International Journal of Modern Research in Science & Engineering
AI-Enabled Self-Healing Cyber-Physical Systems for Industrial Automation
Cyber-Physical Systems (CPSs) are transforming Industry 4.0 by integrating computation, networking, and physical processes to enable intelligent industrial automation. However, increasing system complexity introduces challenges related to reliability, fault tolerance, cybersecurity, and maintenance. This study proposes an AI-enabled self-healing CPS framework that supports autonomous fault detection, diagnosis, prediction, and recovery. The framework combines machine learning, deep learning, digital twin technology, and reinforcement learning to continuously monitor data from industrial sensors, PLCs, robotic systems, and network devices. A closed-loop architecture comprising monitoring, analysis, decision, action, and learning enables real-time anomaly detection and automated corrective actions such as parameter optimization, workload redistribution, and system reconfiguration without interrupting operations. By continuously learning from operational data, the framework enhances predictive maintenance, improves system resilience against hardware, software, and communication failures, and increases operational efficiency. The proposed approach provides a scalable foundation for next-generation smart manufacturing systems with enhanced autonomy, reliability, adaptability, and industrial intelligence.
Graph Neural Network-Based Fault Diagnosis in Smart Power Distribution Systems
Smart power distribution systems integrate advanced sensors, intelligent electronic devices (IEDs), IoT technologies, distributed energy resources, and communication networks to enhance efficiency, reliability, and resilience. However, their increasing complexity creates significant challenges in fault diagnosis due to dynamic network topologies, hidden fault dependencies, and high-dimensional sensor data. Traditional rule-based and signal-processing techniques often struggle to adapt to these complex environments. Graph Neural Networks (GNNs) have emerged as an effective solution by modeling the electrical distribution network as a graph, where nodes represent electrical assets and edges capture their connectivity. GNNs learn both structural and operational information from measurements such as voltage, current, frequency, power flow, and phasor data, enabling accurate fault detection, localization, and classification while reducing feature engineering requirements. This paper reviews recent GNN-based fault diagnosis techniques, discusses their mathematical foundations, identifies current research gaps, and highlights future directions. GNN-driven intelligent diagnostics can significantly improve fault detection accuracy, reduce diagnostic latency, and enhance the reliability and autonomous operation of smart grids.
Generative AI-Assisted Engineering Design Optimization for Sustainable Manufacturing
Sustainable manufacturing increasingly relies on Generative AI to optimize engineering design while reducing environmental impact, resource consumption, and production costs. By integrating deep learning, large language models, diffusion models, reinforcement learning, and digital engineering, Generative AI enables autonomous design generation, multi-objective optimization, and lifecycle sustainability assessment. The proposed framework enhances material efficiency, energy savings, carbon reduction, and manufacturing flexibility while accelerating product development. Despite challenges such as explainability, computational complexity, and design validation, Generative AI offers significant potential to transform sustainable manufacturing through intelligent, environmentally responsible, and data-driven engineering design.
Explainable AI-Based Predictive Maintenance Framework for Industrial Equipment Reliability
Industry 4.0 has transformed manufacturing through the integration of Industrial IoT (IIoT), cyber-physical systems, cloud computing, and artificial intelligence, making predictive maintenance (PdM) a key strategy for improving equipment reliability. Unlike traditional maintenance, AI-driven PdM analyzes real-time sensor data to predict equipment failures before they occur. However, many AI models operate as black boxes, limiting trust and interpretability. The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning (Random Forest, Gradient Boosting, LSTM, and Transformers), and explainability techniques such as SHAP, LIME, and rule extraction. These methods provide transparent fault predictions and maintenance recommendations by highlighting the factors influencing equipment degradation. Continuous learning further enables adaptive model updates as new operational data become available. Overall, the framework improves prediction accuracy, reduces downtime and false alarms, enhances maintenance scheduling, and supports trustworthy, intelligent asset management for next-generation smart factories.
