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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Hu, Hongsheng Xu, Hao Huang, Shipei Zhuhai519082 China National-Local Joint Engineering Laboratory for Digitalize Electrical Design Technology Wenzhou University Wenzhou325035 China

ISBN: (纸本)9789819616930

A finite-time boundedness (FTB) round-robin protocol (RRP) control problem for the 2-D continuous-discrete system described by Roesser model is investigated in the work. Firstly, a novel 2-D RRP is developed to reduce network data congestion and data collisions between controllers and actuators. Under the RRP, a finite-time controller is designed for considered systems. By resorting to the Lyapunov function, deriving finite-time bounded sufficient conditions guarantees that the state is finite-time bounded in a finite-time interval. Finally, the correctness of the proposed result is proved via a simulation example under the RRP in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete time control systems

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Optimal Planning of a Standalone Microgrid by Constrained Multi-objective Population Extremal Optimization

Optimal Planning of a Standalone Microgrid by Constrained Mu...

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International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Qin, Zhen Zeng, Guo-Qiang Lu, Kang-Di Wang, Rong Wu, Jun-Yi National-Local Joint Engineering Laboratory of Digitalize Electrical Design Technology Wenzhou University Wenzhou325035 China College of Information Science and Technology Donghua University Shanghai201620 China

ISBN: (纸本)9789819618637

A standalone microgrid plays an important role in supplying power to residential and remote areas by integrating different types of distributed generation. One of the key open issues in both academic and engineering fields is how to provide an optimal planning strategy for distributed generations and storage systems in a standalone microgrid, considering multi-objective performance indices such as reliability and cost. In this paper, a novel constrained multi-objective population extremal optimization (CMOPEO) is proposed for standalone microgrid optimal planning. The fundamental concept of the suggested approach is to frame the problem as a standard constrained multi-objective problem, where power loss probability, fuel emission, and power cost of the system are considered as the objectives, and the maximum amount of extra power and the availability of renewable resources are the constraints. The superiority of the CMOPEO algorithm is demonstrated through simulation results on a standalone microgrid case over multi-objective particle swarm optimization. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Constrained optimization

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A Cost-Effective Hybrid Si/ SiC Dual Bridge Parallel LLC Resonant Converter for High Current Application

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Ke, Zipeng Wang, Jun Hu, Bo Zhang, Chao Peng, Zishun Dai, Yuxing Shen, Z. John Hunan University College of Electrical and Information Engineering Changsha410012 China Guizhou University Electrical Engineering College Guiyang550025 China Wenzhou University National-Local Joint Engineering Laboratory of Digitalize Electrical Design Technology Wenzhou325035 China Simon Fraser University School of Mechatronic Systems Engineering SurreyBCV3T 0A3 Canada

The SiC-based dual bridge paralleled (DBP) LLC resonant converter offers advantages of low current stress and high efficiency but suffers from high costs. In this article, a cost-effective hybrid Si/SiC DBP LLC resonant converter is proposed, which consists of a high-current low-cost Si IGBTs bridge arm and a low-current SiC MOSFETs bridge arm, obtaining significant cost-reduction. Benefiting the soft-switching operation of the Si IGBTs bridge arm, the proposed hybrid Si/SiC DBP LLC converter can achieve comparable switching losses with full-SiC MOSFETs design. Meanwhile, fewer resonant tanks are required benefiting from the topology reconstruction of the proposed converter, in comparison to the prior-art design of DBP LLC resonant converter, further reducing costs and power loss. A 6-kW prototype of the hybrid DBP LLC resonant converter, utilizing both Si IGBTs and SiC MOSFETs, is constructed as a case study to validate the proposed approach. Compared to the prior art full-SiC DBP LLC converter, the proposed hybrid Si/SiC LLC converter achieves comparable total power loss under high output power, while offering 51.7% switching device cost, 22.9% resonant components cost, and 31.6% total cost reductions. © 2013 IEEE.

关键词： high efficiency Hybrid Si/SiC LLC resonant converter power loss

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A Global Visual Information Intervention Model for Medical Visual Question Answering

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Computers in Biology and Medicine 2025年第Pt A期192卷 110195-110195页

作者： Peng, Peixi Fan, Wanshu Shen, Yue Yang, Xin Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer-Aided Design School of Software Engineering Dalian University Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Medical Visual Question Answering (Med-VQA) aims to furnish precise responses to clinical queries related to medical imagery. While its transformative potential in healthcare is undeniable, current solutions remain nascent and are yet to see widespread clinical adoption. Med-VQA presents heightened complexities compared to standard visual question answering (VQA) tasks due to the myriad of clinical scenarios and the scarcity of labeled medical imagery. This often culminates in language biases and overfitting vulnerabilities. In light of these challenges, this study introduces Global Visual Information Intervention (GVII), an innovative Med-VQA model designed to mitigate language biases and improve model generalizability. GVII is centered on two key branches: the Global Visual Information Branch (GVIB), which extracts and filters holistic visual data to amplify the image's contribution and reduce question dominance, and the Forward Compensation Branch (FCB), which refines multimodal features to counterbalance disruptions introduced by GVIB. These branches work in tandem to enhance predictive accuracy and robustness. Furthermore, a multi-branch fusion mechanism ensures cohesive integration of features and losses across the model. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art models, achieving a 2.6% improvement in accuracy on the PathVQA dataset. In conclusion, the GVII-based Med-VQA model not only successfully mitigates prevalent language biases and overfitting issues but also significantly improves diagnostic precision, offering a considerable stride toward robust, clinically applicable VQA systems. © 2025

