دانلود کتاب Latest Advances in Electrical Engineering, and Electronics: Proceedings of the JIEE 2021 (Lecture Notes in Electrical Engineering, 933)

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Contents\nElectric Power\nElectric Energy Demand Forecasting in an Oil Production Company Using Artificial Neural Networks\n 1 Introduction\n 2 Artificial Neural Networks (ANN)\n 3 Methods\n 3.1 Prior Knowledge of the Company\n 3.2 Selection and Analysis of the Input Variables Historical Data\n 3.3 Data Pre-processing\n 3.4 ANN Implemented\n 3.5 Study Cases\n 3.6 Training, Validation and Storage of the ANN\n 3.7 Electricity Demand Forecasting Using Python\n 4 Results\n 4.1 Historical Data Analysis\n 4.2 ANN Configuration Selection and Training\n 4.3 Electricity Demand Forecasting\n 5 Conclusions\n References\nSecondary Voltage Control Areas Using Hybrid Methods for Systems with High Wind Penetration\n 1 Introduction\n 2 Theoretical Background\n 2.1 Voltage Instability Analysis\n 2.2 Voltage Control Areas and Pilot Buses\n 3 Proposed Methodology for Secondary Voltage Control Areas\n 3.1 Modal Energy Analysis for Control Areas Partitioning\n 3.2 MonteCarlo Simulation Approach\n 3.3 Control Areas Partitioning Validation\n 3.4 Secondary Voltage Control Proposal\n 4 Simulation Results and Analysis\n 4.1 Control Areas Partitioning\n 4.2 Secondary Voltage Control Analysis\n 5 Conclusions\n References\nStudy of Electromagnetic Fields Distribution in Tena Electrical Substation\n 1 Introduction\n 2 Materials and Methods\n 2.1 EMF in Substations\n 2.2 EMF Calculations\n 3 Analysis and Discussion of Results\n 3.1 EMF Simulation on Incoming and Outgoing Lines\n 3.2 Simulation of EMF in Substation Busbars\n 3.3 Simulation of EMF in Substation Busbars\n 4 Conclusions\n References\nMechanical Stress in Power Transformer Winding Conductors: A Support Vector Regression Approach\n 1 Introduction\n 2 Method\n 2.1 Electromagnetic Forces and Mechanical Stress\n 2.2 Support Vector Regression\n 3 Results and Discussion\n 3.1 Results\n 3.2 Discussion\n 4 Conclusion\n References\nIndustrial Control and Automation\nDevelopment of an Industrial IoT Gateway Prototype\n 1 Introduction\n 2 Background\n 3 Selected Protocols and Technologies\n 3.1 Modbus RTU Protocol\n 3.2 MQTT Protocol\n 3.3 Arduino Platform\n 3.4 Node-RED\n 4 Prototype Implementation Requirements\n 5 Proposed System Architecture\n 5.1 IIoT Arduino UNO Gateway (I2C Master) + Ethernet Shield\n 5.2 Arduino Nano (I2C Slave) + TTL/RS-485 Converter\n 5.3 I2C Slave Sensors\n 5.4 Node-RED + Mosquitto MQTT Broker\n 5.5 Inputs and Outputs Conditioning\n 5.6 Router\n 5.7 Users\n 5.8 Modbus RTU Station\n 6 Performed Tests and Results\n 6.1 Devices\n 6.2 Alarms\n 6.3 Real-Time Trends\n 7 Conclusions and Future Works\n References\nThe Quadruple-Tank Process: A Comparison Among Advanced Control Techniques\n 1 Introduction\n 2 Materials\n 2.1 Plant Model\n 3 Advanced Control Designs\n 3.1 PID Control\n 3.2 Sliding Mode Control (SMC)\n 3.3 Model Reference Adaptative Control (ADAPTATIVE)\n 3.4 Linear Algebra with Decouplers (LA-D)\n 3.5 Linear Algebra Without Decouplers (LA)\n 4 Simulation Results\n 4.1 Set-Point Step Change Response\n 4.2 Disturbance is Rejection\n 5 Conclusion\n References\nLearning an Improved LMI Controller Based on Takagi-Sugeno Models via Value Iteration\n 1 Introduction\n 2 Methodology\n 2.1 Takagi-Sugeno Systems\n 2.2 Reinforcement Learning and Optimal Control\n 2.3 Reinforcement Learning for Takagi-Sugeno Systems\n 2.4 Value Iteration for Takagi-Sugeno Systems\n 3 Results and Discussion\n 3.1 Numerical Example\n 4 Conclusions\n References\nA Sliding Mode Controller Approach Based on Particle Swarm Optimization for ECG and Heart Rate Tracking Purposes\n 1 Introduction\n 2 Background\n 2.1 Sliding Mode Control\n 2.2 Particle Swarm Optimization Algorithm\n 3 Heart Models\n 3.1 Zeeman\'s Heartbeat Model\n 3.2 Heart Rate Model\n 4 Controller Design\n 4.1 Sliding Mode Controller Based on PSO for First-Order Models\n 4.2 Sliding Mode Controller Based on PSO for Integrating Systems\n 5 Simulation Results\n 5.1 ECG Tracking and Regulation Test for Zeeman\'s Heartbeat Model Using SMC-FO-PSO\n 5.2 Heart Rate Tracking Test Using SMC-IS-PSO\n 6 Conclusions\n References\nDesign, Simulation, and Implementation of an Artificial Pancreas Prototype for Virtual Patients with Type 1 Diabetes Applying SMC Controller with