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Smart Systems for Industrial Applications [Kõva köide]

Edited by (Anna University, India), Edited by (Sengunthar Engineering College, India), Edited by (Sri Krishna College of Technology, India), Edited by (Nandha Engineering College, India)
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Smart Systems for Industrial ApplicationsSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781119762003.html
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SMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges.

The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthcare, smart grid, virtual assistance, etc.

Audience

The book will be of interest to researchers and postgraduate students in artificial intelligence, electrical and electronic engineering, as well as those engineers working in the application areas such as healthcare, energy systems, education, and others.
Preface xvii
1 AI-Driven Information and Communication Technologies, Services, and Applications for Next-Generation Healthcare System
1(32)
Vijayakumar Ponnusamy
A. Vasuki
J. Christopher Clement
P. Eswaran
1.1 Introduction: Overview of Communication Technology and Services for Healthcare
2(4)
1.2 AI-Driven Communication Technology in Healthcare
6(4)
1.2.1 Technologies Empowering in Healthcare
6(1)
1.2.2 AI in Diagnosis
7(1)
1.2.3 Conversion Protocols
8(1)
1.2.4 AI in Treatment Assistant
9(1)
1.2.5 AI in the Monitoring Process
10(1)
1.2.6 Challenges of AI in Healthcare
10(1)
1.3 AI-Driven mHealth Communication System and Services
10(3)
1.3.1 Embedding of Handheld Imaging Platforms With mHealth Devices
12(1)
1.3.2 The Adaptability of POCUS in Telemedicine
12(1)
1.4 AI-Driven Body Area Network Communication Technologies and Applications
13(7)
1.4.1 Features
16(1)
1.4.2 Communication Architecture of Wireless Body Area Networks
16(1)
1.4.3 Role of AI in WBAN Architecture
17(1)
1.4.4 Medical Applications
18(1)
1.4.5 Nonmedical Applications
18(1)
1.4.6 Challenges
18(2)
1.5 AI-Driven IoT Device Communication Technologies and Healthcare Applications
20(5)
1.5.1 AIs and IoTs Role in Healthcare
20(1)
1.5.2 Creating Efficient Communication Framework for Remote Healthcare Management
21(1)
1.5.3 Developing Autonomous Capability is Key for Remote Healthcare Management
22(2)
1.5.4 Enabling Data Privacy and Security in the Field of Remote Healthcare Management
24(1)
1.6 AI-Driven Augmented and Virtual Reality-Based Communication Technologies and Healthcare Applications
25(8)
1.6.1 Clinical Applications of Communication-Based AI and Augmented Reality
27(1)
1.6.2 Surgical Applications of Communication-Based on Artificial Intelligence and Augmented Reality
28(2)
References
30(3)
2 Pneumatic Position Servo System Using Multi-Variable Multi-Objective Genetic Algorithm--Based Fractional-Order PID Controller
33(30)
D. Magdalin Mary
V. Vanitha
G. Sophia Jasmine
2.1 Introduction
34(2)
2.2 Pneumatic Servo System
