Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]

• Pubblicazione: Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]; ©2021
• Descrizione fisica: 1 online resource (384 pages)
• Disciplina: 621.317
• Soggetto topico: Artificial intelligence - Industrial applications; Electric power supplies to apparatus - Energy conservation - Data processing
• Soggetto genere / forma: Electronic books.
• Persona (resp. second.): TripathiSuman Lata
• Nota di contenuto: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Design of Low Power Junction Less Double-Gate MOSFET -- 1.1 Introduction -- 1.2 MOSFET Performance Parameters -- 1.3 Comparison of Existing MOSFET Architectures -- 1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) -- 1.5 Heavily Doped JL-DG MOSFET for Biomedical Application -- 1.6 Conclusion -- References -- 2 VLSI Implementation of Vedic Multiplier -- 2.1 Introduction -- 2.2 8x8 Vedic Multiplier -- 2.3 The Architecture of 8x8 Vedic Multiplier (VM) -- 2.3.1 Compressor Architecture -- 2.4 Results and Discussion -- 2.4.1 Instance Power -- 2.4.2 Net Power -- 2.4.3 8-Bit Multiplier -- 2.4.4 16-Bit Multiplier -- 2.4.5 Applications of Multiplier -- 2.5 Conclusion -- References -- 3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers -- 3.1 Introduction -- 3.1.1 IOT-Based Sewer Gas Detection -- 3.1.2 Objective -- 3.1.3 Contribution of this Chapter -- 3.1.4 Outline of the Chapter -- 3.2 Related Works -- 3.2.1 Sewer Gas Leakage Detection -- 3.2.2 Crack Detection -- 3.3 Methodology -- 3.3.1 Sewer Gas Detection -- 3.3.2 Crack Detection -- 3.3.3 Experimental Setup -- 3.4 Experimental Results -- 3.5 Conclusion -- References -- 4 Machine Learning for Smart Healthcare Energy-Efficient System -- 4.1 Introduction -- 4.1.1 IoT in the Digital Age -- 4.1.2 Using IoT to Enhance Healthcare Services -- 4.1.3 Edge Computing -- 4.1.4 Machine Learning -- 4.1.5 Application in Healthcare -- 4.2 Related Works -- 4.3 Edge Computing -- 4.3.1 Architecture -- 4.3.2 Advantages of Edge Computing over Cloud Computing -- 4.3.3 Applications of Edge Computing in Healthcare -- 4.3.4 Edge Computing Advantages -- 4.3.5 Challenges -- 4.4 Smart Healthcare System -- 4.4.1 Methodology -- 4.4.2 Data Acquisition and IoT End Device. ; 4.4.3 IoT End Device and Backend Server -- 4.5 Conclusion and Future Directions -- References -- 5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices -- 5.1 Introduction -- 5.2 Types of Attacks -- 5.3 Some Countermeasures for the Attacks -- 5.4 Machine Learning Solutions -- 5.5 Machine Learning Algorithms -- 5.6 Authentication Process Based on Machine Learning -- 5.7 Internet of Things (IoT) -- 5.8 IoT-Based Attacks -- 5.8.1 Botnets -- 5.8.2 Man-in-the-Middle -- 5.9 Information and Identity Theft -- 5.10 Social Engineering -- 5.11 Denial of Service -- 5.12 Concerns -- 5.13 Conclusion -- References -- 6 Smart Energy-Efficient Techniques for Large-Scale Process Industries -- 6.1 Pumps Operation -- 6.1.1 Parts in a Centrifugal Pump -- 6.1.2 Pump Efficiency -- 6.1.3 VFD -- 6.1.4 VFD and Pump Motor -- 6.1.5 Large HT Motors -- 6.1.6 Smart Pumps -- 6.2 Vapour Absorption Refrigeration System -- 6.2.1 Vapour Compression Refrigeration -- 6.2.2 Vapour Absorption Refrigeration -- 6.3 Heat Recovery Equipment -- 6.3.1 Case Study -- 6.3.2 Advantages of Heat Recovery -- 6.4 Lighting System -- 6.4.1 Technical Terms -- 6.4.2 Introduction -- 6.4.3 LED Lighting -- 6.4.4 Energy-Efficiency Techniques -- 6.4.5 Light Control with IoT -- 6.4.6 EU Practices -- 6.5 Air Conditioners -- 6.5.1 Technical Terms -- 6.5.2 Types of Air Conditioners -- 6.5.3 Star Rating of BEE -- 6.5.4 EU Practices -- 6.5.5 Energy-Efficiency Tips -- 6.5.6 Inverter Air Conditioners -- 6.5.7 IoT-Based Air Conditioners -- 6.6 Fans and Other Smart Appliances -- 6.6.1 BLDC Fan Motors -- 6.6.2 Star Ratings -- 6.6.3 Group Drive of Fans -- 6.6.4 Other Smart Appliances -- 6.7 Motors -- 6.7.1 Motor Efficiency -- 6.7.2 Underrated Operation -- 6.7.3 Energy-Efficient Motors -- 6.7.4 Retrofit of Standard Motors with Energy-Efficient Motors. ; 6.7.5 Other Salient Points -- 6.7.6 Use of Star-Delta Starter Motor -- 6.8 Energy-Efficient Transformers -- 6.8.1 IEC Recommendation -- 6.8.2 Super Conducting Transformers -- References -- 7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Existing Techniques -- 7.3 Proposed K-Means Clustering Algorithm -- 7.3.1 Homogenous and Heterogeneous Network Clustering Algorithms -- 7.3.2 Dynamic and Static Clustering -- 7.3.3 Flow Diagram -- 7.3.4 Objective Function -- 7.4 System Model and Assumption -- 7.4.1 Simulation Parameters -- 7.5 Results and Discussion -- 7.6 Conclusions -- References -- 8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications -- 8.1 Introduction -- 8.2 PV Panel as Energy Source -- 8.2.1 Solar Cell -- 8.3 Multi-Level Inverter Topologies -- 8.3.1 Types of Inverters Used for Drives -- 8.3.2 Multi-Level Inverters -- 8.4 Experimental Results and Discussion -- 8.4.1 PV Powered H Bridge Inverter-Fed Drive -- 8.4.2 PV Powered DCMLI Fed Drive -- 8.5 Conclusion and Future Scope -- References -- 9 Analysis and Design of Bidirectional Circuits for Energy Storage Application -- 9.1 Introduction -- 9.2 Modes of Operation Based on Main Converters -- 9.2.1 Single-Stage Rectification -- 9.2.2 Single-Stage Inversion -- 9.2.3 Double-Stage Rectification -- 9.2.4 Double-Stage Inversion -- 9.3 Proposed Methodology for Three-Phase System -- 9.3.1 Control Block of Overall System -- 9.3.2 Proposed Carrier-Based PWM Strategy -- 9.3.3 Experiment Results -- 9.4 Conclusion -- References -- 10 Low-Power IOT-Enabled Energy Systems -- 10.1 Overview -- 10.1.1 Conceptions -- 10.1.2 Motivation -- 10.1.3 Methodology -- 10.2 Empowering Tools -- 10.2.1 Sensing Components -- 10.2.2 Movers -- 10.2.3 Telecommunication Technology. ; 10.2.4 Internet of Things Information and Evaluation -- 10.3 Internet of Things within Power Region -- 10.3.1 Internet of Things along with Vitality Production -- 10.3.2 Smart Metropolises -- 10.3.3 Intelligent Lattice Network -- 10.3.4 Smart Buildings Structures -- 10.3.5 Powerful Usage of Vitality in Production -- 10.3.6 Insightful Transport -- 10.4 Difficulties Relating Internet of Things -- 10.4.1 Vitality Ingestion -- 10.4.2 Synchronization via Internet of Things through Sub-Units -- 10.4.3 Client Confidentiality -- 10.4.4 Safety Challenges -- 10.4.5 IoT Standardization and Architectural Concept -- 10.5 Upcoming Developments -- 10.5.1 IoT and Block Chain -- 10.5.2 Artificial Intelligence and IoT -- 10.5.3 Green IoT -- 10.6 Conclusion -- References -- 11 Efficient Renewable Energy Systems -- Introduction -- 11.1 Renewable-Based Available Technologies -- 11.1.1 