Constraints are a major issue in radio-based communication in Wireless Sensor Networks, where each sensor node has a limited amount of power. Conventional clustering and optimization methods have been inappropriate for dynamic conditions which lead to timely energy drainage and reduce the network lifetime. In this research, the novel Deep Reinforcement Learning-Enhanced Hybrid African Vulture and Aquila Optimizer has been proposed that optimizes the dynamic clustering and energy-based parameters in real time. The proposed model is designed for optimizing the Wireless Sensor Networks, by including Deep Reinforcement Learning to adjust the dynamic formation of the base of the cluster on real-time data which leads to efficient energy utilization among all the sensor nodes. It combines the best properties of the Aquila and African Vulture Optimizer to optimize the network lifetime and energy consumption. The network lifetime, which is one of the most crucial characteristics, is optimized by using the global search algorithm of African Vulture Optimiser. In contrast, it is optimized by the localized search of Aquila optimizer to reduce energy consumption. The presented novel African Vulture and Aquila model outperforms the existing methods used convention-based optimization methods. It shows a 20% improvement in energy efficiency and faster convergence with better robustness while keeping the network scalability. The proposed approach is perfectly suited for the scalable WSNs which are mainly used in the environment such as smart cities and IoT systems where a timely adaptation process is inevitable.

In the field of video surveillance, effective image enhancement is pivotal for extracting valuable information from challenging visual environments. Enhancing images from video surveillance scenes is challenging due to varying lighting conditions ranging from bright daylight to low-light or nighttime settings. Noise, artifacts and distortions in video frames further degrade quality, while real-time processing requirements add complexity. To overcome these issues, this research focuses on developing a specialized neural network tailored for enhancing images captured in video surveillance scenarios. The primary objective is to significantly boost the visual quality of surveillance video frames. To achieve both accuracy and efficiency, Convolutional Neural Network (CNN) based on ResNet-152, is specifically designed for enhancing images in video surveillance settings. The research aims to enhance adaptability to varying lighting conditions, weather patterns and scene complexities. Uniform frame sampling (UFS) ensures simplicity in implementation and computational efficiency by consistently extracting frames at regular intervals. To further enhance the performance of the ResNet-152 CNN, an Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) is employed. Experimental outcomes reveal that the proposed system outperforms traditional approaches, achieving impressive metrics like accuracy of 98

The Industrial Internet of Things is becoming the novel driving force in the automotive industry, assembly travel more suitable for individuals. Despite this, there are still a number of obstacles to overcome, such as detecting illegal drivers, identifying legitimate drivers, and evaluating driving behavior. The use of deep learning networks has been attempted by many academics to address the issues of detecting illicit drivers and identifying legal drivers, however the gathering and analysis of data on driving behavior are still subject to several restrictions. Furthermore, insufficient focus has been placed on examining a driver’s behavior. To deal with the aforementioned concerns, we carried out a thorough investigation on driving behavior patterns and constructed an Multi-Task Learning (MTL) based network. In the first place, we gather real-world data from an automobile and analyze it for traits related to driving. After that, a novel MTL network is built utilizing a Long Short Term Memory network to detect unlawful drivers, identify legal drivers, and evaluate driving behavior. To strengthen their argument, the authors could conduct experiments or case studies comparing the performance and efficiency of MTL with single-task learning or other methods. By quantifying these factors, they could provide more concrete evidence to support their claim that MTL offers time and cost savings.

Quantum Computing observed a significant rise to public and technologies in past three decades, the reason behind for the development of quantum computing is to solve various problems which are so complex that traditional (classical) computers were not able to solve. New technologies, hardware components and software advancements are being discovered all around the world in order to use this powerful tool. But in addition to the development of technologies and the attempt to scale up the quantum computers, new challenges and problems too came in light which makes it tough for further progress in the quest to unlock the true development of quantum computers. Various methods has been identified for Quantum Information Processing (QIP), but the error rates were more than what we would expect often resulting in inappropriate computations which eventually gives inaccurate conclusions.In this work, we discuss about the prominent hardware and software methods to build the quantum computers with low error rates and better accuracy, we will look onto the topics related to qubits and its principles which are incorporated in Quantum Processing Units (QPUs) which govern the working of quantum computers, the topics of quantum algorithms and its methodology are also been discussed to provide a clear understanding of the manipulation of qubits according to the purpose needed. In addition to that we will talk about the applications like quantum teleportation and cryptography which utilizes the quantum computers, and discuss about the future enhancements which can be done using this technology.