36(2)
2.3 Existing System Analysis
38(2)
2.4 Proposed Controller and Its Modeling
40(3)
2.4.1 Modeling of Fractional-Order PID Controller
40(1)
2.4.1.1 Fractional-Order Calculus
40(2)
2.4.1.2 Fractional-Order PID Controller
42(1)
2.5 Genetic Algorithm
43(4)
2.5.1 GA Optimization Methodology
43(1)
2.5.1.1 Initialization
44(1)
2.5.1.2 Fitness Function
44(1)
2.5.1.3 Evaluation and Selection
44(1)
2.5.1.4 Crossover
45(1)
2.5.1.5 Mutation
45(1)
2.5.2 GA Parameter Tuning
46(1)
2.6 Simulation Results and Discussion
47(9)
2.6.1 MATLAB Genetic Algorithm Tool Box
47(1)
2.6.2 Simulation Results
47(1)
2.6.2.1 Reference = 500 (Error)
48(4)
2.6.2.2 Reference = 500
52(1)
2.6.2.3 Reference = 1,500
52(4)
2.6.2.4 Analysis Report
56(1)
2.7 Hardware Results
56(3)
2.7.1 Reference = 500
58(1)
2.7.2 Reference = 1,500
59(1)
2.8 Conclusion
59(4)
References
59(4)
3 Improved Weighted Distance Hop Hyperbolic Prediction-Based Reliable Data Dissemination (IWDH-HP-RDD) Mechanism for Smart Vehicular Environments
63(30)
Sengathir Janakiraman
M. Deva Priya
A. Christy Jeba Malar
3.1 Introduction
64(3)
3.2 Related Work
67(4)
3.2.1 Extract of the Literature
70(1)
3.3 Proposed Improved Weighted Distance Hop Hyperbolic Prediction-Based Reliable Data Dissemination (IWDH-HP-RDD) Mechanism for Smart Vehicular Environments
71(8)
3.4 Simulation Results and Analysis of the Proposed IWDH-HP-RDD Scheme
79(10)
3.5 Conclusion
89(4)
References
90(3)
4 Remaining Useful Life Prediction of Small and Large Signal Analog Circuits Using Filtering Algorithms
93(22)
M. Sathiyanathan
K. Anandha Kumar
S. Jaganathan
C.S. Subash Kumar
4.1 Introduction
94(1)
4.2 Literature Survey
95(3)
4.3 System Architecture
98(1)
4.4 Remaining Useful Life Prediction
99(4)
4.4.1 Initialization
99(1)
4.4.2 Proposal Distribution
100(1)
4.4.3 Time Update
101(1)
4.4.4 Relative Entropy in Particle Resampling
101(1)
4.4.5 RUL Prediction
102(1)
4.5 Results and Discussion
103(8)
4.6 Conclusion
111(4)
References
111(4)
5 AI in Healthcare
115(26)
S. Menaga
J. Paruvathavardhini
5.1 Introduction
116(3)
5.1.1 What is Artificial Intelligence?
117(1)
5.1.2 Machine Learning -- Neural Networks and Deep Learning
117(2)
5.1.3 Natural Language Processing
119(1)
5.2 Need of AI in Electronic Health Record
119(4)
5.2.1 How Does AI/ML Fit Into EHR?
120(1)
5.2.2 Natural Language Processing (NLP)
121(1)
5.2.3 Data Analytics and Representation
122(1)
5.2.4 Predictive Investigation
122(1)
5.2.5 Administrative and Security Consistency
122(1)
5.3 The Trending Role of AI in Pharmaceutical Development
123(4)
5.3.1 Drug Discovery and Design
124(1)
5.3.2 Diagnosis of Biomedical and Clinical Data
125(1)
5.3.3 Rare Diseases and Epidemic Prediction
125(1)
5.3.4 Applications of AI in Pharma
126(1)
5.3.5 AI in Marketing
126(1)
5.3.6 Review of the Companies That Use AI
126(1)
5.4 AI in Surgery
127(4)
5.4.1 3D Printing
127(1)
5.4.2 Stem Cells
128(1)
5.4.3 Patient Care
128(1)
5.4.4 Training and Future Surgical Team
