Articles published in IJEER


A Novel Modified Energy and Throughput Efficient LOYAL UN-Supervised LEACH Method for Wireless Sensor Networks

Data Sensing Devices (DSD’s) have gained lot of traction for various use cases like border control, vehicle tracking. Data Sensing device network (DSDN) is shaped with the aid of combining lot of DSD’s across a random area. Like this multiple groups are formed. In each of the group the specific DSD is elected which is responsible for communication between two independent groups. Each of the group head has multiple attributes with first attribute based on distance, the second attribute based on remaining energy.

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Flyback Micro-Converter Design with an Integrated Octagonal Micro-Transformer for DC-DC Conversion

The work presented in this paper concerns the design of an integrated flyback DC-DC micro-converter operating at high frequencies. The flyback converter consists of only one transformer. The integrated micro-transformer in the flyback micro-converter is composed of two planar stacked coils with spiral octagonal geometry. Basing on Mohan’s method, the geometrical parameters are evaluated.

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Lyapunov based Control Strategy for DFIG based Wind Turbines to Enhance stability and Power

The Doubly Fed Induction Generators (DFIG) based wind turbine is fed with maximum power point tracking is presented in this paper in proposed technique the proportional coefficient tuned adaptively as per wind changes and compare with traditional approaches. This novel method uses three control laws to adjust the proportional gain adaptively to wind speed variations.

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Switched Capacitor-Based Bidirectional Power Converter with Enhanced Voltage Boost and Reduced Switching Strain for Electric Vehicle Applications

This research work presents an improved design of a bidirectional converter for EVs, specifically focusing on its buck and boost operations. The proposed design incorporates a switched capacitor-based double switch converter, which offers enhanced performance compared to conventional converters. The utilization of switched capacitors reduces voltage stress on switches and improves overall efficiency, making it well-suited for electric vehicle applications. Moreover, the inclusion of synchronous rectification enables zero voltage switching, further enhancing the converter's performance.

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Design and Implementation of a Transmitter for IR-UWB Standard

In wireless communication, the Ultrawide Band (UWB) is a technique for achieving a higher data rate, low power consumption, and less complexity. Impulse Radio - Ultra-Wide Band (IR-UWB) uses the baseband signal technique, reducing circuit complexity and power consumption. This work proposes an IR-UWB transmitter block with low power and tunable bandwidth that meets the UWB regulations of LRP (Low-Rate Pulse) UWB. The transmitter proposed uses a time-interleaved architecture using 0.18um CMOS technology using Cadence Virtuoso, which consists of On Off Keying (OOK) Circuit for the four stages of a pulse generator that operates in 6.17GHz-9.10GHz.

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Futuristic Energy Management Solution: Fuzzy logic controller-Enhanced Hybrid Storage for Electric Vehicles with Batteries and Super Capacitors

The core focus of this study was directed towards devising an energy management strategy tailored for hybrid storage systems (HSS) within electric vehicles, with the prime objective of enhancing the longevity of the battery cycle. The batteries employed in electric vehicles (EVs) are prone to expedited deterioration resulting from harsh charging/discharging cycles and the substantial power surges experienced during acceleration and deceleration phases.

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Analysis on Rapid Charging for Electrified Transportation Systems

Background: To improve system resilience when charging electric vehicles, a new control mechanism for converters that convert voltage from sources in micro-grids is presented. Methods: This deals with an evolving continuous current and stable voltage charging method for electric automobiles (EVs) with the objectives of speedy charging, constant voltage stability, deviation from voltage reduction, and cost reduction.

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Lifting Wavelets with OGS for Doppler Profile Estimation

This article discusses the second-generation wavelet transform concept and technique and its application to the noise removal problem of MST radar data. Located near Gadanki in Andhra Pradesh, India, the MST radar is collecting data on climate change. To obtain weather data, the signal collected by the radar needs to be analyzed, which usually requires power spectrum estimation. Most parametric and non-parametric methods cannot predict Doppler at an altitude above 14 KM, which makes to search for introduction of new denoising methods.

