ARAB ACADEMY FOR SCIENCE &

TECHNOLOGY & MARITIME TRANSPORT

College of Engineering & Technology

Computer Engineering Department

Post-Graduate Student

By:

Eng. Ismail Fathalla El-Gayar

Under Supervision Of:

Prof.Dr. Mohamed Taher El-Sonni

Dr. Ahmed Abou-El-Farag

System Science & Engineering Documentation

System Science & Engineering

Contents

Part 1 : System Science & Engineering

Introduction

• Motivation & Applications

• Report Organization

System Concepts & Definitions

Introduction To System

o System Definition

o System Classification

o System science

o System Engineering

System Function, Behavior and Structure

o System Patterns

o System Structure & Dynamics

o System Behavior

o System Properties

o System Characteristics

o System Sustainable

o System Life Cycle

o System Development Life Cycle

Related System Definitions

o Engineering & Scientific Methodology

o Integrated Logistic Support

o Systematic

o Cybernetics

o Ergonomics

o Systemic

Feedback & Feedback Types

o Introduction To Feedback

o Feedback Importance

o Feedback Types

Introduction To System Modeling

Thinking Process

o Analogical

o Inductive

o Deductive

o Abductive

System Modeling

• Linguistic

• Visualization

• Mathematical

• Physical

Part 2: Statistics & Probability

Overview Of Statistics & Probability

Basic Concepts on Statistics & Probability

o Basic Concepts

o Measure Of Dispersions

o Causes of not knowing things precisely

o Probability & Density Functions

o Distributions

Stochastic Process & Markov Chain

o Stochastic Process

o Markov Chain

Principal Component Analysis ( PCA)

o Definition

o Applications

o Graphical Model

o Complete Example

Part 3 : Case Study : Dependability

Introduction To Dependability

Dependability Elements

o Attributes

Availability

Reliability

Safety

Confidentiality

Integrity

Maintainability

o Threats

Fault

Error Failure

o Means

Fault Preventation Fault Removal

Fault Forecasting

Fault Tolerance

Fault, Error & Failure Classifications

o Fault Classes

o Error Classifications o Failure Classes

Measuring Dependability

o Measuring Dependability Concepts

o Fault Tree Analysis Method o Software Tools For Measuring Dependability

Dependability Benchmark

o Benchmark & Dependability Benchmark o Elements of Performance & Dependability Benchmarking

o Basic Definitions on Dependability Benchmarking

Summary & Conclusion

List Of References & Figures

o List Of References

o List Of Figures

Part 1

System Science & Engineering

Chapter 1

Introduction

System Science & Engineering is one of the most important Courses in our life, This

course has a different felling for anyone who take this course, it depend on how you think and

how you imagine the course , this course learn me a lot of things first of all learned me how to

be a philosopher , how to illustrate what I think in a good way , I learned also the scientific

methodology on thinking how to base my idea & how to think in a good way , the

representation of the knowledge how to be so simple & in this document , I tried to make this

concept & trained to be good on it by using system models that I found it so interested as the

visualization ,mathematical , linguistic & physical model , this course that I really enjoyed so

much in learning it and I really want to learn system more & more , I learned also practical

expressions that benefit me in my work in any system , I learned about how system be

Reliable , Available , Usable , maintainable ….etc , The dependability was my case study in

this document I learned how system can be dependable & how to measure this dependability?

, also I learned the fault , error & failure chain which harm any system and how to detect it &

stop it fast before it will be hazard or a Failure of System . Also Another thing which I

learned about Markov chain & Stochastic process which helps me a lot in analysis of any

process also the transforms , probability , statistics ,Principal component analysis , so I

Learned a tools which I can benefit from them a lot in my life in analysis any system or

problem I will find in my life . All of this but still more & more Benefits I don't mention yet

so as many as I talk, I can't explain this course represents what for me.

Motivation & Applications:-

System Science & Engineering was a very successful course to me , I have learned

many topics which will help me in life , all of this topic I have learned from my masters

Prof.Dr.Mohamed Taher El-Sunni & Dr.Ahmed Abo El-Farag which I want to thanks

Them both for their efforts on this course which was very successful to me, So from this

beginning point my masters in this course was the first to motivate me to make this

documentation to illustrate what I have been learned in this course , I really enjoying making

this document because it's content is what I have learned for 5 months being in this term as a

topic & more of this as a methodologies of how I can think & How can I simplify The

information & See all things from A holistic view, In my point of view I see that a course like

system science & Engineering must be learned to all engineers in the world so that they can

know how to deal with a system well, how to control & know the performance of this system,

how to develop the system….etc . So this was my second motivate to make this document & I

will give it to all the engineers I know to be an abstract & a reference to one of the most

important courses in the world.

This Course Application is too many in any factory, any system in your house as an example :

car, refrigerator, television, computer … etc , you will need the basic of system science to

know this system well.

Report Organization:

The Report Organized in an illustrative flow which makes the reader can imagine &

understand the topic well as follow:

Part 1: System Science & Engineering

This Part introduces The Meaning Of System , Characteristics , properties , attributes ,

classification & Some definitions that relate to system science & System engineering also

this chapter has many exciting topics like system like system life cycles & Development life

cycles , System feedbacks & it's types , thinking process types & what is meant by system

process ? , System modeling & its importance & Types, As We See this part talking generally

about System Science & its Related Topics.

Part 2: Statistics & Probability

This Part introduces probability & statistic Concepts, Importance, Applications. Also talking

about Joint probability & Distributions Types & Normal Distribution as An Example, Also

Talked about exciting topics most use this days like Stochastic Processes & Markov chain,

Principal Component Analysis…etc.

Part 3: Case Study

The Last Part talking about my Case Study which is the Dependability of Any System as a

whole view to the dependability, performance, measures, I also take a look about another

attributes like Reliability, Availability, Maintainability, Safety, Confidentiality, Integrity also

I take a look about the threats of dependability which cause dependability failure & minimize

the dependability of a system like the faults, errors & failure chain and the way to preventing,

removing, forecasting, tolerance this error , Also talked about dependability Benchmark &

Software used for the benchmarking.

Chapter 2

System Concept & Definitions

Introduction To System:-

Definition of SYSTEM:- A set of components integrated together to perform

a certain goal surrounded by a certain Environment within a boundary observed by a

set of observers

Comparing between Some Definitions Of important

Organizations & Known Authors in SYSTEM:-

Goal Integration Components Define

To achieve a given purpose. aggregation end products , enabling

products

ANSI/EIA[1]

whose behavior satisfies

customer/operational needs and

provides for life cycle sustainment

of the products

A set or arrangement -

related

elements and processes IEEE[2]

to achieve one or more stated

purposes

A combination -

interacting elements -

organized

elements ISO/IEC[3]

to produce the capability to meet a

need.