129(2)
5.5 Artificial Intelligence in Medical Imaging
131(3)
5.5.1 In Cardio Vascular Abnormalities
131(1)
5.5.2 In Fractures and Musculoskeletal Injuries
132(1)
5.5.3 In Neurological Diseases and Thoracic Complications
133(1)
5.5.4 In Detecting Cancers
134(1)
5.6 AI in Patient Monitoring and Wearable Health Devices
134(3)
5.6.1 Monitoring Health Through Wearable's and Personal Devices
135(1)
5.6.2 Making Smartphone Selfies Into Powerful Diagnostic Tools
136(1)
5.7 Revolutionizing of AI in Medicinal Decision-Making at the Bedside
137(1)
5.8 Future of AI in Healthcare
137(2)
5.9 Conclusion
139(2)
References
139(2)
6 Introduction of Artificial Intelligence
141(32)
R. Vishalakshi
S. Mangai
6.1 Introduction
142(3)
6.1.1 Intelligence
142(1)
6.1.2 Types of Intelligence
143(1)
6.1.3 A Brief History of Artificial Intelligence From 1923 till 2000
144(1)
6.2 Introduction to the Philosophy Behind Artificial Intelligence
145(2)
6.2.1 Programming With and Without AI
147(1)
6.3 Basic Functions of Artificial Intelligence
147(2)
6.3.1 Categories of Artificial Intelligence
148(1)
6.3.1.1 Reactive Machines
148(1)
6.3.1.2 Limited Memory
148(1)
6.3.1.3 Theory of Mind
149(1)
6.3.1.4 Self-Awareness
149(1)
6.4 Existing Technology and Its Review
149(8)
6.4.1 Tesla's Autopilot
149(1)
6.4.2 Boxever
150(1)
6.4.3 Fin Gesture
150(2)
6.4.4 AI Robot
152(1)
6.4.5 Vinci
153(1)
6.4.6 AI Glasses
153(1)
6.4.7 Affectiva
153(1)
6.4.8 AlphaGo Beats
154(1)
6.4.9 Cogito
154(1)
6.4.10 Siri and Alexa
155(2)
6.4.11 Pandora's
157(1)
6.5 Objectives
157(2)
6.5.1 Major Goals
157(1)
6.5.2 Need for Artificial Intelligence
158(1)
6.5.3 Distinction Between Artificial Intelligence and Business Intelligence
158(1)
6.6 Significance of the Study
159(5)
6.6.1 Segments of Master Frameworks
160(2)
6.6.1.1 User Interface
162(1)
6.6.1.2 Expert Systems
163(1)
6.6.1.3 Inference Engine
163(1)
6.6.1.4 Voice Recognition
164(1)
6.6.1.5 Robots
164(1)
6.7 Discussion
164(3)
6.7.1 Artificial Intelligence and Design Practice
164(3)
6.8 Applications of AI
167(2)
6.8.1 AI Has Been Developing a Huge Number of Tools Necessary to Find a Solution to the Most Challenging Problems in Computer Science
168(1)
6.8.2 Future of AI
168(1)
6.9 Conclusion
169(4)
References
170(3)
7 Artificial Intelligence in Healthcare: Algorithms and Decision Support Systems
173(1)
S. Palanivel Rajan
M. Paranthaman
7 A Introduction
173(26)
7.2 Machine Learning Work Flow and Applications in Healthcare
176(11)
7.2.1 Formatting and Cleaning Data
177(1)
7.2.2 Supervised and Unsupervised Learning
178(1)
7.2.3 Linear Discriminant Analysis
178(1)
7.2.4 K-Nearest Neighbor
179(1)
7.2.5 K-Means Clustering
180(1)
7.2.6 Random Forest
181(1)
7.2.7 Decision Tree
181(1)
7.2.8 Support Vector Machine
182(1)
7.2.9 Artificial Neural Network
183(1)
7.2.10 Natural Language Processing
184(1)
7.2.11 Deep Learning
185(1)
7.2.12 Ensembles
186(1)
7.3 Commercial Decision Support Systems Based on AI
187(6)
7.3.1 Personal Genome Diagnostics
