet switched data network
    сеть телетайпной передачи данных
    teletype broadcast network
    система автоматизированного обмена данными
    automated data interchange system
    система обмена данными
    data interchange system
    система обработки данных
    1. data handling system
    2. data processing system система передачи данных
    1. data communication system
    2. data link system система предварительной обработки данных
    preprocessed data system
    система регистрации данных
    data-record system
    служба обмена данными
    data interchange service
    (о полете) справочные данные
    reference data
    спутниковая линия передачи данных
    satellite link
    температура в данной точке
    local temperature
    центровочные данные
    balance data
    эксплуатационные данные
    operating data
    электронная обработка данных
    electronic data processing
    электронная передача данных
    electronic data transmission

    Русско-английский авиационный словарь > данные

  • 6 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

  • 7 оптимизация

    1. optimization

     

    оптимизация
    Процесс отыскания варианта, соответствующего критерию оптимальности
    [Терминологический словарь по строительству на 12 языках (ВНИИИС Госстроя СССР)]
    оптимизация
    1. Процесс нахождения экстремума функции, т.е. выбор наилучшего варианта из множества возможных, процесс выработки оптимальных решений; 2. Процесс приведения системы в наилучшее (оптимальное) состояние. Иначе говоря, первое определение трактует термин «О.» как факт выработки и принятия оптимального решения (в широком смысле этих слов); мы выясняем, какое состояние изучаемой системы будет наилучшим с точки зрения предъявляемых к ней требований (критерия оптимальности) и рассматриваем такое состояние как цель. В этом смысле применяется также термин «субоптимизация» в случаях, когда отыскивается оптимум по какому-либо одному критерию из нескольких в векторной задаче оптимизации (см. Оптимальность по Парето, Векторная оптимизация). Второе определение имеет в виду процесс выполнения этого решения: т.е. перевод системы от существующего к искомому оптимальному состоянию. В зависимости от вида используемых критериев оптимальности (целевых функций или функционалов) и ограничений модели (множества допустимых решений) различают скалярную О., векторную О., мно¬гокритериальную О., стохастическую О (см. Стохастическое программирование), гладкую и негладкую (см. Гладкая функция), дискретную и непрерывную (см. Дискретность, Непрерывность), выпуклую и вогнутую (см. Выпуклость, вогнутость) и др. Численные методы О., т.е. методы построения алгоритмов нахождения оп¬тимальных значений целевых функций и соответствующих точек области допустимых значений — развитой отдел современной вычислительной математики. См. Оптимальная задача.
    [ http://slovar-lopatnikov.ru/]

    Параллельные тексты EN-RU из ABB Review. Перевод компании Интент

    The quest for the optimum

    Вопрос оптимизации

    Throughout the history of industry, there has been one factor that has spurred on progress more than any other. That factor is productivity. From the invention of the first pump to advanced computer-based optimization methods, the key to the success of new ideas was that they permitted more to be achieved with less. This meant that consumers could, over time and measured in real terms, afford to buy more with less money. Luxuries restricted to a tiny minority not much more than a generation ago are now available to almost everybody in developed countries, with many developing countries rapidly catching up.

    На протяжении всей истории промышленности существует один фактор, подстегивающий ее развитие сильнее всего. Он называется «производительность». Начиная с изобретения первого насоса и заканчивая передовыми методами компьютерной оптимизации, успех новых идей зависел от того, позволяют ли они добиться большего результата меньшими усилиями. На языке потребителей это значит, что они всегда хотят купить больше, а заплатить меньше. Меньше чем поколение назад, многие предметы считались роскошью и были доступны лишь немногим. Сейчас в развитых странах, число которых быстро увеличивается, подобное может позволить себе почти каждый.

    With industry and consumers expecting the trend towards higher productivity to continue, engineering companies are faced with the challenge of identifying and realizing further optimization potential. The solution often lies in taking a step back and looking at the bigger picture. Rather than optimizing every step individually, many modern optimization techniques look at a process as a whole, and sometimes even beyond it. They can, for example, take into account factors such as the volatility of fuel quality and price, the performance of maintenance and service practices or even improved data tracking and handling. All this would not be possible without the advanced processing capability of modern computer and control systems, able to handle numerous variables over large domains, and so solve optimization problems that would otherwise remain intractable.

    На фоне общей заинтересованности в дальнейшем росте производительности, машиностроительные и проектировочные компании сталкиваются с необходимостью определения и реализации возможностей по оптимизации своей деятельности. Для того чтобы найти решение, часто нужно сделать шаг назад, поскольку большое видится на расстоянии. И поэтому вместо того, чтобы оптимизировать каждый этап производства по отдельности, многие современные решения охватывают процесс целиком, а иногда и выходят за его пределы. Например, они могут учитывать такие факторы, как изменение качества и цены топлива, результативность ремонта и обслуживания, и даже возможности по сбору и обработке данных. Все это невозможно без использования мощных современных компьютеров и систем управления, способных оперировать множеством переменных, связанных с крупномасштабными объектами, и решать проблемы оптимизации, которые другим способом решить нереально.

