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All Journal Pancasila International Journal of Applied Social Science Control Systems and Optimization Letters
Islam, Monirul
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Journal : Control Systems and Optimization Letters

 1 
Nanomaterials in Industry: A Review of Emerging Applications and Development Kumar, Swarup; Khan, Saidul Islam; Neidhe, Md Musfiqur Rahman; Islam, Monirul; Hasan, Md Mehedi
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.135

Abstract

Nanomaterials are materials where at least one dimension is smaller than 100 nanometers, unlocking a realm of extraordinary properties that set them apart from their bulk counterparts. These materials exhibit unique behaviors, such as enhanced electrical conductivity, superior mechanical strength, and heightened chemical reactivity. Due to these qualities, they are widely used in sectors like as electronics, healthcare, energy, and environmental preservation. Nanomaterials have made it possible for electronics to get smaller, and they have enhanced medication delivery and diagnostics in the medical field. They are perfect for energy conversion and storage technologies like solar cells and batteries because of their large surface area and conductivity. Furthermore, the use of nanoparticles in sustainable agriculture and environmental remediation is being investigated. Nevertheless, there are still difficulties in meeting regulatory requirements, guaranteeing safety, and increasing output. This paper looks at the many uses for nanomaterials, emphasizes their promise, and discusses the obstacles preventing a wider industrial acceptance of them.
AI-Driven Microgrid Solutions for Enhancing Energy Access and Reliability in Rural and Remote Areas: A Comprehensive Review Ahmed, Faisal; Uzzaman, Asif; Adam, Md Ibrahim; Islam, Monirul; Rahman, Md Moklesur; Islam, Asm Mohaimenul
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.183

Abstract

As localized energy systems, microgrids provide a viable way to solve problems with energy dependability and access in rural and isolated locations. These regions often have inadequate and unstable grid infrastructure, which restricts their access to energy. Artificial Intelligence (AI) improves the overall performance, flexibility, and efficiency of microgrid systems. AI ensures a steady and dependable power supply by enabling predictive maintenance, optimal load forecasting, energy storage management, and renewable energy resource optimization. AI may help microgrids anticipate system faults, better control energy consumption, and prolong the life of vital parts. Additionally, AI ensures the sustainability of microgrids in resource-constrained places by optimizing the usage of renewable energy sources like solar and wind. Successful case studies from places like the US, India, and Africa have shown the promise of AI-enhanced microgrids in raising the standard of living for marginalized areas, despite obstacles like data infrastructure and upfront installation costs. Microgrids have a bright future thanks to developments in artificial intelligence (AI), which might increase electricity availability and promote economic growth in rural and isolated regions of the world.
Testing Autonomous Vehicles in Virtual Environments: A Review of Simulation Tools and Techniques Uzzaman, Asif; Islam, Monirul; Hossain, Md Shimul
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.196

Abstract

Autonomous vehicles (AVs) have the potential to transform the transportation industry by improving road safety, reducing traffic congestion, and enhancing fuel efficiency. Significant progress has been made in autonomous vehicle (AV) technologies, especially in sensor systems, machine learning, and artificial intelligence. These advancements enable vehicles to navigate complex environments and make real-time decisions. Despite these advancements, numerous challenges remain in ensuring the safety, reliability, and acceptance of AVs. Key issues include sensor fusion, the ability to handle unpredictable scenarios, the development of universally accepted regulatory frameworks, and public trust in autonomous systems. Furthermore, ethical dilemmas, such as decision-making in unavoidable accident situations, present additional concerns. The deployment of AVs also raises questions about the impact on employment in driving-dependent industries and the infrastructure needed to support these technologies. This paper reviews the current state of AV development, examining the progress made in simulation-based testing, sensor technology, and decision-making algorithms. Additionally, it discusses the challenges that still need to be addressed, including safety concerns, regulatory barriers, and societal implications. The paper concludes by outlining potential areas for future research, such as improving sensor reliability, enhancing machine learning algorithms, integrates an analysis of simulation-based testing, decision-making algorithms, and sensor technologies with a forward-looking discussion on legal frameworks, public trust, and employment impacts, offering a holistic perspective on the path toward AV integration.
Bio-Inspired Hybrid Control for Autonomous Vehicles: Improving Real-Time Navigation Through the Integration of ACO and PSO Uzzaman, Asif; Islam, Monirul; Ahmed, Shishir
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.204

Abstract

This research demonstrates nature-inspired control systems for the navigation of autonomous vehicles (AVs), utilizing algorithms derived from nature Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) to tackle challenges posed by dynamic environments. ACO is based on the pheromone trails of ants to facilitate adaptive route selection, PSO draws inspiration from bird flocking behavior for optimal pathfinding, and ABC imitates the division of labor seen in bee swarms for decentralized decision-making. A combined ACO-PSO model merges ACO's capability for local adaptability with PSO's ability for global convergence, allowing for real-time modifications to paths. Simulations conducted on the CARLA and SUMO platforms illustrate improvements in navigation stability and responsiveness, showcasing enhancements in trajectory smoothness by 15%, collision avoidance by 22%, and congestion reduction by 18% when faced with unexpected obstacles and variable traffic conditions. The findings support the notion that bio-inspired systems can serve as scalable and resilient alternatives to conventional algorithms, providing strong solutions for the emergence of next-generation AV technologies. This study connects biological concepts with artificial autonomy to develop intelligent transportation systems using hybrid algorithms and real-time adaptive learning. Biologically inspired models enhance decision-making in complex environments. However, limitations such as high computational complexity and challenges in scaling the system for real-world applications are acknowledged.
Co-Authors Adam, Md Ibrahim Ahmed, Faisal Ahmed, Shishir Al Mamun, Firoz Hasan, Md Mehedi Hasan, Mehabub Hossain, Md Shimul Islam, Asm Mohaimenul Islam, Hafizul Khan, Saidul Islam Kumar, Swarup Neidhe, Md Musfiqur Rahman Rahman, Md Moklesur Ruhul Amin, Ruhul Uzzaman, Asif