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A Symmetric Multi-Level Cascaded H-Bridge Inverter for Renewable Energy Integration

The advanced multi-level cascaded H-Bridge inverter system described in this paper is novel and intended for effective integration of renewable energy sources. Phase-displacement pulse width modulation (PD-PWM) control has been employed in the proposed five-level topology to produce output voltage with better quality. The system incorporates proficient filtering methods with a low total harmonic distortion (THD) desired outcome. With a stable output of 230 V at 50 Hz and a 2.3 kW capacity, the inverter system has been satisfied the exacting IEEE 519 standards for power quality.

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A Novel Passive Islanding Detection Method for Distributed Generation

Off-grid and On-grid are two technologies that allow renewable energy sources to run continuously. The system can be networked in the first scenario, and it can operate independently or as a microgrid in the second. The decentralised generator (DG) can run in island mode even if there isn't an external power supply accessible. This circumstance may prohibit the equipment from correctly joining, endangering the auxiliary system. In order to find island patterns at particular times, this research suggests a passive method.

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An Evaluation of the Signal to Noise Ratio (SNR) of Next Generation Wireless Communication Systems using Large Intelligent Surfaces: Deep Learning Approach

The existing data rate must be greatly increased in order to support the numerous applications of the next generation communication systems. Using Large Intelligent Surfaces (LIS), which are a panel with mounted reflective components, is one way to address this problem. Their primary function is to divert the electromagnetic signal to the intended user. As a result, the received signal's strength and reception quality both improve, improving the Quality of Service (QoS). Machine Learning algorithms have been used to implement LIS in a number of ways, including channel estimation and the calculation of phase shifts (discrete), to mention a few.

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Interconnection Study in Utilizing of Solar Energy for 150 MW Photovoltaic Power Generation through 150 kV Transmission Line

The interconnection of utility-scale photovoltaic (PV) power plants with the electric grid is a crucial factor that requires comprehensive analysis and assessment. The focus of this research article is on a specific photovoltaic (PV) power plant that is planned for construction in the X Power System located in Indonesia which has 150 MW capacity which has intermittent behavior, experiencing fluctuations in power generation based on the availability of sunlight and the cloud movement. The objective of this paper is to explore the feasibility, technical prerequisites, and potential solutions for the successful integration of the PV power plant into the existing power system.

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Analysis Optimization and Comparison to Detect Failures in the Squirrel-Cage Rotor using High-Level Wavelets

The methods and tools used for signal analysis extracted from the induction motors, such as the motor current signature analysis (MCSA) used for data collection on a non-invasive basis, the multi-resolution analysis (MRA) and discrete wavelet transform (DWT), are efficient tools for the signal analysis at different levels or resolutions, these tools have been applied together to improve detection of failures in the rotor of induction motors in condition of no-load. This work focuses on the study of rotor cage end ring, in a condition with lower-load or no-load where uncertainty predominates, this area of study is complicated to analyze correctly with conventional methods, but in these circumstances, the analysis using TDW has better performance.

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Black Widow Optimization for Power System Load Frequency Control: A Comparative Study

This research paper mainly engrossed on developing a suitable novel tuning methodology named Black Widow Optimization (BWO) Algorithm for power system optimization problems. Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR), two of the most important control systems in the power system arena, are employed as test systems to assess the efficiency of the suggested BWO approach. Various analyses, such as transient analysis, are employed to evaluate the efficiency of the suggested BWO approach in LFC and AVR systems. robustness analysis and convergence analysis.

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Design and Implementation of a Two-Frequency Power Amplifier without Interference for Wireless Communication

In this research paper, we will delve into a type of circuit known as a power amplifier. This circuit is designed to operate at two frequencies, allowing it to perform tasks at each frequency. The main goals of the design are to separate signals and ensure proper operation of the circuit in different modes. One notable feature of this power amplifier is its ability to work without any distortion, especially when both frequencies are used simultaneously. Achieving this has been made possible by combining a method that ensures signal termination with a strategy that enables their separation. To evaluate its performance, we conducted computer simulations as tests using both large signals.

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Optimal Location and Size of Solar photovoltaic Generator to Improve the Stability of Iraqi National Super Grid Power System

The Iraqi National Super Grid Power System is facing significant challenges in terms of stability and reliability, leading to power outages and disruptions. One potential solution to this problem is the integration of solar photovoltaic generator (SPVG) into the grid system. This article explores the optimal location and size of solar PV generators in order to improve the stability and reliability of the Iraqi National Super Grid Power System (INSGPS).