188(1)
7.3.2 Tempus
188(1)
7.3.3 Icarbonx---Manage Your Digital Life
189(1)
7.3.4 H2O.ai
189(1)
7.3.5 Google DeepMind
189(1)
7.3.6 Buoy Health
189(1)
7.3.7 PathAI
190(1)
7.3.8 Beth Israel Deaconess Medical Center
190(1)
7.3.9 Bioxcel Therapeutics
190(1)
7.3.10 BERG
191(1)
7.3.11 Enlitic
191(1)
7.3.12 Deep Genomics
191(1)
7.3.13 Freenome
192(1)
7.3.14 CloudMedX
192(1)
7.3.15 Proscia
192(1)
7.4 Conclusion
193(6)
References
193(6)
8 Smart Homes and Smart Cities
199(26)
C. N. Marimuthu
G. Arthy
8.1 Smart Homes
199(9)
8.1.1 Introduction
199(1)
8.1.2 Evolution of Smart Home
200(2)
8.1.3 Smart Home Architecture
202(1)
8.1.3.1 Smart Electrical Devices or Smart Plugs
202(1)
8.1.3.2 Home Intelligent Terminals or Home Area Networks
203(1)
8.1.3.3 Master Network
203(1)
8.1.4 Smart Home Technologies
204(2)
8.1.5 Smart Grid Technology
206(1)
8.1.6 Smart Home Applications
206(1)
8.1.6.1 Smart Home in the Healthcare of Elderly People
206(1)
8.1.6.2 Smart Home in Education
207(1)
8.1.6.3 Smart Lighting
207(1)
8.1.6.4 Smart Surveillance
207(1)
8.1.7 Advantages and Disadvantages of Smart Homes
207(1)
8.2 Smart Cities
208(17)
8.2.1 Introduction
208(1)
8.2.2 Smart City Framework
209(1)
8.2.3 Architecture of Smart Cities
210(1)
8.2.4 Components of Smart Cities
211(1)
8.2.4.1 Smart Technology
212(1)
8.2.4.2 Smart Infrastructure
212(2)
8.2.4.3 Smart Mobility
214(1)
8.2.4.4 Smart Buildings
215(1)
8.2.4.5 Smart Energy
216(1)
8.2.4.6 Smart Governance
217(1)
8.2.4.7 Smart Healthcare
218(1)
8.2.5 Characteristics of Smart Cities
219(2)
8.2.6 Challenges in Smart Cities
221(1)
8.2.7 Conclusion
222(1)
References
222(3)
9 Application of AI in Healthcare
225(24)
V. Priya
S. Prabu
9.1 Introduction
226(6)
9.1.1 Supervised Learning Process
226(1)
9.1.2 Unsupervised Learning Process
227(1)
9.1.3 Semi-Supervised Learning Process
227(1)
9.1.4 Reinforcement Learning Process
227(1)
9.1.5 Healthcare System Using ML
228(1)
9.1.6 Primary Examples of ML's Implementation in the Healthcare
228(1)
9.1.6.1 AI-Assisted Radiology and Pathology
228(1)
9.1.6.2 Physical Robots for Surgery Assistance
229(2)
9.1.6.3 With the Assistance of AI/ML Techniques, Drug Discovery
231(1)
9.1.6.4 Precision Medicine and Preventive Healthcare in the Future
232(1)
9.2 Related Works
232(8)
9.2.1 In Healthcare, Data Driven AI Models
232(1)
9.2.2 Support Vector Machine
233(1)
9.2.3 Artificial Neural Networks
233(2)
9.2.4 Logistic Regression
235(1)
9.2.5 Random Forest
235(1)
9.2.6 Discriminant Analysis
236(1)
9.2.7 Naive Bayes
236(1)
9.2.8 Natural Language Processing
236(1)
9.2.9 TF-IDF
236(1)
9.2.10 Word Vectors
237(1)
9.2.11 Deep Learning
237(1)
9.2.12 Convolutional Neural Network
237(3)
9.3 DL Frameworks for Identifying Disease
240(1)
9.3.1 TensorFlow
240(1)
9.3.2 High Level APIs
240(1)
9.3.3 Estimators
240(1)
9.3.4 Accelerators
241(1)
9.3.5 Low Level APIs
241(1)
9.4 Proposed Work
241(3)
9.4.1 Application of AI in Finding Heart Disease
241(1)
