6.Обучение чтению литературы на английском языке по специальности «Автономные информационные и управляющие системы»

Copyright ОАО «ЦКБ «БИБКОМ» & ООО «Aгентство Kнига-Cервис»
Московский государственный технический университет
имени Н.Э. Баумана
Е. А. Яковлева
ОБУЧЕНИЕ ЧТЕНИЮ ЛИТЕРАТУРЫ
НА АНГЛИЙСКОМ ЯЗЫКЕ
ПО СПЕЦИАЛЬНОСТИ
«АВТОНОМНЫЕ ИНФОРМАЦИОННЫЕ
И УПРАВЛЯЮЩИЕ СИСТЕМЫ»
Учебно-методическое пособие
Москва
Издательство МГТУ им. Н.Э. Баумана
2007
Copyright ОАО «ЦКБ «БИБКОМ» & ООО «Aгентство Kнига-Cервис»
УДК 802.0
ББК 81.2 Англ-923
Я-474
Рецензент И.В. Стасенко
Яковлева Е.А.
Я-474
Обучение чтению литературы на английском языке по специальности «Автономные информационные и управляющие системы»: Учеб.-метод. пособие. – М.: Изд-во МГТУ
им. Н.Э. Баумана, 2007. – 28 с.
Пособие содержит оригинальные тексты из английской и американской научно-технической литературы, а также упражнения и
задания, позволяющие освоить и закрепить лексико-грамматический материал, необходимый для понимания и перевода иностранных изданий по специальности.
Для студентов старших курсов, обучающихся по специальности
«Автономные информационные и управляющие системы».
Ил. 1. Библиогр. 6 назв.
УДК 802.0
ББК 81.2 Англ-923
Учебное издание
Екатерина Александровна Яковлева
Обучение чтению литературы на английском языке
по специальности «Автономные информационные
и управляющие системы»
Редактор Н.М. Маслова
Корректор Р.В. Царева
Компьютерная верстка А.Ю. Ураловой
Подписано в печать 12.03.2007. Формат 60×84/16. Бумага офсетная.
Печ. л. 1,75. Усл. печ. л. 1,63. Уч.-изд. л. 1,45.
Тираж 100 экз. Изд. № 137. Заказ
Издательство МГТУ им. Н.Э. Баумана
105005, Москва, 2-я Бауманская ул., 5
© МГТУ им. Н.Э. Баумана, 2007
Copyright ОАО «ЦКБ «БИБКОМ» & ООО «Aгентство Kнига-Cервис»
ПРЕДИСЛОВИЕ
Пособие содержит оригинальные тексты из английской и американской научно-технической литературы (с элементами адаптации лишь в особенно трудных для перевода случаях) как для самостоятельной работы студентов, так и для аудиторных занятий под
руководством преподавателя.
Каждый из трех разделов включает лексические и грамматические упражнения и задания, выполняя которые, студенты овладевают навыками перевода с английского языка на русский и с
русского на английский, а также развивают навыки устного обсуждения вопросов, связанных с изучаемой тематикой.
Овладение терминологией в рамках специальности и необходимой общеупотребительной лексикой позволяет студентам и будущим специалистам получать из иностранных источников информацию, нужную им в исследовательской и другой работе.
Пособие предназначено для студентов старших курсов, обучающихся по специальности «Автономные информационные и
управляющие системы» на факультете «Специальное машиностроение».
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UNIT I
1. Learn the new words and word combinations:
adaptability n
adoptable a
artilicial intelligence (AI)
assertion n
boom n
bridge v
claim v
cognitive a
consciousness n
core n
disregard v
emulate v
encode v
feasibility n
for the time being
gap n
heavy parallelism
ingenious a
intentionality n
magnitude n
multistrategy approach
neural network
predominant a
query n
real-life a
reasoning n
tie v
адаптивность
приемлемый
искусственный интеллект
утверждение, суждение
звуковой удар; быстрое развитие
преодолевать препятствие
претендовать
познавательный
сознание
сущность, ядро
игнорировать, пренебрегать
имитировать
кодировать
возможность
в данный момент
брешь, разрыв
значительный параллелизм
изобретательный, искусный, оригинальный
умысел
величина
зд. комплексный подход
нейронная сеть
преобладающий
вопрос
реалистический
логическое мышление; доводы
обязывать; ограничивать
2. Match Russian and English equivalents.
(1) artificial intelligence
(2) to produce a boom
(3) to bridge the gap
(4) to launch a project
(5) to give on-line answers
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(a) навязать свой взгляд
(b) сравнение доводов «за» и
«против»
(c) широкий выбор логических устройств
(d) создать системы
(e) заявить права на …
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(6) a wide range of reasoning fa- (f) ликвидировать разрыв
cilities
(7) comparison of pros and cons
(g) дать ответ в режиме реального времени
(8) order of magnitude
(h) искусственный интеллект
(9) to adopt an approach
(i) обеспечить быстрый рост
(10) to claim one’s rights
(j) принять научный метод
(11) to establish a system
(k) придерживаться одностороннего подхода
(12) to rely on one-sided approach (l) начать работу над проектом
(13) to impose its own view
(m) порядок величины
3. Translate the following sentences paying attention to contextual
meanings of the italicized words.