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A Highly Compatible Optical/Acoustic Modem based on MIMO-OFDM for Underwater Wireless Communication using FPGA

This work is on Underwater Wireless Communication using optical/acoustic modem based on MIMO–OFDM method. Underwater Communication is a vast field where data analysis is performed on sea exploration, aquatic animals, aquatic species etc. But the current system is based on sound as medium for communication. This system faces many significant problems which plays a key role in affecting the performance of the entire system lost the data and resulting in efficiency of the system only for a few meters radius of transmission and reception. In this paper, the modem designed for both optical (light) and acoustic (ultrasound) signals using MATLAB Simulink and implemented on Xilinx System generator using FPGA.

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Innovative Approach of Spectral Efficiency Optimization over Various Pilot Reuse Factors

The Massive MIMO with TDD is breakthrough technology for spectral efficiency gains. The CSI is essential for spectral efficiency gains and CSI can be obtained by channel estimation methods. The channel estimation methods employ known pilot s sequences to estimate the channel before actual data transmission. However, the channel coherence is time and frequency limited, which reflects the trade-off between the resources available for pilots and those available for data in coherent block for transmission. The pilot sequences reuse in other cells can reduce pilot overhead, called pilot reuse. However potential interference is introduced, by pilot reuse, in the channel estimation phase, called pilot contamination.

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Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods

This paper introduces a pioneering Hybrid Parallel Multi-linear Face Recognition algorithm that capitalizes on multi-linear methodologies, such as Multi-linear Principal Component Analysis (MPCA), Linear Discriminant Analysis (LDA), and Histogram of Oriented Gradients (HOG), to attain exceptional recognition performance. The Hybrid Feature Selection (HFS) algorithm is meticulously crafted to augment the classification performance on the CK+ and FERET datasets by amalgamating the strengths of feature extraction techniques and feature selection methods.

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Performance Analysis of Sensorless Induction Motor Drive using Improved Control Techniques

AC Drives demand robust motor design with rugged construction, low cost, high reliability in service, and simple maintenance. In modern power drives, Sensorless Induction motor drives are more popular than other drives. A speed sensor/encoder-based drive is costlier and requires more space in case more parallel units are coupled together in drive operation. To address these difficulties, speed sensorless drives are introduced without loss of efficiency and reliability. However, Sensorless speed drive requires advanced control techniques in which complex calculations are there due to the nonlinearity of IM.

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An Enhanced Authenticated Key Agreement Scheme for Cloud-Based IoT in Wireless Sensor Networks

Recent advancements in mobile and wireless technology have fundamentally impacted the underpinnings of cloud computing and IoEs. These changes have changed the way data is communicated across numerous channels, allowing for intelligent discovery and operation. The Internet of Things (IoT) is highly reliant on wireless sensor networks (WSNs), which have several applications in industries ranging from smart medicine to military operations to farming. The IoT's substantial reliance on these activities generates a large amount of data. All the above-specified data is transferred to a remote server for storage and processing. As a result, it is critical to enable safe data access in WSNs by authenticating individuals in altered states of awareness.

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Brain Tumor Classification and Identification using PSO and ANFIs

Fast Computer-Aided Diagnostic Systems (CAD) have become instrumental in diagnosing diseases. Brain tumors, in particular, pose a significant health challenge. Traditional tumor detection methods relied on radiologists and biopsy, which are time-consuming and detrimental to patients. Early detection is crucial for effective treatment. This system leverages image processing, SWARM intelligence, and Support Vector Machines (SVMs) to detect and classify brain tumors swiftly and accurately.

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Application of Delta PLC on Battery Management System in AC/DC Microgrid

To keep the energy balance and power quality in check, a proper controller must be made for all the parts of the microgrid to work together. The sizing of the microgrid is done by considering the distributed generators and their connected ACDC loads. The performance of DC will obtain according to the state of charge condition of the battery bank. So it is essential to operate the battery bank by observing the state of the charge condition.

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Performance Analysis of Multi-Hop Hybrid FSO/mm Wave Communication System for Next-Generation Wireless Networks

Next-generation wireless networks are facing increasing demand for high data rates, low latency, and seamless connectivity. To address these challenges, a multi-hop hybrid communication system integrating Free Space Optics (FSO) and millimeter wave (mm Wave) technologies for backhaul communication is proposed. This system combines the advantages of FSO, such as high bandwidth and low latency, with the robustness and reliability of mm Wave technology. The multi-hop architecture enables the formation of a network of interconnected nodes, providing improved coverage and flexibility.

