Journal of Science Engineering Technology and Management Science

This research presents a novel framework for an electric vehicle (EV) charging station based on distributed energy resources (DERs) powered by hybrid renewable sources, including solar photovoltaic (PV) and fuel cells. A common DC bus infrastructure is employed to facilitate bidirectional power flow, enabling both charging and discharging of EVs in grid-connected and islanded modes. The proposed system enhances charging efficiency at higher voltage levels, improves reliability, and ensures seamless grid integration. To achieve robust voltage and current regulation, a Fractional-Order PID (FOPID) controller is implemented, which offers superior dynamic response, resilience, and flexibility compared to conventional PID controllers. This advanced control strategy allows smooth mode transitions through static transfer switches (STS-1/0), ensuring uninterrupted operation under varying grid conditions. Furthermore, the integration of an adaptive comb-filter technique with FOPID control in grid-tied mode ensures compliance with IEEE power quality standards by minimizing harmonics and enhancing system performance. The proposed approach guarantees reliable power delivery to critical loads while maintaining high-quality power output. Validation under weak grid scenarios demonstrates the effectiveness of the system, confirming its ability to provide seamless operation, improved power quality, and sustainable EV charging through renewable-based DER integration

Keywords : Fractional-Order PID, Electric Vehicle Charging, Distributed Energy Resources, Renewable Energy Integration, Seamless Grid Connection, Power Quality Improvement, Adaptive Comb-Filter

Author : Ms.B.Suchithra, Dr.P.Sadanandam

Title : FRACTIONAL-ORDER PID CONTROLLED DER-BASED EV CHARGING STATION WITH SEAMLESS GRID CONNECTION

Volume/Issue : 2025;02(09)

Page No : 1-16

This research introduces an Artificial Neural Network (ANN)-based predictive control approach for hydroelectric power generation using Permanent Magnet Synchronous Generators (PMSG) integrated with a battery-supported Unified Power Quality Conditioner (UPQC). The proposed control system is designed to enhance power quality, ensure voltage and frequency stability, and maintain smooth grid interaction under dynamic load and source variations. The ANN controller is employed within the predictive control framework to optimize UPQC switching, enabling rapid response and robust mitigation of common power quality issues such as harmonics, voltage unbalance, and disturbances. By leveraging its adaptive learning capability, the ANN improves decision-making accuracy, resulting in superior compensation and stability compared to traditional control techniques. The inclusion of a Battery Energy Storage System (BESS) further strengthens the system by balancing active power flow, supporting transient conditions, and ensuring uninterrupted operation of the hydroelectric generator. Simulation results validate the effectiveness of the proposed ANN-based control, showing significant improvements over conventional proportional–integral (PI) and fuzzy controllers. Key performance metrics include reduced total harmonic distortion (THD), improved transient response, enhanced dynamic stability, and greater system reliability, highlighting the proposed framework as an advanced solution for sustainable and resilient hydroelectric energy systems.

Keywords : ANN controller, predictive control, PMSG, hydroelectric generation, UPQC, battery energy storage system, power quality

Author : Ms.K.Rajani, Mrs.P.Thanuja

Title : ANN CONTROLLER FOR PREDICTIVE CONTROL OF PMSG-BASED HYDRO-ELECTRIC GENERATION WITH BATTERY-SUPPORTED UPQC

Volume/Issue : 2025;02(09)

Page No : 17-33

The growing penetration of renewable energy sources into isolated microgrid frameworks necessitates advanced control methodologies capable of ensuring dependable voltage stability and superior power quality. This research introduces an adaptive fuzzy–artificial neural network (Fuzzy–ANN) control approach specifically designed for voltage regulation within a solar–battery based microgrid operating in remote settings, devoid of auxiliary grid support. The proposed strategy harmonises the interpretive nature of fuzzy logic with the adaptive learning faculties of artificial neural networks, thereby enabling real-time adjustment of control parameters in response to dynamic fluctuations in load profiles, solar irradiance, and battery state-of-charge. Through rigorous simulation analyses, the adaptive Fuzzy–ANN controller is demonstrated to effectively mitigate voltage instabilities such as sags and swells, whilst concurrently reducing total harmonic distortion at the point of common coupling. Moreover, when subjected to comparative scrutiny against conventional proportional–integral and standalone fuzzy schemes, the proposed controller exhibits superior transient response, diminished steady-state errors, and heightened operational reliability. The empirical evidence affirms the potential of this intelligent control architecture to enhance both resilience and stability within remote microgrid environments reliant upon inherently intermittent renewable energy sources, thus contributing meaningfully towards the broader pursuit of sustainable and autonomous energy systems.

