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5th International Conference on Power engineering, ICPE 2024

作者： Li, Mingxue Mei, Xinyu Zhang, Yue Yu, Dongsheng China University of Mining and Technology School of Electrical Engineering Xuzhou China China University of Mining and Technology School of Computer Science and Technology Xuzhou China

ISBN: (纸本)9798331531140

For dual active bridge (DAB) DC/DC converters, DC bias of the inductor current caused by the system parameters variation increases system losses, leads to core saturation, and even jeopardizes the safe and reliable operation. To solve it, a transient DC bias suppression strategy based on adaptive virtual resistance compensation is proposed in this paper. The transient mathematical model of a DAB converter with DC bias is built. On this basis, the phase shift is compensated to simulate the transient characteristic of real-resistance compensation, where the virtual resistance can be dynamically adjusted according to the DC bias. Thus, the transient DC bias can be suppressed without additional circuits, and the simulation results verify its feasibility and effectiveness. © 2024 IEEE.

关键词： Electromagnetic transients

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Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation

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Energy and AI 2024年第4期18卷 258-280页

作者： Junhao Song Yingfang Yuan Kaiwen Chang Bing Xu Jin Xuan Wei Pang School of Mathematical and Computer Sciences Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering Imperial College LondonLondonUnited Kingdom Edinburgh Business School Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering and Physical Sciences University of SurreySurreyUnited Kingdom School of Management Science and Engineering Nanjing University of Information Science and TechnologyNanjingChina

To advance the circular economy(CE),it is crucial to gain insights into the evolution of public attention,cognitive pathways related to circular products,and key public *** achieve these objectives,we collected data from diverse platforms,including Twitter,Reddit,and The Guardian,and utilised three topic models to analyse the *** the performance of topic modelling may vary depending on hyperparameter settings,we proposed a novel framework that integrates twin(single-and multi-objective)hyperparameter timisation op-for CE *** experiments were conducted to determine appropriate hyperparameters under different constraints,providing valuable insights into the correlations between CE and public *** findings reveal that economic implications of sustainability and circular practices,particularly around recyclable materials and environmentally sustainable technologies,remain a significant public *** related to sustainable development and environmental protection technologies are particularly prominent on The Guardian,while Twitter discussions are comparatively *** insights highlight the importance of targeted education programmes,business incentives adopt CE practices,and stringent waste management policies alongside improved recycling processes.

关键词： Circular economy Pulic attention Topic modelling Machine learning Hyperparameter optimisation

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U-Net based Pancreas Segmentation from Computed Tomography Images 3

U-Net based Pancreas Segmentation from Computed Tomography I...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Devendhar, Tirumalasetti Lakshmipriya, B. Jayalakshmy, S. Vellore Institute of Technology Chennai Campus School of Computer Science & Engineering Chennai India Jawaharlal Nehru University School of Engineering New Delhi India IFET College of Engineering Electronics & Communication Engineering India

ISBN: (纸本)9798350391565

Delineation of pancreas from computed tomography (CT) scan images is burdensome owing to its anatomic variation in size, shape and its presence with respect to adjacent organs. This work explores the U-Net architecture to delineate pancreas from the CT volume of abdomen. U-Net was trained for variable number of epochs to identify the optimal learning environment for better segmentation performance. U-Net when trained for pancreas segmentation using CT dataset taken from The Cancer Imaging Archive (TCIA) has resulted in a segmentation performance of dice similarity coefficient of 0.8138, intersection over union of 0.7962 and boundary F1 score of 0.8036. © 2024 IEEE.