关键词： Question answering

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CNsum:Automatic Summarization for Chinese News Text

arXiv

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arXiv 2025年

作者： Zhao, Yu Huang, Songping Zhou, Dongsheng Ding, Zhaoyun Wang, Fei Nian, Aixin Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha410000 China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian116000 China

Obtaining valuable information from massive data efficiently has become our research goal in the era of Big Data. Text summarization technology has been continuously developed to meet this demand. Recent work has also shown that transformer-based pre-trained language models have achieved great success on various tasks in Natural Language Processing (NLP). Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese, this paper proposes a Chinese news text summarization model (CNsum) based on Transformer structure, and tests it on Chinese datasets such as THUCNews. The results of the conducted experiments show that CNsum achieves better ROUGE score than the baseline models, which verifies the outperformance of the model. © 2025, CC BY.

关键词： Big data

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FDC: Feature Dropout Consistency for unsupervised domain adaptation semantic segmentation

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Neural Networks 2025年 189卷 107527页

作者： Rao, Chaoyu Fan, Wanshu Wang, Cong Yang, Xin Wei, Xiaopeng Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Dalian University of Technology China

In Unsupervised Domain Adaptation Semantic Segmentation (UDASS), while self-training techniques have become one of the most effective methods to date, the absence of target labels makes models susceptible to overfitting. To address this problem, consistency techniques regularize the model by perturbing the target domain and enforcing consistent pixel predictions before and after the perturbation. Consistency techniques are based on the idea that a well-performing model should yield consistent predictions for the target data stream before and after perturbations. In this paper, we introduce the Feature Dropout Consistency (FDC) module, a novel approach that instigates perturbations at the feature level. This is achieved by implementing random feature dropout between the encoder and decoder of the student network. Subsequently, a feature dropout consistency loss is applied to minimize discrepancies in predictions between the perturbed student network and its teacher counterpart. Notably, FDC seamlessly integrates with existing self-training methodologies and, when paired with input perturbation, delves into an even broader perturbation spectrum. Through rigorous experimentation in standard UDA settings — training with synthetic labeled and real unlabeled data — FDC consistently outperforms baseline models, establishing new benchmarks on GTAV → Cityscapes and SYNTHIA → Cityscapes. © 2025

关键词： Semantic Segmentation

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Iterative Optimal Attention and local Model for Single Image Rain Streak Removal

arXiv

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arXiv 2025年

作者： Li, Xiangyu Fan, Wanshu Shen, Yue Wang, Cong Wang, Wei Yang, Xin Zhang, Qiang Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer Science and Technology Dalian University of Technology Dalian China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University School of Computer Science and Technology Dalian University of Technology Dalian China

High-fidelity imaging is crucial for the successful safety supervision and intelligent deployment of vision-based measurement systems (VBMS). It ensures high-quality imaging in VBMS, which is fundamental for reliable visual measurement and analysis. However, imaging quality can be significantly impaired by adverse weather conditions, particularly rain, leading to blurred images and reduced contrast. Such impairments increase the risk of inaccurate evaluations and misinterpretations in VBMS. To address these limitations, we propose an Expectation Maximization Reconstruction Transformer (EMResformer) for single image rain streak removal. The EMResformer retains the key self-attention values for feature aggregation, enhancing local features to produce superior image reconstruction. Specifically, we propose an Expectation Maximization Block seamlessly integrated into the single image rain streak removal network, enhancing its ability to eliminate superfluous information and restore a cleaner background image. Additionally, to further enhance local information for improved detail rendition, we introduce a local Model Residual Block, which integrates two local model blocks along with a sequence of convolutions and activation functions. This integration synergistically facilitates the extraction of more pertinent features for enhanced single image rain streak removal. Extensive experiments validate that our proposed EMResformer surpasses current state-of-the-art single image rain streak removal methods on both synthetic and real-world datasets, achieving an improved balance between model complexity and single image deraining performance. Furthermore, we evaluate the effectiveness of our method in VBMS scenarios, demonstrating that high-quality imaging significantly improves the accuracy and reliability of VBMS tasks. Codes are at https://***/ghfkahfk/EMResformer. Copyright © 2025, The Authors. All rights reserved.

