Muutke küpsiste eelistusi

New Foundation Of Artificial Intelligence [Kõva köide]

(Ntu, S'pore), (Tongji Univ, China), (Kumamoto Univ, Japan)
  • Formaat: Hardback, 404 pages
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814271632
  • ISBN-13: 9789814271639
  • Formaat: Hardback, 404 pages
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814271632
  • ISBN-13: 9789814271639
This book lays a new foundation toward achieving artificial self-intelligence by future machines such as intelligent vehicles. Its chapters provide a broad coverage to the three key modules behind the design and development of intelligent vehicles for the ultimate purpose of actively ensuring driving safety as well as preventing accidents from all possible causes. Self-contained and unified in presentation, the book explains in details the fundamental solutions of vehicle's perception, vehicle's decision-making, and vehicle's action-taking in a pedagogic order.Besides the fundamental knowledge and concepts of intelligent vehicle's perception, decision and action, this book includes a comprehensive set of real-life application scenarios in which intelligent vehicles will play a major role or contribution. These case studies of real-life applications will help motivate students to learn this exciting subject. With concise and simple explanations, and boasting a rich set of graphical illustrations, the book is an invaluable source for both undergraduate and postgraduate courses, on artificial intelligence, intelligent vehicle, and robotics, which are offered in automotive engineering, computer engineering, electronic engineering, and mechanical engineering. In addition, the book will help strengthen the knowledge and skills of young researchers who want to venture into the research and development of artificial self-intelligence for intelligent vehicles of the future.Related Link(s)
Preface vii
Part 1 Knowledge Representation
1(74)
1 Basics of Knowledge
3(12)
1.1 Introduction
3(1)
1.2 Physical World
3(4)
1.2.1 Entity
4(1)
1.2.2 Properties of Entity
5(1)
1.2.3 Constraints of Entity
6(1)
1.3 Conceptual Worlds
7(4)
1.3.1 Entity with Self-Intelligence
8(1)
1.3.2 Role of Human Language
9(1)
1.3.3 Conceptual World and Its Definition
10(1)
1.4 Knowledge and Its Definition
11(1)
1.5 Artificial Self-Intelligence and Its Roadmap
12(1)
1.6 Summary
13(1)
1.7 Exercises
14(1)
2 Basics of Human Language
15(14)
2.1 Introduction
15(1)
2.2 Use of Human Language
15(1)
2.3 Invention of Nouns
16(1)
2.4 Invention of Pronouns
17(1)
2.5 Invention of Verbs
18(2)
2.6 Invention of Adverbs
20(1)
2.7 Invention of Adjectives
20(1)
2.8 Invention of Prepositions
21(1)
2.9 Invention of Grammar
21(3)
2.9.1 Language Models of Phrases
22(1)
2.9.2 Language Models of Sentences
23(1)
2.10 Summary
24(1)
2.11 Exercises
25(4)
3 Basics of Technical Language
29(28)
3.1 Introduction
29(1)
3.2 Use of Technical Language
29(1)
3.3 Point
30(1)
3.4 Vector
31(2)
3.5 Matrix
33(2)
3.6 Polynomials
35(1)
3.7 Integration
35(1)
3.8 Derivative
36(1)
3.9 Logic
37(4)
3.10 Probability
41(4)
3.11 Possibility
45(3)
3.12 Fourier Transform
48(2)
3.13 Laplace Transform
50(4)
3.14 Summary
54(1)
3.15 Exercises
55(2)
4 Basics of Programming Language
57(18)
4.1 Introduction
57(1)
4.2 Use of Programming Language
57(2)
4.3 Data Structure
59(2)
4.4 Instruction Set
61(1)
4.5 Function
62(5)
4.6 Library
67(1)
4.7 Dynamic Class
68(5)
4.8 Programmable Script
73(1)
4.9 Summary
74(1)
4.10 Exercises
74(1)
Part 2 Knowledge Acquisition
75(242)
5 Knowledge Acquisition from Text
