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Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

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computers, Materials & Continua 2025年第3期82卷 4503-4533页

作者： Yaming Ren Xing Deng College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.

关键词： Multi-area economic dispatch problem proximal point algorithm trend theory

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An Automated Approach to Discovering Software Refactorings by Comparing Successive Versions

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IEEE Transactions on Software engineering 2025年第05期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： Software quality

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Convolutional Neural Network Image Classification Based on Different Color Spaces

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Tsinghua Science and technology 2025年第1期30卷 402-417页

作者： Zixiang Xian Rubing Huang Dave Towey Chuan Yue School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is *** paper explores the impact of different color spaces on image classification using *** compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color *** find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than *** color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,*** leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network *** experiments show that our proposed model can outperform the single-color-space models on most *** also find that our method is simple,flexible,and compatible with any CNN and image dataset.

关键词： color space Convolutional Neural Network(CNN) image classification pseudo-Siamese network

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HGTFM: Hierarchical Gating-Driven Transformer Fusion Model for Robust Multimodal Sentiment Analysis

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IEEE Access 2025年 13卷 74430-74445页

作者： Yang, Chengcheng Liang, Zhiyao Yan, Dashun Hu, Zeng Wu, Ting Macau University of Science and Technology School of Computer Science and Engineering Avenida Wai Long Taipa China Zhongkai University of Agriculture and Engineering School of Information Science and Technology Guangzhou510550 China

In multimodal sentiment analysis, a significant challenge lies in quantifying the contribution of each modality and achieving effective modality fusion. This paper presents a Hierarchical Gating-Driven Transformer Fusion Model (HGTFM), which effectively achieves multimodal data fusion through an advanced transformer architecture and a Hierarchical Gated Fusion Module (HGFM). The model first encodes audio and video features using LSTM and CNN, respectively, while employing BERT to encode text features. This approach effectively enhances the model ability to manage long-range temporal dependencies. Furthermore, the model introduces HGFM, which dynamically integrates multimodal features across different levels after processing by the modality interaction network (MIN), thus intelligently adjusting the contribution weights of each modality. In particular, the model incorporates a Unimodal Label Generation Module (ULGM), which considers the differences between the modalities. This enables HGFM to more accurately identify the contributions of samples with significant modal variations, thus optimizing the module potential. The experimental results on several established benchmark datasets demonstrate that HGTFM achieves comparable performance improvements in multimodal sentiment analysis tasks. © 2013 IEEE.

关键词： Sentiment analysis Transformers Analytical models Data models Adaptation models Tensors Accuracy Noise measurement Training Robustness

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Institute of Applied Physics Graduate School of Pure and Applied Sciences University of Tsukuba Institute of Special Environments Physical Sciences Harbin Institute of Technology School of Computer Science and Technology Harbin Institute of Technology

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction（HER） performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H2SO4,with only a 3.8% degradation in performance over a 120-h continuous *** proton exchange membrane（PEM） water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm-2,in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Acidic media Graphene encapsulation Hydrogen evolution Interface Proton exchange membrane

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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Photonic generation of multi-format and reconfigurable microwave signals based on cascaded external modulation

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Optical and Quantum Electronics 2025年第3期57卷 1-14页

作者： Chen, Mo Chen, Hongyao Zhang, HongYu Li, ZhuoYang Wang, Jianping Feng, LiFang Lu, HuiMin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing10083 China

This paper presents a photonic scheme for generating multi-format, multi-band, and reconfigurable microwave photonic signals through cascaded external modulation. The proposed system utilize dual-parallel Mach–Zehnder modulators (DP-MZM) and Mach–Zehnder modulators (MZM) to produce high-order optical sidebands, enabling flexible frequency multiplication factors switching and pulse waveform reconstruction. Additionally, using a polarization control structure allows for spurious suppression and encoded signal loading. The system can generate various signal formats, including rectangular optical frequency combs, frequency-multiplied signals, dual-frequency encoded signals, and multiple pulse waveforms. Simulation results demonstrate the successful generation of 16 GHz frequency-doubled signal and 32 GHz frequency-quadrupled signal, with spurious suppression ratios of 37.9 dB and 44.8 dB, respectively. It can also generate four-line rectangular optical frequency combs with frequency multiplication factors of 2 and 4, achieving flatness values of 0.24 dB and 0.16 dB. Furthermore, the system can generate symmetric triangular pulses and Sinc-Nyquist pulses with adjustable repetition frequencies, as well as encoded signals with good pulse compression performance, having PCR values close to the theoretical value of 13. This work demonstrates a versatile and reconfigurable approach to microwave photonic signal generation, offering promising applications in advanced radar systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Microwave photonics

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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Gloss-driven Conditional Diffusion Models for Sign Language Production

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷 1-17页

作者： Tang, Shengeng Xue, Feng Wu, Jingjing Wang, Shuo Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China School of Data Science School of Information Science and Technology University of Science and Technology of China Hefei China

Sign Language Production (SLP) aims to convert text or audio sentences into sign language videos corresponding to their semantics, which is challenging due to the diversity and complexity of sign languages, and cross-modal semantic mapping issues. In this work, we propose a Gloss-driven Conditional Diffusion Model (GCDM) for SLP. The core of the GCDM is a diffusion model architecture, in which the sign gloss sequence is encoded by a Transformer-based encoder and input into the diffusion model as a semantic prior condition. In the process of sign pose generation, the textual semantic priors carried in the encoded gloss features are integrated into the embedded Gaussian noise via cross-attention. Subsequently, the model converts the fused features into sign language pose sequences through T-round denoising steps. During the training process, the model uses the ground-truth labels of sign poses as the starting point, generates Gaussian noise through T rounds of noise, and then performs T rounds of denoising to approximate the real sign language gestures. The entire process is constrained by the MAE loss function to ensure that the generated sign language gestures are as close as possible to the real labels. In the inference phase, the model directly randomly samples a set of Gaussian noise, generates multiple sign language gesture sequence hypotheses under the guidance of the gloss sequence, and outputs a high-confidence sign language gesture video by averaging multiple hypotheses. Experimental results on the Phoenix2014T dataset show that the proposed GCDM method achieves competitiveness in both quantitative performance and qualitative visualization. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Forming

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BEV-Locator:an end-to-end visual semantic localization network using multi-view images

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Science China(Information Sciences) 2025年第2期68卷 134-150页

作者： Zhihuang ZHANG Meng XU Wenqiang ZHOU Tao PENG Liang LI Stefan POSLAD School of Vehicle and Mobility Tsinghua University School of Information Technology & Management University of International Business and Economics Qcraft Inc. School of Electronic Engineering and Computer Science Queen Mary University of London

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV(bird-eye-view) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map query sequences. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. This end-to-end model speaks to its broad applicability across different driving environments, including high-speed scenarios. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-Locator is capable of estimating the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052 m, 0.135 m and 0.251° in lateral, longitudinal translation and heading angle degree.

关键词： visual localization semantic map bird-eye-view transformer pose estimation

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