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Design and Development of Efficient Energy Systems


Design and Development of Efficient Energy Systems


Artificial Intelligence and Soft Computing for Industrial Transformation 1. Aufl.

von: Suman Lata Tripathi, Dushyant Kumar Singh, Sanjeevikumar Padmanaban, P. Raja

197,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 26.03.2021
ISBN/EAN: 9781119761792
Sprache: englisch
Anzahl Seiten: 384

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Beschreibungen

<p>There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these &ldquo;game changers,&rdquo; governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society.</p> <p>This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation.</p> <p>The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications.&nbsp; Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.&nbsp;</p>
<p>Preface xv</p> <p><b>1 Design of Low Power Junction-Less Double-Gate MOSFET 1<br /></b><i>Namrata Mendiratta and Suman Lata Tripathi</i></p> <p>1.1 Introduction 1</p> <p>1.2 MOSFET Performance Parameters 2</p> <p>1.3 Comparison of Existing MOSFET Architectures 3</p> <p>1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) 3</p> <p>1.5 Heavily Doped JL-DG MOSFET for Biomedical Application 8</p> <p>1.6 Conclusion 9</p> <p>References 10</p> <p><b>2 VLSI Implementation of Vedic Multiplier 13<br /></b><i>Abhishek Kumar</i></p> <p>2.1 Introduction 13</p> <p>2.2 8x8 Vedic Multiplier 14</p> <p>2.3 The Architecture of 8x8 Vedic Multiplier (VM) 16</p> <p>2.3.1 Compressor Architecture 17</p> <p>2.3.1.1 3:2 Compressor 18</p> <p>2.3.1.2 4:3 Compressor 18</p> <p>2.3.1.3 5:3 Compressor 18</p> <p>2.3.1.4 8:4 Compressor 19</p> <p>2.3.1.5 10:4 Compressor 19</p> <p>2.3.1.6 12:5 Compressor 20</p> <p>2.3.1.7 15:5 Compressor 21</p> <p>2.3.1.8 20:5 Compressor 21</p> <p>2.4 Results and Discussion 23</p> <p>2.4.1 Instance Power 23</p> <p>2.4.2 Net Power 24</p> <p>2.4.3 8-Bit Multiplier 25</p> <p>2.4.4 16-Bit Multiplier 26</p> <p>2.4.5 Applications of Multiplier 27</p> <p>2.5 Conclusion 28</p> <p>References 28</p> <p><b>3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers 31<br /></b><i>Dr. D. Jeyabharathi, Dr. D. Kesavaraja and D. Sasireka</i></p> <p>3.1 Introduction 31</p> <p>3.1.1 IOT-Based Sewer Gas Detection 31</p> <p>3.1.1.1 IoT Sensors 32</p> <p>3.1.2 Objective 32</p> <p>3.1.3 Contribution of this Chapter 33</p> <p>3.1.4 Outline of the Chapter 33</p> <p>3.2 Related Works 33</p> <p>3.2.1 Sewer Gas Leakage Detection 33</p> <p>3.2.2 Crack Detection 34</p> <p>3.3 Methodology 34</p> <p>3.3.1 Sewer Gas Detection 34</p> <p>3.3.1.1 Proposed Tristate Pattern 35</p> <p>3.3.2 Crack Detection 36</p> <p>3.3.3 Experimental Setup 37</p> <p>3.4 Experimental Results 39</p> <p>3.5 Conclusion 40</p> <p>References 40</p> <p><b>4 Machine Learning for Smart Healthcare Energy-Efficient System 43<br /></b><i>S. Porkodi, Dr. D. Kesavaraja and Dr. Sivanthi Aditanar</i></p> <p>4.1 Introduction 43</p> <p>4.1.1 IoT in the Digital Age 43</p> <p>4.1.2 Using IoT to Enhance Healthcare Services 44</p> <p>4.1.3 Edge Computing 44</p> <p>4.1.4 Machine Learning 44</p> <p>4.1.5 Application in Healthcare 45</p> <p>4.2 Related Works 45</p> <p>4.3 Edge Computing 47</p> <p>4.3.1 Architecture 47</p> <p>4.3.2 Advantages of Edge Computing over Cloud Computing 47</p> <p>4.3.3 Applications