1. The artificial satellite launched was manufactured in Russia. The
fifth generation project was launched in Japan. A launch of a space vehicle is a complicated process.
2. Ferrous metals are subject to corrosion. Artificial intelligence is
probably the subject to challenge human interest forever. Data were
subjected to thorough verification.
3. Reasoning is done through argumentation, comparison and pro
and con arguments. The power of reasoning belongs to “homo
sapiens”.
4. Both projects have adopted the computationally strong-AI approach. Tsiolkovsky’s ideas were adopted by his followers.
5. AI technology is undergoing a boom. Aircraft manufacturers
strive to make the boom of aircraft lower.
6. To bridge a gap between the two theories scientists worked more
than a decade. The arches of the bridge were high enough to let a tall
ship pass.
4. Translate the following words stating the part of speech they
belong to.
Regard – disregard – regardless; general – generality; adopt –
adoption – adoptability; conscious – consciousness – unconscious; intend – intention – intentionality – unintended; execute –
executive – execution.
5. Read and translate the text.
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Text IA. Artificial Intelligence
To understand current trends in artificial intelligence, the history of
AI can be of great help.
The first AI era was impressed by the fact that human brains are
several orders of magnitude slower than computers (in transmission as
well as coupling speed). Therefore, making a copy of a human brain on
a computer would have to result in something ingeniously better. Three
subjects were predominant: (1) learning without knowledge, (2) neural
modeling (self organizing systems and decision space techniques), and
(3) evolutionary learning.
Major AI projects have not resulted in intelligent or commercially
successful products. But intelligence cannot be easily achieved on
digital computers with existing approaches. Today’s computers as
well as existing approaches basically do not differ much from those of
30 years ago (apart from being faster and having better storing capacities) and, therefore, are very unlikely to approach not only human
level but also any level of intelligence established by biological intelligent systems.
Several other projects were started, based on logic programming
(LP). The project was heavily based on logic programming to bridge
the gap between applications and machines.
Japan has already launched the Sixth Generation project, based on
real-life domains, neural networks, optical connections, and heavy
parallelism. This project addresses the tremendous task of codifying
a vast quantity of knowledge possessed by a typical human into
a workable system. General assertions have been put into CYC’s
knowledge base, using a vocabulary with approximately 10³ atomic
terms. CYC (cyclic computer) is intended to be able to give on-line
sensible answers to all sensible queries. CYC includes a wide range of
reasoning facilities, including general deduction and analogical inference. Reasoning is done through argumentation, through comparison
of pros and cons.
CYC is the first project of this magnitude. All knowledge in CYC is
encoded in the form of logical sentences, and not in diagrams, procedures, semantic nets, or neural networks.
The two projects mentioned could not produce intelligent systems
at all. They have adopted the computationally strong AI approach instead of at least combining it with others, e.g. cognitive weak-AI. Both
projects relied on a one-sided approach, disregarding the new school of
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AI. This new approach claims that to design an intelligent system, none
has to give it all properties of intelligent creatures: unity (i.e. multiple
knowledge and multistrategy approach), intentionality, consciousness
and autonomy along with generality and adaptability.
One algorithm executed on a single processor cannot emulate intelligence. The process must consist of many interleaving and intensively communicating sub processes. The general approach seems
promising, yet it is not clear in which particular direction the discovery of true intelligence lies. For the time being it seems that new AI is
strongly related to interdisciplinary sciences, especially biological and
cognitive sciences.
The strong thesis of multiple knowledge states that multiple semantic models are an integral and necessary part of intelligence in any
machine or being in real-life domains. A single model cannot achieve
as good performance as multiple models because each model ties to
fit data and noise according to its own structure and therefore ties to
impose its own view. In other words, although multiple models can be
at any time transformed into one single model with the same performance as a set of models, in general it is not possible to construct such
a single model in the process of learning without designing multiple
models.
Integration of models after they are designed seems not only feasible but also sensible because of reduction in storage and classification time.
99% of all existing computer systems and most current AI orientations are based of a single model. Intelligent systems seem to have special properties, e.g. multiplicity.
(3500 characters)
6. Answer the following questions.
1. What three subjects were predominant in making a copy of
a human brain?
2. What was logic programming aimed at?
3. What is the sixth generation project based on?
4. What does the CYC project address?
5. What facilities is reasoning done through?
6. How is knowledge encoded in CYC projects?
7. What are the characteristic properties of intelligent creatures?
8. Which are weak and strong points of AI modeling?
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7. Translate the following sentences paying attention to the grammar.