关键词： Image segmentation

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Knowledge Distillation with Adaptive Category Temperatures for Classifying High-Similar Objects 21

Knowledge Distillation with Adaptive Category Temperatures f...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Wang, Heng Zhang, Yu Hu, Wang Lin, Kezhen Hu, Junjie School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science Sichuan University Chengdu611731 China

ISBN: (纸本)9798331519254

Grayscale images play a crucial role in X-ray-based welding flaw identification, where variations among different types of flaws primarily arise from disparities in shape and intensity. Consequently, different flaw types often exhibit considerable similarity in their feature descriptions. To tackle this challenge, we propose an enhanced method termed Knowledge Distillation with Adaptive Category Temperatures (KD-ACT) to effectively classify objects with high similarity. To extract implicit information inherent in objects, KD-ACT incorporates an adaptive category temperatures adjustment mechanism, which is guided by feedback from the validation dataset. The proposed method dynamically adjusts the critical temperature parameters of the distillation process, assigning an independent temperature to each category to maximize flexibility in temperature variations. This tailored temperature adjustment enables the welding flaw model to acquire rich information inherent in different flaw types, thereby enhancing its overall performance. Compared to traditional knowledge distillation algorithms, KD-ACT has achieved a maximum improvement of about 4%, with an average improvement of approximately 2 %. © 2024 IEEE.

关键词： Deep learning

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Mitigating Malware Propagation in Social Internet of Things Using an Exact Markov Chain-Based Epidemic Method

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IEEE Internet of Things Journal 2025年第13期12卷 24104-24118页

作者： Zhang, Hong Hu, Xinyi Shen, Yizhou Xu, Huibin Shen, Shigen Li, Ruidong Donghua University School of Computer Science and Technology Shanghai201620 China Newcastle University School of Computing Newcastle upon TyneNE1 7RU United Kingdom Huzhou University School of Information Engineering Huzhou313000 China Kanazawa University Institute of Science and Engineering Kakuma Kanazawa920-1192 Japan

In the Social Internet of Things (SIoT) environment, malware propagation is attracting more and more attention due to increasing damages. Markov chain models have been used to predict epidemic behavior qualitatively and quantitatively, but most of them model random propagation as a basic multiplicative factor. In this paper, we propose an epidemic model Susceptible-Infected without command-Infected with command (SII’), and derive an exact Markov chain for SIoT malware propagation. We also employ a Markov chain for an SIoT malware mitigation system that groups random devices alongside those with detected infections during the malware eradication process. This mitigation mechanism operates at the network scale, addressing the risks associated with large-scale SIoT deployments through a strategic, yet assertive, approach of widespread disconnections. Such a system effectively drives down the basic reproduction number to less than 1, preventing malware from gaining dominance over the network–all accomplished without modifying the recovery rate. We conducted experimental simulations of the proposed model’s dynamic predictions, and the experimental results show that the use of an exact Markov chain model better matches the benchmark results of our proposed model and also verifies the different effects of group-based mitigation in different SIoT contexts. © 2014 IEEE.

关键词： Markov chains

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Label-Enhanced Cross-Modal Hashing with Dual-Semantic Learning 25th

Label-Enhanced Cross-Modal Hashing with Dual-Semantic Learn...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Fang, Ziye Teng, Luyao Zheng, Zefeng Zhang, Wei Teng, Shaohua School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou511483 China

ISBN: (纸本)9789819605668

Due to its excellent storage and retrieval performance, Cross-Modal Hashing (CMH) has garnered widespread attention in recent years. However, current methods still face two major limitations: 1) most methods fail to adequately address noise in the labels, 2) many methods neglect learning the correlations between labels. To address these issues, we propose Label-enhanced Cross-modal Hashing with Dual-semantic Learning (LCHDL) in this study. LCHDL mainly consists of two parts, i.e., DeNoise Label Semantics Learning (DNLSL) and Static Label Correlations Embedding (SLCE). DNLSL uses low-rank and sparse representation to reduce noise containing in the labels. SLCE employs a relaxation strategy to achieve static label correlations embedding. Experiments on two benchmark datasets demonstrate that the performance of LCHDL surpasses many state-of-the-art methods. The source code for LCHDL is publicly available at https://***/Fizzyf/LCHDL. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A comprehensive strategy for identifying plagiarism in academic submissions

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Journal of Umm Al-Qura University for engineering and Architecture 2025年第2期16卷 310-325页

作者： Setu, Deblina Mazumder Islam, Tania Erfan, Md. Dey, Samrat Kumar Asif, Md. Rashid Al Samsuddoha, Md. Department of Computer Science and Engineering University of Barishal Barishal 8200 Bangladesh School of Science and Technology (SST) Bangladesh Open University (BOU) Gazipur 1705 Bangladesh