of Edge Computing in Healthcare 48</p> <p>4.3.4 Edge Computing Advantages 49</p> <p>4.3.5 Challenges 50</p> <p>4.4 Smart Healthcare System 50</p> <p>4.4.1 Methodology 50</p> <p>4.4.2 Data Acquisition and IoT End Device 51</p> <p>4.4.3 IoT End Device and Backend Server 51</p> <p>4.5 Conclusion and Future Directions 52</p> <p>References 52</p> <p><b>5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices 57<br /></b><i>Dr. Jeyabharathi, Dr. A. Sherly Alphonse, Ms. E.L. Dhivya Priya and Dr. M. Kowsigan</i></p> <p>5.1 Introduction 57</p> <p>5.2 Types of Attacks 58</p> <p>5.3 Some Countermeasures for the Attacks 59</p> <p>5.4 Machine Learning Solutions 59</p> <p>5.5 Machine Learning Algorithms 59</p> <p>5.6 Authentication Process Based on Machine Learning 60</p> <p>5.7 Internet of Things (IoT) 62</p> <p>5.8 IoT-Based Attacks 62</p> <p>5.8.1 Botnets 62</p> <p>5.8.2 Man-in-the-Middle 62</p> <p>5.9 Information and Identity Theft 62</p> <p>5.10 Social Engineering 63</p> <p>5.11 Denial of Service 63</p> <p>5.12 Concerns 63</p> <p>5.13 Conclusion 64</p> <p>References 64</p> <p><b>6 Smart Energy-Efficient Techniques for Large-Scale Process Industries 67<br /></b><i>B Koti Reddy and N V Raghavaiah</i></p> <p>6.1 Pumps Operation 67</p> <p>6.1.1 Parts in a Centrifugal Pump 68</p> <p>6.1.2 Pump Efficiency 68</p> <p>6.1.3 VFD 70</p> <p>6.1.4 VFD and Pump Motor 72</p> <p>6.1.5 Large HT Motors 73</p> <p>6.1.6 Smart Pumps 73</p> <p>6.2 Vapour Absorption Refrigeration System 74</p> <p>6.2.1 Vapour Compression Refrigeration 74</p> <p>6.2.2 Vapour Absorption Refrigeration 75</p> <p>6.3 Heat Recovery Equipment 77</p> <p>6.3.1 Case Study 77</p> <p>6.3.2 Advantages of Heat Recovery 78</p> <p>6.4 Lighting System 78</p> <p>6.4.1 Technical Terms 78</p> <p>6.4.2 Introduction 78</p> <p>6.4.3 LED Lighting 79</p> <p>6.4.4 Energy-Efficiency Techniques 79</p> <p>6.4.5 Light Control with IoT 80</p> <p>6.4.5.1 Wipro Scheme 80</p> <p>6.4.5.2 Tata Scheme 80</p> <p>6.4.6 EU Practices 81</p> <p>6.5 Air Conditioners 82</p> <p>6.5.1 Technical Terms 82</p> <p>6.5.2 Types of Air Conditioners 82</p> <p>6.5.3 Star Rating of BEE 83</p> <p>6.5.4 EU Practices 83</p> <p>6.5.5 Energy-Efficiency Tips 83</p> <p>6.5.6 Inverter Air Conditioners 85</p> <p>6.5.7 IoT-Based Air Conditioners 85</p> <p>6.6 Fans and Other Smart Appliances 86</p> <p>6.6.1 BLDC Fan Motors 87</p> <p>6.6.2 Star Ratings 87</p> <p>6.6.3 Group Drive of Fans 88</p> <p>6.6.4 Other Smart Appliances 88</p> <p>6.7 Motors 92</p> <p>6.7.1 Motor Efficiency 92</p> <p>6.7.2 Underrated Operation 93</p> <p>6.7.3 Energy-Efficient Motors 94</p> <p>6.7.3.1 Energy-Efficiency Ratings of BEE 94</p> <p>6.7.3.2 Energy-Efficiency Ratings of IEC 94</p> <p>6.7.4 Retrofit of Standard Motors with Energy-Efficient Motors 96</p> <p>6.7.5 Other Salient Points 97</p> <p>6.7.6 Use of Star-Delta Starter Motor 97</p> <p>6.8 Energy-Efficient Transformers 98</p> <p>6.8.1 IEC Recommendation 98</p> <p>6.8.2 Super Conducting Transformers 99</p> <p>References 99</p> <p><b>7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network 101<br /></b><i>Manwinder Singh and Anudeep Gandam</i></p> <p>7.1 Introduction 101</p> <p>7.2 Related Work 103</p> <p>7.2.1 Existing Techniques 105</p> <p>7.3 Proposed K-Means Clustering Algorithm 105</p> <p>7.3.1 Homogenous and Heterogeneous Network Clustering Algorithms 105</p> <p>7.3.2 Dynamic and Static