Intelligent Multi-Sensor Data Fusion Framework for Autonomous Engineering Systems
Autonomous engineering systems require accurate perception, intelligent decision-making, and adaptive control to operate safely in dynamic environments. This paper proposes an AI-driven Intelligent Multi-Sensor Data Fusion Framework that integrates heterogeneous sensors with deep learning, probabilistic fusion, and reinforcement learning for enhanced environmental perception, autonomous reasoning, and real-time control. The framework consists of four layers: multi-sensor acquisition, intelligent preprocessing, AI-based fusion and reasoning, and autonomous execution. By employing adaptive confidence weighting and context-aware learning, it improves object detection, localization, fault diagnosis, decision reliability, and system robustness while addressing challenges such as sensor uncertainty, synchronization, failures, and cybersecurity. The scalable architecture is applicable to autonomous vehicles, robotics, smart manufacturing, infrastructure monitoring, and Industry 5.0 cyber-physical systems, with future research focusing on explainable AI, federated learning, digital twins, edge intelligence, and trustworthy autonomous decision-making.
Intelligent Human-Centric Cyber-Physical Systems for Industry 5.0 Smart Manufacturing
The transition from Industry 4.0 to Industry 5.0 emphasizes human-centric, sustainable, and intelligent manufacturing by integrating human expertise with advanced technologies such as Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cyber-Physical Systems (CPS), Digital Twins (DT), Edge Computing, Cloud Computing, Collaborative Robots (Cobots), and Explainable AI (XAI). This paper proposes an Intelligent Human-Centric Cyber-Physical System (HC-CPS) framework comprising six interconnected layers for real-time monitoring, predictive maintenance, adaptive production scheduling, quality optimization, and human-centered decision support. A multi-objective optimization model and AI-driven closed-loop decision-making algorithm enhance production efficiency, equipment reliability, energy utilization, product quality, and human–machine collaboration while reducing downtime and operational costs. Human operators remain actively involved through collaborative interfaces that validate or modify AI recommendations. Comparative evaluation demonstrates significant improvements over conventional Industry 4.0 systems in Overall Equipment Effectiveness (OEE), predictive maintenance, operational safety, and manufacturing flexibility, providing a scalable, resilient, and sustainable foundation for future Industry 5.0 smart factories.
Human-Centric Cyber-Physical Systems (HC-CPS)
Explainable Artificial Intelligence (XAI)
Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations
The rapid advancement of Industry 4.0 has accelerated the development of intelligent and autonomous smart factories powered by Industrial Internet of Things (IIoT) devices, cyber-physical systems (CPS), and advanced manufacturing technologies. Although cloud computing offers significant computational and storage capabilities, it suffers from latency, bandwidth limitations, privacy concerns, and delayed decision-making in time-critical industrial environments. This paper proposes an Integrated Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations, combining edge computing, Artificial Intelligence (AI), digital twins, and predictive analytics to enable real-time local data processing with seamless cloud integration. The framework employs machine learning for anomaly detection, deep learning for automated quality inspection, reinforcement learning for adaptive production scheduling, and predictive models for equipment health monitoring. Secure communication protocols enhance data protection and system reliability. Experimental results demonstrate improved latency, prediction accuracy, manufacturing efficiency, energy utilization, fault detection, and operational resilience compared with conventional cloud-based approaches. The proposed architecture provides a scalable and sustainable solution for next-generation autonomous smart factories, improving equipment reliability, reducing operational costs, and increasing manufacturing productivity.