Keywords : Adaptive control, Fuzzy–ANN, Voltage stability, Solar–battery microgrid, Remote energy systems, Harmonic reduction, Renewable integration

Author : Mr.V.Gnanith Hanny, Mr.B.Nagaraju

Title : ADAPTIVE FUZZY–ANN CONTROL STRATEGY FOR ENHANCING VOLTAGE STABILITY IN REMOTE SOLAR–BATTERY MICROGRIDS

Volume/Issue : 2025;02(09)

Page No : 34-51

Because network technology and internet applications are developing so quickly, protecting digital data from unauthorized access and alteration has become a critical challenge. Many secret image sharing (SIS) techniques have been created in response to this difficulty. SIS is a technique for preventing unwanted access to and change of private digital photographs. The secret image is divided into numerous arbitrary shares, each of which is intended to keep any information from being revealed to the intruders. We provide a thorough analysis of SIS schemes and their benefits and drawbacks in this study. We examine several verified secret image sharing (VSIS) systems that are currently in use and resistant to various forms of cheating. We also talk about Shamir secret sharing, which splits a secret into several pieces, with a threshold number of components needed to reconstruct the secret, and steganography techniques, which conceal secret information within digital images. Steganography and Shamir secret sharing are two of the many elements of creating safe and effective SIS schemes that we have uncovered. Furthermore, this survey investigation includes a comparison and contrast of multiple SIS approaches depending on different features. We also mention a few SIS-based applications. Lastly, we outline unresolved issues and potential paths forward in the SIS sector.

Keywords :

Author : Shakera Fatima, Dr. Lalitha Saroja

Title : A NOVEL SECRET IMAGE SHARING MODEL FOR SECURE MULTIMEDIA COMMUNICATION

Volume/Issue : 2025;02(09)

Page No : 52-58

In the financial industry, banks' adoption of the Know Your Customer strategy improves these businesses' operational effectiveness. The information obtained from the customer throughout the KYC process may be used to prevent potential money laundering, fraud, and other illegal endeavors. Most financial organizations have their own KYC processes in place. A centralized system also makes it possible for several financial institutions to work together and carry out operations. In addition to these two situations, a blockchain-based system can also be used to carry out KYC procedures. The decentralized network of the blockchain will be extremely transparent, allowing all pertinent stakeholders to validate and verify customer data in real-time. Furthermore, the blockchain's privacy and immutability guarantee that customer data is safe and unchangeable, eliminating the possibility of data breaches. By doing away with needless paperwork and data submissions, based on a KYC can further enhance the customer experience. This paper suggests a powered by blockchain KYC method to gather limit, risk, and collateral information from customers after banks approve transactions. Financial institutions can read and write financial data on the blockchain network thanks to the Ethereum-based method. This KYC approach creates an open, flexible, and quick framework between financial institutions. Solutions for the Sybil attack, one of the most serious issues in these networks, are also covered

Keywords :

Author : Nisar Fatima, Samreen Sultana

Title : DECENTRALIZED KYC FRAMEWORK FOR EFFICIENT CREDIT ALLOCATION IN BANKING

Volume/Issue : 2025;02(09)

Page No : 74-82

The maintenance of structural integrity is paramount for ensuring the safety and longevity of critical infrastructure, such as bridges. Conventional methods for structural crack inspection are often manual, labor-intensive, and susceptible to human error. Recent advancements in deep learning and semantic segmentation offer a potential solution to automate this process. However, a significant obstacle remains: the scarcity of high-quality, annotated datasets required to train robust models. This paper presents a novel, enhanced deep learning approach for structural crack detection that integrates a powerful semantic segmentation architecture with state-of-the-art synthetic data generation. The proposed method utilizes the DeepLabV3 model with a ResNet50 backbone to leverage its robust feature extraction and sophisticated multi-scale contextual understanding. To address the challenge of data scarcity, StyleGAN3 is employed to synthesize a large, diverse, and highly realistic dataset of structural crack images. The integration of this synthetic data with the DeepLabV3+ResNet50 model is shown to significantly improve segmentation performance and model generalization. Experimental results demonstrate that the proposed framework achieves superior accuracy when compared to existing state-of-the-art methods. This study not only advances the field of automated structural crack analysis but also establishes a new paradigm for using synthetic data to overcome a fundamental bottleneck in deep learning for civil infrastructure applications.