Plagiarism is a major problem in education, especially in higher education environments. To address this problem, a comprehensive detection method is proposed, utilizing cutting-edge models like Bidirectional Encoder Representations from Transformers (BERT) and cosine similarity for detecting direct copy and semantic changes. The methodology involves evaluating the BERT model and cosine similarity first, then performing pairwise comparisons to determine how similar a student’s work is to a reference text. If the semantic changes are higher, the content is classified as paraphrased;otherwise, it is considered direct copy in cases where the similarity threshold is greater than 30%. When the similarity threshold is less than or equal to 30%, the method moves to online plagiarism detection. This includes web scraping, generating search queries, and employing Term Frequency-Inverse Document Frequency (TF-IDF) vectorization with cosine similarity to assess the similarities between students’ content and online content. As a result, the study detects online plagiarism and distinguishes between plagiarizing and authentic content. The purpose is to provide an effective method to maintain academic integrity and enhance originality in student work. In similarity testing on MIT plagiarism detection datasets, this study demonstrated an average recall of 80%, precision of 68%, accuracy of 71%, and f1-score of 74%. With a threshold of 0.2, the average precision for online plagiarism detection reached 65%, accompanied by a 56% recall, an accuracy of 60%, and an f1-score of 60%. The results of the study demonstrate the efficiency of the strategy in enhancing plagiarism detection accuracy while reducing false positives. © The Author(s) 2025.

关键词： Bidirectional encoder representations from transformers Cosine similarity Paraphrasing Plagiarism Term frequency-inverse document frequency Web scraping

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MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction 39

MSR: A Multifaceted Self-Retrieval Framework for Microscopic...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hong, Dongsheng Chen, Chao Li, Xujia Wang, Shuhui Lin, Wen Liao, Xiangwen College of Computer and Data Science Fuzhou University China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong College of Computer and Control Engineering Minjiang University China

ISBN: (纸本)157735897X

The microscopic cascade prediction task has wide applications in downstream areas like "rumor detection". Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of "infected" users while neglecting those of "uninfected" users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent "behavioral preferences" and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Taxonomies

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Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment 2

Transformer-Based Classification Outcome Prediction for Mult...

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2nd IEEE International Conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Ma, Danqing Wang, Meng Xiang, Ao Qi, Zongqing Yang, Qin Computer Science Stevens Institute of Technology HobokenNJ United States University of Sofia Department of Computer Science Palo AltoCA United States School of Computer Science & Engineering University of Electronic Science and Technology of China Sichuan China School of Integrated Circult Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Stevens Institute of Technology Computer Science HobokenNJ United States

ISBN: (纸本)9798350373646

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects. © 2024 IEEE.

关键词： computerized tomography

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Distributed Event-Triggered Voltage Secondary Control Strategy for Microgrids Considering Load Forecasting

Distributed Event-Triggered Voltage Secondary Control Strate...

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2024 China Automation Congress, CAC 2024

作者： Qin, Chao Zhang, Jiancheng Ma, Lingyu Pang, Shanchen School of Computer Science and Technology China University of Petroleum Qingdao China School of Information Science and Engineering Qilu Normal University Jinan China School of Information Science and Engineering Shandong Management University Jinan China

ISBN: (纸本)9798350368604

Voltage regulation in microgrids (MGs) is increasingly challenging due to the integration of renewable energy sources and load variability. This paper presents a distributed event-triggered voltage secondary control strategy for MGs, incorporating load forecasting using semi-Markov theory. By predicting load variations, the proposed method utilizes an event-triggered communication mechanism to adjust the voltage of distributed generation units, effectively solving voltage regulation issues caused by line impedance mismatches and load fluctuations. Lyapunov stability theory is employed to prove the stability of the event-triggered system with semi-Markov switching topology. Simulation results demonstrate that the proposed control strategy significantly reduces communication frequency while maintaining voltage at its rated value, even under varying load conditions. This approach enhances the reliability and efficiency of MG operations, demonstrating its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Microgrids

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