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High-frequency Differential Mode Modeling of Universal Motor's Windings

The universal motor is a rotating electrical machine that can operate on either direct current or single-phase alternating current, similar to a DC motor. It has been widely used in various small and inexpensive drives for a long time, mostly in home appliances and hand tools. The noise generated by a universal motor is believed to be closely associated with the electromagnetic torque fluctuations of the machine, which are caused by variations in the current supplied to the motor. The power electronics utilized for controlling the motor's speed are responsible for these current changes. Accurate high-frequency motor models are crucial for reliable electromagnetic interference simulations in motor drive power electronic systems.

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Optimal Location of Electric Vehicle Charging Station in Reconfigured Radial Distribution Network

Electric vehicles are becoming increasingly popular because they are cleaner-burning and more efficient than combustion-engine automobiles. Due to the diminishing availability of fossil fuels and the carbon emissions produced by cars, electric vehicles (EV) have become a need for mobility in the near future. Electric car charging stations were established as a consequence of the increase in EVs. Electric car charging lowers voltage and increases real power loss in the radial distribution network. In order to mitigate real power loss and provide a stable voltage profile, the charging station has to be placed as efficiently as possible.

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Skin Cancer Detection and Classification using Deep learning methods

Skin cancer is a very dangerous disease that needs to be found early, so that it can be treated effectively. In the past few years, classifiers built on convolutional neural networks (CNNs) have become the best way to find melanoma. According to the review, the CNN-based classifier is as accurate as dermatologist in classifying skin cancer images, allowing for faster and more accurate detection. This article examines the most recent studies on Machine learning and deep learning-based melanoma categorization in depth. We provide a comprehensive description of the machine learning and deep learning classifier, including details on the accuracy of these classifiers.

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Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

Due to its general use in various practical applications, number of innovations in Wireless Sensor Networks (WSN) is receiving a lot of consideration from researchers. It shows significant technological development with excessive capacity since it gives useful information to users in a particular field through real-time monitoring. Due to its characteristics, such as infrastructure-less adoption and resource limitations, wireless sensor networks bring several problems that could impair the system's operation. Cluster based routing in WSN is the major concern in this field that could conflicts with the effectiveness of energy, suitable Cluster Head (CH) selection, protected data transport as well as network lifetime augmentation, demand major consideration, etc.

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A New Dragonfly Optimized Fuzzy Logic-based MPPT Technique for Photovoltaic Systems

Photovoltaic (PV) power systems should be operated at the maximum power point (MPP) for best solar energy utilization, which can be achieved using maximum power point tracking (MPPT) techniques. Perturb & Observe (P&O) and Fuzzy logic MPPT approaches were two of the various strategies that were suggested as effective ways to achieve Maximum Power under Continuous Irradiation. When exposed to changes in environmental conditions, these approaches perform poorly dynamically and exhibit substantial steady-state oscillations around the MPP. To overcome this problem, this paper proposes the Dragonfly optimization-based fuzzy logic MPPT approach for maximum power extraction of photovoltaic (PV) systems.

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A Novel Optimized Neural Network Model for Ink Selection in Printed Electronics

The field of Printed Electronics (PE) is experiencing significant growth in the industrial sector and generating considerable interest across various industries due to its ability to produce intricate components. The functionality of printed electronic products heavily relies on the utilization of conductive ink during the printing process, which plays a vital role in developing flexible electronic circuits and improving the communicative functionalities of objects. Selecting the right ink for printing is crucial to meet consumer requirements. However, the conventional approach to this process has been manual, labor-intensive, and time-consuming, relying on the expertise of designers.

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Optimized Multi Agent System for Stability Enhancement of Inter Connected Power System

Due to the rising use of renewable energy sources and the use of contemporary power electronic equipment, power system stability has become a major challenge in current power systems. Controlling the power system characteristics can increase the stability of the power system. The traditional techniques for improving power system stability, such as the use of FACTS devices, are costly and may not be effective in handling the dynamic changes of the power system. As a result, by optimizing the power system parameters, an optimization-based multi-agent system can improve the stability of the power system. The Grey Wolf Optimizer based Multi Agent System (GWO-MAS) is proposed in this paper to improve power system stability.