Object clause
1. While it is not yet clear whether digital machines can achieve intelligence at all, it is becoming accepted that on digital computers, systems simulating human thought processes will be essentially different
from humans.
2. A procedure which determines whether it will stop or not, does
not exist.
3. The question remains whether this approach is applicable in all
the cases.
4. The problem is whether the results of the test could be recorded,
whether successful or not.
Complex subject
1. Intelligent systems seem to have special properties, e.g. multiplicity.
2. The new version has been formally proven to be wrong.
3. The subject is likely to stimulate the reader’s interest.
4. This change seems to have begun towards the end of the last
century.
5. However, the common practice of designing such devices has
been shown to be inadequate.
6. Such has indeed proved to be the case.
7. The reaction could appear to involve a rearrangement.
Participle I
1. Today’s computers as well as existing approaches basically do
not differ much from those of 30 years ago (apart from being faster and
having better storing capacities) and, therefore, are very unlikely to approach not only human level but also any level of intelligence established by biological intelligent systems.
2. CYC includes a wide range of reasoning facilities including general deduction and analogical inference.
3. Both projects relied on a one-sided approach disregarding
the new school of AI.
8. Read and translate the text without a dictionary. Give your own
title. Answer the questions.
1. What new technologies are available to aerospace manufacturers?
2. What are the applications of the new technologies?
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Text IB. New Generation of Computers
New generation of powerful computers will soon be available to
aerospace manufacturers. Neural networks, fly-by-speech advances1,
smaller processors and integrated avionics will accelerate the process of
automation.
Neural networks are widely believed to be the next step in enhancing2 computer intelligence. Neurocomputers will be able to understand
highly complex tasks which are extremely difficult to perform with current computers. Research is centering on image processing, target and
feature recognition. Neural computing is also considered particularly
suitable for speech recognition, enabling pilots to tell the aircraft to
change course or arm missiles. Full voice control3 in combat aircraft
may become a reality soon but it is unlikely to be introduced on commercial aircraft as crews do not fly hand-on-throttle4 stick as in combat
aircraft.
One major benefit of growing computer capacity will be the sophistication of monitoring systems, which detect and display error and
damages requiring maintenance5.
The latest programme is supposed to develop technologies for the
new generation equipment using microwave integrated circuit technology based on one chip, incorporating the latest advances in parallel
processing. New packaging6 and cooling techniques7 are certain to improve reliability.
(1220 characters)
9. Speak on the main trends of AI creation from the text below.
А. Use the following words and word combinations:
intelligent systems; commercially successful; cognitive; approach; to bridge a gap; applications; general assertions; reasoning; argumentation.
B. Use the following starting phrases:
I’d like to speak about…, I’ll begin with…, I shall speak in detail about…, to sum it up…, a major advance was made…
1
fly-by-speech advances – передовая технология управления голосом.
enhance – усиливать, увеличивать.
3
voice control – управление голосом.
4
hand-on-throttle – в ручном режиме.
5
maintenance – ремонт, обслуживание.
6
packaging – компоновка, герметизация.
7
cooling technique – криогенная технология.
2
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Text IC. Applications of AI
Game playing
You can buy machines that can play master level chess for a few
hundred dollars. There is some AI in them, but they play well against
people mainly through brute force computation—looking at hundreds
of thousands of positions. To beat a world champion by brute force and
known reliable heuristics requires being able to look at 200 million positions per second.
Speech recognition
In the 1990s, computer speech recognition reached a practical level
for limited purposes. Thus United Airlines has replaced its keyboard
tree for flight information by a system using speech recognition of
flight numbers and city names. It is quite convenient. On the the other
hand, while it is possible to instruct some computers using speech, most
users have gone back to the keyboard and the mouse as still more convenient.
Understanding natural language
Just getting a sequence of words into a computer is not enough.
Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is
presently possible only for very limited domains.
Computer vision
The world is composed of three-dimensional objects, but the inputs
to the human eye and computers' TV cameras are two dimensional.
Some useful programs can work solely in two dimensions, but full
computer vision requires partial three-dimensional information that is
not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and
they are not as good as what humans evidently use.
Expert systems
A «knowledge engineer» interviews experts in a certain domain and
tries to embody their knowledge in a computer program for carrying out
some task. How well this work depends on whether the intellectual
mechanisms required for the task are within the present state of AI.
When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did
better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals,
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death, recovery, and events occurring in time. Its interactions depended
on a single patient being considered. Since the experts consulted by the
knowledge engineers knew about patients, doctors, death, recovery,
etc., it is clear that the knowledge engineers forced what the experts
told them into a predetermined framework. In the present state of AI,
this has to be true. The usefulness of current expert systems depends on
their users having common sense.
Heuristic classification
One of the most feasible kinds of expert system given the present
knowledge of AI is to put some information in one of a fixed set of
categories using several sources of information. An example is advising
whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also
about the item he is buying and about the establishment from which he
is buying it (e.g., about whether there have been previous credit card
frauds at this establishment).