Biomass-integrated gas turbines for electricity generation are competitive with coal and nuclear energy production. Gas turbine-based power generation system looks to be an excellent solution amid the emerging environmental conditions and the increased energy crisis. This paper presents a simplified dynamic analysis model for biomass-based, Woodward governor-controlled, twin-shaft heavy-duty gas turbine (HDGT) plants, alongside an intelligent controller for stability enhancement. Grid-connected HDGTs can become unstable under load disturbances, potentially causing system shutdowns. A simplified model for twin-shaft gas turbines, rated from 18.2 MW to 106.7 MW, has been identified based on control characteristics during startup. The speed controller is dominant, while the acceleration controller is only active at startup, and the temperature controller's effect is minimal during normal operation. This model suits all dynamic studies of twin-shaft gas turbines, regardless of varying power ratings and rotor time constants. For improving the grid stability, a Takagi-Sugeno-Kang (TSK) fuzzy gain scheduling PID controller has been proposed in this work and its behavior is validated with 5001M, 7001Ea, and 9001Ea models. Step response of fuzzy PID controller under load disturbances and set-point variations are compared with fixed gain PID controllers tuned by Ziegler-Nichols (ZN) and performance index-based tuning using the typical gas turbine model. Further the controller behavior is validated with field test-based model parameters as derived from the real-world combustion turbines used in Alaskan Railbelt system. Extensive research simulation and validation results reveal that the fuzzy self-tuning PID controller showed superior adaptability for grid-interactive twin-shaft HDGTs under load variations and set-point variations. Time domain specifications and the performance indices confirms that the proposed fuzzy tuned PID controller ensure reliable and stable operation for the energy management in grid-operative mode. The proposed simplified model and intelligent control logic enable various dynamic studies in grid-connected environments for both simple cycle and combined cycle operations.

SummaryWireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision‐making with respect to their objective of application. However, the implementation of energy‐effective data gathering strategies in large‐scale WSNs is the most challenging due to the limited energy resources. Clustering‐based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)‐based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade‐off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra‐cluster distance, inter‐cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches.

New electric vehicle charging infrastructure is proposed in this chapter to implement the enhanced operation and control of microgrids for commercial practices. The development of intended solutions was initiated with the study of different sources of expansion in the microgrid between 2018 and 2021. The prediction value was computed for 2022 to 2025. The four charging levels (level 1, level 2, level 3, and level 4) are classified from the literature to utilize the existing resources in an optimal way. Changes identified in the charging infrastructure are reflected in the existing process through charging levels based on the number of electric vehicles and environmental conditions. The voltage profile in a day is computed for each charging level to normalize the number of electric vehicles utilizing the same infrastructure to improve the commercial systems. The mentioned parameters are loaded in the new forecasting model with a minimum convolution level. The proposed forecasting model's level of improvement demonstrates that, with the existing charging infrastructure, improved microgrid operation is possible. Location-wise classification is performed for the state of charging for all the days in a year with possible profit improvement. Furthermore, the charging delay in each level of charging was simulated with an incremental cost saving for different electric vehicle counts. Finally, the total electric vehicle counts revealed enhancements in a day after the computation of actual and expected movement. The energy saving improved by approximately 5% to 15% for every 5-kWh charging, which shows the future adaptability of the proposed method.

