Standalone Operation of Inverter-Based Variable Speed Wind Turbines on DC Distribution Network by Hossein Amini, Reza Noroozian

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Scalable AP Clustering With Deep Reinforcement Learning for Cell-Free Massive MIMO by Yu Tsukamoto, Akio Ikami, Takahide Murakami, Amr Amrallah, Hiroyuki Shinbo, Yoshiaki Amano

“…Cell-free massive MIMO (CF-mMIMO) is a promising approach for future mobile networks, utilizing centralized MIMO processing for densely distributed access points (APs). …”
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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…First, the StarNet architecture is introduced into the Backbone to enhance the extraction of shallow image features and significantly reduce computational complexity. Next, Star Blocks from the StarNet framework are employed to optimize the C3k2 module in the Neck stage, thereby improving the localization accuracy of deep features without increasing network complexity. …”
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Back Propagation Neural Network model for analysis of hyperspectral images to predict apple firmness by Shuiping Li, Yueyue Chen, Xiaobo Zhang, Junbo Wang, Xuanxiang Gao, Yunhong Jiang, Zhaojun Ban, Cunkun Chen

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Memristor-Based Neuromorphic System for Unsupervised Online Learning and Network Anomaly Detection on Edge Devices by Md Shahanur Alam, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha

“…Threshold optimization and anomaly detection are achieved through a fully analog Euclidean Distance (ED) computation circuit, eliminating the need for floating-point processing units. …”
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MFN: Multi-Scale Frequency Feature Fusion Network for Multi-Classification Image Segmentation by Ji Xiao, Li Jianfang, Zhao Peng, Li Xiaochen, Zhang Chengchun, Zheng Junyi, Pang Yonghui, Huang Xiangsheng

“…Finer-grained atrous convolutions are introduced in its channels to capture multi-scale semantic information, enabling the extraction of richer and more detailed features. The network incorporates a hybrid attention module to enhance the importance of specific spatial points and strengthen feature dependencies between channels. …”
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Methods for Applying Matrices when Creating Models of Group Pursuit by A. A. Dubanov

“…This requires the development of models of group pursuit. Note that optimization in pursuit tasks is reduced to the construction of optimal trajectories (shortest trajectories, trajectories with differential constraints, fuel consumption indicators). …”
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3DMeshNet: A three-dimensional differential neural network for structured mesh generation by Jiaming Peng, Xinhai Chen, Jie Liu

“…It takes geometric points as input to learn the potential mapping between parametric and computational spaces. …”
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Incorporating Risk in Operational Water Resources Management: Probabilistic Forecasting, Scenario Generation, and Optimal Control by Ties van derHeijden, Miguel Angel Mendoza‐Lugo, Peter Palensky, Nick van deGiesen, Edo Abraham

“…The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. …”
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Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation by Muhammad Adnan Pramudito, Yunendah Nur Fuadah, Ali Ikhsanul Qauli, Aroli Marcellinus, Ki Moo Lim

“…Abstract The Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) risk, utilizing computational models to predict cardiac drug toxicity. …”
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Multi-factor optimization for electric bus charging stations: Integrating electrical, social, and environmental perspectives by Mustafa Nurmuhammed, Ozan Akdağ, Teoman Karadağ, Maged Kafafy˘

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Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source by Parisa Sadeghli Dizaji, Hamidreza Habibiyan

“…Our results demonstrate that by adaptively finding the coupling coefficient through BO, the model has identified optimal points in the over-coupled regions with superior performance. …”
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Precision evaluation criteria for simulation algorithms in infinite systems: A network model-based approach by Yonglong Ding

“…The network architecture enables precise prediction of the phase transition point in the two-dimensional Ising model with 98.4% accuracy.…”
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RGB-D camera and graph neural network-based SLAM for dynamic and low-texture environments by Xiaoran Liu, Xiaowei Zhang, Xiaochen Yan, Peng Liu, Haksrun Lao

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Optimization of Characteristic Diagram Based Thermal Error Compensation via Load Case Dependent Model Updates by Christian Naumann, Janine Glänzel, Martin Dix, Steffen Ihlenfeldt, Philipp Klimant

“…This paper presents a new method for updating characteristic diagram based compensation models by combining existing models with new measurements. This allows the optimization of the compensation for serial production load cases without the effort of computing a new model. …”
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Analysis of User Personalized Retrieval of Multimedia Digital Archives Dependent on BP Neural Network Algorithm by Zhongke Wang

“…Researchers have put forward personalized retrieval of multimedia files based on the BP neural network computing. In this way, the interest model of customers can be analyzed based on the characteristics of the different classification areas of users. …”
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Combining the A* Algorithm with Neural Networks to Solve the Team Orienteering Problem with Obstacles and Environmental Factors by Alfons Freixes, Javier Panadero, Angel A. Juan, Carles Serrat

“…Furthermore, computation times remain within 5–10% of the baseline, showing that the added predictive layer maintains computational efficiency.…”
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Lag-Specific Transfer Entropy for Root Cause Diagnosis and Delay Estimation in Industrial Sensor Networks by Rui Chen, Shu Liang, Jian-Guo Wang, Yuan Yao, Jing-Ru Su, Li-Lan Liu

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Novel Node Localization Algorithm Based on Nonlinear Weighting Least Square for Wireless Sensor Networks by Fu Xiao, Mingtan Wu, Haiping Huang, Ruchuan Wang, Sudan Wang

“…Regarding ranging equation error-weighted sum as a whole, this method starts with the initial iteration point of stepwise refinement to explore the optimal solution and further reduces the positioning computational complexity by the simplification of the Taylor equation. …”
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Robust Model Predictive Control-Based Recurrent Neural Networks for Autonomous Vehicles in Avoidance Collisions by Hung Duy Nguyen, Duc Thinh Le, Tung Lam Nguyen, Minh Nhat Vu

“…However, due to its computational complexity, we employ a data-driven approach by collecting measurements under different road adhesion conditions to train deep neural networks with a long short-term memory layer (DNN-LSTM). …”
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