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UNIT II
1. Learn the new words:
accommodate v
actuator n
bleed v
booster n
coil n
comparator n
contamination n
convert v
groove n
hose n
incorporate v
instantly a
leakage n
lever n
manifold n
monitor v
mounting orientation
opt v
pneumatic a
relieve v
respond v
rigid a
rust n
scale n
solid-state a
susceptible a
приспосабливать
исполнительный механизм
отбирать; стравливать
ускоритель
спираль, катушка
сравнивающее устройство (компаратор)
загрязнение
преобразовывать
винтовая нарезка, бороздка
шланг
включать в себя
мгновенно
утечка
рычаг
коллектор
отслеживать
схема расположения узлов
предусмотреть вариант
пневматический
зд. снизить
реагировать, отвечать
жесткий; устойчивый
ржавчина
окалина
полупроводниковый
восприимчивый
2. Translate the following word combinations and phrases:
A
board-mounted
low-wattage
unlike other units
threaded ports
machined in intercom
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due to air leakage
set point
mounting orientation susceptible
branch line pressure
hose-related leakage
threaded ports
solid aluminium manifold
B
cut off pressure
require additional air consumption
reach a set point
affect the mounting orientation
be susceptible to
cause blockage of valve
eliminate above problems
unscrew a faulty component
regulate main air supply
accommodate the air valves and the pressure sensor
3. Find pairs of synonyms among the following words:
сonvert, design, circuit, reliable, contaminant, consumption, respond,
loop, pollutant, expenses, react, transform, structure, durable.
4. Translate the following words stating the part of speech they belong to.
Maintain – maintenance; constant – constantly; respond – response;
disposable – dispose; opt – option – optional; corporation – corporate –
incorporate; contaminate – contamination – contaminator; sufficient –
insufficient – suffice; manufacture – manufacturer.
5. Read and translate the text.
Text IIA. EP-310
Model EP-310 incorporates a revolutionary new design to convert an analog signal into proportional pneumatic pressure. The EP310 transducer does not use any springs, diaphragms, levers, or any
other mechanical components. Unlike other units, which act like air
regulators and bleed air to maintain pressure, the EP-310 incorpo13
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rates a 100 % solid-state piezoresistive silicon pressure sensor to
measure the pneumatic branch line pressure going to the operator.
This information is used by an ultra fast integrated circuit comparator to compare the analog input from the system to the branch line
pressure. If the pressure is below, the logic energizes an air valve to
increase the pressure to the operator while constantly monitoring
the pressure change. The moment the branch line pressure reaches
set point the airflow is instantly cut off. If the pressure is above set
point a second air valve is opened to relieve pressure.
Unlike other units, which constantly bleed air, the EP-310 does not
require any additional air consumption once desired1 pressure is
achieved. By using 100 % solid-state electronics and fast, reliable I.C.
logic, the EP-310 not only responds to a change instantly, but also does
so without “hunting”. Unlike other units, which require a filtered and
regulated main air supply, the EP-310 can use non-regulated, unfiltered
plant air and can achieve branch line pressure with a zero differential to
main line pressure. Since the EP-310 is 100 % solid state, it is not affected by mounting orientation, vibration, temperature, or other environmental conditions.
These Systems pioneered the concept of using a two-solenoid
and pressure sensor electro pneumatic converter in the early 1980s.
This extensive experience has taught us many things. We have found
that units which rely on hoses to interconnect components often develop “chattering,” “hunting,” or a needless air consumption problems due to air leakage in the hoses caused by age, heat, moisture,
and contamination. Our competition has chosen to use PC boardmounted, sealed, low-wattage, inexpensive needle valves, which, as
experience has demonstrated, are susceptible to mounting orientation. If a needle valve is mounted horizontally, the needle tends to
eventually cut a groove in the sleeve, causing “sticking” problems.
Needle valves also incorporate a spring to put tension on the needle
against the seat. The spring may lose tension over time, resulting in
air leaks. Other manufacturers have also opted to use inexpensive,
low-wattage (1 watt) coils to switch the valves. If the coil wattage is
insufficient, it may not have the power to pull the needle, especially
if the needle has cut a groove in the sleeve. Use of a low-wattage
coil and needle valves has forced our competition to minimize the
output airflow, resulting in insufficient output for the operator/actuator and creating a need for an expensive output booster. Our
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desired (p.p. от desire) – желаемый.
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extensive experience has established that the main air supply usually
contains moisture, scale, rust particles, and other contaminants.
These contaminants may cause blockage in a sealed valve such as
the needle valve our competitors employ.
These Systems, with more than four years of concentrated R & D
effort, has produced a unique, revolutionary electro pneumatic converter assembly, which eliminates all of the above problems. Our electro pneumatic converter consists of a solid aluminum manifold with all
the interconnections machined within, thus eliminating all hoses and
hose-related leakage problems. This solid manifold also has threaded1
ports machined in to accommodate the air valves and the pressure sensor, forming a rigid, interchangeable assembly. If any component fails,
it can be easily replaced in the field by unscrewing the faulty component and substituting a new one.