Abstract When every single cell in the body has the same voltage and capacity, it is said to be in cell balance. Maintaining the identical state-of-charge for each cell in a multi-cell pack by balancing contributes to the pack’s maximum capacity and service life. Two basic techniques of balancing exist: Overcharged cells lose charge due to passive balancing, which dissipates the stored energy as heat. Active balancing distributes energy from abundant cells to least cells to preserve battery pack energy. Battery management systems (BMS) use resistors to achieve passive cell balancing, which balances the states of charge in individual cells. Every cell has a resistor connected in parallel with it. The matching resistor is triggered when a cell is charged too much, releasing extra energy as heat. Until all cells have voltages that are comparable, this process is repeated. Active balancing is more efficient than passive balancing, and less expensive. In BMS, energy is transferred between cells to balance their state of charge, a process known as active cell balancing. It controls the flow of energy by using electronic parts like capacitors and inductors. Active balancing can quickly balance cells or correct large voltage imbalances since it is more sophisticated and effective than passive balancing. A cell’s state of charge (SOC) represents its current capacity as a function of its rated capacity. This technology optimizes battery performance, prolonging overall lifespan and promoting reliable energy management in electric vehicles. By minimizing voltage deviations, LC-based balancing contributes to increased driving range and sustained battery health.

Everyone considers security to be a big concern when they are separated from their family. There is currently no viable solution to the aforementioned problem. An electrical security system that works with an Arduino UNO is described in this article. The Arduino UNO microcontroller board is part of the UBER family. It is an open-source tool that is convenient to use. Applications can be found, screened, stored, and controlled. The Arduino Mega 2560 board is used to regulate entryway access. This job demonstrates a keyless locking and opening system that uses an OTP and a pre-programmed number lock. Unauthorised access can be prevented by sending an OTP to a way of obtaining an OTP password that requires the recipient to speak with the administrator. To access it, employ a 2.8 display, which welcomes user involvement and displays all UI messages.[1] The system uses a fingerprint sensor to confirm a user's verification when they are authorised before sending a password or one-time password (OTP) through the SIM and GSM module, to the registered mobile number of the individual saved in the local SD card database. If the entered password is correct, the door will open automatically; else, On the notification given to the owner that the security was being attempted to be compromised, a message indicating the incorrect password will be TFT displayed. This hardware idea uses less electricity and three levels of security with commonly available components. In an emergency, the ADMIN can also use this system's SMS feature to unlock the door.

Smart Grid Systems, consisting of interconnected energy sources and consumers, often face challenges in managing energy resources. This manuscript presents a novel approach to address these issues by combining two advanced techniques Beluga Whale Optimization (BWO) and Tree Hierarchical Deep Convolutional Neural Network (THDCNN). The goal is to minimize total operating costs under various constraints, providing committed units and economical load dispatch for each operational hour. BWO optimizes cost, CO2 emissions, and unit losses, while THDCNN predicts the optimal solution. The BWO-THDCNN approach is evaluated using statistical methods to compare cost and carbon dioxide emission reductions, unit losses, and load demand against existing methods like Color Harmony Algorithm (CHA), Gannet Optimization Algorithm (GOA), and Heap-Based Optimizer (HBO). Carbon dioxide emission reductions are 4.8 include smart grid data, unit demand, and operational constraints.The hybrid approach integrates BWO and THDCNNmethods to optimize scheduling. Outputs consist of committed units, cost reduction, high power delivery, and Carbon dioxide (CO2) emission reduction. The approach is compared with existing processesto demonstrate its effectiveness in minimizing costs and emissions while ensuring a reliable power supply.

This paper targets Hybrid Energy Storage System (HESS) in EVs which utilizes a supercapacitor in addition to a battery. This system employs a bidirectional DC-to-DC converter to enable the power flow between the battery, supercapacitor, and motor (PMSM). The presence of supercapacitors provides a smoother and even more comfortable driving experience. In addition, Solar PV panels are installed to keep the supercapacitor charged during daylight operations. Supercapacitors are preferred to aid batteries due to their fast charge-discharge cycles, making them suitable for regenerative braking and supplying extra power to the motor during peak demands. HESS implementation results in increased vehicle acceleration performance, reduced battery size, and weight, as well as a reduced overall cost. The HESS system was simulated using MATLAB to understand its operation. Simulations show power flow, voltage and current profiles, and energy efficiency. EV's performance and energy efficiency are improved significantly with HESS.