9.4.2 Data Pre-Processing and Classification of Heart Disease
241(3)
9.5 Results and Discussions
244(2)
9.6 Conclusion
246(3)
References
246(3)
10 Battery Life and Electric Vehicle Range Prediction
249(20)
S. Ravikrishna
C.S. Subash Kumar
M. Sundaram
10.1 Introduction
250(3)
10.2 Different Stages of Electrification of Electric Vehicles
253(1)
10.2.1 Starting and Stopping
253(1)
10.2.2 Regenerative Braking
253(1)
10.2.3 Motor Control
253(1)
10.2.4 EV Drive
254(1)
10.3 Estimating SoC
254(3)
10.3.1 Cell Capacity
254(1)
10.3.2 Calendar Life
255(1)
10.3.3 Cycling Life
255(1)
10.3.4 SoH Based on Capacity Fade
255(1)
10.3.5 SoH Based on Power Fade
255(1)
10.3.6 Open Circuit Voltage
255(1)
10.3.7 Impedance Spectroscopy
255(1)
10.3.8 Model-Based Approach
256(1)
10.4 Kalman Filter
257(3)
10.4.1 Sigma Point Kalman Filter
257(1)
10.4.2 Six Step Process
258(2)
10.5 Estimating SoH
260(2)
10.6 Results and Discussion
262(5)
10.7 Conclusion
267(2)
References
267(2)
11 AI-Driven Healthcare Analysis
269(18)
N. Kasthuri
T. Meeradevi
11.1 Introduction
270(1)
11.2 Literature Review
271(4)
11.3 Feature Extraction
275(1)
11.3.1 GLCM Feature Descriptors
275(1)
11.4 Classifiers
276(6)
11.4.1 Stochastic Gradient Descent Classifier
276(1)
11.4.2 Naive Bayes Classifier
276(1)
11.4.3 K-Nearest Neighbor Classifier
277(1)
11.4.4 Support Vector Machine Classifier
277(1)
11.4.5 Random Forest Classifier
278(1)
11.4.6 Working of Random Forest Algorithm
278(1)
11.4.7 Convolutional Neural Network
278(3)
11.4.7.1 Activation Function
281(1)
11.4.7.2 Pooling Layer
281(1)
11.4.7.3 Fully Connected Layer (FC)
281(1)
11.5 Results and Conclusion
282(5)
11.5.1 5,000 Images
282(1)
11.5.2 10,000 Images
283(1)
References
284(3)
12 A Novel Technique for Continuous Monitoring of Fuel Adulteration
287(20)
P. Rajalakshmy
R. Varun
P. Subha Hency Jose
K. Rajasekaran
12.1 Introduction
288(2)
12.1.1 Literature Review
289(1)
12.1.2 Overview
290(1)
12.1.3 Objective
290(1)
12.2 Existing Method
290(9)
12.2.1 Module-1 Water
291(2)
12.2.2 Module-2 Petrol
293(1)
12.2.3 Petrol Density Measurement
293(1)
12.2.4 Block Diagram
293(1)
12.2.5 Components of the System
294(1)
12.2.5.1 Pressure Instrument
294(1)
12.2.5.2 Sensor
294(1)
12.2.6 Personal Computer
295(1)
12.2.7 Petrol Density Measurement Instrument Setup
295(1)
12.2.7.1 Setup 1
296(2)
12.2.7.2 Setup 2
298(1)
12.2.7.3 Setup 3
298(1)
12.2.7.4 Setup 4
298(1)
12.2.7.5 Final Setup
298(1)
12.3 Interfacing MPX2010DP with INA114
299(3)
12.3.1 I2C Bus Configuration for Honeywell Sensor
299(1)
12.3.2 Pressure and Temperature Output Through I2C
300(2)
12.4 Results and Discussion
302(1)
12.5 Conclusion
303(4)
References
304(3)
13 Improved Merkle Hash and Trapdoor Function-Based Secure Mutual Authentication (IMH-TF-SMA) Mechanism for Securing Smart Home Environment
307(26)
M. Deva Priya
Sengathir Janakiraman
A. Christy Jeba Malar
13.1 Introduction
308(2)
13.2 Related Work
310(6)