(3800 characters)
6. Answer the questions.
1. What is Model EP-310 designed for?
2. How is the information obtained processed?
3. What type of converter is used in the unit?
4. What contaminants does the main air supply usually contain?
5. How are leakage problems eliminated in the converter under
study?
7. Translate the following sentences paying attention to the Infinitives.
1. The model incorporates the design to convert an analog signal
into proportional pneumatic pressure.
2. The model incorporates a solid silicon pressure sensor to measure
the line pressure going into operation.
3. The needle tends to eventually cut a groove in a sleeve.
4. To put pressure on the needle designers thought of a special
spring.
5. Use of low-wattage coil and needle valves has forced our competition to minimize the output airflow.
6. The airflow may be expected to contain contaminants.
7. The contaminants are likely to cause blockage in a sealed valve.
1
threaded (p.p. от thread) – с резьбой.
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8. It is for the manufacturers to explore effective approaches to
eliminating the drawbacks.
9. Efforts are underway to develop new converters which make it
possible to replace any component that fails.
8. Translate the following word combinations from Russian into
English:
быстрый компаратор интегральной цепи;
основной источник снабжения воздухом;
выходной ускоритель;
электро-пневматический конвертер в сборе;
коллектор из твердого алюминия;
течь, связанная с использованием шланга;
порты с резьбой.
9. Write a short account on the topic of your interest in English (e.g.
design, assembly, troubleshooting) using the following vocabulary:
the design incorporates, reliable components, to pioneer a concept,
to opt, to lose some qualities over time, interchangeable assembly,
to utilize, to eliminate drawbacks, to improve, to work thoroughly,
promising tendency.
10. Write a short summary of Text II A using the following expressions:
firstly, secondly, thirdly;
however, in contrast to, unlike;
to sum it up, in conclusion, finally;
in addition, as well, too.
11. A. Read and translate the text into Russian. B. Give your own
title. C. Summarize the text in a few sentenses.
Text II B. Converter Valves
Our electropneumatic converter also incorporates a unique singlecomponent poppet valve1 which has no needle or springs. Instead, it
1
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poppet valve – трубчатый (тарельчатый) клапан.
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employs a low-mass steel disc with a Teflon seat which is immune to1
mounting orientation or vibration. Unlike needles, this poppet has
minimum travel and cannot lose tension over time, thus providing years
of trouble-free service. In case of blockages caused by contaminants,
this unique valve can be easily serviced in the field by merely unscrewing2 the coil3 assembly and removing the contaminants. Similarly, if
the poppet is damaged, it can be replaced in the field without removing
the entire valve. If the contaminant is blocking the valve seat, the customer may easily unscrew the lower portion of the valve, replace it with
a new seat and poppet, and screw back the existing coil assembly. In
the same way, the pressure sensor can also be easily field serviced if
needed. If a problem exists within the manifold ports, the complete
electropneumatic converter may be replaced in the field and the EP-310
brought back on line. In order to ensure that sufficient airflow is available to move any actuator/operator without an external booster4, the air
valves in our electropneumatic converter incorporate a large port and
a hefty 3 watt coil to maximize the output airflow.
By using a solid manifold without any hoses, poppet valves versus5
needle valves, a 3 watt coil instead of a 1 watt, and a totally interchangeable and field serviceable assembly, the EP-310 offers a reliable
alternative to inexpensive, disposable6 units.
1
immune to – не подверженный чему-либо.
unscrew – отвинчивать.
3
coil – катушка.
4
booster – двигатель, ускоритель.
5
versus – по сравнению с чем-либо.
6
disposable – одноразовый.
2
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UNIT III
1. Learn the new words:
assume v
classifier n
cluster v
deinterleaver n
derive v
elevation n
extractor n
frequency n
intrinsic a
list v
sample n
simulate v
sophistication n
tag v
tentative a
update v
принимать; допускать
классификатор, сортировщик
объединять по категориям
обращенный перемежитель
извлекать; выводить; брать производную
повышение; угол возвышения
фильтр, сепаратор
частота, повторяемость
подлинный, присущий
вносить в список
образец, модель, шаблон
моделировать
подделка, усложнение
наносить бирку; метить
экспериментальный, предварительный
модернизировать, усовершенствовать
2. Translate the following word combinations and expressions into
Russian:
pulse pattern extractor;
observed parameters;
tentative identification;
reference book;
feature extractor;
off-line computations;
via computers;
long-term memory;
real time values;
to be referred to as;
to arrive in fractions of seconds;
to make sense;
to pass pulses;
to act as long-term menu;
to track and identify pulses;
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to dominate by eigenvectors;
to tag with;
to pass forward measures.
