arXiv subjects

Minhajur Rahman

Publications and source records attributed to Minhajur Rahman.

5 recordsLinked to original sources

arXiv2025

Design and Analysis of a Grid-connected DC Fast Charging Station for Dhaka-Chittagong Highway

The growing adoption of electric vehicles (EVs) necessitates the development of efficient and reliable charging infrastructure, particularly fast charging stations (FCS) for addressing challenges such as range anxiety and long charging times. This paper presents the design and feasibility analysis of a grid-connected DC fast charging station for the Dhaka-Chittagong highway, a critical transportation corridor in Bangladesh. The proposed system incorporates advanced components, including a step-down transformer, Vienna Rectifier, and LC filter, to convert high-voltage AC power from the grid into a stable DC output. Simulated using MATLAB Simulink, the model delivers a peak output of 400V DC and 120 kW power, enabling rapid and efficient EV charging. The study also evaluates the system's performance, analyzing charging times, energy consumption, and distance ranges for representative EVs. By addressing key technical, environmental, and economic considerations, this paper provides a comprehensive roadmap for deploying fast charging infrastructure, fostering EV adoption, and advancing sustainable transportation in Bangladesh.

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arXiv2024

Scaled and Inter-token Relation Enhanced Transformer for Sample-restricted Residential NILM

Transformers have demonstrated exceptional performance across various domains due to their self-attention mechanism, which captures complex relationships in data. However, training on smaller datasets poses challenges, as standard attention mechanisms can over-smooth attention scores and overly prioritize intra-token relationships, reducing the capture of meaningful inter-token dependencies critical for tasks like Non-Intrusive Load Monitoring (NILM). To address this, we propose a novel transformer architecture with two key innovations: inter-token relation enhancement and dynamic temperature tuning. The inter-token relation enhancement mechanism removes diagonal entries in the similarity matrix to improve attention focus on inter-token relations. The dynamic temperature tuning mechanism, a learnable parameter, adapts attention sharpness during training, preventing over-smoothing and enhancing sensitivity to token relationships. We validate our method on the REDD dataset and show that it outperforms the original transformer and state-of-the-art models by 10-15\% in F1 score across various appliance types, demonstrating its efficacy for training on smaller datasets.

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arXiv2024

ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals

Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%.

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arXiv2024

EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN

Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerable challenges in the realm of neuromuscular disorder diagnostics. In this study, we employ advanced deep-learning techniques to address the detection of ALS and Myopathy, two debilitating conditions. Our methodology begins with the extraction of informative features from raw electromyography (EMG) signals, leveraging the Log-spectrum, and Delta Log spectrum, which capture the frequency contents, and spectral and temporal characteristics of the signals. Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined with Convolutional Neural Networks (CNNs) and Attention for the classification of three classes. The robustness of our approach is rigorously evaluated, demonstrating its remarkable performance in distinguishing among the classes: Myopathy, Normal, and ALS, with an outstanding overall accuracy of 92\%. This study marks a contribution to addressing the diagnostic challenges posed by neuromuscular disorders through a data-driven, multi-class classification approach, providing valuable insights into the potential for early and accurate detection.

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arXiv2024

Towards a Deeper Understanding of Transformer for Residential Non-intrusive Load Monitoring

Transformer models have demonstrated impressive performance in Non-Intrusive Load Monitoring (NILM) applications in recent years. Despite their success, existing studies have not thoroughly examined the impact of various hyper-parameters on model performance, which is crucial for advancing high-performing transformer models. In this work, a comprehensive series of experiments have been conducted to analyze the influence of these hyper-parameters in the context of residential NILM. This study delves into the effects of the number of hidden dimensions in the attention layer, the number of attention layers, the number of attention heads, and the dropout ratio on transformer performance. Furthermore, the role of the masking ratio has explored in BERT-style transformer training, providing a detailed investigation into its impact on NILM tasks. Based on these experiments, the optimal hyper-parameters have been selected and used them to train a transformer model, which surpasses the performance of existing models. The experimental findings offer valuable insights and guidelines for optimizing transformer architectures, aiming to enhance their effectiveness and efficiency in NILM applications. It is expected that this work will serve as a foundation for future research and development of more robust and capable transformer models for NILM.

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