Clustering 105</p> <p>7.3.2.1 Routing 106</p> <p>7.3.3 Flow Diagram 106</p> <p>7.3.4 Objective Function 106</p> <p>7.4 System Model and Assumption 108</p> <p>7.4.1 Simulation Parameters 108</p> <p>7.4.1.1 Residual Energy 108</p> <p>7.4.1.2 End-to-End Delay 109</p> <p>7.4.1.3 Number of Hops or Hop Count in the Network 109</p> <p>7.5 Results and Discussion 109</p> <p>7.6 Conclusions 114</p> <p>References 115</p> <p><b>8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications 119<br /></b><i>Arunkumar S, Mohana Sundaram N and K. Malarvizhi</i></p> <p>8.1 Introduction 119</p> <p>8.2 PV Panel as Energy Source 120</p> <p>8.2.1 Solar Cell 120</p> <p>8.3 Multi-Level Inverter Topologies 121</p> <p>8.3.1 Types of Inverters Used for Drives 121</p> <p>8.3.2 Multi-Level Inverters 121</p> <p>8.4 Experimental Results and Discussion 122</p> <p>8.4.1 PV Powered H Bridge Inverter-Fed Drive 123</p> <p>8.4.2 PV Powered DCMLI Fed Drive 126</p> <p>8.5 Conclusion and Future Scope 128</p> <p>References 129</p> <p><b>9 Analysis and Design of Bidirectional Circuits for Energy Storage Application 131<br /></b><i>Suresh K, Sanjeevikumar Padmanaban and S Vivek</i></p> <p>9.1 Introduction 131</p> <p>9.2 Modes of Operation Based on Main Converters 133</p> <p>9.2.1 Single-Stage Rectification 134</p> <p>9.2.2 Single-Stage Inversion 135</p> <p>9.2.3 Double-Stage Rectification 137</p> <p>9.2.3.1 Duty Mode - Interval -I 137</p> <p>9.2.3.2 Freewheeling Mode - Interval -II 138</p> <p>9.2.4 Double-Stage Inversion 139</p> <p>9.2.4.1 Charging Mode - Interval -I 140</p> <p>9.2.4.2 Duty Mode - Interval -II 141</p> <p>9.3 Proposed Methodology for Three-Phase System 141</p> <p>9.3.1 Control Block of Overall System 143</p> <p>9.3.2 Proposed Carrier-Based PWM Strategy 144</p> <p>9.3.3 Experiment Results 145</p> <p>9.4 Conclusion 148</p> <p>References 148</p> <p><b>10 Low-Power IOT-Enabled Energy Systems 151<br /></b><i>Yogini Dilip Borole and Dr. C. G. Dethe</i></p> <p>10.1 Overview 151</p> <p>10.1.1 Conceptions 151</p> <p>10.1.2 Motivation 152</p> <p>10.1.3 Methodology 154</p> <p>10.2 Empowering Tools 156</p> <p>10.2.1 Sensing Components 156</p> <p>10.2.2 Movers 159</p> <p>10.2.3 Telecommunication Technology 160</p> <p>10.2.4 Internet of Things Information and Evaluation 166</p> <p>10.2.4.1 Distributed Evaluation 166</p> <p>10.2.4.2 Fog Computing (Edge Computing) 167</p> <p>10.3 Internet of Things within Power Region 167</p> <p>10.3.1 Internet of Things along with Vitality Production 168</p> <p>10.3.2 Smart Metropolises 168</p> <p>10.3.3 Intelligent Lattice Network 171</p> <p>10.3.4 Smart Buildings Structures 172</p> <p>10.3.5 Powerful Usage of Vitality in Production 173</p> <p>10.3.6 Insightful Transport 174</p> <p>10.4 Difficulties - Relating Internet of Things 174</p> <p>10.4.1 Vitality Ingestion 178</p> <p>10.4.2 Synchronization via Internet of Things through Sub-Units 178</p> <p>10.4.3 Client Confidentiality 180</p> <p>10.4.4 Safety Challenges 180</p> <p>10.4.5 IoT Standardization and Architectural Concept 181</p> <p>10.5 Upcoming Developments 182</p> <p>10.5.1 IoT and Block Chain 182</p> <p>10.5.2 Artificial Intelligence and IoT 184</p> <p>10.5.3 Green IoT 185</p> <p>10.6 Conclusion 187</p> <p>References 188</p> <p><b>11 Efficient Renewable Energy Systems 199<br /></b><i>Prabhansu and Nayan Kumar</i></p> <p>Introduction 199</p> <p>11.1 Renewable-Based