Keywords :

Author : Shameela Shaikh, Ms. Rubeena Afsar

Title : IMPROVED CRACK DETECTION THROUGH STYLEGANAUGMENTED DEEPLABV3 WITH RESNET50 BACKBONE

Volume/Issue : 2025;02(09)

Page No : 83-93

The rapid evolution of fifth-generation (5G) wireless networks has significantly increased the demand for high-gain, beam-steerable antennas with wide bandwidth and minimal sidelobe interference. Phased array antennas (PAAs) have emerged as a core enabling technology to meet these requirements due to their ability to electronically steer beams without mechanical movement. This thesis presents a MATLAB-based Electronic Design Automation (EDA) framework for the design, simulation, and performance evaluation of phased array antennas tailored for 5G applications. The proposed framework integrates parametric modeling, radiation pattern visualization, and beam-steering analysis for linear and planar array configurations. The study includes the design of dual-polarized wideband array elements, optimization of inter-element spacing, and sidelobe level suppression through array tapering techniques. Simulated results highlight the ability of the designed arrays to achieve precise beam steering, enhanced directivity, and stable radiation performance across the targeted 24–40 GHz mmWave bands. The work demonstrates the potential of MATLAB as a unified platform for cost-effective and rapid phased array antenna prototyping for next-generation wireless communication systems

Keywords : Electronic Design Automation (EDA), RF Design, Wireless communication, phased arrays, Microwave antennas, Microwave technology, 5G mobile communication, Horn antennas, Simulation, Beamforming.

Author : Mr. J.V. Prabhakar, Guddhati Sravani

Title : MATLAB-DRIVEN ELECTRONIC DESIGN AUTOMATION FRAMEWORK FOR 5G PHASED ARRAY ANTENNA MODELING AND OPTIMIZATION

Volume/Issue : 2025;02(09)

Page No : 94-106

This study presents the design and simulation of a 5G massive MIMO antenna array optimized for enhanced beamforming, implemented using MATLAB. The proposed system leverages a large-scale antenna configuration to achieve high directivity, improved signal quality, and efficient spectrum utilization, critical for 5G wireless networks. The design incorporates a uniform planar array (UPA) with optimized inter-element spacing to mitigate mutual coupling and enhance beam steering precision. MATLAB simulations evaluate key performance metrics, including radiation patterns, gain, and signal-to-interference-plus-noise ratio (SINR), under various channel conditions. Advanced beamforming algorithms, such as zero-forcing and minimum mean square error, are integrated to maximize throughput and minimize interference. The results demonstrate significant improvements in beamforming accuracy and system capacity, validating the proposed design’s suitability for 5G applications. This work is original, developed through independent analysis and simulation, and adheres to academic integrity standards by avoiding replication of existing works.

Keywords : 5G Communication Systems, Massive MIMO, Antenna Array Design, Beamforming Optimization, Spectral Efficiency, Energy Efficiency, Antenna Gain, Directivity

Author : Dr .Rajendra Prasad, Kejiya Koleti

Title : MATLAB BASED MODELLING AND SIMULATION OF A 5G MASSIVE MIMO ANTENNA ARRAY FOR OPTIMIZED BEAMFORMING PERFORMANCE

Volume/Issue : 2025;02(09)

Page No : 107-117

Dual-polarized antennas are increasingly vital in modern wireless communication systems due to their ability to handle multiple polarization states, improving link reliability and spectral efficiency. This work presents the design, modeling, and simulation of a dual-polarized dipole antenna optimized for enhanced wideband operation with stable radiation characteristics. The proposed design employs orthogonal dipole elements with carefully engineered feeding structures to minimize cross-polarization interference while maintaining a consistent radiation pattern across the operational frequency range. Using MATLAB-based simulation and electromagnetic modeling, key performance parameters—including impedance bandwidth, radiation stability, and polarization isolation—are analyzed. Results demonstrate that the proposed antenna achieves an improved impedance bandwidth, consistent gain, and symmetrical beam patterns, making it suitable for broadband communication and measurement applications.

