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

 

Московский государственный технический университет 
 имени Н.Э. Баумана 
 
 
 
 
 
 
 
 
 
О.С. Никитина, О.В. Ноздрина 
 
 
 
Обучение чтению литературы  
на английском языке по специальности  
«Автономные информационные  
и управляющие системы»  
 
 
 
Учебно-методическое пособие 
 
 
 
 
 
 
 
 
 
 
Москва 
Издательство МГТУ им. Н.Э. Баумана  
2007 

 

УДК 802.0 
ББК 81.2 Англ-923 
        Н624 
Рецензент  Т.И. Кузнецова  
Никитина О.С., Ноздрина О.В. 
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. – 32 с. 
  

Пособие, состоящее из трех разделов, содержит оригинальные тексты 
на английском языке по изучаемой студентами специальности, комплексы 
лексических и грамматических упражнений для развития навыков перевода научно-технической литературы, а также умения вести беседы на 
профессиональные темы на английском языке.  
Для студентов старших курсов, обучающихся по специальности 
«Автономные информационные и управляющие системы» на факультете 
«Специальное машиностроение». 
                                                                             УДК 802.0 
ББК 81.2 Англ-923 
Учебное издание 
 
Ольга Станиславовна Никитина 
Ольга Вячеславовна Ноздрина  
 
Обучение чтению литературы  
на английском языке по специальности  
«Автономные информационные  
и управляющие системы» 
 
Редактор Н.М. Маслова 
Корректор  М.А. Василевская 
Компьютерная верстка  Е.В. Зимакова 
 
Подписано в печать 02.05.2007. Формат  60х84/16. Бумага офсетная. 
Печ. л. 2,0. Усл. печ. л. 1.86.  Уч.-изд. л. 1,75. Тираж 100 экз.  
Изд. № 148. Заказ  
 
Издательство МГТУ им. Н.Э. Баумана 
105005, Москва, 2-я Бауманская, 5 
 
                                                        ©МГТУ им. Н.Э. Баумана, 2007 

Н624 

ПРЕДИСЛОВИЕ 

Пособие состоит из трех разделов, подготовленных на основе 
оригинальных научно-технических текстов (с очень незначительными элементами адаптации) из английских и американских периодических изданий, профиль которых соответствует специальности, изучаемой студентами.  
Помимо текстов каждый раздел содержит задания, позволяющие развить навыки перевода литературы по специальности с английского языка на русский, овладеть ключевой терминологией, 
связанной с предложенными в пособии темами, закрепить некоторые грамматические конструкции, характерные для научнотехнической литературы, а также развить умение вести беседы на 
английском языке на темы, представляющие профессиональный 
интерес. 
Пособие предназначено для студентов старших курсов, обучающихся по специальности «Автономные информационные и 
управляющие системы» на факультете «Специальное машиностроение».  

UNIT I  

New Words and Word Combinations 

adjacent a – смежный, соседний 
automation system − автоматическая система  
constraint v – ограничивать 
customization n − обеспечение соответствия требованиям  
заказчика 
diagnoses problem − проблема диагностики 
decompose v – paскладывать (на части) 
encode v – кодировать 
extend the range of smth. − расширить диапазон 
exigency n − острая необходимость 
fault diagnosis – диагностика неисправностей 
fuzzy logic – нечеткая логика  
generality n – обобщение 
high-level tool – прибор высокого уровня  
high-level language − язык высокого уровня 
intended scope – санкционированный диапазон действия 
inference n – логический вывод, следствие 
inference mechanism − устройство механизма логического  
   вывода 
inference engine – механизм логического вывода 
knowledge based system (KBS) − система, основанная на  
  представлении знаний 
kernal n – ядро 
knowledge representation mechanism − механизм представления  
знаний 
ordering n − упорядочение 
propensity n – склонность  
reason v − размышлять, делать выводы 
task dependent architecture − проблемно-ориентированная  
архитектурная система 
target application – адрес применения 
toolkit n − комплект инструментальных средств разработки  
программы 

undertake v – предпринимать; гарантировать 
validate v – проверять достоверность; контролировать 

1. Read and translate the text with a dictionary. 

Text IA. The Toolkit Concept as a Task Dependent Tool  
for Intelligent Automation 