Multi-Agent Autonomous Control Framework for Intelligent Transportation Infrastructure
Rapid urbanization and increasing traffic demand require intelligent transportation systems that can adapt in real time. Traditional centralized traffic management is limited in handling dynamic traffic conditions, leading to congestion, delays, higher fuel consumption, and reduced road safety. This study proposes a Multi-Agent Autonomous Control Framework that integrates Multi-Agent Systems (MAS), Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), Internet of Things (IoT), Vehicle-to-Everything (V2X) communication, and edge-cloud computing. The framework enables traffic signals, vehicles, roadside units, and other transportation entities to operate as autonomous, cooperative agents that exchange real-time information and make distributed decisions. By analyzing traffic, environmental, and infrastructure data, the proposed architecture dynamically optimizes signal control, routing, and emergency response. Simulation results demonstrate improvements in traffic flow, congestion reduction, travel time, fuel efficiency, road safety, scalability, and system resilience. The framework provides a scalable and intelligent foundation for future smart cities, connected autonomous transportation, and sustainable urban mobility.
AI-Driven Digital Twin Framework for Smart Industrial Process Optimization
Industry 4.0 has accelerated intelligent manufacturing through AI, IIoT, cloud-edge computing, and Digital Twin technologies. This study proposes an AI-driven Digital Twin framework that integrates real-time sensing, machine learning, deep learning, and predictive analytics for intelligent process monitoring, predictive maintenance, anomaly detection, energy optimization, and autonomous decision-making. The framework enables continuous synchronization between physical assets and their digital counterparts, supporting closed-loop optimization with low-latency edge computing and cloud-based analytics. Reinforcement learning further improves production efficiency, equipment reliability, and resource utilization while reducing downtime and operational costs. Applicable across multiple industrial sectors, the framework also addresses interoperability, cybersecurity, and data governance challenges, providing a scalable foundation for sustainable and human-centric Industry 5.0 manufacturing systems.
Quantum-Inspired Optimization Models for Renewable Energy Scheduling and Grid Stability
The growing integration of renewable energy sources creates challenges in energy scheduling and grid stability. This paper presents a quantum-inspired optimization framework for efficient renewable energy scheduling under uncertainty. The proposed model optimizes energy dispatch by considering renewable forecasting, battery storage, load variation, and grid constraints while minimizing operating cost, renewable curtailment, voltage deviation, and frequency fluctuations. The framework combines quantum-inspired solution encoding, adaptive evolutionary search, and stability-aware optimization to improve scheduling performance. Simulation results demonstrate superior renewable utilization, reduced scheduling errors, enhanced grid stability, and higher computational efficiency compared with conventional optimization methods such as GA and PSO. The proposed approach provides a scalable foundation for future intelligent and autonomous renewable energy management systems.
Federated Learning Framework for Privacy-Preserving Smart Infrastructure Monitoring
Smart cities, intelligent transportation systems, and industrial infrastructures increasingly rely on IoT, edge computing, and AI to enable real-time monitoring and predictive maintenance. However, centralized machine learning raises concerns regarding data privacy, communication overhead, security, and data ownership. This paper proposes a Federated Learning Framework for Privacy-Preserving Smart Infrastructure Monitoring (FL-PSIM), which enables decentralized model training without transferring raw infrastructure data. Edge devices collaboratively share encrypted model updates using secure aggregation, differential privacy, and adaptive encryption techniques to preserve confidentiality. The framework also incorporates edge-cloud collaboration to balance computational efficiency, model accuracy, and network resource utilization. Designed to support heterogeneous sensor environments across transportation, energy, industrial, and urban systems, FL-PSIM optimizes global learning while maintaining local data privacy. Experimental results demonstrate improved monitoring accuracy, anomaly detection, communication efficiency, scalability, and resilience against cyber threats compared with centralized AI approaches. The proposed framework provides a secure, privacy-preserving, and scalable foundation for next-generation smart infrastructure, supporting sustainable digital transformation, smart cities, and Industry 5.0 applications.