    Whether through a stunning example of how to improve the rolling of metal, or in a more general overview of progress in optimization algorithms, this edition of ABB Review brings you closer to the challenges and successes of real world computer-based optimization tasks. But it is not in optimization and solving alone that information technology is making a difference: Who would have thought 10 years ago, that a technician would today be able to diagnose equipment and advise on maintenance without even visiting the factory? ABB’s Remote Service makes this possible. In another article, ABB Review shows how the company is reducing paperwork while at the same time leveraging quality control through the computer-based tracking of production. And if you believed that so-called “Internet communities” were just about fun, you will be surprised to read how a spin-off of this idea is already leveraging production efficiency in real terms. Devices are able to form “social networks” and so facilitate maintenance.

    Рассказывая об ошеломляющем примере того, как был усовершенствован процесс прокатки металла, или давая общий обзор развития алгоритмов оптимизации, этот выпуск АББ Ревю знакомит вас с практическими задачами и достигнутыми успехами оптимизации на основе компьютерных технологий. Но информационные технологии способны не только оптимизировать процесс производства. Кто бы мог представить 10 лет назад, что сервисный специалист может диагностировать производственное оборудование и давать рекомендации по его обслуживанию, не выходя из офиса? Это стало возможно с пакетом Remote Service от АББ. В другой статье этого номера АББ Ревю рассказывается о том, как компания смогла уменьшить бумажный документооборот и одновременно повысить качество управления с помощью компьютерного контроля производства. Если вы считаете, что так называемые «интернет-сообщества» служат только для развлечения,
    то очень удивитесь, узнав, что на основе этой идеи можно реально повысить производительность. Формирование «социальной сети» из автоматов значительно облегчает их обслуживание.

    This edition of ABB Review also features several stories of service and consulting successes, demonstrating how ABB’s expertise has helped customers achieve higher levels of productivity. In a more fundamental look at the question of what reliability is really about, a thought-provoking analysis sets out to find the definition of that term that makes the greatest difference to overall production.

    В этом номере АББ Ревю есть несколько статей, рассказывающих об успешных решениях по организации дистанционного сервиса и консультирования. Из них видно, как опыт АББ помогает нашим заказчикам повысить производительность своих предприятий. Углубленные размышления о самой природе термина «надежность» приводят к парадоксальным выводам, способным в корне изменить представления об оптимизации производства.

    Robots have often been called “the extended arm of man.” They are continuously advancing productivity by meeting ever-tightening demands on precision and efficiency. This edition of ABB Review dedicates two articles to robots.

    Робот – это могучее «продолжение» человеческой руки. Применение роботов способствует постоянному повышению производительности, поскольку они отвечают самым строгим требованиям точности и эффективности. Две статьи в этом номере АББ Ревю посвящены роботам.

    Further technological breakthroughs discussed in this issue look at how ABB is keeping water clean or enabling gas to be shipped more efficiently.

    Говоря о других технологических достижениях, обсуждаемых на страницах журнала, следует упомянуть о том, как компания АББ обеспечивает чистоту воды, а также более эффективную перевозку сжиженного газа морским транспортом.

    The publication of this edition of ABB Review is timed to coincide with ABB Automation and Power World 2009, one of the company’s greatest customer events. Readers visiting this event will doubtlessly recognize many technologies and products that have been covered in this and recent editions of the journal. Among the new products ABB is launching at the event is a caliper permitting the flatness of paper to be measured optically. We are proud to carry a report on this product on the very day of its launch.

    Публикация этого номера АББ Ревю совпала по времени с крупнейшей конференцией для наших заказчиков «ABB Automation and Power World 2009». Читатели, посетившие ее, смогли воочию увидеть многие технологии и изделия, описанные в этом и предыдущих выпусках журнала. Среди новинок, представленных АББ на этой конференции, был датчик, позволяющий измерять толщину бумаги оптическим способом. Мы рады сообщить, что сегодня он готов к выпуску.

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    Русско-английский словарь нормативно-технической терминологии > оптимизация

  • 8 дата

    дата выдачи — date of issue, date issued
    дата изготовления, дата выпуска — date of manufacture, date manufactured
    дата испытания — date of test, date tested
    дата консервации — date of preservation, date of slushing, date of processing
    дата осмотра — date of inspection, date inspected
    дата отгрузки — date of shipment, date shipped
    дата отмены (документа и т. д.) — date of cancellation
    дата получения — date of reception, date received
    дата поставки — date of delivery, date delivered
    дата проведения следующего осмотра, дата проведения следующей поверки и т. д. (графа) — due date (e. g., inspection due date)
    дата проведения измерений — date of measurement, date measured
    дата проверки — date of check, date checked
    дата сдачи, дата ввода в эксплуатацию — date of putting into operation, date of putting into service, date of commissioning
    дата снятия (с объекта) — date of removal, date removed
    дата снятия характеристики упаковки — date of taking performance data date of packing, date packed
    дата установки оборудования — date of installation, date installed

    Поставки машин и оборудования. Русско-английский словарь > дата

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