People are not only Homo sapience – creatures capable of forming 
abstract ideas and reasoning – but also Homo Faber – makers of tools. 
Apart from minor exceptions, man alone has the propensity for making 
tools to aid in the pursuit of happiness and comfort. The mechanization 
and the increasing rate of production moved towards the automation. 
Spurred on by the Second World War, significant advances in the techniques for automatic control of machines and processes have been 
made and resulted in spectacular achievements. This has led to the present so called Information Technology Revolution where the digital 
computer is the most significant tool yet developed. The enormous 
computational power of this machines provides significant opportunity 
to develop information processing tools capable of controlling and interacting with today’s complex industrial processes. Thereby further 
enhancing the automation of our production processes is carried out.  
The other distinguishing feature of a man – the ability to abstract  
a reason on data obtained from observations. This resulted in rapidly 
developing area of Artificial Intelligence, particularly in the significant 
advances of Expert Systems. They have led to the considerable interest 
in developing techniques for extending the range of automation systems 
to include some of the techniques which currently can be undertaken by 
people.  
The crucial aspect is the ability of the system to abstract the important information from the data and obtain an understanding of the situation and to decide on an action react accordingly. 
Substantial world-wide interest is now developing in this area of so 
called Real-Time Knowledge Systems1. 
The next step in this progression – the development of tools for 
automating the development of intelligent automation systems, thereby 
combining the abilities of humans to reason with abstract information 
with the provision of tools to enable the efficient building of complex 
information progressing systems for applications in industrial automation. 

TOOLKIT is currently being validated on three important demonstrator applications relevant to industrial control diagnosis problems. 
The diagnostic system using the toolkit is one of the systems validated 
on demonstrator applications. 
The TOOLKIT is organized as a task-dependent architecture consisting of five conceptual layers: strategic, tactical, teleological, functional and object. The tools are defined by a systematic task classification and are constructed from a set of tools components consisting of 
the representation languages and their associated inference mechanisms. In addition, other tool components include the provision of truth 
maintenance and causal ordering. 
In developing a KBS architecture for Industrial Control Applications, the intended scope must be very carefully considered that is a 
highly specific KBS would not generate a sufficient user base to justify 
its development. Whereas an extremely general system would be expensive to develop and would require extensive customization for  
a given application project. 
The most general and also least powerful for a specific application 
is a high-level language. Within this category AI languages are included, e.g. Common Lisp and Prolog. These languages must be used to 
generate both the knowledge representation and the reasoning mechanism provided by Prolog is sufficient. By providing a knowledge representation mechanism and an inference engine, expert system shells 
bring more power to applications but limit the scope to those applications for which the representation and inference are adequate. 
More recent attempts have been focusing on developing “specialized” toolkits. These attempts are to provide a restricted set of representations and control mechanisms (tools) for a particular application domain. In addition, these toolkits may provide additional domain 
dependent facilities such as graphic capabilities, interfaces, etc. It is 
suggested that this reduces the scope, compared with environments, but 
significantly increase the power for within the target domain. 
Consistent with the general trend, it has been developed a number of 
high level tools designed to solve a range of problems in Industrial 
Automation applications and utilizing different types and sources of 
knowledge. This approach allows the KBS designer to directly encode 
his knowledge using primitives that naturally describe the problem to be 
solved rather than focusing on implementation details. Compared to 

conventional KBS shells, the toolkit approach provides a much more 
powerful functionality at the expense of generality. In the case of  
the QUIC2 toolkit the scope of the application domain has been a priori 
restricted to Industrial Automation, and hence powerful tools have been 
developed for his specific but large and economically important domain. 
The large spectrum and specific nature of the targeted application 
domain in this project as well as the different approaches to knowledge 
representation are required in modeling dynamic physical systems. This 
led to the development of a specific KBS building environment according the toolkit approach. This provides a much more specialized working environment, enabling the KBS designer to build a target application by selecting appropriate toolkit modules. It also allows tools to be 
included that are optimized to provide a specific functionality for a generic task, e.g. fault diagnosis.  
In general, a toolkit comprises: 
• A general system architecture that serves as a common reference 
framework. The architecture specifies the basic function of the main 
modules. These include: knowledge bases, external interfaces, working 
memories, inference operators. The architecture specifies how  
the modules interact and defines the flow of communication information between them.  
• A set of special-purpose building tools that are tailored together 
and assembled to construct a complete KBS application system. This 
set will include all tools necessary for the implementation of the inference mechanisms and special facilities (e.g. explanation and justification) specified in the reference architecture.  
• A set of engineering support tools to avoid the system designer in 
the construction of the knowledge bases (i.e. editing, refining, the updating the represented knowledge). 
• A set of specifications for the construction of external interfaces 
to traditional programming environments (e.g. simulation packages, 
DBMS) and towards the industrial process (data acquisition, sensors, 
instrumentation actuators, etc.). 
• A KBS analysis and design methodology for supporting the correct and effective use of the toolkit over the range of tasks covered by 
application domain. 
The paradigm of the toolkit allows high flexibility to explore different architectures for structured and efficient knowledge organization, 

and still retains the classical advantages of openness of the KBS approach. The toolkit concept embodies the proper compromise between 
the contrasting exigencies of generality and specific, effective usability. 
____________________ 