Available Technologies 200</p> <p>11.1.1 Wind Power 201</p> <p>11.1.1.1 Modeling of the Wind Turbine Generator (WTG) 201</p> <p>11.1.1.2 Categorization of Wind Turbine 202</p> <p>11.1.2 Solar Power 202</p> <p>11.1.2.1 PV System 202</p> <p>11.1.2.2 Network-Linked Photovoltaic Grid-Connected PV Set-Up 203</p> <p>11.1.3 Tidal Energy 203</p> <p>11.1.4 Battery Storage System 204</p> <p>11.1.5 Solid Oxide Energy Units for Enhancing Power Life 204</p> <p>11.1.5.1 Common Utility of SOFC 204</p> <p>11.1.5.2 Integrated Solid Oxide Energy Components and Sustainable Power Life 205</p> <p>11.2 Adaptability Frameworks 206</p> <p>11.2.1 Distributed Energy Resources (DER) 206</p> <p>11.2.2 New Age Grid Connection 209</p> <p>11.3 Conclusion 210</p> <p>References 211</p> <p><b>12 Efficient Renewable Energy Systems 215<br /></b><i>Dr. Arvind Dhingra</i></p> <p>12.1 Introduction 215</p> <p>12.1.1 World Energy Scenario 215</p> <p>12.2 Sources of Energy: Classification 217</p> <p>12.3 Renewable Energy Systems 217</p> <p>12.3.1 Solar Energy 218</p> <p>12.3.2 Wind 218</p> <p>12.3.3 Geothermal 218</p> <p>12.3.4 Biomass 218</p> <p>12.3.5 Ocean 218</p> <p>12.3.6 Hydrogen 218</p> <p>12.4 Solar Energy 218</p> <p>12.5 Wind Energy 223</p> <p>12.6 Geothermal Energy 225</p> <p>12.7 Biomass 226</p> <p>12.7.1 Forms of Biomass 226</p> <p>12.8 Ocean Power 227</p> <p>12.9 Hydrogen 227</p> <p>12.10 Hydro Power 227</p> <p>12.11 Conclusion 227</p> <p>References 227</p> <p><b>13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management 229<br /></b><i>Dilip Kumar and Ujala Choudhury</i></p> <p>13.1 Introduction 229</p> <p>13.1.1 Novelty of the Work 232</p> <p>13.1.2 Benefit to Society 232</p> <p>13.2 Development of the Proposed System 233</p> <p>13.3 System Description 233</p> <p>13.3.1 Study of the Crop Under Experiment 233</p> <p>13.3.2 Hardware of the System 235</p> <p>13.3.3 Software of the System 235</p> <p>13.4 Layers of the System Architecture 236</p> <p>13.4.1 Application Layer 236</p> <p>13.4.2 Cloud Layer 237</p> <p>13.4.3 Network Layer 237</p> <p>13.4.4 Physical Layer 237</p> <p>13.5 Calibration 237</p> <p>13.6 Layout of the Sprinkler System 239</p> <p>13.7 Testing 239</p> <p>13.8 Results and Discussion 241</p> <p>13.9 Conclusion 242</p> <p>References 242</p> <p><b>14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning 245<br /></b><i>Mohit Goyal and Durgesh Srivastava</i></p> <p>14.1 Introduction 246</p> <p>14.2 Basics of Internet of Things (IoT) 246</p> <p>14.2.1 The IoT Reference Model 248</p> <p>14.2.2 Working of IoT 249</p> <p>14.2.2.1 Device 249</p> <p>14.2.2.2 Connectivity to Cloud 250</p> <p>14.2.2.3 Data Analysis 250</p> <p>14.2.2.4 User Interface 250</p> <p>14.2.3 Utilization of Internet of Things (IoT) 250</p> <p>14.3 Authentication in IoT 251</p> <p>14.3.1 Methods of Authentication 251</p> <p>14.3.1.1 Authentication Based on Knowledge 252</p> <p>14.3.1.2 Authentication Based on Possession 252</p> <p>14.3.1.3 Authentication Based on Biometric 253</p> <p>14.4 User Authentication Based on Behavioral-Biometric 255</p> <p>14.4.1 Machine Learning 256</p> <p>14.4.1.1 Supervised Machine Learning 256</p> <p>14.4.1.2 Unsupervised Machine Learning 256</p> <p>14.4.2 Machine Learning Algorithms 257</p> <p>14.4.2.1 RIPPER 257</p> <p>14.4.2.2 Multilayer Perceptron 257</p> <p>14.4.2.3 Decision Tree 257</p> <p>14.4.2.4 Random Forest 258</p> <p>14.4.2.5 