In this paper, to provide a constant voltage supply to the load using sustainable energy resources, a PV array is integrated along with a lithium-ion battery. The performance of the combined supply of the energy storage system has been analyzed and tested in the MATLAB environment. The solar power as a resource is used to produce power for the consumption of the loads. The behavior of the battery and the PV array are recorded from the output of the simulation. This paper also discusses how the irradiance level of sun produces different power output and how the loads are satisfied for their power requirement. Simulation results are offered to highlight the performance of the load and supply under diverse environments. The proposed system shows us the pathway to produce power from sustainable source and proves us to maintain the battery lifecycle longer than the provided period.

In Wireless Sensor Network (WSN) number of nodes involved varies from thousand to thousand. Data communication among the nodes creates a lot of research issues. Hence routing is the essential mechanism for providing path and ensures efficient and reliable multi-hop communication. Furthermore, routing is to ensure the transfer rate of data in the network and at the same time aggregation ensures the reduction of transaction rate between the nodes. In the proposed system, Multi cluster architecture is developed and analyzed for enhancing the performance in routing and data aggregation. Supporting nodes are introduced for performing energy efficient multi path routing and secure data aggregation (EEMPRSDA). To achieve it in real time hardware an experimental setup is developed using three LPC2148 processors and corresponding Xbee series 1 wireless interface cards. The secured data aggregation algorithm is developed using privacy homomorphism techniques and implemented in the proposed ARM based sensor nodes.

This paper demonstrates Raspberry-Pi microcontroller can be used to replace Programmable Logic Controller (PLC), which is installed in the automation industry for weighing machine control. Weighing machine Controller, is an application for automatic packaging of food products such as seeds, grains, nuts, etc. The weighing machine controller includes a PLC application that calculates the weight and controls the filling of the machine. PLC weighing machine control application usually uses the Allen Bradley 5000 PLC, but this project implements the same functional program in the CODESYS programming environment on Raspberry Pi. In this project Raspberry-pi is programmed with Ladder Logic programming using CODESYS environment. Load Cell Transmitter transmits the data continuously to the Raspberry-pi which enhances and resolves the identified issues and improves the application reliability. The result of the project is a functional and reliable solution that has been developed to be able to use the Raspberry Pi to control the weighing machine device instead of the Allen Bradley PLC. The developed solution allows for a significant increase in profitability, user-friendly, and tool flexibility. It is concluded that Raspberry Pi can replace PLC’s that is used in industry for some industrial automation process.

This study demonstrated that the production data from the weighing machine of the industry need to be monitored by the owner from anywhere in the world through wireless communication. Weighing machine in the industry is used for packing the items in a bag with proper measured quantity. These bags packed in a given time period must be calculated and the data must be sent to remote web server like Thing speak. In the industry, PLC ladder logic is used in the weighing machine for the control of the machine and the data from the machine i.e., the number of bags packed by the machine for a specific time period is recorded and the data is transmitted using 2G network now. Thus, this may delay the transmission of the recorded data to the cloud which reduces the efficiency of the data. This cannot be the assured data used for calculating the bags packed in the given time period. To overcome this problem, 4G network is used here for the faster transmission of the data. As 4G LTE has more advantages and additional features than 2G and 3G network such as faster than 3G, has extremely voice quality, easy to access the internet and better upload and download speed. Hence the ATSAMD21 Microcontroller is integrated with the 4G GSM module for the faster and wireless transmission of the data to the cloud. This improves the efficiency of the data transmission and thus helps the owner to monitor the production data from anywhere and the data is accurate.

The development of the electric vehicles and hybrid vehicles are increasing rapidly due to depletion of fossil fuels, which cause the implementation of new technologies and highly efficient power converters to decrease the dependency on fossil fuels. However, the safety becomes the main issue in the higher voltage level. Therefore, this article is proposed for desirable working, which enhances the usage of low voltage motor drives and improves the safety of the vehicle. The proposed voltage level is 48V, and this voltage is beneficiary for the battery integration and additional source of energies. The suggested bidirectional converter can be operated with input voltage ranging between 12V-48V and wide output voltage ranges with various power level. The presence of additional energy sources in the system aids to enhance the control response necessary during acceleration and braking. Overall, this study proposes a bidirectional converter for connecting vehicles to energy sources and in turn connecting the same converter for connecting energy source to the vehicles.