The combination -

function together

elements (include all

hardware, software,

equipment, facilities,

personnel, processes, and

procedures needed for

this purpose)

NASA[4]

Figure .121

Classification of Systems:

Natural System and Human-Made System:

Natural System – a high degree of order and equilibrium, such as

seasons, food chains, water cycle

Human-made system – technology based system

Physical and Conceptual System:

Physical system – in physical form or space

Conceptual system – in ideas, plans, concepts, hypotheses

Static and dynamic System:

Static system – structure without activity

Dynamic system – structural components with activity

Closed and Open System:

Closed system – one that does not interact with its environment

Open system – one that interact with its environment

What is SYSTEM SCIENCE:-

• Is an interdisciplinary field of science that studies the nature of complex

systems in nature, society, and science, It aims to

develop interdisciplinary foundations, which are applicable in a variety of

areas, such as engineering, biology, medicine and social sciences.

What is SYSTEM ENGINEERING:-

• is Defined as the art of designing & Optimizing Systems , Starting with

expressed needs & ending up with the complete set of specifications for all the

system elements (Aslaksin& Belcher 992)

Idioms

Design

Architecture

System Function, Behavior and Structure:-

System Patterns:-

A pattern is more than either just the problem or just the solution

structure:

It includes both the problem and the solution, along with the rationale that binds them

together. A problem is considered with respect to conflicting forces, detailing why the

problem is a problem. A proposed solution is described in terms of its structure, and

includes a clear presentation of the consequences both benefits and liabilities—of

applying the solution.

Types of Patterns:-

Architecture Pattern:-

Expresses a fundamental structural organization schema for any system .it provides a

set of predefined sub-systems, specifies their responsibilities, and includes rules and

guidelines for organizing the relationships between them [Buschmann, Meunier,

Rohnert, Sommerland]

Design Pattern:-

Describes a commonly- recurring structure of communicating components that solve a

general design problem in a particular context [Gamma , Helm , Johnson]

Idioms Pattern:-

Describes how to implement particular aspects of components or the relationships

between them. [Buschmann, Meunier, Rohnert, Sommerland][*]

Low level pattern to solve implementation

specific problems

Medium scale pattern to organize sub-

system functionality in application domain

in independent way

High Level pattern to help to specify the

fundamental structure of the system

Figure .122

Note: Each pattern is a three-part rule, which expresses a relation between:-

( a certain context, a problem, and a solution).

System Structure & Dynamics:-

System Structure: - A graphical representation of the pattern. Class

diagrams and Interaction diagrams may be used for this

System dynamics: -

is an approach to understanding the behavior of complex over time. It deals

with internal feedback loops and time delays that affect the behavior of the entire

system. What makes using system dynamics different from other approaches to

studying complex systems is the use of feedback loops and stocks and flows. These

elements help describe how even seemingly simple systems display

baffling nonlinearity.

System Behavior:-is what the system does to implement its function and is described

by a sequence of states.

System Attributes:-

The term attributes classifies functional or physical features of a system.

Examples include gender; unit cost; nationality, state, and city of residence; type of

sport; organizational position manager; and fixed wing aircraft versus rotor.(Wasson)

System Properties:-

The term, properties, refers to the mass properties of a system.(Wasson)

Examples include composition; weight; density; and size such as length, width, or

height.

System Characteristics:-

The term characteristics refer to the behavioral and physical qualities that

uniquely identify each system. (Wasson)

- Behavioral characteristics examples include predictability and

responsively.

- Physical characteristics examples include equipment warm-up

and stabilization profiles; equipment thermal signatures; aircraft radar

cross-sections; vehicle acceleration to cruise speed, handling, or stopping;

and whale fluke markings.

When we characterize system, there are four basic types of characteristics we consider:

.

•stated in marketing brochures where key features are emphasized to capture a client

General Characteristics

•describe system features related to usability, survivability, and performance

Operating or Behavioral Characteristics

•relate to nonfunctional attributes such as size, weight, color, capacity

Physical Characteristics

•relate to the “look and feel” of a system

System Aesthetics

Figure .123

System Sustainable:-

Sustainability refers to a quality and system of life that allows people to meet

their current needs without compromising the resources available for future

generations to meet their future needs. Sustainability rests on the belief that we can

coexist with the environment if we work to ensure our actions are not harmful to it.

Essentially, it means ensuring that we leave our environment no worse than we found

it.

System Life Cycle

Development

Production

Operation

Disposal

Figure .124

System Development Life Cycle

Figure .125

Related System Definitions:-

System thinking:-

Is a framework that is based on the belief that the component parts of a system

can best be understood in the context of relationships with each other and with other

systems, rather than in isolation.

Systematic

• is a study of systems and their application to the problem of understanding

ourselves and the world,

– Formal Systematic

– Pure Systematic

– Applied Systematic

– Practical Systematic

Cybernetics

Is the interdisciplinary study of the structure of regulatory systems.

Cybernetics is closely related to control theory and systems theory. cybernetics is

equally applicable to physical and social (that is, language-based) systems

Systemic

To study systems from a holistic point of view. It is an attempt at

developing logical, mathematical, engineering and philosophical paradigms and

frameworks in which physical, technological, biological, social, cognitive, and

metaphysical systems can be studied and modeled.(Bunge (1979))

Ergonomics

Is the scientific discipline concerned with the understanding of

interactions among humans and other elements of a system, and the profession that

applies theory, principles, data and methods to design in order to optimize human

well-being and overall system performance.(International Ergonomics Association)

Methodology:

"the analysis of the principles of methods, rules, and postulates

employed by a discipline"

"the systematic study of methods that are, can be, or have been

applied within a discipline"

Scientific Methodology: - (deduced from Definition of Methodology)

Is To Analysis by a scientific way ( Methods , Rules )

Engineering Methodology: - (deduced from Definition of Methodology)

Is To Analysis by an Engineering way ( Methods , Rules )

Integrated Logistic Support (ILS):-

Is the management organization that plans and directs the activities of many

technical disciplines associated with the identification and development

of logistics support and system requirements for military systems or equipment / parts

Feedback & Feedback Types

Introduction to Feedback

When the system is part of a chain of cause-and-effect that forms a circuit or

loop, then the event is said to "feed back" into itself.