关键词： Rain

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Semantic-Guided Global-local Collaborative Networks for Lightweight Image Super-Resolution

arXiv

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arXiv 2025年

作者： Fan, Wanshu Wang, Yue Wang, Cong Zhang, Yunzhe Wang, Wei Zhou, Dongsheng The National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China Hong Kong School of Cyber Science and Technology Sun Yat-Sen University Shen Zhen China

Single-Image Super-Resolution (SISR) plays a pivotal role in enhancing the accuracy and reliability of measurement systems, which are integral to various vision-based instrumentation and measurement applications. These systems often require clear and detailed images for precise object detection and recognition. However, images captured by visual measurement tools frequently suffer from degradation, including blurring and loss of detail, which can impede measurement accuracy. As a potential remedy, we in this paper propose a Semantic-Guided Global-local Collaborative Network (SGGLC-Net) for lightweight SISR. Our SGGLC-Net leverages semantic priors extracted from a pre-trained model to guide the super-resolution process, enhancing image detail quality effectively. Specifically, we propose a Semantic Guidance Module that seamlessly integrates the semantic priors into the super-resolution network, enabling the network to more adeptly capture and utilize semantic priors, thereby enhancing image details. To further explore both local and non-local interactions for improved detail rendition, we propose a Global-local Collaborative Module, which features three Global and local Detail Enhancement Modules, as well as a Hybrid Attention Mechanism to work together to efficiently learn more useful features. Our extensive experiments show that SGGLC-Net achieves competitive PSNR and SSIM values across multiple benchmark datasets, demonstrating higher performance with the multi-adds reduction of 12.81G compared to state-of-the-art lightweight super-resolution approaches. These improvements underscore the potential of our approach to enhance the precision and effectiveness of visual measurement systems. Codes are at https://***/fanamber831/SGGLC-Net. © 2025, CC BY.

关键词： Object detection

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State of charge estimation for lithium-ion battery based on dynamic data reconciliation combined with unscented Kalman filtering and long short-term memory network

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Journal of Energy Storage 2025年 124卷

作者： Yan, Jin Zhang, Zhengjiang Wang, Sigao Hong, Zhihui Zhao, Sheng Wu, Guichu The National and Local Joint Engineering Research Center of Electrical Digital Design Technology Wenzhou University Zhejiang Wenzhou325000 China The Key Laboratory of Low-Voltage Apparatus Intellectual Technology of Zhejiang Wenzhou University Zhejiang Wenzhou325000 China Zhejiang Juchuang Intelligent Technology Company Limited Zhejiang Wenzhou325000 China

For new energy vehicles, their safety and reliability are closely related to the accuracy of the state of charge (SOC) estimation of lithium batteries. However, the single SOC estimation methods have their limitations, and the accuracy of the estimation is limited. To improve the accuracy of SOC estimation, this study proposes a hybrid method based on dynamic data reconciliation (DDR) for data fusion. The method combines the Unscented Kalman Filter (UKF) based on the Thevenin model and the long short-term memory (LSTM) method, which operate in parallel to generate two sets of SOC prediction data, after which the two sets of data are fused by DDR to obtain the final SOC estimation result. The results from both the simulation and experimental set, show that the proposed method not only integrates the advantages of the two single methods, but also demonstrates high robustness and accuracy. © 2025 Elsevier Ltd

关键词： Lithium-ion batteries

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CmERFⅠ-5 and CmERFⅤ-2 affect ethylene synthesis and sucrose accumulation in postharvest oriental melon fruit at low temperature

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Postharvest Biology and technology 2025年 219卷

作者： Yang, Fan Gao, Ge Wang, Cheng Guan, Jingyue Chen, Junqin Qi, Hongyan College of Horticulture Shenyang Agricultural University Shenyang 110866 China Key Laboratory of Protected Horticulture of Education of Ministry and Liaoning Province China National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology Shenyang 110866 China

Postharvest low-temperature storage crucially affects ethylene-dependent fruit ripening and the sucrose accumulation of climacteric melon fruit;however, the molecular mechanism by which ethylene affects sugar content at low temperature remains unclear. Here, ‘High Sucrose’ (‘HS’) melon fruit were harvested at the initial stage of ethylene release (30 d after anthesis, DAA) and treated at different temperature (25 ℃, 10 ℃, and 4 ℃). The results revealed that both low temperature treatments had a lower sucrose content and sucrose percentage while also having a higher starch content, hexose level, vacuolar invertase (VINV) activity, and CmVINV2 expression in the melon fruit. Moreover, the ethylene production and CmACO1 (ACC oxidase 1) expression were lower at low temperature. Both low-temperature treatments positively and negatively regulated the expression of the ethylene response factors CmERFV-2 and CmERFI-5, respectively. We found that CmERFV-2 and CmERFI-5 could positively regulate the promoters of CmVINV2 and CmACO1, respectively. The agrobacterium-mediated transient transformation further verified that CmERFV-2 silencing and CmERFI-5 overexpression resulted in higher sucrose levels and produced more ethylene. In short, postharvest low-temperature treatments promoted CmERFV-2 expression and inhibited CmERFI-5 expression, which activated the transcription of downstream CmVINV2 and CmACO1, respectively, ultimately affecting sugar content and ethylene biosynthesis. The lower production of ethylene at low temperature reduced starch degradation, thereby affecting sugar accumulation in melon fruit. This study explained the cause for the lower sweetness of postharvest oriental melon fruit at low temperature and provided new insights into maintaining flavour quality at low temperature. © 2024 Elsevier B.V.

关键词： CmACO1 CmVINV2 Ripening Sugar content Vacuolar invertase

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