Efficient energy supply and consumption play a substantial role in the energy grid, especially with renewable energy sources. Renewable power sources are unreliable which made the grid difficult to handle. A smart energy grid architecture provides an effective management structure in energy distribution. The essential grid factors, load balancing, and demand monitoring are enhanced by the emerging technologies incorporated with a distributed energy framework. In this paper, the interconnection of various grid domains and their role in effective operations are discussed. Both the energy and communication sector need to be working in parallel. The demand handling, loss reduction, issue identification in energy transmission, and the data storage, data analyzation, traffic controlling in communication are addressed by smart grids. This paper analyses a technique that is implemented in various grid architectures, the potential of the distributed system, the challenges in the existing grid, and the research to overcome those difficulties.

In Wireless Sensor Networks (WSNs) an effectual scheme for selection of Cluster Head (CH) is vital for attaining improved co-operative data processing. Swarm-intelligent Meta-Heuristic (MH)-based methods for choice of CH are found to be efficient for designing energy-proficient methods which choose ideal CHs from nodes. In this article, Modified Grasshopper and Differential Evolution-based Optimization Algorithm (MGHDEOA) is proposed for achieving network stability in homogenous as well as heterogeneous networks. MGHDEOA incorporates a dynamic mechanism into Differential Evolution (DE) for enhancing global searching ability during optimization. The proposed scheme is designed for enhancing convergence efficacy and preserving diversity of population. It owns the proficiency of improving convergence speed as well as precision. Incorporation of Grasshopper Optimisation Algorithm (GOA) into DE avoids early convergence of the algorithm, as deviation among scaling entities persuades population to be arbitrarily disseminated, focusing on retaining population diversity. Simulation investigation of the proposed scheme is performed well in terms of network lifespan, convergence rate, computation time and communication cost when compared to standard mechanisms taken for examination.

The property of low carbon emissions and high energy efficiency has made the Electric vehicles (EVs) recently popular in the market. However, the limited availability of charging infrastructure remains a significant barrier to their widespread adoption. To address this issue, it is crucial to develop efficient and affordable EV chargers that can accommodate a large range of battery voltages. The idea proposed here implements a converter circuit for an EV battery charger. One promising solution is a multi-voltage charger circuit that employs a non-inverting DC-DC converter known as the SEPIC (Single-Ended Primary Inductor converter) converter. This converter can either step up or step down the input voltage to provide the required output voltage, enabling the charging of batteries with different voltages, including 24V, 36V, and 48V. A key advantage of the multi-voltage charger circuit is its versatility in charging batteries with different voltages. However, the design of a multi-voltage charger circuit must consider several factors, including the voltage range, maximum charging current, and control circuitry required for proper operation. To ensure the safety and reliability of the charging process, appropriate control measures, such as voltage regulation and current limiting, must be implemented. Overall, a multi-voltage charger circuit utilizing a SEPIC converter offers a promising solution to improve the accessibility and convenience of EV charging infrastructure, potentially accelerating the adoption of EVs and reducing carbon emissions.

This manuscript proposes a hybrid method for measuring the battery's dynamic electrical response as it is compressed by an external-force. The proposed hybrid technique is the wrapper of the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network, commonly called as WSO-HDLNN technique. The main aim of the proposed method is to lessen the battery-voltage error. The War Strategy Optimization method detects the parameters of the battery method. The Hierarchical Deep Learning Neural Network is used to predict the dynamic-electrical-response of the battery when it deforms during external-force. By using the proposed method, the estimated voltage and measured voltage error are reduced, and identifies the parameter effectively. Finally, the proposed method is done in the MATLAB platform and it is compared with different existing approaches. The error of the proposed method is 4 mV, the Jellyfish Search Optimizer method error is 6 mV, the Heap-based Optimizer method error is 12 mV, and the Grey Wolf Optimizer method error is 14 mV. The proposed method time is 0.7 s The proposed method shows better results in all methods, like Jellyfish Search Optimizer, Heap-based Optimizer, and Grey Wolf Optimizer, The proposed method provides less computation time and error than the existing one is proved from the simulation outcome.