Feedback Importance

Feedback used to give indicator about the output is the output is

good or we need to change in the input or in the system.

It is very important in any system to develop the performance of

the system feedback methods also used in community systems & society

systems not also the systems related to engineering.

Feedback Types

Feedback has many types we can't mentioned it all so we will mention as an

example:-

Positive & Negative Feedback

Positive when the feedback signal can amplify the input signal, leading to

more modification.

Negative when the feedback signals dampen the effect of the input signal,

leading to less modification.

Introduction to System Modeling:-

System modeling is a technique to express, visualize, analyze and transform

the architecture of a system. Here, a system may consist of software components,

hardware components, or both and the connections between these components. A

system model is then a skeletal model of the system.

Thinking Process:-

Is any process of estimating or inferring how local policies, actions, or changes

influence the state of the neighboring universe

It also can be defined, as an approach to problem solving, as viewing "problems" as

parts of an overall system, rather than reacting to present outcomes or events and

potentially contributing to further development of the undesired issue or problem

Analogical

Refers to a process of finding and using a known experience or domain to

understand an unknown phenomenon or domain.

Inductive

Moving from specific observations to broader generalizations and theories.

Informally, we sometimes call this a "bottom up" approach (please note that it's

"bottom up" which is the kind of thing the bartender says to customers when he's

trying to close for the night!).

Deductive

Works from the more general to the more specific. Sometimes this is informally

called a "top-down" approach. We might begin with thinking up a theory about our

topic of interest. We then narrow that down into more specific hypotheses that we can

test. We narrow down even further when we collect observations to address the

hypotheses.

Abductive

Starts from a set of accepted facts and infers their most likely, or best, explanations.

The term abduction is also sometimes used to just mean the generation of hypotheses

to explain observations or conclusions

Chapter 3

System Modeling

Model:-

A model is a simplification of another entity, which can be a physical thing or

another model. The model contains exactly those characteristics and properties

of the modeled entity which are relevant for a given task. A model is minimal

with respect to a task, if it does not contain any other characteristics than those

relevant for the task.

A model is a representation of one or more concepts that may be realized

in the physical world. It generally describes a domain of interest. A key

feature of a model is that it is an abstraction that does not contain all the detail

of the modeled entities within the domain of interest. Models are represented

in many forms including graphical, mathematical, and logical representations,

and physical prototypes.

For example, a model of a building may include a blueprint and a scaled prototype

physical model. The building blueprint is a specification for one or more buildings

that are built. The blueprint is an abstraction that does not contain all the building's

detail such as the characteristics of its materials.

A model must:

Relates to an entity

be a simplification of that entity

be a related to a task and an objective

may relate to a not yet existing entity

Modeling Types:-

Linguistic Modeling:-

It is a method for Modeling by using Language describe our system by

language Expressions

Example: Description Of A Car:- It is a block in which has 4 tires , it moves

forward & Backward , this block consist of a Salon , Engine ,Electrical &

Mechanical Sub-systems , Used for traveling distances.

Visualization Modeling:-

It is a method for modeling by using a visualize images & Diagrams to express

The system idea, relationships, components ….etc

Modeling Methods

Linguistic Modeling

Visualization Modeling

Mathematical Modeling

Physical Modeling

Describe by

Words

Describe by

Graphs &

Animations

Describe by

Mathematical

Equations

Describe by

tangible Materials

Figure .131

Metaphor visualization

Compound visualization

Strategy visualization

Concept visualization

Information visualization

Data visualization

As Seen In The Figure (A Periodic Table Of Visualization Method):

The Table Consist Of category for Visualization (by Colors):-

Figure .132

Figure .133

Mathematical Modeling:-

It is a method for modeling by using mathematical equations to express the

system as an equation & variables

A representation of the essential aspects of an existing system , which presents

knowledge of that system in usable form'. (Eykhoff (1974))

Example:-

Physical Modeling:-

It is a method for modeling by using tangible materials to express the system,

can be a physical object such as an architectural model of a building. Uses of

an architectural model include visualization of internal relationships within the

structure or external relationships of the structure to the environment.

Example As An Empty Cup :-

Figure .134

Part 2

Statistics & Probability

Chapter 1

Overview Of Statistics & Probability

In This Part We will go to a journey around probability & statistics & Method

used in system science applications & Computations we will take a look about

probability computations & how to analyze data on statistics & classify them by

different method & how we can deal with different types of data & variables

(Continuous & discrete) also we will learn about markov chain & its application on

dependability methods, also we will talk about principal component analysis method

and its role in researches area, we will talk about how we can get another dimensions

by an illustrative example

Importance

Statistics & Probability are very important in this course to simplify the

analysis of the data & help us to improve performance of the system. we can measure

system performance, availability, reliability, dependability, usability and all the

abilities of the system using probability & statistics method which will be illustrate in

this chapter, this chapter will help us to practically apply these definitions & concepts

on any system we want.

Applications

Applications of these chapter varies in many science & situations we meet in

our life, we will learn some concepts must be understood well, for solving problems

in our life, we will learn how to measure the meaning by a different methods, we can

benchmarking systems by these methods, these methods is the practical view for

system science & Engineering course, in which we can develop ourselves & practice

these in our field.

Chapter 2

Basic Concept On Statistics & Probability

Basic Definitions:-

Probability

It is the likelihood—or chances—of something to happened

Do we have a better chance of it occurring or do we have a better chance of it

not occurring?

Types Of Probability:-

- Empirical Probability

It is determined from repeated experimentation and observation, recording

results.

- Theoretical Probability

It is determined using mathematical computations based on possible results,

or outcomes.

Statistics

Analysis and Interpretation of numerical data

A number summarizing a bunch of values

Data

Collection and compilation of relevant information

Data are a bunch of values of one or more variables.

Variable

A variable is something that has different values

Discreet variable

Continuous variable

Independent Events

Two events are called independent if the occurrence of one event does not in

any way affect the probability of the other event

Random Variable

A variable is called a random variable if it takes one of a specified set of

values with a specified probability.

Arithmetic Geometric Harmonic

Measure of Dispersions:-

Measure By Central Tendency :-

* The Mean Types:-

• Arithmetic Average Value

The Mean*

• Most frequently Used Value

The Mode

• Middle value after arranging data

The Median

Figure .221

Figure .222

When We Use Each Of The Central Tendency Measures???

Figure .223

Measure By Dispersion :-

Range - (minimum, maximum)

Variance and Standard deviation

- Variance =

- Standard deviation ( ) = (Measure of spread)

- Standard error =

o Causes of not knowing things precisely

2

1

1

n

i xxn

Variancex

n

Figure .224

Probability & Cumulative Density Functions:-

The Sample Space:-

The space of all possible outcomes of a given process or situation is called the sample

space S

An event:-

An event A is a subset of the sample space.

