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All Journal Indonesian Journal of Disability Studies Journal of Central Banking Law and Institutions Control Systems and Optimization Letters
ISLAM, MD MONIRUL
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Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Control Systems and Optimization Letters

 1 
The Role of IoT and Artificial Intelligence in Advancing Nanotechnology: A Brief Review Islam, Md Monirul; Hossain, Ikram; Martin, Md. Hasnat Hanjala
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.124

Abstract

The main objective of this research is to review the importance of IoT and Artificial Intelligence for Nanotechnology. Several industries are seeing notable breakthroughs due to the convergence of nanotechnology, artificial intelligence, and the Internet of Things. This succinct overview examines how IoT and AI are essential for improving the capabilities and uses of nanotechnology. Real-time monitoring, data gathering, and control at the nanoscale are made possible by IoT, improving the accuracy and efficiency of operations including industrial manufacturing, healthcare monitoring, and environmental sensing. The design, optimization, and predictive modeling of nanomaterials and systems are made easier by artificial intelligence (AI), which provides strong tools for evaluating the complicated data produced by nanoscale devices. The convergence of IoT, AI, and nanotechnology facilitates the creation of intelligent systems that possess the ability to monitor themselves and make decisions on their own. IoT and AI amplify the potential of nanotechnology by enabling real-time data collection, advanced data analytics, and autonomous decision-making, with vast applications across industries from healthcare to energy. Even while this integration seems promising, there are still issues to be resolved, such as privacy issues, data security, and technical difficulties in creating dependable nanoscale Internet of Things devices. It is anticipated that as research advances, the confluence of these technologies will transform industries including smart manufacturing, environmental monitoring, and medicine, making this a critical area for future innovation.
Potential Applications and Limitations of Artificial Intelligence in Remote Sensing Data Interpretation: A Case Study Hossain, Ikram; Islam, Md Monirul; Martin, Md. Hasnat Hanjala
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.v2i3.128

Abstract

This research aims to comprehensively review the applications and limitations of artificial intelligence (AI) in interpreting remote sensing data, highlighting its potential through a detailed case study. AI technologies, particularly machine learning and deep learning, have shown remarkable promise in enhancing the accuracy and efficiency of data interpretation tasks in remote sensing, such as anomaly detection, change detection, and land cover classification. AI-driven analysis has a lot of options because to remote sensing, which can gather massive amounts of environmental data via drones, satellites, and other aerial platforms. AI approaches, in particular machine learning and deep learning, have demonstrated potential to improve the precision and effectiveness of data interpretation tasks, including anomaly identification, change detection, and land cover classification. Nevertheless, the research also points to a number of drawbacks, including challenges related to data quality, the need for large labeled datasets, and the risk of model overfitting. Furthermore, the intricacy of AI models can occasionally result in a lack of transparency, which makes it challenging to understand and accept the outcomes. The case study emphasizes the necessity for a balanced strategy that makes use of the advantages of both AI and conventional techniques by highlighting both effective applications of AI in remote sensing and areas where traditional methods still perform better than AI. This research concludes that while AI holds significant potential for advancing remote sensing data interpretation, careful consideration of its limitations is crucial for its effective application in real-world scenarios.
A Review on Employing Weather Forecasts for Microgrids to Predict Solar Energy Generation with IoT and Artificial Neural Networks Islam, Md Monirul; Akter, Mst. Tamanna; Tahrim, H M; Elme, Nafisa Sultana; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.108

Abstract

In this study, an artificial neural network (ANN) based approach is studied about the prediction of solar energy generation in a microgrid using weather forecasting. The ANN is trained using historical data of solar energy generation and weather forecast data. The input parameters for the ANN include weather variables such as temperature, humidity, wind speed, and solar irradiance. The output parameter is the solar energy generation in kilowatt-hour (kWh). The proposed approach is implemented and tested using real-world data from a microgrid. The results indicate that the ANN-based approach is effective in predicting the solar energy generation with high accuracy. The proposed approach can be used for optimizing the operation of microgrids and facilitating the integration of renewable energy sources into the power grid. This study proposes the use of an Artificial Neural Network (ANN) to predict the solar energy generation in a microgrid using weather forecast data. Weather forecasting has become more precise and dynamic with the integration of IoT data with advanced analytics and machine learning models. These models are quite accurate at predicting solar irradiance and analyzing patterns. The microgrid comprises of a photovoltaic (PV) system which generates solar energy and a battery storage system which stores and supplies the energy to the load. Accurate prediction of solar energy generation is crucial for optimizing management of the microgrid. The inputs to the ANN model include temperature, humidity, wind speed, cloud cover and solar irradiance, which are obtained from weather forecast data. The output of the model is the predicted solar energy generation. The performance of the ANN model is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). This study presents a practical approach for predicting solar energy generation in a microgrid using weather forecast data, which can be used for efficient management of the microgrid.
Comparative Analysis of IoT and AI-Based Control Strategies for Community Micro-Grids Islam, Md Monirul; Akter, Mst. Tamanna; Elme, Nafisa Sultana; Khan, Md. Yakub Ali
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.v3i2.191

Abstract

The main objective of this paper is to review the centralized, decentralized, and hybrid control approaches based on key performance metrics such as efficiency, reliability, and scalability. By improving sustainability, dependability, and efficiency, the combination of artificial intelligence (AI) and the Internet of Things (IoT) in community micro-grids has completely changed energy management. The Internet of Things (IoT) and artificial intelligence (AI) have been used more and more in microgrid control to improve autonomy, dependability, and efficiency. Sensors, smart meters, distributed energy resources (DERs), and energy storage systems are just a few of the microgrid's components that can communicate and monitor in real time thanks to the Internet of Things.AI uses this data to make smart decisions on activities like fault detection, load forecasting, renewable energy prediction, and optimal power dispatch.  To optimize power distribution, load balancing, and fault detection in micro-grids, this article compares several control systems that make use of IoT and AI. The study looks at decentralized, hybrid, and centralized control strategies, emphasizing their benefits, drawbacks, and applicability in various operational scenarios. Important performance indicators are assessed, including cost-effectiveness, responsiveness, energy efficiency, and flexibility about renewable energy sources. The results contribute to the development of smart energy systems by shedding light on the best control schemes for enhancing microgrid performance.
Co-Authors Akter, Mst. Tamanna Elme, Nafisa Sultana Hossain, Ikram Khan, Md. Yakub Ali Mariev , Oleg Martin, Md. Hasnat Hanjala Sohag, Kazi Tahrim, H M