77(28)
5.1 Introduction
77(1)
5.2 Representation of Human Language
77(9)
5.2.1 Representation of Physical Meanings
78(3)
5.2.2 Representation of Conceptual Meanings
81(3)
5.2.3 Representation of Concept-physical Meanings
84(2)
5.3 Transformation From Text to Speech
86(4)
5.4 Transformation From Speech to Text
90(6)
5.4.1 Sensing of Analogue Sound Waves
90(1)
5.4.2 Sampling of Analogue Sound Signals
91(1)
5.4.3 ADC of Discrete-time Sound Signals
91(1)
5.4.4 Segmentation of Digital Sound Signals
91(1)
5.4.5 Fourier Transform of Digital Sound Signals
92(2)
5.4.6 Symbol Grounding of Digital Sound Unit
94(1)
5.4.7 Composition of Words
95(1)
5.4.8 Composition of Sentences
96(1)
5.5 Transformation From Image to Text
96(6)
5.5.1 Digitization of Input Image
96(1)
5.5.2 Identification of Background Color
97(1)
5.5.3 Segmentation of Text Image
98(1)
5.5.4 Alignment of Text Image
98(1)
5.5.5 Segmentation of Word Image
98(1)
5.5.6 Symbol Grounding of Word Image
99(3)
5.6 Transformation From Text to Meanings
102(1)
5.7 Summary
103(1)
5.8 Exercises
104(1)
6 Basics of Visual Perception
105(34)
6.1 Nature of Human Vision
105(17)
6.1.1 Human Vision Is Composite
106(1)
6.1.2 Human Vision Is Parallel
106(1)
6.1.3 Human Vision is Quantitative
107(1)
6.1.4 Human Vision Is Qualitative
108(1)
6.1.5 Human Vision Is Situated
109(2)
6.1.6 Human Vision Is Developmental
111(2)
6.1.7 Human Vision Is Cognitive
113(6)
6.1.8 Human Vision Is Attentive
119(3)
6.2 Nature of Machine Vision
122(14)
6.2.1 What Is Machine Vision
122(3)
6.2.2 Machine Vision Is Computational
125(1)
6.2.3 Machine Vision Is Cognitive
126(10)
6.3 Summary
136(1)
6.4 Exercises
136(3)
7 Knowledge Acquisition from Color
139(44)
7.1 Introduction
139(1)
7.2 Transformation From Light to Color Image
140(6)
7.2.1 Light Gathering
140(2)
7.2.2 Color Sensing
142(2)
7.2.3 Color Synthesis
144(1)
7.2.4 Color Image Formation
145(1)
7.3 Representation of Color
146(17)
7.3.1 Color's Properties in Physical World
146(14)
7.3.2 Color's Properties in Conceptual Worlds
160(2)
7.3.3 Data Structure of Organized Meanings
162(1)
7.4 Cognition of Color
163(10)
7.4.1 Visual Computing
163(5)
7.4.2 Visual Learning
168(5)
7.5 Recognition of Color
173(5)
7.5.1 Use of Symbol Grounding Network
174(1)
7.5.2 Use of Fuzzy Logic
175(1)
7.5.3 Use of Multi-scale Color Indices
176(2)
7.6 Summary
178(3)
7.7 Exercises
181(2)
8 Knowledge Acquisition from Shape
183(62)
8.1 Introduction
183(1)
8.2 Transformation from Image to Shape
183(24)
8.2.1 Definition of Shape
185(1)
8.2.2 Use of Color Histogram
186(3)
8.2.3 Use of Color Region
189(7)
8.2.4 Use of Color Edge
196(4)
8.2.5 Use of Color Texture
200(7)
8.3 Representation of Shape
207(22)
8.3.1 Properties of Shape in Physical World
207(2)
8.3.2 Representation of Shape's Physical Properties
209(16)
8.3.3 Representation of Shape's Conceptual Properties
225(3)
8.3.4 Data Structure of Organized Meanings
228(1)
8.4 Cognition of Shape
229(8)
8.4.1 Visual Computing
230(5)
8.4.2 Visual Learning
235(2)
8.5 Recognition of Shape
237(4)
8.5.1 Open-loop Approach to Shape Recognition
237(3)
8.5.2 Closed-loop Approach to Shape Recognition
240(1)
8.6 Summary
241(1)
8.7 Exercises
242(3)
9 Knowledge Acquisition from Depth
245(72)
9.1 Introduction
245(2)
9.2 Transformation From Depth to Image
247(21)
9.2.1 Properties of Image Plane
248(4)
9.2.2 Properties of Camera
252(3)
9.2.3 Relative Pose Between Camera's Coordinate Systems
255(4)
9.2.4 Relative Motion Between Camera's Coordinate Systems
259(2)
9.2.5 Baseline Between Camera's Coordinate Systems