The Laws of Probability:-

The probability of the sample space S is 1, P(S) = 1

The probability of any event A is such that

0 <= P (A) <= 1.

Law of Addition

If A and B are mutually exclusive events, then

P (A or B) = P (A) + P (B)

If A and B are not mutually exclusive:

P (A or B) = P (A) + P (B) – P (A and B)

Union:-

Elements in at least one of the two sets:

AB = { x | x A x B }

Figure .225

Figure .226

Figure .227

Intersection:-

Elements in exactly one of the two sets:

Disjoint Sets

DEF: If A and B have no common elements, they are said to be disjoint,

i.e. A B = .(Mutual Exclusive)

Disjoint Union

When A and B are disjoint, the disjoint union operation is well defined. The

circle above the union symbol indicates disjointedness.

Figure .228

Figure .229

Figure .2210

Set Difference

Elements in first set but not second:

A-B = { x | x A x B }

Symmetric Difference

Elements in exactly one of the two sets:

AB = { x | x A x B }

Complement

Elements not in the set (unary operator):

A = {x | x A}

Figure .2211

Figure .2212

Figure .2213

Conditional Probabilities:-

It means that what is the probability of occurring A if B has been already

happened.

The conditional probability of A given B is

P (A|B) = P (A, B) / P (B)

If A and B are independent then

P (A, B) =P (A)*P (B) P (A|B) =P (A)

In general:

min(P(A),P(B) P(A)*P(B) max(0,1-P(A)-P(B))

For example:-

If P (A) =0.7 and P (B) =0.5 then P (A, B) has to be between 0.2 and

0.5, but not necessarily be 0.35.

Probability Density function:-

a probability function that maps the possible values of x against their respective

probabilities of occurrence, p(x)

P(x) is a number from 0 to 1.0.

][)( xXPxpX

Figure .2214

Cumulative Distribution function:-

For a given x, there is a fixed possibility that the random variable will not

exceed certain value x, it is non-decreasing in x

Permutation & Combination:-

Permutation: How many different sets of r objects can be chosen from n objects

][)( xXPxFX

)()( 2121 xFxFxx

1...21 rnnnnpn

r

!!

rn

npn

r

Figure .2215

and , where and

0 , if the pdf of isX2 2( ) /(2 )1

( )2

xf x e x

Combination: Without regard to order of drawing.

• Number of n things taken r at a time.

Distributions:

Some Examples on Distributions:-

Normal distribution:

A continuous random variable X is said to have a normal distribution with parameters

Bernouli Distribution

Binomial distribution

Poisson distribution

Negative binomial

distribution

Normal Distribution

!!

!

rnr

nc n

r

n

r

Figure .2216

Mean or Expected Value

Variance: The expected value of the square of distance between x and its

mean

Figure .2217

Figure .2218

)])([( yx yxE

Coefficient of Variation

Covariance

Measures the strength of the linear relationship between two variables

cov(X,Y) > 0 X and Y are positively correlated

cov(X,Y) < 0 X and Y are inversely correlated

cov(X,Y) = 0 X and Y are independent

Correlation Coefficient: normalized value of covariance

The correlation always lies between -1 and +1

Joint Probability:-

The joint CDF of X and Y is:

),())((σ1

N

i

iiyixixy yxPyx

Chapter 3

Stochastic Process & Markov Chain

Coin Toss Process

-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10

Flips

Valu

e

Stochastic Process

Is a Series of variables represent a process that goes through time and has some

random component

To model any variable over time, we need an algorithm or formula that tells us how

the variable changes from one period to the next.

We calculate the variable by applying the formula to an initial value to get the second

value, applying it to the second value to get the third, etc.

Start with a deterministic process:

0, 2, 4, 6, 8…

The deterministic process is to add the value of 2 to the previous value., we could

describe this algorithm as:

Stochastic Process is similar to deterministic process, except that they add a chance element

to each change.

A simple example:

Flip a coin.

If (heads) add 1 & If (tails) subtract 1.

Here are the results from my home experiment:

T, H, T, H, H, H, H, T, H, H which produces -1, 0, -1, 0, 1, 2, 3, 2, 3, 4

1 02, 0t tX X X

Figure .231

So We Can Define stochastic process as an another definition as a collection of random

variables indexed on a set;

Usually the index denotes time.

Continuous-time stochastic process:

Discrete-time stochastic process:

First order to n-order distribution can characterize the stochastic process.

First order:

Second order:

Strict stationary

For all n, k and N

Markov Chain

Is an example of mathematical model to model a system

Convenient to give transition probabilities in matrix form

As an Example:-

The Following markov chain with a representation on

matrix form of state A, B, C, D

State

t

State

t+1

Probability P(t, t+1)

0.2 0.5

0.95

0.2 0.05

0.8

1

0.3

Figure .232

Figure .233 Figure .234

Another Example on Markov Chain:-

This is an illustrative example of markov chain for CPU in

which the states & the process are illustrated with their

probability and the corresponding representation using

matrix form.

S1

S3

S0

S2

0.99

0.92

0.98

0.900.02

0.01

0.02

0.01

0.04

0.01

0.09

0.01

WAIT

LOOP

USER

SUPERVISOR

USER

PROGRAMS

SYSTEM

SUPERVISOR

PROBLEM

STATE

SUPERVISOR

STATES

IDLE

STATE

Figure .235

Figure .236

Chapter 4

Principal Component Analysis(PCA)

Principal Component Analysis ( PCA)

The Principal components method summarizes data by finding the major correlations

in linear combinations of the observations.

Reduce the dimensionality of a data set by finding a new set of variables, smaller than

the original set of variables

—

— PCA is a statistical method to transform the data to a new coordinate system.

* Little information lost in process, usually

Applications

Used Scientifically in Compression & Classification of data in this Application:

◦ Face Recognition

◦ Voice Recognition

◦ Image Compression

◦ Pattern Recognition

◦ Handwriting Analysis

◦ Lip Reading

◦ Marketing

◦ Social Science Researches

◦ And many more other fields.

Graphical Model

Complete Example

1- Get Some Data: First we will gather some data that can be represented in 2

dimensions.