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Control of Three-Phase Squirrel Cage Induction Motor by Field Acceleration Method (FAM) for E-Mobility

The rapid electrification of mobility systems has fuelled the demand for advanced control techniques that can enhance the performance and efficiency of electric vehicles (EVs). In this context, this paper introduces the Field Acceleration Method (FAM) as a control strategy for three-phase squirrel cage induction motors, specifically tailored for e-mobility applications. FAM has not been previously simulated or tested practically in the context of electric mobility, making this study a pioneering effort. Induction motors are widely employed in electric vehicles, and various control methods such as the Indirect Field-Oriented Control (IFOC) gained popularity for its effectiveness in achieving precise and efficient motor control.

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MRI Intracranial Neoplasm Localization Using Convolution Neural Network Based Residual Block ResUnet: A Systematic Approach

Analysis of intracranial neoplasm using multimodal MR images requires accurate and automatic segmentation. However, manually classifying tumors with similar structures or appearances in magnetic resonance imaging (MRI) with similar anatomy or appearances is more challenging, requiring experience to detect brain tumors. Precise segmentation of brain tumors gives clinicians with a foundation for surgical planning and treatment. Due to its capacity to segment brain tumor images automatically, Deep Neural Networks (DNN) have been widely used in image segmentation applications.

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A Fuzzy Logic Based Cluster Head Election Technique for Energy Consumption Reduction in Wireless Sensor Networks

Wireless sensor networks deploy sensor nodes to different areas for data collection. The small size of these sensor nodes allows limited energy storage capacity, and most applications of the networks do not support recharging the batteries once their energy is depleted. Research on energy efficiency in wireless sensor networks is thus an active area that seeks to minimize energy consumption so that the sensor nodes can live longer. Clustering, one of the energy consumption optimization techniques, is employed in this research. It splits the network into smaller groups for data collection and forwards the data to the base station via appointed cluster heads.

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Reconfigurable Converter Topologies for EV Fast Charging Stations

Infrastructure for charging electric vehicles (EVs) is highly demanded due to the rising number of EVs on the road. Stations for charging electric vehicles are necessary for the ongoing transportation e-mobility. In particular, fast charging infrastructures increase the computing ability of transmission grids that are already under a lot of pressure. The market's current energizing foundation takes a ton of room and incidentally causes gridlocks, which raises the risk of mishaps and hinders crisis vehicles. The cost of installing this charging infrastructure increases significantly because the current system needs a lot of room.

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GWOPID-MRAS based Speed Estimation and Speed Control of Sensorless Induction Motors

Sensorless AC motor drives have grown in popularity recently in a variety of applications, from industrial to domestic electrical equipment. FOC and DTC are popular control methodologies in contemporary alternating current (AC) structures. They can achieve good performance for AC motor drives by creating a decupled flux and torque control. However, both have limitations and drawbacks, such as the vector control's dependence on machine parameters and DTC's high flux and torque ripples. This paper proposes a new control method, in which the speed of an induction motor is controlled by Grey Wolf Optimizer (GWO) based PID controller.

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Modeling, Design and Control of Speed DC motor using chopper

An electric motor, a power controller, and an energy-transmitting shaft make up an electrical drive. Power electronics converters are utilized as power controllers in contemporary electrically driven systems. DC actuators and AC drives are the two primary categories of electric drives. This study presents design and modeling techniques for very effective individually stimulated DC motor speed control. A DC motor speed controller can be implemented using a chopper circuit as a converter. The controller transmits a signal into the chopper firing circuit, which in turn generates the desired speed of the chopper by varying the voltage supplied to the motor's armature.

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Design of Boost Integrated Luo Converter for Grid Tied EV Based Charging Station

The combination of Renewable Energy Source (RES) and storage element in charging station is a possible solution for meeting the growing energy requirement of electric vehicles (EVs). In a grid-tied RE system, the converters are essential for attaining the process of power conditioning. Conventional DC-DC converters suffer from issues like voltage spikes, electromagnetic interference, and efficiency losses. The necessity for integrated converters arises from the demand for improved power conditioning, increased power density, and enhanced overall system performance.