Anticipated Carbohydrate Information\n 1 Introduction\n 2 Materials and Methods\n 2.1 Theoretical Foundation\n 2.2 Describing Modeling Equations\n 2.3 Sliding Mode Controller\n 2.4 Pancreas Artificial Prototype Control Loop\n 2.5 The Graphical Interface (HMI)\n 2.6 Electronic Devices for Assembly\n 2.7 Injection System\n 2.8 Printed Circuit Board (PCB)\n 2.9 Prototype Case\n 3 Results\n 3.1 Controller Simulation with Hovorka\'s Patient Model\n 3.2 Controller Validation with UVA/PADOVA Simulator\n 3.3 Prototype Results\n 3.4 Pump Validation\n 4 Conclusions\n References\nDesign and Characterization of a Wireless Illuminance Meter with IoT-Based Systems for Smart Lighting Applications\n 1 Introduction\n 2 Development of the Illuminance Meter\n 2.1 Characteristics of the System Detection\n 2.2 Device Selection\n 2.3 Test Setup and Traceability\n 2.4 Test Lamps\n 3 Test Results and Discussions\n 4 Conclusions\n References\nDecoupled Distributed State Estimator with Reduced Number of Measurements for Power System Applications\n 1 Introduction\n 2 State Estimation in Power Systems\n 2.1 General Features\n 2.2 WLS Formulation for State Estimation\n 3 Decoupled Distributed State Estimator Formulation\n 3.1 Measurement Functions\n 3.2 Jacobian Elements\n 3.3 Minimum Spanning Tree for Reduction of Measurements\n 4 Distributed State Estimator Implementation\n 5 Test and Results\n 5.1 Operating Scenario Description\n 5.2 Testing Procedures\n 5.3 Results\n 6 Conclusions\n References\nObject Detection and Tracking Based on Artificial Vision for a Single Board Computer (SBC)\n 1 Introduction\n 2 Software Review\n 2.1 Neural Networks for Object Identification\n 2.2 Tracking Algorithms\n 3 Methodology\n 3.1 Hardware Analysis\n 3.2 Algorithm Design\n 3.3 Algorithm Walk-Through\n 4 Test and Results\n 5 Conclusions\n References\nDevelopment of an Industrial Communication Driver for Profinet Devices\n 1 Introduction\n 2 Theoretical Framework\n 2.1 Profinet IO\n 2.2 Ethernet Protocol\n 2.3 IPv4 Protocol\n 2.4 TCP Protocol\n 3 Proposal for Industrial Driver Communication Implementation\n 3.1 Commercial Data Servers\n 3.2 Proposed Architecture\n 3.3 Software Selection\n 3.4 Existing Variables in the PLC and Their Management\n 3.5 Existing Variables in the MySQL Database and Their Management\n 4 Communication Driver Implementation\n 4.1 Profinet Data Management in Siemens\n 4.2 Profinet Protocol Initialization\n 4.3 Software Implementation\n 4.4 Driver Configuration Interface\n 5 Test and Results\n 5.1 Testing Requirements\n 5.2 Lecture Process\n 5.3 Writing Process\n 6 Conclusions\n References\nInformation Networks\nProposal for Information Security Risk Mitigation Practices Based on a Regulatory Approach\n 1 Introduction\n 2 Methodology\n 3 Results and Discussion\n 3.1 Information Security Management in FONDOSP\n 3.2 Insights of the Regulatory Exposure Analysis at FONDOSP in Terms of Information Security\n 3.3 Mapping of Mitigation Practices Based on ISO 27002\n 4 Conclusion\n References\nTelecommunications\nAnalysis and Simulation of Downlink Scheduling Algorithms on 5G NSA Networks Under FTP Traffic\n 1 Introduction\n 2 Background\n 2.1 Scheduling Techniques\n 2.2 Data Traffic Model\n 3 Related Work\n 4 Research Method\n 5 Result and Analysis\n 5.1 Throughput Simulation Results\n 5.2 Delay Simulation Results\n 5.3 Spectral Efficiency Simulation Results\n 5.4 Simulation Results Summary vs Requirements\n 6 Conclusions and Future Works\n References\nA Comparative Analysis of External Optical Modulators Operating in O and C Bands\n 1 Introduction\n 2 External Optical Modulators\n 2.1 Electro-Optical Modulator\n 2.2 Electro-Absorption Modulator\n 3 Simulation Scheme\n 3.1 Transmitter\n 3.2 Modulators\n 3.3 Transmission Channel\n 3.4 Receiver\n 4 Results Analysis\n 4.1 Results with the Electro-Optical Modulators\n 4.2 Results with the Electro-Absorption Modulators\n 5 Conclusions\n References\nLogarithmic Antennas for Electromagnetic Energy Harvesting Applications\n 1 Introduction\n 2 Methodology\n 2.1 Logarithmic Spiral Antenna (LSA) Design\n 2.2 Logarithmic Periodic Antenna (LPA) Design\n 2.3 Voltage Multiplier Circuit Design\n 3 Discussion of Results\n 3.1 Electromagnetic Energy Harvesting System Performance Tests\n 3.2 Frequency Tests Using the MiniVNA-Tiny Spectrum Analyzer\n 3.3 Comparison Between Simulated and Measured Value\n 4 Conclusions\n References\nAuthor Index