1 Real-Time Knowlege Based System – система, основанная на знаниях и работающая в режиме реального времени. 
2 QUIC – четвертьдюймовый картридж. 

2. Find English equivalents for the following phrases in text IA: 

− 
цепь обратной связи; 

− 
обеспечивать многократное представление и логические 
выводы; 

− 
обеспечивать ограниченный набор представлений и управляющих механизмов для определенного домена; 

− 
большой спектр и специфическое происхождение домена к 
адресам применения; 

− 
создать желаемую область применения с помощью выборки 
из соответствующего модуля; 

− 
базовая архитектура;  

− 
механизм логического вывода; 

− 
это обеспечивает большую гибкость системы и определяет 
рабочую среду; 

− 
обеспечивать эффективное построение систем по обработке 
сложной информации; 

− 
раскладывать на функциональные идентификационные 
блоки; 

− 
нужно внимательно проанализировать диапазон действия; 

− 
ядро тулкита; 

− 
системы, подходящие для наглядного показа.  

3. Translate the following word combinations from English into 
Russian: 

− 
qualitative reasoning;  

− 
task specific architecture; 

− 
task dependant tools; 

− 
truth maintenance;  

− 
causal ordering; 

− 
motive power; 

− 
a model-based diagnostic reasoner;  

− 
component based language;  

− 
assumption-based truth maintenance; 

− 
complex information processing system; 

− 
a knowledge representation mechanism; 

− 
expert system shell;  

− 
event graphs;  

− 
additional domain;  

− 
dependent facilities. 

4. Answer the questions to text IA. 

1. What spectacular achievements in mechanization have been 
made after the Second World War?  
2. Why is the digital computer called the most significant tool of the 
Information Technology Revolution?  
3. What automation systems can be currently undertaken by people? 
Why?  
4. What sort of tools are of the current interest for humans? 
 
5. Сomplete the following sentences using the information from  
text IA. 
1.Toolkit is a _________________________ refered to a composite 
software environment for supporting the design of the system. 
2. The ability to reason and abstract any information _________ 
__________ . 
3. The toolkit consists of _______________ and is organized as 
_______________ . 
4. A high-level language category __________ that are not specific 
for a particular domain. 
 5. The adequacy of the inference and representation is provided by 
______________ . 
6. The toolkit system represents _________________ that enables 
__________________________ .  

6. Write a brief summary of text IA using the phrases. 

1. This article is entitled ... 
2. It runs about ... 
3. The main points described are ... 

4. It is also mentioned ... 
5. In addition ...  
6. As a conclusion, it may be stressed that ... 

7. Translate the sentences paying attention to the modal verbs and 
the Passive constructions. 

1. The distinction between ontological and empirical knowledge has 
been endorsed by the industrial partners and by the experience of validation exercise. 
2. The shallow system can be regarded as “brittle” as it doesn’t reason about problems even slightly out of its range of experience.  
3. A new “deep” knowledge approach has been developed as a system to explain the sequence of rules used to determine an action.  
4. The deep-level approach should be considered to be advantageous in deriving solutions for anticipated situations.  
5. The internal states for reasoning about the behaviour of the mechanism must be defined by an internal structure of a given system.  
6. There is no general agreement on how these deeper knowledge 
structures should be.  
7. The deep knowledge is defined to be relative, i.e. it turns to be 
deep only with respect to a particular shallow description and can itself 
be a shallow with respect to a deeper modal. 
8. Empirical knowledge takes the word as we find it, it must be obtained from the experience based on observations. 
9. Deep empirical models are called causal on which representations for the internal causal structure of a system are to be provided and 
generated.  
________________ 

Note: internal causal structure – внутренняя структура причинноследственных связей.  

8. Give the reverse meaning of the following words: 

weak, ordered, to construct, dependent, fault, internal, significant, flexible, to include, conventional, complex, adjacent, integrated. 

9. Match English and Russian equivalents from two columns.  

(1) supporting 
(a) модернизация