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Proficient Bayesian Classifier for Predicting Congestion and Active Node Sensing Classification in Wireless Cognitive Radio

This study researches into fixed range designation systems with diverse applications in remote sensing, specifically addressing the emerging issue of range deficiency, particularly concerning access points with reduced range delivery services for remote hubs. An analysis of the existing system reveals limitations in current approaches. To overcome these challenges, the study proposes leveraging remote cognitive radio, a dynamic range access approach that optimally utilizes existing resources. The central focus of cognitive radio is on acquiring sensing data, addressing the deficiencies observed in the existing system.

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Development of Static Model for a Current Based UPFC

The power system loads are dispersed across the network and generators are concentrated in a few key locations. In between generation and loads, there exist transmission systems. FACTS devices have applications to regulate voltage magnitude and angle, and impedance of the system. In FACTS devices, UPFC is one of its kind. Generally, line outages are occurred due to the faults on the transmission lines. One of the sensitive measures to understand the line outages is Line Outage Distribution Factor (LODF).

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Multiple Grid-Connected Microgrids with Distributed Generators Energy Sources Voltage Control in Radial Distribution Network Using ANFIS to Enhance Energy Management

Voltage conditions and power quality for customers and utility equipment are significantly impacted by the addition of microgrid-generating sources within distribution networks. Designing the right control for distributed generators for the various generating units of a Microgrid is important in enabling the synchronization of renewable energy generation sources, energy storage unity, and integration of Microgrids into a radial distribution network. This research provides control mechanisms based on an adaptive technique employing ANFIS, to reduce fluctuation of voltage and current difficulties faced when multiple renewable energy sources and storage systems are incorporated into a distribution network.

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L-M Based ANN for Predicting the Location of DG under Contingency Condition

The continuing monitoring of online voltage stability and the increased loadability of the transmission lines for the existing electrical power system are the two major challenges that today's energy management systems must deal with. As a result, evaluating online voltage stability under various loading situations is extremely challenging and time-consuming. The line voltage stability indices using an Artificial Neural Network (ANN), the system describes online voltage monitoring and warns the operator before voltage dips.

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Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments

This study leverages the Semantic Segmentation of Underwater Imagery (SUIM) dataset, encompassing over 1,500 meticulously annotated images that delineate eight distinct object categories. These categories encompass a diverse array, ranging from vertebrate fish and invertebrate reefs to aquatic vegetation, wreckage, human divers, robots, and the seafloor. The use of this dataset involves a methodical synthesis of data through extensive oceanic expeditions and collaborative experiments, featuring both human participants and robots. The research extends its scope to evaluating cutting-edge semantic segmentation techniques, employing established metrics to gauge their performance comprehensively.

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Development of Wireless Smart Current Sensor for Power Monitoring System

The traditional distribution network must be replaced by a smart grid, durable, and dependable, due to the rising demand for power by the customer. The smart grid will need a smart monitoring system based on the smart current sensor at all buildings to determine the power consumption with its costs. This paper proposed a wireless monitoring system for measuring the three-phase currents in the building by using three HW-666 current sensors with Arduino and ESP32 microcontrollers to transmit the data according to the Internet of Things (IoT) technique to the Telegram app on the mobile phone.

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Vertebra Segmentation Based Vertebral Compression Fracture Determination from Reconstructed Spine X-Ray Images

The vertebral compression fracture represents the vertebral body deformity appeared over lateral spine imageries. In order to evaluate the vertebral compression fracture (VCF), the vertebral compression ratio (VCR) has to be accurately measured. In most of the existing vertebral segmentation approaches, degraded accuracy, increased possibilities of error and time complexity are found to be the major drawbacks. Hence to conquer these issues and to enhance the overall segmentation performance, rapid automated vertebral segmentation approach is proposed for evaluating the VCR.

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Frequency Stability of Multi-source Power System using Whale Optimization Algorithm

The Whale Optimization Algorithm (WOA), an evolutionary computing approach, is presented in this study and is used to auto regulated frequency of many composed power systems including thermoelectric power station, hydroelectric, and gas power plants. The purpose of this process follows the concept of a hunting mechanism of fish through water bubbles. The WOA is first applied to a single region with a multi-source power system for optimal gain adjustment of proportional integral controllers (PID). This approach is then applied to two areas, each having six generating sources with AC and AC-DC links.

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