Wind Power -- 11.1.2 Solar Power -- 11.1.3 Tidal Energy -- 11.1.4 Battery Storage System -- 11.1.5 Solid Oxide Energy Units for Enhancing Power Life -- 11.2 Adaptability Frameworks -- 11.2.1 Distributed Energy Resources (DER) -- 11.2.2 New Age Grid Connection -- 11.3 Conclusion -- References -- 12 Efficient Renewable Energy Systems -- 12.1 Introduction -- 12.1.1 World Energy Scenario -- 12.2 Sources of Energy: Classification -- 12.3 Renewable Energy Systems -- 12.3.1 Solar Energy -- 12.3.2 Wind -- 12.3.3 Geothermal -- 12.3.4 Biomass -- 12.3.5 Ocean -- 12.3.6 Hydrogen -- 12.4 Solar Energy -- 12.5 Wind Energy -- 12.6 Geothermal Energy -- 12.7 Biomass -- 12.7.1 Forms of Biomass -- 12.8 Ocean Power -- 12.9 Hydrogen -- 12.10 Hydro Power -- 12.11 Conclusion -- References -- 13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management -- 13.1 Introduction -- 13.1.1 Novelty of the Work -- 13.1.2 Benefit to Society -- 13.2 Development of the Proposed System. ; 13.3 System Description -- 13.3.1 Study of the Crop Under Experiment -- 13.3.2 Hardware of the System -- 13.3.3 Software of the System -- 13.4 Layers of the System Architecture -- 13.4.1 Application Layer -- 13.4.2 Cloud Layer -- 13.4.3 Network Layer -- 13.4.4 Physical Layer -- 13.5 Calibration -- 13.6 Layout of the Sprinkler System -- 13.7 Testing -- 13.8 Results and Discussion -- 13.9 Conclusion -- References -- 14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning -- 14.1 Introduction -- 14.2 Basics of Internet of Things (IoT) -- 14.2.1 The IoT Reference Model -- 14.2.2 Working of IoT -- 14.2.3 Utilization of Internet of Things (IoT) -- 14.3 Authentication in IoT -- 14.3.1 Methods of Authentication -- 14.4 User Authentication Based on Behavioral-Biometric -- 14.4.1 Machine Learning -- 14.4.2 Machine Learning Algorithms -- 14.5 Threats and Challenges in the Current Security Solution for IoT -- 14.6 Proposed Methodology -- 14.6.1 Collection of Gait Dataset -- 14.6.2 Gait Data Preprocessing -- 14.6.3 Reduction in Data Size -- 14.6.4 Gaits Feature -- 14.6.5 Classification -- 14.7 Conclusion and Future Work -- References -- 15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas -- 15.1 Introduction -- 15.2 Proposed System -- 15.2.1 Problem Statement -- 15.2.2 Overview -- 15.2.3 System Components -- 15.3 Work Process -- 15.3.1 System Hardware -- 15.3.2 Connections and Circuitry -- 15.4 Optimization Framework -- 15.4.1 Fuzzy Goal Description -- 15.4.2 Characterizing Fuzzy Membership Function -- 15.4.3 Construction of FGP Model -- 15.4.4 Definition of Variables and Parameters -- 15.4.5 Fuzzy Goal Description -- 15.5 Creation of Database and Website -- 15.5.1 Hosting PHP Application and Creation of MySQL Database -- 15.5.2 Creation of API (Application Programming Interfaces) Key. ; 15.6 Libraries Used and Code Snipped.
• Sommario/riassunto: "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. 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. 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"-- Provided by publisher
• Titolo autorizzato: Design and development of efficient energy systems
• ISBN: 1-5231-4332-0; 1-119-76178-6; 1-119-76177-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910555117803321
• Lo trovi qui: Univ. Federico II
• Serie: Artificial Intelligence and Soft Computing for Industrial Transformation.