Instance-Based Learning 258</p> <p>14.4.2.6 Bootstrap Aggregating 258</p> <p>14.4.2.7 Na&iuml;ve Bayes 258</p> <p>14.5 Threats and Challenges in the Current Security Solution for IoT 258</p> <p>14.6 Proposed Methodology 259</p> <p>14.6.1 Collection of Gait Dataset 259</p> <p>14.6.2 Gait Data Preprocessing 259</p> <p>14.6.3 Reduction in Data Size 260</p> <p>14.6.4 Gaits Feature 260</p> <p>14.6.5 Classification 260</p> <p>14.7 Conclusion and Future Work 261</p> <p>References 261</p> <p><b>15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas 265<br /></b><i>Pushan Kr. Dutta, Somsubhra Gupta, Simran Kumari and Akshay Vinayak</i></p> <p>15.1 Introduction 265</p> <p>15.2 Proposed System 266</p> <p>15.2.1 Problem Statement 266</p> <p>15.2.2 Overview 266</p> <p>15.2.3 System Components 268</p> <p>15.3 Work Process 271</p> <p>15.3.1 System Hardware 271</p> <p>15.3.2 Connections and Circuitry 271</p> <p>15.4 Optimization Framework 271</p> <p>15.4.1 Fuzzy Goal Description 271</p> <p>15.4.2 Characterizing Fuzzy Membership Function 272</p> <p>15.4.3 Construction of FGP Model 272</p> <p>15.4.4 Definition of Variables and Parameters 273</p> <p>15.4.5 Fuzzy Goal Description 274</p> <p>15.5 Creation of Database and Website 275</p> <p>15.5.1 Hosting PHP Application and Creation of MySQL Database 275</p> <p>15.5.2 Creation of API (Application Programming Interfaces) Key 275</p> <p>15.5.2.1 $api_key_value = &ldquo;3mM44UaC2DjFcV_63GZ14aWJcRDNmYBMsxceu&rdquo;; 275</p> <p>15.5.2.2 Preparing Mysql Database 275</p> <p>15.5.2.3 Structured Query Language (SQL) 275</p> <p>15.5.2.4 Use of HTTP (Hypertext Transfer Protocol) in Posting Request 276</p> <p>15.5.2.5 Adding a Dynamic Map to the Website 277</p> <p>15.5.2.6 Adding Dynamic Graph to the Website 277</p> <p>15.5.2.7 Adding the Download Option of the Data Set 278</p> <p>15.6 Libraries Used and Code Snipped 278</p> <p>15.7 Mode of Communication 280</p> <p>15.8 Conclusion 280</p> <p>Abbreviations 282</p> <p>References 282</p> <p><b>16 Internet of Things &ndash; Definition, Architecture, Applications, Requirements and Key Research Challenges 285<br /></b><i>Dushyant Kumar Singh, Himani Jerath and P. Raja</i></p> <p>16.1 Introduction 285</p> <p>16.2 Defining the Term Internet of Things (IoT) 286</p> <p>16.3 IoT Architecture 287</p> <p>16.4 Applications of Internet of Things (IoT) 289</p> <p>16.5 Requirement for Internet of Things (IoT) Implementation 290</p> <p>16.6 Key Research Challenges in Internet of Things (IoT) 291</p> <p>16.6.1 Computing, Communication and Identification 291</p> <p>16.6.2 Network Technology 292</p> <p>16.6.3 Greening of Internet of Things (IoT) 292</p> <p>16.6.4 Security 293</p> <p>16.6.5 Diversity 293</p> <p>16.6.6 Object Safety and Security 293</p> <p>16.6.7 Data Confidentiality and Unauthorized Access 293</p> <p>16.6.8 Architecture 293</p> <p>16.6.9 Network and Routing Information Security 293</p> <p>References 294</p> <p><b>17 FinFET Technology for Low-Power Applications 297<br /></b><i>Bindu Madhavi, Suman Lata Tripathi and Bhagwan Shree Ram</i></p> <p>17.1 Introduction 297</p> <p>17.2 Exiting Multiple-Gate MOSFET Architectures 299</p> <p>17.3 FinFET Design and Analysis 301</p> <p>17.4 Low-Power Applications 304</p> <p>17.4.1 FinFET-Based Digital Circuit Design 304</p> <p>17.4.2 FinFET-Based Memory Design 304</p> <p>17.4.3 FinFET-Based Biosensors 304</p> <p>17.5 