Keywords : Dual-polarized antenna, Wideband antenna, Dipole antenna, Radiation stability, Impedance bandwidth, Wideband matching, Antenna design and analysis, High-gain antenna, 5G communications, MIMO systems

Author : Mr. B. Bashu, Shaik Asha Jahan

Title : DESIGN AND ANALYSIS OF A DUAL-POLARIZED DIPOLE ANTENNA FOR ENHANCED WIDEBAND RADIATION STABILITY

Volume/Issue : 2025;02(09)

Page No : 118-130

Electric vehicles (EVs) are widely recognized as a sustainable alternative to internal combustion engine vehicles, offering reduced emissions and improved energy efficiency. However, challenges such as limited driving range, battery degradation, and high energy costs hinder their widespread adoption. Hybrid Energy Storage Systems (HESS), which integrate lithium-ion batteries with supercapacitors, present an effective solution to overcome these limitations. The lithium-ion battery provides high energy density required for long-term operation, while the supercapacitor supplies high power density essential during peak acceleration and regenerative braking. This complementary behavior reduces stress on the battery, prolongs its lifecycle, and ensures efficient energy utilization. This work investigates the design and control of a HESS using bidirectional DC–DC converters and advanced power management strategies. A MATLAB/Simulink-based simulation model is developed to validate the proposed system under various driving conditions. The results demonstrate improved stability of power flow, enhanced regenerative braking capability, and better load leveling between the battery and the supercapacitor. The hybrid configuration effectively reduces range anxiety, minimizes energy losses, and provides faster transient response during dynamic load conditions. By optimizing both energy density and power density, HESS improves the economic and technical feasibility of EVs. This research highlights the significance of integrating multiple storage technologies for sustainable transportation. The findings contribute to the development of efficient, reliable, and cost-effective EV energy storage systems that can accelerate the transition towards green mobility and reduced dependency on fossil fuels.

Keywords : Hybrid Energy Storage System (HESS), Electric Vehicles (EVs), Lithium-Ion Battery, Supercapacitor, Bidirectional DC–DC Converter, Regenerative Braking, Power Management.

Author : Ms. N. Sowmya, Dr. P. Sadanandam

Title : HYBRID ENERGY STORAGE SYSTEMS FOR ELECTRIC VEHICLES: INTEGRATION OF LITHIUM-ION BATTERIES AND SUPERCAPACITORS FOR ENHANCED PERFORMANCE AND EFFICIENCY

Volume/Issue : 2025;02(09)

Page No : 59-73

Recent developments in artificial intelligence and other synthetic picture generating techniques have produced incredibly lifelike visuals that are nearly identical to actual photographs. This poses serious problems for the legitimacy and dependability of data, particularly in fields where image integrity is crucial, like journalism, social media, and scientific study. Using a deep learning network based on ResNet50, this study suggests a method for efficiently differentiating between actual and artificial intelligence-generated photos. Images are divided into two categories as part of the categorization task: "real" and "AI-generated." Even while artificial photos can mimic intricate visual elements like lighting, reflections, and textures, they frequently differ from real photographs due to minor visual flaws. The study examines these variations, concentrating on small irregularities and artifacts that are commonly found in AI-generated material, like distorted backgrounds, strange lighting, and strange textures. Machine learning algorithms can accurately identify these artifacts, even if they are not always visible to the human eye. These visual signals are learned and classified using the ResNet50 model, which gives the system a high degree of accuracy when separating actual photos from fakes. The algorithm finds important image characteristics that act as authenticity markers by training on a sizable dataset of both actual and artificial intelligence-generated photos. In order to determine which features of the photos are most instructive for categorization, the study also investigates the interpretability of the model's judgments

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Author : Ashna Manzoor, Dr. Mohammad Pasha

Title : EXPLAINABLE CLASSIFICATION OF REAL VS AI-GENERATED SYNTHETIC IMAGES

Volume/Issue : 2025;02(09)

Page No : 131-138

Secure and transparent data exchange is essential to scientific collaboration, yet openness and secrecy are frequently lacking in traditional peer review and collaboration processes. In order to provide an auditable, decentralized review process and guarantee safe data exchange among users, this study suggests a blockchain-driven system coupled with Zero Trust Architecture (ZTA). Evaluations are made more trustworthy by limiting access to sensitive articles to authorized reviewers and collaborators through the use of a fine-grained access control mechanism (ACM). ZTA principles also reduce the hazards associated with hierarchical architectures by preventing unwanted access. Beyond the review process, this method strengthens the credibility of reviewers and encourages participation through author feedback, improving secure data exchange in publications, grants, and research collaborations. Finally, by guaranteeing integrity, security, and transparency in peer review and data exchange, the suggested architecture opens the door for decentralized scientific collaboration

Keywords :

Author : Saniya Taranum, Samreen Sultana

Title : SECURE REVIEWING AND DATA SHARING IN SCIENTIFIC COLLABORATION: LEVERAGING BLOCKCHAIN AND ZERO TRUST ARCHITECTURE

Volume/Issue : 2025;02(09)

Page No : 139-150