Digital Twin-Assisted Optimization of Electric Vehicle Charging Infrastructure
The rapid adoption of electric vehicles (EVs) has increased the need for intelligent charging infrastructure capable of addressing challenges such as charging congestion, uneven energy distribution, grid instability, and long waiting times. Conventional charging management approaches based on static scheduling are inadequate for dynamic charging environments. This paper proposes a Digital Twin-Assisted Optimization Framework for Electric Vehicle Charging Infrastructure (DTO-EVCI) that integrates IoT, cloud computing, artificial intelligence (AI), machine learning, and optimization techniques to enable real-time monitoring, predictive analytics, and intelligent charging management. The framework synchronizes physical charging stations with a virtual digital twin, enabling accurate simulation, charging demand prediction, occupancy forecasting, optimized scheduling, predictive maintenance, and adaptive energy management. By improving charging efficiency, resource utilization, grid reliability, and renewable energy integration, the proposed framework reduces operational costs, minimizes charging delays, and supports sustainable large-scale EV deployment while contributing to smart city and carbon-neutral transportation initiatives.
Blockchain-Enabled Secure Industrial IoT Architecture for Smart Engineering Applications
Industry 4.0 has accelerated the adoption of the Industrial Internet of Things (IIoT), enabling intelligent communication among industrial devices, edge systems, and cloud platforms for smart manufacturing. However, conventional centralized security approaches are increasingly vulnerable to cyber threats, data tampering, and single-point failures. This paper proposes a secure blockchain-enabled IIoT architecture that integrates industrial sensing, edge computing, distributed ledger technology, cloud analytics, and intelligent decision-making. The framework employs device authentication, encrypted communication, decentralized consensus, smart contracts, and machine learning-based anomaly detection to enhance data integrity, secure information sharing, and cyber resilience. Experimental evaluation demonstrates improvements in communication security, authentication accuracy, transparency, scalability, latency, and throughput, making the proposed architecture a robust and scalable solution for secure next-generation smart engineering and industrial automation.
Hybrid Digital Twin and IoT Framework for Smart Building Energy Optimization
Rapid urbanization, increasing energy demand, and stringent environmental regulations have accelerated the adoption of smart building management systems. However, conventional Building Energy Management Systems (BEMS) rely on static control strategies and limited predictive capabilities, resulting in inefficient energy utilization. This paper proposes a Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization. Environmental and operational data from sensors, including temperature, humidity, occupancy, lighting, CO₂ concentration, and energy meters, are processed at the edge and synchronized with a cloud-based Digital Twin for real-time monitoring and predictive analytics. The framework forecasts energy demand, occupant behavior, and HVAC performance while optimizing building operations to reduce energy consumption and operational costs without compromising occupant comfort. Continuous interaction between the physical building and its virtual counterpart enables predictive maintenance, adaptive control, intelligent fault diagnosis, and renewable energy integration, delivering scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making.
Building Energy Management System (BEMS)
Knowledge Graph-Based Engineering Asset Management for Predictive Decision Intelligence
Engineering Asset Management (EAM) is evolving through Industry 4.0 technologies such as IoT, AI, Digital Twins, and cloud computing, which generate vast amounts of asset-related data. However, conventional asset management systems often fail to integrate heterogeneous data sources, resulting in fragmented information, reactive maintenance, and increased operational costs. This study proposes a Knowledge Graph-Based Engineering Asset Management (KG-EAM) framework to enhance predictive decision intelligence for smart industrial assets. The framework integrates data from Industrial IoT devices, SCADA systems, Enterprise Asset Management (EAM) platforms, Computerized Maintenance Management Systems (CMMS), and engineering documentation into a unified semantic knowledge ecosystem. By combining ontology modeling, graph databases, graph embeddings, machine learning, and explainable AI (XAI), the framework enables predictive maintenance, root cause analysis, asset health prediction, and risk-aware decision-making. The proposed approach improves asset reliability, maintenance planning, operational efficiency, knowledge sharing, and lifecycle management while reducing downtime, maintenance costs, and unexpected equipment failures across diverse industrial sectors.