3. Translate the following words stating the part of speech they belong to.
Reliable – rely; acknowledge – know – knowledge – know-how;
see – foresee – foreseeable; assumption – assume; speak – outspoken – unspoken; requirement – require; bring – upbringing; qualification – qualify; conception – conceive – misconception; memory –
memorize – commemoration – commemorize; identify – identification – unidentified.
4. Put the capitalized words into the proper grammar form by supplying appropriate affixes and insert the m in the sentences.
1. Please ___ our letter of the 14th. We have not had a reply.
2. Keith’s exam results turned out to be ___ .
3. There will be no pay rises in the ___ future.
4. Jim is one of the most ___ Members of the committee.
5. What are the entry ___ at the university?
6. Jackie suffered as a child from a very strict ___ .
7. I think that your ___ about the cost are wrong.
8. This statue ___ the soldiers who died in the war.
9. The idea that the sun «rises» is a popular ___ .
10. Ruth has gone back to college to get a teaching ___ .
KNOW
DISASTER
SEE
SPEAK
REQUIRE
BRING
ASSUME
MEMORY
CONCEIVE
QUALIFY
5. Read and translate the text.
Text III A. Radar Signal Categorization Using
a Neural Network
We have used neural networks to cluster, characterize and identify
radar signals from different emitters. The approach assumes the ability
to monitor a region of the microwave spectrum and to detect and measure properties of received radar pulses. The microwave environment is
assumed to be complex, so there are pulses from a number of different
emitters present, and pulses from the same emitter are noisy or their
properties are not measured with great accuracy.
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For several practical applications, it is important to be able to tell
quickly, first, how many emitters are present and, second, what their
properties are. In other words time average prototypes must be derived
from time dependent data without a tutor. Finally the system must tentatively identify the prototypes as members of previously seen classes
of emitters.
Feature extractor
Deinterleaver
Pulse Pattern
extractor
Classifier
OUTPUT
Type priority
Tracker
OUTPUT
Az, El
Fig. 1
Stages of Processing
We accomplish this task in several stages. Figure 1 shows a block
diagram of the resulting system, which contains several neural networks. The system as a whole is referred to as the Adaptive Network
Sensor Processor (ANSP).
In the block diagram given in Fig.1 the first block is a feature extractor. We start by assuming a microwave radar receiver of some sophistication at the input to the system. This receiver is capable of
processing each pulse into feature values, i.e. azimuth, elevation, signal to noise ratio (normalized intensity), frequency, and pulse width.
This data is then listed in a pulse buffer and tagged with time of arrival of the pulse. In a complex radar environment, hundreds of thousands of pulses can arrive in fractions of seconds, so there is no lack
of data. The problem, as in many data rich environments, is making
sense of it.
The second block in Fig. 1 is deinterleaver which clusters incoming
radar pulses into groups, each group formed by pulses from a single
emitter. A number of pulses are observed, and a neural network com20
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putes, off line, how many emitters are present, based on the sample, and
estimates their properties. That is, solves the so-called deinterleaving
problem by identifying pulses as being produced by a particular emitter.
This block also produces and passes forward measures of each cluster’s
azimuth, elevation, SNR1, frequency, and pulse width.
The third block, the pulse extractor, uses the deinterleaved information to compute the pulse repetition pattern of an emitter by using the
times of arrival for the pulses that are contained in the given cluster.
His information will be used for emitter classification.
The fourth block, the tracker, acts as a long term memory for
the cluster found by the second block, storing the average azimuth,
elevation, SNR, frequency, and pulse width. Since the diagram in Fig. 1
is organized via initial computational functionality, the tracking module follows the deinterleaver so as to store its outputs. In an operationally organized diagram, the tracker is the first block to receive
pulse data from feature extractor. It must identify most of the pulses
in real time as previously learned by the deinterleaver module and
only pass a small number of unknown pulses back to the deinterleaver
module for further learning. The tracker also updates the cluster averages. Their properties can change with time because of emitter of receiver motion, for example.
The fourth and fifth blocks, the tracker and the classifier operate as
a unit to classify the observed emitters, based on information stored in
a data base of emitter types. Intrinsic emitter properties stored in these
blocks are frequency, pulse width and pulse repetition pattern.
The most important question of the ANSP to answer is what
the emitters might be and what they can do. That is “who is looking at me,
should I be concerned, and should I (or can I) do something about it?”
(3800 characters)
5. Answer the questions.
1. What have neural networks been used for?
2. What is the microwave environment assumed as?
3. What is especially important to assess for practical applications?
4. What is «feature extractor»?
5. Which block clusters incoming pulses into groups?
1
signal-to-noise ratio – отношение сигнала к помехам.
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6. Which parameters does the deinterleaver measure and pass on?
7. How does the tracker act as a long-term memory block?
8. Where do the intrinsic emitter properties stored?
6. Translate the sentences from Russian into English using the active vocabulary.
1. Усредненные по времени прототипы необходимо получать
из данных, зависящих от временного коэффициента, без дополнительных инструкций.