Figure .241

Figure .242

2- Substract The mean: we have to subtract the mean from all the data

3- Calculate the covariance matrix

4- Calculate Eigenvector and Eigen values of the covariance matrix

5- Choosing components and forming a feature vector

We then choose the eigenvector with the highest eigenvalue

6- Deriving the new dataset

Figure .243

Final Data = Feature Vector x Data Adjusted.

Shown Example by using Matlab Function:-

Figure .244

Figure .245

Part 3

Case Study: Dependability

Chapter 1

Introduction To Dependability

Definition Of Dependability:-

• Is a value showing the reliability of a person to others because of his/her integrity,

truthfulness, and trustfulness, traits that can encourage someone to depend on

him/her.

• The collective term used to describe the availability performance and its influencing

factors: reliability performance, maintainability performance and maintenance

support performance. [Belcher][1]

• Is the system property that integrates such attributes as reliability, availability,

safety, security, survivability, maintainability.

Performance Concept Diagram:-

This Diagram illustrate the relation between the Quality Of Service (QOS) &

The Dependability (which depend on Availability, Reliability, Maintainability)

Figure .311

Dependability and Survivability are the same as shown that The

Goals of Each others are common:

Dependability Goal

1) Ability to deliver service that can justifiably be trusted

2) Ability of a system to avoid failures that are more frequent or more severe, and

outage durations that are longer, than is acceptable to the user(s)

Survivability Goal

Capability of a system to fulfill its mission in a timely manner

Also As we will see in the threats we will find that Dependability &

survivability has same meaning also:

Dependability Threats:

1) Design faults (e.g., software flaws, hardware errata, malicious logics)

2) Physical faults (e.g., production defects, physical deterioration)

3) Interaction faults (e.g., physical interference, input mistakes, attacks, including

viruses, worms, intrusions)

Survivability Threats:

1) Attacks (e.g., intrusions, probes, denials of service)

2) Failures (internally generated events due to, e.g., software design errors, hardware

degradation, human errors, corrupted data)

3) Accidents (externally generated events such as natural disasters)

Chapter 2

Dependability Elements

•A way to asses (to measure) the Dependability of a system

Attributes

•An understanding of the things that can affect the Dependability of a system

Threats

•Ways to increase the Dependability of a system prevention, fault tolerance, fault removal and fault forecasting.

Means

Dependability can be thought of as being composed of three elements:-

Collecting Together As A Tree Called (Dependability Tree) :-

Figure .322

Figure .321

Availability

• readiness for correct service.

Reliability

• continuity of correct service.

Safety

• absence of catastrophic consequences on the user(s) and the environment .

Integrity

• absence of improper system alteration

Maintainability

• ability to undergo modifications and repairs .

Confidentiality

• i.e. the absence of unauthorized disclosure of information

Attributes Of Dependability:-

Attributes are the qualities of a system. Which can be assessed to determine

its overall dependability using Qualitative or Quantitative measures.

The following is The Dependability Attributes:-

Figure .323

Availability:-

Will be up and running and able to deliver useful services at

any given time?

The availability of a system is the probability that it.

Reliability:-

The reliability of a system is the probability, over a given

period of time, that the system will correctly deliver services

as expected by the user.

continuity of correct service

Safety:-

The safety of a system is a judgment of how likely it is that

the system will cause damage to people or its environment?

Absence of catastrophic consequences on the user(s) and the

environment

Confidentiality-

Absence of unauthorized disclosure of information.

Integrity:-

absence of improper system alteration

Integrity is a pre-requisite for availability, reliability and

safety

Maintainability:-

ability to undergo modifications and repairs

Threats Of Dependability:-

Are things that can affect a system and cause a drop in Dependability

There are three main terms that must be clearly understood:

Figure .324

Fault: A fault is a defect in a system. The presence of a fault in a

system may or may not lead to a failure, for instance although a

system may contain a fault its input and state conditions may never

cause this fault to be executed so that an error occurs and thus

never exhibits as a failure.

* Activation of Fault Leads to Error

Fault

Error

Activation

Figure .325

Error: An error is a discrepancy between the intended behavior of

a system and its actual behavior inside the system boundary. Errors

occur at runtime when some part of the system enters an

unexpected state due to the activation of a fault. Since errors are

generated from invalid states they are hard to observe without

special mechanisms, such as debuggers or debug output to logs.

* Assume (1, 2, 3&4) is The Processes of the System.

* If a Fault has happened (Activated) The Process will go to the

Error State (invalid State).

* An Observer inside the Boundary of the System (e.g: Debugger)

1 2 3 4

Error

Observer

Fault Activated

Figure .326

Failure: A failure is an instance in time when a system displays

behavior that is contrary to its specification. An error may not

necessarily cause a failure, for instance an exception may be

thrown by a system but this may be caught and handled using fault

tolerance techniques so the overall operation of the system will

conform to the specification.

* When the Error propagate it will causes Failure

Error

Failure

Propagate

Figure .327

It is important to note that Failures are recorded at the system boundary.

They are basically Errors that have propagated to the system boundary

and have become observable. Faults, Errors and Failures operate

according to a mechanism. This mechanism is sometimes known as a

Fault-Error-Failure chain. As a general rule a fault, when activated, can

lead to an error (which is an invalid state) and the invalid state generated

by an error may lead to another error or a failure (which is an observable

deviation from the specified behavior at the system boundary).

Once a fault is activated an error is created. An error may act in the same

way as a fault in that it can create further error conditions, therefore an

error may propagate multiple times within a system boundary without

causing an observable failure. If an error propagates outside the system

boundary a failure is said to occur.

* A failure is basically the point at which it can be said that a service is

failing to meet its specification. Since the output data from one service

may be fed into another, a failure in one service may propagate into

another service as a fault so a chain can be formed of the form: Fault

leading to Error leading to Failure leading to Error, etc.

Figure .328

Fault Removal

•How Can Be Removed?

Fault Preventation

•How Can We Prevent?

Fault Forecasting

•How Can We Forecast?

Fault Tolerance

•How Can We Tolerant?

Means Of Dependability:-

Since the mechanism of a Fault-Error-Chain is understood, it is possible to

construct means to break these chains and thereby increase the dependability of a

system.

Four means have been identified so far:

Figure .329

Fault Removal: - can be sub-divided into two sub-categories:

Removal During Development

Removal During Use.

-Removal during development: requires verification so that

faults can be detected and removed before a system is put

into production. Once systems have been put into production

a system is needed to record failures and remove them via a

maintenance cycle.

-Removal during Use: happen after system put into

production.

Fault Prevention: - deals with preventing faults being

incorporated into a system. This can be accomplished by use of

development methodologies and good implementation techniques.