Autonomous Robotics with Vision-Language AI for Industrial Inspection and Maintenance
Industrial environments are rapidly evolving toward Industry 4.0, where autonomous robots and AI enable intelligent inspection and maintenance with minimal human intervention. Traditional inspection methods rely on manual operations, fixed robotic programming, and isolated computer vision techniques that struggle in dynamic industrial environments. This work proposes FACTS, an autonomous industrial inspection and maintenance framework integrating robotics with vision-language AI. The framework combines multimodal sensing, advanced computer vision models (CNNs, Vision Transformers, and vision-language foundation models), and natural language understanding to interpret equipment conditions, detect defects, reason about maintenance requirements, and execute autonomous corrective actions. Vision-language feature fusion enhances contextual understanding, while mathematical optimization improves decision accuracy and computational efficiency. The proposed system supports applications in manufacturing, power plants, aerospace, and critical infrastructure by enabling defect detection, predictive maintenance, safety monitoring, and remote assistance. Experimental evaluation demonstrates significant improvements in inspection accuracy, fault classification, decision-making, maintenance prediction, reduced downtime, and lower human intervention, providing a foundation for next-generation intelligent industrial automation.
Edge AI-Based Autonomous Monitoring System for Smart Manufacturing Environments
The rapid evolution of Industry 4.0 has accelerated the adoption of intelligent manufacturing systems requiring real-time monitoring, predictive maintenance, and autonomous decision-making. Traditional cloud-based solutions often suffer from latency, bandwidth limitations, and data privacy concerns, making them unsuitable for time-critical industrial applications. This paper presents an Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things (IIoT) sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring. Real-time sensor data, including temperature, vibration, pressure, humidity, and power consumption, are processed locally using Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and anomaly detection algorithms to identify equipment faults and optimize operations. Only summarized insights are transmitted to the cloud, reducing communication overhead while enabling scalable enterprise-level analytics. The proposed framework improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing. It provides a scalable and resilient solution for smart factories across industries, enabling intelligent automation, predictive analytics, and efficient autonomous industrial operations.
AI-Driven Sustainable Materials Discovery for Next-Generation Engineering Applications
The increasing demand for sustainable, high-performance materials has accelerated the adoption of Artificial Intelligence (AI) in materials discovery. Traditional material development relies on time-consuming experiments and computationally intensive simulations, limiting scalability and innovation. AI-driven materials discovery integrates machine learning, deep learning, data analytics, and computational materials science to rapidly predict, optimize, and design advanced materials with improved mechanical, thermal, electrical, and environmental performance. The proposed framework combines data preprocessing, feature engineering, predictive modeling, optimization, and sustainability assessment to identify materials that satisfy both engineering and environmental requirements. Advanced algorithms, including Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting, accurately predict material properties, while generative AI enables inverse design of novel, recyclable, and energy-efficient materials. Sustainability metrics such as life-cycle assessment and carbon footprint guide multi-objective optimization. Despite challenges in data quality and validation, emerging technologies including federated learning, physics-informed neural networks, digital twins, and autonomous laboratories are expected to further advance AI-enabled sustainable materials discovery for Industry 5.0 and resource-efficient engineering.
Intelligent Energy Management in Microgrids Using Multi-Agent Reinforcement Learning
The increasing integration of renewable energy sources and distributed energy resources has made smart microgrid energy management more complex due to renewable intermittency and dynamic operating conditions. This paper proposes a Multi-Agent Reinforcement Learning (MARL)-based energy management framework in which intelligent agents coordinate renewable generation, energy storage, controllable loads, and grid interaction. Through distributed learning and real-time decision-making, the framework optimizes energy scheduling, improves system reliability, reduces operational cost and carbon emissions, enhances renewable energy utilization, and provides a scalable, autonomous, and resilient solution for next-generation smart microgrids.
