B. Huang, Z. Wang, J. Chen, B. Zhou, Y. Zhu, Y. Liu [references] [full-text] [DOI: 10.13164/re.2025.0181] [Download Citations]
Research on Site Selection and Capacity Determination of Electric Vehicle Public Charging Stations by Integrating K-Means++ and Improved RODDPSO

To address the suboptimal spatial distribution and low comprehensive utilization of existing electric vehicle (EV) public charging infrastructure, this study proposes an innovative charging station placement and capacity determination methodology integrating K-Means++ clustering with an enhanced RODDPSO variant. Building upon conventional K-Means and RODDPSO frameworks, we develop an improved hybrid algorithm incorporating three critical advancements: 1) an adaptive mutation mechanism within the RODDPSO architecture to enhance global search capabilities and prevent premature convergence; 2) synergistic optimization of K-Means++ cluster centroids through the enhanced RODDPSO operator; and 3) a novel cluster validation metric based on real-world utilization patterns. The proposed methodology effectively resolves the inherent limitations of conventional K-Means approaches, particularly their sensitivity to initial centroid selection and tendency toward local optima. Empirical validation through a case study of Nanjing's charging infrastructure demonstrates the algorithm's superior performance: stations sited using the proposed hybrid method exhibit 63.8% greater spatial correlation with high-utilization zones (>15% operational utilization) compared to baseline K-Means implementations. The advancements provide both methodological contributions to spatial optimization algorithms and practical insights for urban EV infrastructure planning.

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Keywords: K-Means++, variation randomly occurring distributedly delayed particle swarm optimization, public charging station, siting and capacity determination

T. Sivaranjani, B. Sasikumar, G. Sugitha [references] [full-text] [DOI: 10.13164/re.2025.0195] [Download Citations]
Rectified Adam Optimizer and LSTM with Attention Mechanism for ECG-Based Multi-class Classification of Cardiac Arrhythmia

Cardiac Arrhythmia (CA) is one of the most prevalent cardiac conditions and prime reasons for sudden death. The current CA detection methods face challenges in noise removal, R-peak detection, and low-level feature selection, which can impact diagnostic accuracy and signal stability. The research aims to develop an effective framework for detecting and classifying CA using advanced signal processing, feature extraction, feature selection, and classification for reliable medical diagnosis. The input electrocardiogram (ECG) signals are processed using hybrid noise reduction techniques such as cascaded variable step size normalized least mean square and sparse low-rank filter. The complex and high-level features are extracted using higher-order spectral energy distributed image, wavelet transform, and R-wave peak to R-wave peak interval to enhance the representation of cardiac data. Recursive feature elimination is applied to select the most relevant diagnostic features and the Rectified Adam optimizer is used to fine-tune parameters to achieve better training stability. The model integrates long-term memory with an attention mechanism to enhance the classification performance of arrhythmia detection. Simulation results demonstrate that the proposed model achieves 99.40% accuracy, outperforming existing models and showing its efficiency in classifying CA for better diagnosis and early treatments.

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Keywords: Cardiac arrhythmia, electrocardiogram, sparse low-rank filter, recursive feature elimination, long short-term memory, rectified Adam optimizer, attention mechanism