Fault Forecasting: - predicts likely faults so that they can be

removed or their effects can be circumvented.

Fault Tolerance: - deals with putting mechanisms in place that

will allow a system to still deliver the required service in the

presence of faults, although that service may be at a degraded level.

*Dependability means are intended to reduce the number of failures

presented to the user of a system. Failures are traditionally recorded over

time and it is useful to understand how their frequency is measured so

that the effectiveness of means can be assessed.

Chapter 3

Fault, Error & Failure Classifications

Fault Classes:-

Represented as follow:-

Persistence Domain:-

• Transient fault:

– E.g. hardware components which have an adverse reaction to radioactivity.

• Permanent fault:

– E.g., a broken wire or a software design error.

• Intermittent fault:

– E.g. a hardware component that is heat sensitive, it works for a time, stops

working, cools down and then starts to work again.

Figure .331

Phenomenological Cause

• physical faults

- Which are due to adverse physical phenomena,

• human-made faults

- Which result from human imperfections.

Nature of fault

• accidental faults

- Which appear or are created fortuitously;

• intentional faults

- Which are created deliberately, with or without a malicious intention.

Phase of creation

• Development faults

- Which result from imperfections arising either

a) During the development of the system (from requirement

specification to implementation) or during subsequent modifications

b) During the establishment of the procedures for operating or

maintaining the system

• Operational faults

- Which appear during the system’s exploitation.

System boundaries

• internal faults

- Which are those parts of the state of a system which, when invoked by the

computation activity, will produce an error,

• external faults

- Which result from interference or from interaction with its physical

(Electromagnet perturbations, radiation, temperature, vibration, etc.) Or

human Environment.

Combined Fault

It's Used by Laprie, he used to make the faults classes in which we can represent any

type of faults, in which may faults has many types of classes so he illustrate this faults

by the intersection of the classes with each other

Matrix Representation:-

Figure .332

More combinations may be identified in the future. The combined fault classes as

shown belong to three major partially overlapping groupings:

• Development faults that include all fault classes occurring during development.

• Physical faults that include all fault classes that affect hardware. • Interaction faults that include all external faults.

As An illustrative example on this Diagram as shown in:

Natural Fault: The fault caused by nature must be hardware fault (physical fault), it can't be

software fault.

Human-made Fault: The fault caused by human-made may be software, hardware or

external

------------------------------------------------------

Error:

An error is detected if its presence is indicated by an error message or error

signal. Errors that are present but not detected are latent errors.

Whether or not an error will actually lead to a service failure depends on two

factors:

1. The structure of the system, and especially the nature of any redundancy that

exists in it:

• Protective redundancy, introduced to provide fault tolerance, that is

explicitly intended to prevent an error from leading to service failure;

• Unintentional redundancy (it is in practice difficult if not impossible to

build a system without any form of redundancy) that may have the same

presumably unexpected — result as intentional redundancy.

2. The behavior of the system: the part of the state that contains an error may

never be needed for service, or an error may be eliminated (e.g., when

overwritten) before it leads to a failure.

Error Classifications:

A convenient classification of errors is to describe them in terms of the

elementary service failures that they cause:

o content vs. timing errors

o detected vs. latent errors

o consistent vs. inconsistent errors when the service goes to two or more

users

o Minor vs. catastrophic errors. In the field of error control codes

Content errors are further classified according to the damage pattern: single,

double, triple, byte, burst, erasure, arithmetic, track, etc., errors.

Some faults (e.g., a burst of electromagnetic radiation) can simultaneously

cause errors in more than one component. Such errors are called multiple

related errors. Single errors are errors that affect one component only.

-----------------------------------------------------

Failure classes:

Domain Classes

Content (value) failures:

- The content of the information delivered at the service interface (i.e., the

service content) deviates from implementing the system function;

Timing failures:

-The time of arrival or the duration of the information delivered at the service

interface (i.e., the timing of service delivery) deviates from implementing the

system function.

Figure .333

Perception by several users

Consistent failures:

- The incorrect service is perceived identically by all system users.

Inconsistent failures:

- Some or all system users perceive differently incorrect service (some users

may actually perceive correct service).

Consequence of environment

Minor failures

- Where the harmful consequences are of similar cost to the benefits provided

by correct service delivery;

Catastrophic failures

- Where the cost of harmful consequences is orders of magnitude, or even

incommensurably, higher than the benefit provided by correct service

delivery.

Figure .334

Chapter 4

Measuring Dependability

Measuring Dependability Varies by the type of system & What used for,

many types of methods used to measuring dependability, As an example:-

Measuring By Attributes of Dependability ( Reliability, Maintainability,

Availability, safety, Confidentiality …etc)

Measuring Using Fault Tree Analysis Method.

Measuring Using Stochastic Petri-nets Method & Markov Chain.

I will talk in this chapter about some methods & concepts used in Dependability

Measurements.

-------------------------------------------

Some Concepts we use to measure dependability:-

As we learn in the dependability elements is the dependability attribute

which we try to use them to find equations for computing dependability of a

system.

Only Availability and Reliability are quantifiable by direct measurements

whilst others are more subjective.

Safety cannot be measured directly via metrics but is a subjective assessment

that requires judgmental information to be applied to give a level of

confidence; while Reliability can be measured as failures over time.

While Reliability can be measured as failures over time.

Reliability = Failure / Time

Applying security measures to the appliances of a system generally improves

the dependability by limiting the number of externally-originated errors.

When Measuring Reliability and Availability Time from an initial instant to

the next failure event Typical measures:

– MTTF: mean time to failure

– MTBF: mean time between failures

– MTTR: mean time to repair

– MFC: mean failure cost

Availability = MTTF / MTBF

Ratio of service time to elapsed time

Computing Mean Time Between Failure:-

MTBF = MTTF + MTTR

As it is usually true that MTTR is a small fraction of MTTF, it is

usually allowed to assume that MTBF ≈ MTTF.

Measuring Maintainability which is a function of time representing the

probability that a failed system will be repaired in a time less than or equal to

(t). Which can be estimated as:

M (t) = 1 - exp-μt

(Where μ being the repair rate)

Applying security measures to the appliances of a system generally improves

the dependability by limiting the number of externally-originated errors.

Security is the concurrent existence of:-

a) Availability: for authorized users only,

b) Confidentiality

c) Integrity: with ‘improper’ meaning ‘unauthorized’.

Informally, the security of a system is a judgment of how likely it is that

the system can resist accidental or deliberate intrusion.