Conclusion 305</p> <p>References 305</p> <p><b>18 An Enhanced Power Quality Single-Source Large Step-Up Switched-Capacitor Based Multi-Level Inverter Configuration with Natural Voltage Balancing of Capacitors 307<br /></b><i>Mahdi Karimi, Paria Kargar, Kazem Varesi and Sanjeevikumar Padmanaban</i></p> <p>18.1 Introduction 307</p> <p>18.2 Suggested Topology 309</p> <p>18.2.1 Circuit Configuration 309</p> <p>18.2.2 Generation of Output Voltage Steps 310</p> <p>18.2.3 Voltage Stress of Switches 320</p> <p>18.3 Cascaded Configuration of Suggested Topology 320</p> <p>18.4 Modulation Technique 321</p> <p>18.5 Power Loss Analysis 324</p> <p>18.5.1 Conduction Losses 324</p> <p>18.5.2 Switching Losses 326</p> <p>18.5.3 Capacitor Losses 327</p> <p>18.6 Design of Capacitors 328</p> <p>18.7 Comparative Analysis 330</p> <p>18.8 Simulation Results 333</p> <p>18.9 Conclusions 336</p> <p>References 336</p> <p>Index 339</p>
<p><b>Suman Lata Tripathi, PhD</b>, is a professor at Lovely Professional with more than seventeen years of experience in academics. She has published more than 45 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published one edited book and currently has multiple volumes scheduled for publication, including volumes available from Wiley-Scrivener.</p><p><b>Dushyant Kumar Singh</b>, is an assistant professor and Head of Embedded Systems Domain at Lovely Professional University. With a masters degree from Punjab Engineering College, University of Technology, Chandigarh, he has several years of industrial experience and more than ten years of teaching experience.</p><p><b>Sanjeevikumar Padmanaban, PhD</b>, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching.</p><p><b>P. Raja</b> is currently working as an assistant professor at Lovely Professional University. His expertise is in VLSI and embedded systems. He has more than 14 years of experience with 5 years in embedded industry. He has 14 publications in UGC-approved and other reputable journals. He also has 10 patents to his credit.</p>
<p><b>Covering the concepts and fundamentals of efficient energy systems, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple industries, for both the engineer and the student.</b></p><p>There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these “game changers,” governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society.</p><p>This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation.</p><p>The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.</p><p>This outstanding new volume:</p><ul><li>Handles the fundamentals of system design including the concept of energy saving aspects at different levels</li><li>Is useful for all engineering students for learning the fundamentals of system design and automation with machine learning and IoT</li><li>Will be helpful for researchers and designers to find out key parameters for future projects and current applications</li></ul><p><b>Audience:</b></p><p>Engineers and scientists across many fields, including petroleum and process engineers, chemical engineers, electrical engineers working with power systems, and students at the university and post-graduate level studying energy topics</p>

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Handbook of Virtual Humans
Handbook of Virtual Humans
von: Nadia Magnenat-Thalmann, Daniel Thalmann
PDF ebook
136,99 €