Physics-Informed Machine Learning for Structural Integrity Assessment of Critical Infrastructure
Structural integrity assessment is essential for ensuring the safety and reliability of critical infrastructure such as bridges, buildings, dams, tunnels, pipelines, and power plants. Traditional Structural Health Monitoring (SHM) methods rely on either physics-based simulations or data-driven machine learning, each having limitations in computational cost, data requirements, and generalization. Physics-Informed Machine Learning (PIML) integrates physical laws with deep learning to improve damage detection, structural condition assessment, and predictive maintenance using limited and noisy sensor data. This paper reviews recent PIML approaches, including PINNs, hybrid finite element–AI models, digital twins, and uncertainty-aware learning, while identifying key research challenges. A generalized PIML framework is proposed to support accurate, explainable, and intelligent structural health monitoring for resilient next-generation infrastructure.
Smart Water Distribution Systems Using Real-Time Sensor Networks
Smart water distribution systems play a vital role in addressing modern water management challenges caused by urbanization, population growth, industrial expansion, and climate change. Traditional water supply networks often suffer from high water losses, inefficient monitoring, delayed fault detection, and increased operational costs due to manual management practices. To overcome these limitations, smart water management integrates real-time sensor networks, Internet of Things (IoT) technologies, wireless communication, cloud computing, and data analytics. Advanced sensors continuously monitor critical parameters such as water pressure, flow rate, water quality, leakage, temperature, pH, and contamination levels across pipelines, reservoirs, treatment plants, and consumer endpoints. This study examines the architecture, communication mechanisms, sensing technologies, and optimization techniques used in smart water distribution systems. The proposed framework employs layered deployment of pressure, flow, and water-quality sensors combined with cloud-based analytics and predictive control algorithms. Wireless communication technologies such as ZigBee, LoRaWAN, GSM, and Wi-Fi enable efficient data transmission across distributed infrastructure. The system supports real-time leakage detection, pressure regulation, contamination monitoring, predictive maintenance, and energy-efficient pump scheduling. Simulation results demonstrate significant improvements in leakage detection accuracy, operational efficiency, water conservation, energy savings, and infrastructure reliability compared to conventional distribution networks. The findings indicate that smart water distribution systems powered by real-time sensor networks can transform traditional water utilities into intelligent, adaptive, and sustainable platforms, supporting resilient water management and future smart city development.
Thermal Management Techniques for High-Performance Electronic Devices
Efficient thermal management has become essential for modern high-performance electronic devices due to increasing heat generation in microprocessors, GPUs, power electronics, electric vehicles, and communication systems. Traditional cooling methods are often insufficient for compact and high-power systems, leading to the development of advanced techniques such as liquid cooling, heat pipes, vapor chambers, thermoelectric cooling, phase-change materials, and nanofluid-based systems. Modern thermal management focuses on improving heat dissipation, reducing thermal resistance, enhancing reliability, and maintaining temperature uniformity. Advanced materials, nanotechnology, and computational modelling techniques like CFD and FEA further improve cooling efficiency. Studies show that hybrid cooling systems combining passive and active methods provide superior thermal performance for next-generation electronic devices.
Advanced Control Strategies for Autonomous Engineering Systems
Autonomous engineering systems are a major technological advancement in modern industry and science, operating with minimal human intervention while ensuring high accuracy, reliability, adaptability, and efficiency. Before 2019, developments in control engineering, artificial intelligence, robotics, and embedded systems led to advanced autonomous control architectures. Applications such as intelligent transportation, industrial automation, aerospace systems, smart manufacturing, underwater exploration, and unmanned aerial vehicles increased the demand for advanced control strategies capable of handling nonlinearities, uncertainties, and environmental disturbances. Traditional methods like PID control were effective for linear systems but limited in dynamic environments. Therefore, advanced techniques such as adaptive control, robust control, model predictive control, fuzzy logic, neural network-based control, sliding mode control, and hybrid intelligent control became widely used. These methods improved stability, trajectory tracking, fault tolerance, and decision-making. This study reviews advanced control strategies for autonomous engineering systems up to 2019, covering theoretical concepts, mathematical models, architectures, and practical applications in robotics, aerospace, industrial automation, and intelligent transportation. It also highlights challenges including sensor uncertainty, communication delays, computational complexity, and environmental disturbances. Comparative analysis shows that advanced control methods outperform traditional techniques in accuracy, robustness, energy efficiency, and disturbance rejection. The study concludes that these strategies are the foundation of next-generation autonomous systems, with future research focusing on adaptive optimization, distributed intelligence, deep reinforcement learning, and collaborative robotics.