Measuring Security by :

MTTD (Mean Time to Detection)

MTTE (Mean Time to Exploitation)

And Gate

OR Gate

Basic Event

Compound Event

Transfer

Fault Tree Analysis Method:-

• Developed in 1962 by Bell Labs

• Using probabilities in analysis:-

- Assignment of probabilities to specific events

- Computation of probabilities for compound events

• Basic Structure Of The Fault Tree is :

Figure .341

Fault Tree Structure As Shown In Figure:-

Fault Tree Calculation As An Example:-

Figure .342

Figure .343

An Example Of Analysis Of Dual-Core Computer:-

An Example Of Heart – Pulse Mechanism:-

Figure .344

Figure .345

When Trigger Fails: it Fails As The Following Tree:-

* Fault trees can be used to analyze security issues and it called attack

trees

Software Tools Using For Measuring Dependability:

Figure .346

Figure .347

Chapter 5

Dependability Benchmark

Benchmarking:-

Is the act of running a computer program, a set of programs, or other operations, in order to assess the relative performance of an object, normally by running a number of

standard tests and trials against it. The term 'benchmark' is also mostly utilized for the

purposes of elaborately-designed benchmarking programs themselves. Benchmarking is

usually associated with assessing performance characteristics of the System.

Dependability Benchmarking:-

Is a specification of all elements required to assess certain measures related to

the behavior of a System in the presence of faults.

Is performance benchmarking extended to dependability aspects.

The main elements of a Performance benchmark are:

SUT: System under Test

WL: Workload PM: Performance Measures

+ =

Workload

Performance Measures

Pricing Information

Full Disclosure Rules

Performance Benchmark

Faultload

Dependability Measures

Pricing Information

Full Disclosure Rules

Dependability Extensions Dependability Benchmark

Workload

Faultload

Performance Measures

Dependability Measures

Pricing Information

Full Disclosure Rules

Figure .351

To extend a performance benchmarking into the dependability domain, a

fault load has to be provided.

The main elements of Dependability benchmark are:

SUB: System under Benchmark

IFL: Interaction Fault Load

DM: Dependability Measures

FMD: Failure Mode Detector

FMC: Failure Mode Classes

WL: Workload PM: Performance Measures

Some Definitions:-

FMD: Failure Mode Detector:-

Identifies and classifies the failure modes in a

dependability benchmark experiment,

SUB: System under Benchmark:-

Is a system where the measures apply DBT: Dependability benchmark target:-

The SUB could be larger than the component or subsystem that the benchmark user wants to characterize

DBE: Dependability Benchmarking Experiments:-

Benchmarking is performing tests on the SUB.

DBC (dependability benchmark configuration):- Is the implementation of the benchmark elements and the

experimental setup.

Figure .352

Scope Oriented

RepresentativePortable

Workload: Is the entire load explicitly or implicitly applied to the System

under Benchmarking.

• User Workload

• Operator Load

• Background Load

* The workload must be:

Performance Measures

• tmpC: transactions per minute

• $/ tmpC

• Response Time

Dependability Measures

• Conditional probability of occurrence of failure mode

classes.

• Availability

• Mean Down Time

Figure .353

Summary

In These Document We Take a Tour around Definition of System

what it means, by different definitions, its structure, patters we know

now what is system science we imagine the word of system we can

distinguish its component, their relations, the interaction with the

environment also we knows the types of models & visualizations we can

distinguish when we use each model to describe something, we knows

the type of feedbacks and the importance of feedbacks, we know more

about thinking process types, also the probability part has a very high

priority in our documentation because this part serves the course well

by learning probability & statistics methods , deterministic & stochastic

process, principal component analysis & how to simplify data , markov

chain also has a big role in my documentation because also it's one of

methods that used for measuring dependability which is my case study

which I illustrate its importance, elements which used for measuring it

which called attributes , threats which used to less its performance like

fault, error & failure ,also its means which used for preventing less of

dependability like fault tolerance, forecasting, removing & preventive ,

in measuring dependability, illustrating some concepts of measuring & a

method like fault tree analysis was so important for the reader to know

a practical method for representing dependability , all of us now knows

the importance of system science & Engineering course, I'm very proud

that I make a documentation to this course that all of my friends can

read and can understand the importance of this course in all practical

fields in our life,

Conclusion

Conclusion of This Documentation That I introduced a good topic

which is good for a beginner engineer who has no idea about system

science to know this field which will benefit him much in his life & will

make him has an imagination & measurements more that anybody for

knowing the performance of the system & this will help him for

designing a new better system or maintain this system , I thought that

this value is the most important in this book beside a new knowledge in

a field which is not known enough in our Arab country, So I hope that

this documentation will be good enough to benefit all the engineers &

can found themselves developed in that field , Also another conclusion

that I knew more in this course when I write this documentation & revise

my information that I have been read for a five months, I really benefit a

lot from this course,& I hope that I will continue learning & researches In

this area of science.

List Of References & Figures

References

[1] "Processes for Engineering a System", ANSI/EIA-632-1999, ANSI/EIA, 1999

[2] "Standard for Application and Management of the Systems Engineering Process -

Description", IEEE Std 1220-1998, IEEE, 1998.

[3] "Systems and software engineering - System life cycle processes", ISO/IEC, 2008.

[4] "NASA Systems Engineering Handbook", Revision 1, NASA, 2007

*5+ “System engineering principles & practice” , William Sweet - & Kossiakoff

*6+ “System Engineering & Analysis” , Blanchard & Walter 1998.

*7+ “The web of life: a new scientific understanding of living systems” ,Capra (1996).

*8+ “GENERAL SYSTEMATICS” , J.G. Bennett (1963) .

[9] “Introduction to Cybernetics”, W. Ross Ashby (1956).

*10+” A world of systems” , Mario Bunge (1979).

*11+ “What is Ergonomics” , International Ergonomics Association(2008).

[12] "System Engineering", Erik Aslaksin & Rod belcher , 1992

[13] Muller, "System Modeling and Analysis: a Practical Approach", 2009

[14] wikipedia, "Systems_Modeling_Language"

[15] Lecture Notes , "http://www.ict.kth.se/courses/IL2202/Slides/lec-01-intro.pdf"

[16] Periodic Tabe Of Visualization, "http://www.visual-

literacy.org/periodic_table/periodic_table.html"

[17] Frank Bushmann , Regine Meunier , Hans Rohnert , Peter Sommerland ,Michael Stal

:"Asystem of Patterns" ,John Wiley &Sons , 1996

[18] Erich Gamma,Richard Helm , Ralph Johnson , John Vlissides, "Design Patterns" ,

Addison- Wisely , 1995

[19] Patterns, http://hillside.net/patterns/patterns.html

[20] www.tml.tkk.fi/Opinnot/Tik-109.450/1998/niska/

[21] http://www.cmcrossroads.com/bradapp/docs/patterns-intro.html

[22] Wasson, "System Analysis, Design, and Development - Concepts, Principles, and

Practices" - 0471393339

[23] Methodology, http://www.merriam-webster.com/dictionary/methodology

[24] Principal Component Analysis,

"http://en.wikipedia.org/wiki/Principal_component_analysis"

[25] Lindsay I Smith, “A tutorial on Principal Components Analysis”, 2002

[26] Jonathon Shlens, “A tutorial on Principal Component Analysis”, April, 2009.