2. Система должна примерно определять прототипы.
3. Импульсный фильтр – это система, находящаяся на входе.
4. Приемник может преобразовывать каждый импульс в категорийную величину: азимут, возвышение, соотношение сигнал/импульс и т. д.
5. В доли секунды поступают тысячи импульсов.
6. Нейронная сеть в автономном режиме обсчитывает количество источников импульсов.
7. В зависимости от образца делается обсчет качеств источника
импульса.
8. Присущие импульсу свойства – это частота, ширина, модем
повтора и т. д.
7. Revise the Gerund. Translate the following sentences into Russian.
1. We start by assuming a microwave radar receiver.
2. This receiver is capable of processing each pulse into feature
values.
3. The pattern extractor computes the pulse repetition pattern by using
the times of arrival for the pulses that are contained in a given cluster.
4. Many neural networks are supervised algorithms, that is, they are
trained by seeing correctly classified examples of training data.
5. We couldn’t help elaborating the new pattern of neural networks.
6. We started calculating without knowing the main intrinsic parameters.
7. They objected to our introducing any changes into their pattern.
8. We insisted on his making use of all precedents in cognitive sciences.
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9. They succeeded in accomplishing the work in time.
10. No one minded our stabilizing the system by bounding
the element activities within limits.
11. The prototype forming systems often involve taking into consideration a kind of average.
8. Put one suitable word into each space. Mind using relative
clauses (which, who, whom, that etc.)
Example:
Midway through the second half of the City scored their fourth
goal, at which point United gave up completely.
1. There is one person to ___ I owe more than I can say.
2. It was the kind of accident for ___ nobody was really to blame.
3. ___ leaves last should turn off the lights.
4. Mary was late yesterday, ___ was unusual for her.
5. The first time I saw you was ___ you answered the bell.
6. Mrs. Brown was the first owner ___ dog won three prizes in
the same show.
7. I’ve just spoken to Sally, ___ sends you her love.
9. Revise modal verbs. Translate the following sentences into Russian.
1. We do not have to organize system from zero.
2. It must identify most of the pulses in real time.
3. Their properties can change.
4. Should I be concerned and should I do something?
5. The system responds as if it had repeatedly learned only one patterh, p, and responds best to it, even though p, in fact, may never have
been learned.
10. Translate the following texts in writing. Use а dictionary.
Text III B. PW-610
The PW-610 incorporates a revolutionary circuit consisting of
a precision oscillator, binary counter, D/A converter, 100 % solid state
piezoresistive silicon pressure sensor, high-speed comparator, and
a proprietary electropneumatic converter, which eliminates all the
hoses. This unique combination of state-of-the-art electronic circuitry
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and revolutionary electropneumatic converter assures extremely accurate and repeatable pulse width conversion to a pneumatic signal.
The PW-610 follows a foolproof logic sequence at the start and end
of each pulse to ensure that there will be no glitches, count errors, or
other conversion problems. At the start of each pulse, the PW-610 holds
the previous pneumatic value, thereby making sure that the output remains the same as before during the counting process. The unit resets
the counter to zero and then begins the new count. By incorporating
a precision oscillator and by maintaining a very fast count pulse, the
PW-610 can resolve a pulse width to 10 milliseconds (0.01 seconds).
At the completion of the pulse, the count is set as an 8-bit digital
word, and a precision D/A converter translates the digital word into
an analog signal. The logic comparator compares this analog value to
the branch line pressure signal provided by the pressure sensor. If the
pressure is below, the logic energizes an air valve to increase the pressure to the operator while constantly monitoring the pressure change.
The moment the branch line pressure reaches the desired value, the airflow is instantly cut off. If the pressure is above the desired value, a
second air valve is opened to relieve pressure. Unlike other units which
constantly bleed (consume) air (energy), the PW-610 does not require
any additional air consumption once the desired pressure is achieved.
By holding the count as a digital word, the PW-610 can maintain the
desired pressure indefinitely and does not require a periodic refresh
pulse as long as power is available.
Text III C. Deinterleaving
Most of the initial and simulation effort in this project was focused
on the deinterleaving problem. This is because this project was focused
on the deinterleaving problem. This is because the ANSP is being asked
for form a conception of the emitter environment from the data itself.
A teacher does not exist for most interesting situations.
In the simplest case each emitter emits with constant properties, i.e.,
no noise is present. Then, determining how many emitters were present
would be trivial: simply count the number of unique pulse via a look up
table. Unfortunately, data is often moderately noisy because of receiver,
environmental and emitter variability, and, sometimes, because of change
of one or another emitter property at the emitter. Therefore, simple identity checks will not work. It is these cases, which this paper will address.