Performance Analysis of Hybrid Renewable Energy Systems for Rural Electrification
Rural electrification remains a major challenge in developing countries, particularly in remote and economically weak regions where grid extension is difficult and expensive. Reliable electricity is essential for socio-economic growth, education, healthcare, agriculture, and industrial activities. Conventional fossil-fuel-based electrification methods suffer from high transmission losses, environmental pollution, fuel price fluctuations, and high investment costs. Hybrid Renewable Energy Systems (HRES), which combine renewable sources such as solar PV, wind, biomass, micro-hydro, and energy storage, provide an effective solution for decentralized rural electrification. These systems improve reliability by reducing the intermittency of individual renewable sources and ensuring continuous power supply through energy storage and intelligent energy management. This study evaluates different hybrid system configurations, including PV-Battery, PV-Wind-Battery, PV-Biomass-Battery, and PV-Wind-Biomass-Battery systems, based on technical, economic, environmental, and reliability factors. Key performance indicators such as renewable energy fraction, levelized cost of energy, net present cost, system efficiency, and carbon emission reduction are analyzed. The results show that multi-source hybrid systems can achieve more than 90% renewable energy penetration while providing reliable and cost-effective electricity. Biomass backup generation further improves power availability and reduces storage requirements. Economic analysis indicates a 20–35% reduction in electricity costs compared to diesel-based systems, along with significant reductions in greenhouse gas emissions. Overall, the study confirms that Hybrid Renewable Energy Systems are technically feasible, economically viable, and environmentally sustainable solutions for rural electrification, supporting universal energy access and sustainable development.
Development of Smart Materials for Adaptive Structural Applications
Smart materials are advanced materials that respond to external stimuli such as temperature, pressure, stress, electric, or magnetic fields. They improve structural adaptability, efficiency, durability, and safety in engineering systems. Common smart materials include piezoelectric materials, shape memory alloys, magnetostrictive materials, electroactive polymers, and self-healing composites. These materials are widely used in aerospace, civil engineering, robotics, automotive, and biomedical applications for sensing, actuation, vibration control, and structural health monitoring. This study reviews the types, properties, applications, and performance of smart materials for adaptive structures. The results show that hybrid smart material systems provide better multifunctional performance, although challenges like high cost, material degradation, and fabrication complexity still exist.
Advanced Signal Processing Techniques for Engineering Measurements
Advanced signal processing plays a vital role in improving the accuracy, reliability, and efficiency of engineering measurement systems used in automation, biomedical instrumentation, aerospace, communications, and structural monitoring. Traditional measurement systems often face challenges such as noise, signal distortion, interference, sensor nonlinearity, and bandwidth limitations. This study reviews advanced techniques including digital filtering, spectral analysis, wavelet transforms, adaptive filtering, statistical signal analysis, and time-frequency processing. A systematic framework involving signal acquisition, preprocessing, feature extraction, adaptive filtering, and performance evaluation is proposed to enhance measurement quality and reduce uncertainty. The analysis shows that wavelet-based methods are highly effective for transient signal processing, while adaptive filtering significantly reduces noise and interference. Hybrid approaches combining multiple signal processing techniques achieve superior measurement accuracy compared to individual methods. The findings highlight the growing importance of intelligent signal processing in developing reliable, high-precision engineering measurement systems for applications such as smart sensing, predictive maintenance, industrial diagnostics, and automated monitoring.