[27] Signals and Systems group, Uppsala Univ., “Instruction for Image Compression using

PCA”, 2005.

[28] M. Mudrova et al., “Principal Component Analysis In Image Processing”.

[29] I.T. Jolliffe, “Principal Component Analysis”, Springer, 2002.

[30] Mendenhall, Beaver , Introduction To Probability & statistics ,2009

[31] Laprie, Randell, & Landwehr, "Basic Concepts and Taxonomy of Dependable and Secure

Computing," IEEE Transactions on Dependable and Secure Computing(2004)

[32] Randell,"Software Dependability: A Personal View", in the Proc of the 25th International

Symposium on Fault-Tolerant Computing(1995)

[33] Laprie. "Dependable Computing and Fault Tolerance: Concepts and terminology”(1985)

[34+ Randell, Laprie “Fundamental Concepts of Dependability”(2001)

[35] Xing, "Dependability Analysis of Hierarchical Systems with Modular Imperfect Coverage"

[36] Baquero, "PETRI NET WORKFLOW MODELING FOR DIGITAL PUBLISHING MEASURING

QUANTITATIVE DEPENDABILITY ATTRIBUTES", 2006

[37] Mikael Asplund, "Lecture Notes: Dependability and fault tolerance"

[38] Robert Brill, "MEADEP and Its Application in Dependability Analysis for A Nuclear Power

Plant Safety System", 1997

[39] Lorenzo Strigini, "Resilience assessment and dependability benchmarking: challenges of

prediction", 2008

[40] Mili, ": Measuring Dependability as a Mean Failure Cost", 2007

[41] Tang, "MEADEP and Its Applications in Evaluating Dependability for Air Traffic Control

Systems" ,1998

[42] Laprie, Avizˇienis, "Fundamental Concepts of Computer System Dependability", 2001

[43] IPLU team, "The dependability of an IP network – what is it?", 2006

[44] Hecht , "An Approach to Measuring and Assessing Dependability for Critical Software

Systems", 1997

[45] Hossam A. Ramadan, "Towards More Comprehensive Measurable Dependability",2008

[46] Laprie, "Basic Concepts and Taxonomy of Dependable and Secure Computing", 2004

[47] Eusgeld, "Introduction to Dependability Metrics",2008

[48] Oliver Tschache , "Dependability Benchmarking of Linux based Systems"

[49] Dependability Management "CONCEPT OF DEPENDABILITY",2009

[50] Sommerville, " Software Engineering: Ch16.Dependability",2000

[51] Performance and Dependability Benchmarking Slides.

[52] IGI Global, "Chapter I: Dependability and Fault-Tolerance: Basic Concepts and

Terminology" , 2009

[53] Knapskog, Sallhammar, "A Framework for Predicting Security and Dependability

Measures in Real-time", 2007

[54] Chaparro, "Measuring quantitative dependability attributes in Digital Publishing using

Petri Net Workflow Modeling",

[55] Miller, " MEADEP — A Dependability Evaluation Tool for Engineers"

[56] Siewiorek, "Measuring Software Dependability by Robustness Benchmarking",1994

[57] Knight," Dependability Analysis Techniques – 1 Including Probabilistic Risk Analysis

(PRA)", 2009

Figures

Part(1)

Figure Represent

Figure .121 System Definition Representation

Figure .122 System Pattern Classifications

Figure .123 System Characteristics types

Figure .124 System Life cycle

Figure .125 System Development Life cycle

Figure .131 Modeling Methods

Figure .132 Periodic Table Of Visualization

Figure .133 Category Of Visualization

Figure .134 Example : Physical Modeling

Part(2)

Figure Represent

Figure .221 Measure by Central tendency

Figure .222 The Mean Types

Figure .223 When We Use Each Of The Central Tendency Measures

Figure .224 Causes of not knowing things precisely

Figure .225 Sample space

Figure .226 An Event

Figure .227 Union

Figure .228 Intersection

Figure .229 Disjoint sets

Figure .2210 Disjoint Union

Figure .2211 Set Differences

Figure .2212 Symmetric Differences

Figure .2213 Complement

Figure .2214 Example: Probability density function

Figure .2215 Example: Cumulative Distribution function

Figure .2216 Distribution Types as examples

Figure .2217 Standard Deviation Showing The Mean

Figure .2218 Standard Deviation Showing The sigma

Figure .231 Coin Toss Process

Figure .232 State Transition on markov chain

Figure .233 Example: State transition with weight

Figure .234 Example: Matrix representation

Figure .235 Example2: state transition with weight

Figure .236 Example2: Matrix Representation

Figure .241 PCA: Graphical Model

Figure .242 Example : Data

Figure .243 Example: Choosing Component

Figure .244 Example: New Data

Figure .245 Example: Matlab function

Part(3)

Figure .311 Performance Concept

Figure .321 Dependability Elements

Figure .322 Dependability Tree

Figure .323 Dependability Attributes

Figure .324 Dependability threats

Figure .325 Fault – Error Relation

Figure .326 Error State

Figure .327 Error-Failure Relation

Figure .328 Fault-Error-Failure Chain

Figure .329 Dependability Means

Figure .331 Elementary Fault Classes

Figure .332 Combined Fault Matrix Representation

Figure .333 The Failure Classes

Figure .334 Failure With respect to domain mode

Figure .341 Basic Structure of The fault Tree

Figure .342 Fault Tree Structure

Figure .343 Fault Tree Calculations

Figure .344 Example: Analysis Of Dual-Core Computer

Figure .345 Example: Heart Pulse Mechanism

Figure .346 Example: Heart Pulse Mechanism – Trigger Pulse

Figure .347 Software Tools For Measuring Dependability

Figure .351 Dependability Benchmark Elements Extraction

Figure .352 Dependability Benchmark Elements

Figure .353 Workload Essential Elements