261(4)
9.2.6 Projection of Points from Scene to Image
265(1)
9.2.7 Projection of Velocities from Scene to Image
266(2)
9.3 Transformation from Image to Apparent Motion
268(14)
9.3.1 Formation of Apparent Motion
268(1)
9.3.2 Registration of Image Sequence
269(1)
9.3.3 Use of Conglomerated Appearance
270(5)
9.3.4 Matching of Conglomerated Appearances
275(3)
9.3.5 Match Search Space
278(4)
9.4 Representation of Depth
282(11)
9.4.1 Use of Depth-map
282(2)
9.4.2 Continuous Depth-map in Camera Frame
284(1)
9.4.3 Discrete Depth-map in Camera Frame
284(2)
9.4.4 Discrete Scene in Viewer Frame
286(2)
9.4.5 Generalized Depth-map in Viewer Frame
288(1)
9.4.6 Concept of Look-through Scene
289(1)
9.4.7 Concept of Depth Redundancy
289(2)
9.4.8 Conceptual Meaning of Depth
291(1)
9.4.9 Data Structure of Organized Meanings
291(2)
9.5 Cognition of Depth
293(16)
9.5.1 Depth from Visual Computing
293(14)
9.5.2 Visual Learning of Depth
307(2)
9.6 Recognition of Depth
309(5)
9.6.1 Problem Statement
310(1)
9.6.2 Use of Predication and Verification Loop
311(2)
9.6.3 Estimation of Recognized View's Pose
313(1)
9.7 Summary
314(1)
9.8 Exercises
315(2)
Part 3 Knowledge Computation
317(26)
10 Computing Statics of Generic Data
319(8)
10.1 Introduction
319(1)
10.2 Computation of Data's Center
319(1)
10.3 Computation of Data's Spread
320(1)
10.4 Computation of Data's Occurrence
320(1)
10.5 Computation of Data's Similarity
321(1)
10.6 Computation of Data's Dissimilarity
321(1)
10.7 Computation of Data's Correlation
322(2)
10.8 Computation of Data's Principal Components
324(2)
10.9 Summary
326(1)
10.10 Exercises
326(1)
11 Computing Dynamics of Mechanical Systems
327(16)
11.1 Introduction
327(1)
11.2 Computation of Mechanism's Motion
327(7)
11.2.1 Equation of Path
328(1)
11.2.2 Equation of Trajectory
329(5)
11.3 Computation of Mechanism's Kinematics
334(3)
11.3.1 Equation of Forward Kinematics
334(2)
11.3.2 Equation of Inverse Kinematics
336(1)
11.4 Computation of Mechanism's Dynamics
337(3)
11.4.1 Equation of Forward Dynamics
337(3)
11.4.2 Equation of Inverse Dynamics
340(1)
11.5 Summary
340(1)
11.6 Exercises
341(2)
Part 4 Knowledge Application
343(34)
12 Knowledge-enabled Applications
345(32)
12.1 Introduction
345(1)
12.2 Vision-centric Intelligence and Its Applications
345(14)
12.2.1 Vision-based Measurement
346(4)
12.2.2 Vision-based Guidance
350(3)
12.2.3 Visual Recognition
353(3)
12.2.4 Visual Understanding
356(3)
12.3 Speech-centric Intelligence and Its Applications
359(4)
12.3.1 Conversational Dialogue
360(1)
12.3.2 Voice-controlled Action
360(1)
12.3.3 Massive Sense-making
361(1)
12.3.4 Meaning-based Machine Translation
361(1)
12.3.5 Voice-based Inquiry
362(1)
12.4 Planning-centric Intelligence and Its Applications
363(2)
12.4.1 Task Planning
363(1)
12.4.2 Action Planning
364(1)
12.4.3 Motion Planning
365(1)
12.5 Control-centric Intelligence and Its Applications
365(2)
12.5.1 Achieving Stability
366(1)
12.5.2 Achieving Smallest Delay in Time
366(1)
12.5.3 Achieving Smallest Error in Output
366(1)
12.6 Data-centric Intelligence and Its Applications
367(4)
12.6.1 Strategy for Market Reward and Market Acceptance
369(1)
12.6.2 Development of Internal Capabilities
370(1)
12.6.3 Optimization of Internal Processes
371(1)
12.7 Design-centric Intelligence and Its Applications
371(5)
12.7.1 Designing Products
371(2)
12.7.2 Designing Factories
373(1)
12.7.3 Designing Universities
374(1)
12.7.4 Designing Intelligent Transportation Systems
375(1)
12.8 Summary
376(1)
12.9 Exercises
376(1)
Bibliography 377(4)
Index 381