Optimization of Renewable Energy Storage Systems Using Intelligent Algorithms
The increasing integration of renewable energy sources such as solar and wind has created challenges related to intermittency, uncertainty, and grid stability. Energy storage systems help address these issues by storing excess energy and supplying it during periods of low generation. This study explores the use of intelligent optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN), Fuzzy Logic Systems (FLS), Ant Colony Optimization (ACO), and hybrid approaches, for efficient renewable energy storage management. Various storage technologies, such as battery storage, pumped hydro, compressed air, and hybrid systems, are examined. The proposed framework combines renewable energy forecasting, storage scheduling, and intelligent optimization to improve energy efficiency, storage utilization, reliability, and cost-effectiveness. Results show that intelligent algorithms outperform traditional optimization methods by providing greater adaptability, robustness, cost savings, and grid stability, making them promising solutions for future smart grids and sustainable energy systems.
Development of Autonomous Monitoring Drones for Infrastructure Inspection
Infrastructure assets such as bridges, roads, highways, tunnels, dams, power transmission lines, and high-rise buildings require regular inspection to ensure safety and reliability. Conventional visual inspection methods are time-consuming, expensive, and often expose inspectors to hazardous environments. Autonomous monitoring drones (UAVs) provide a faster, safer, and more cost-effective alternative by using advanced sensors, GPS, cameras, and AI-based technologies to collect and analyze structural data. This study proposes an AI-enabled autonomous drone framework for infrastructure inspection that integrates intelligent flight planning, automated data collection, computer vision-based defect detection, and condition assessment. The system accurately identifies cracks, corrosion, and other structural defects while optimizing flight paths for improved efficiency. The proposed framework enhances inspection accuracy, operational safety, and scalability compared to traditional methods, demonstrating significant potential for modern infrastructure management. Future developments may integrate swarm intelligence, edge computing, and digital twins to enable fully autonomous infrastructure monitoring and maintenance.
Energy Harvesting Techniques for Self-Powered Sensor Networks
Wireless Sensor Networks (WSNs) are extensively used in environmental monitoring, healthcare, industrial automation, military surveillance, and smart infrastructure. However, the limited battery life of sensor nodes restricts their long-term operation, particularly in remote areas where battery replacement is difficult and expensive. Energy harvesting offers a sustainable solution by converting ambient energy sources such as solar, thermal, vibration, wind, and radio frequency (RF) energy into electrical power for sensor nodes. This study reviews various energy harvesting techniques, their operating principles, advantages, limitations, and suitability for WSN applications. It also highlights the importance of integrating energy harvesters with energy storage devices and power management systems to ensure reliable operation. A framework involving energy source assessment, harvesting module selection, power conditioning, storage management, and adaptive duty-cycle control is presented to optimize energy utilization. Results indicate that solar energy provides the highest power output under favorable conditions, while vibration and RF harvesting are effective in indoor and industrial environments. Hybrid energy harvesting systems offer greater reliability by combining multiple energy sources. Overall, energy harvesting enhances network lifetime, reduces maintenance costs, and enables sustainable, self-powered WSNs, making it a key technology for future Internet of Things (IoT) and smart environment applications.
Smart Automation Systems for Precision Engineering Applications
Precision engineering is a critical manufacturing field that demands high dimensional accuracy, repeatability, productivity, and quality. With the rise of Industry 4.0, smart automation systems integrating cyber-physical systems, IIoT, machine learning, intelligent control, and real-time monitoring have transformed precision manufacturing. These technologies enable autonomous decision-making, process optimization, predictive maintenance, and improved quality control through advanced sensors, intelligent actuators, and AI-based inspection systems. This survey reviews the evolution of smart automation technologies, examines key research prior to 2018, and proposes an integrated framework combining sensor networks, intelligent controllers, data acquisition, predictive analytics, and automated feedback. The results demonstrate significant improvements in manufacturing accuracy, productivity, defect reduction, machine utilization, and overall equipment effectiveness, highlighting the importance of smart automation in future precision engineering and sustainable manufacturing systems.