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Many neural networks are supervised algorithms, that is, they are
trained by seeing correctly classified examples of training data and,
when new data is presented will identify it according to their past experience. Emitter identification does not fall into this category because
the correct answers are not known ahead of time. That, after all, is
the purpose of the system.
11. Make up three dialogues in which you will:
– discuss the prospects of participating in a conference;
– explain your boss the importance of signal categorization by neural network;
– instruct an apprentice how to treat neural network.
12. Render the text in English.
Text III D. BMSTU SM 5 Department
Our department trains1 specialists in the field of systems engineering and electric circuit engineering. Our specialists carry out research
work in the development of autonomous information and control systems (AICS). AICS are widely used in military systems, avionics, space
technology, medicine, transport etc.
AICS perform detection, recognition and processing of video,
acoustic, and radar information obtained via different sensors. They
also develop controlling excitations2 for actuators.
1. Specialists in radio direction finding AICS deal with the systems
which perform detection, recognition, filtration and measurement of
radio signal parameters. These systems are characterized by high speed
of response3 and operation4 within highly distributed conditions.
2. Specialists on signal processing sections in AICS, disturbances5
and signal information features6. Neural network technique7 is applied
for the development of signal processing algorithms.
1
train – готовить.
excitations – возбуждения.
3
response – отклик, реакция.
4
operation – работа.
5
disturbances – возмущения.
6
feature – характеристика, свойство.
7
technique – методика.
2
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3. Specialists in autonomous mechatronic control systems deal with
complex objects containing microprocessor devices, information processing modules and blocks controlling excitation. In such devices electronic and mechanic components are integrated with computer ones.
It should be noted that our engineers are capable of developing
mathematical models for real-life designs. Specialists create fast algorithms and software for automatic control of the objects operating
within extreme conditions. For example, they are used in automatic
spacecraft docking1 and landing, control of complex manufacturing
processes, medical and technical diagnostics.
Detonation fuze is regarded as an automatic controlling device intended to be used for weapon activation in a special point of trajectory. Besides, it processes all properties of information – measuring
and control system. Thus, the intelligent detonation fuze is able to
perceive2 information due to signal processing according to3 a special
algorithm.
13. Translate the following word combinations and expressions:
electric circuit engineering; space technology; signal processing; to
be capable of developing mathematical models; complex manufacturing processes; a device intended to be used for weapon activation
14. Translate the following sentences with the Participles:
1. AICS perform detection, recognition and processing of video,
acoustic, and radar information obtained via different sensors.
2. Specialists in radio direction finding AICS deal with the systems
which perform detection, recognition, filtration and measurement of
radio signal parameters.
3. Specialists on autonomous mechatronic control systems deal with
complex objects containing microprocessor devices, information processing modules and blocks controlling excitations.
4. Specialists create fast algorithms and software for automatic control of the objects operating within extreme conditions.
1
spacecraft docking – стыковка космических летательных аппаратов.
perceive – осознавать, постигать, различать.
3
according to – в соответствии.
2
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15. Answer the questions.
1. What kind of specialists does our faculty train?
2. Where is AICS used?
3. What do specialists in radio detection deal with?
4. What are specialists in signal processing focused on?
5. What can you tell about the specialists in autonomous mechatronic control systems?
6. What examples of real-life designs can you give?
7. What is a denotation fuze intended for?
СПИСОК ЛИТЕРАТУРЫ И САЙТОВ
Англо-русский политехнический словарь / Авт.-сост. М.В. Адамчик. –
Минск: Харвест, 2004.
Большой англо-русский политехнический словарь / С.М. Баринов,
А.Б. Борковский, В.А. Владимиров и др. – М.: Руссо, 2002.
Климзо Б.Н. Ремесло технического переводчика: Об английском языке, переводе и переводчиках научно-технической литературы. – 2-е изд.,
перераб. и доп. – М.: Р. Валент, 2006.
Рубцова М.Г. Чтение и перевод английской научно-технической литературы: Лексико-грамматич. cправочник. – М.: АСТ, 2003.
Hornby F.S. Oxford Advanced Learner’s Dictionary, 7th ed. – Oxford: Oxford Univ. Press, 2006.
Vince M. Advanced Language Practice. – Madrid: Macmillan Heinemann
Publishers Ltd., 2004.
***
aima.cs.berkeley.edu/
liinwww.ira.uka.de/bibliography/Ai/
www.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/0.html
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CONTENTS
Предисловие ................................................................................................
3
Unit I
Text IA. Artificial Intelligence ...................................................................... 6
Text IB. New Generation of Computers ....................................................... 9
Text IC. Applications of AI .......................................................................... 10
Unit II
Text IIA. EP-310 .......................................................................................... 13
Text IIB. Converter Valves ........................................................................... 16
Unit III
Text IIIA. Radar Signal Categorizations Using a Neural Network ...............
Text IIIB. PW-610 ........................................................................................
Text IIIC. Deinterleaving ..............................................................................
Text IIID. BMSTU SM 5 Department ..........................................................
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