13.3 Proposed Improved Merkle Hash and Trapdoor Function-Based Secure Mutual Authentication (IMH-TF-SMA) Mechanism for Securing Smart Home Environment
316(9)
13.3.1 Threat Model
317(1)
13.3.2 IMH-TF-SMA Mechanism
317(3)
13.3.2.1 Phase of Initialization
320(1)
13.3.2.2 Phase of Addressing
320(1)
13.3.2.3 Phase of Registration
320(1)
13.3.2.4 Phase of Login Authentication
321(1)
13.3.2.5 Phase of Session Agreement
321(4)
13.4 Results and Discussion
325(5)
13.5 Conclusion
330(3)
References
330(3)
14 Smart Sensing Technology
333(32)
S. Palanivel Rajan
T. Abirami
14.1 Introduction
333(32)
14.1.1 Sensor
333(1)
14.1.1.1 Real-Time Example of Sensor
334(1)
14.1.1.2 Definition of Sensors
335(1)
14.1.1.3 Characteristics of Sensors
335(1)
14.1.1.4 Classification of Sensors
336(1)
14.1.1.5 Types of Sensors
336(4)
14.1.2 IoT (Internet of Things)
340(1)
14.1.2.1 Trends and Characteristics
340(1)
14.1.2.2 Definition
340(1)
14.1.2.3 Flow Chart of IoT
341(1)
14.1.2.4 IoT Phases
341(1)
14.1.2.5 Phase Chart
342(1)
14.1.2.6 IoT Protocol
342(1)
14.1.3 WPAN
343(1)
14.1.3.1 IEEE 802.15.1 Overview
344(1)
14.1.3.2 Bluetooth
344(1)
14.1.3.3 History of Bluetooth
344(1)
14.1.3.4 How Bluetooth Works
345(1)
14.1.3.5 Bluetooth Specifications
345(1)
14.1.3.6 Advantages of Bluetooth Technology
346(1)
14.1.3.7 Applications
347(1)
14.1.4 Zigbee (IEEE 802.15.4)
348(1)
14.1.4.1 Introduction
348(1)
14.1.4.2 Architecture of Zigbee
349(2)
14.1.4.3 Zigbee Devices
351(1)
14.1.4.4 Operating Modes of Zigbee
351(1)
14.1.4.5 Zigbee Topologies
352(1)
14.1.4.6 Applications of Zigbee Technology
353(1)
14.1.5 WLAN
353(1)
14.1.5.1 Introduction
353(2)
14.1.5.2 Advantages of WLANs
355(1)
14.1.5.3 Drawbacks of WLAN
355(1)
14.1.6 GSM
356(1)
14.1.6.1 Introduction
356(1)
14.1.6.2 Composition of GSM Networks
356(2)
14.1.6.3 Security
358(1)
14.1.7 Smart Sensor
358(1)
14.1.7.1 Development History of Smart Sensors
358(1)
14.1.7.2 Internal Parts of Smart Transmitter
359(2)
14.1.7.3 Applications
361(4)
14.1.8 Conclusion
365(1)
References 365(2)
Index 367
C. Venkatesh, PhD is Professor and Principal, Sengunthar Engineering College, India, and has 28 years of teaching experience. He has published 5 patents, about 80 research papers in international journals, and about 70 papers in international and national conferences.

N. Rengarajan, PhD is Professor and Principal, Nandha Engineering College, India and has more than three decades of experience. He has published 8 patents, 70 papers in international journals, and 20 papers in national and international conferences.

P. Ponmurugan, PhD is an associate professor, Sri Krishna College of Technology, India has almost a decade of experience in academics. He has published 11 patents and about 40 papers in international journals and conferences. He was awarded the Best Young Engineer by IEI Erode Local Centre and Young Scientist by the International Association of Research and Developed Organization (IARDO).

S. Balamurugan, PhD, SMIEEE and ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.