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Joining copper to thermosetting polymer via “Mortise and Tenon” interlocking and chemical-bonding both in-situ generated during friction stir spot welding

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Journal of Manufacturing Processes 2025年 149卷 318-330页

作者： Yidi Wu Jiaxun Li Zijie Huang Fangzheng Shi Licheng Sun Huihong Liu Gaoqiang Chen Peng Li Yongbing Li Zhongqin Lin Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures Shanghai Jiao Tong University Shanghai 200240 PR China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai 200240 PR China State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment Department of Mechanical Engineering Tsinghua University Beijing 100084 PR China Key Laboratory for Advanced Materials Processing Technology Ministry of Education Beijing 100084 PR China Shanghai Radio Equipments Research Institute 201109 Shanghai PR China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai 200240 PR China

This study successfully joined the hard-to-weld combination of Cu and thermosetting polyimide (PI) for the first time using a novel strategy combining chemical plating and friction stir spot welding (FSSW). A thin Cu layer was first chemical-plated on a surface-roughened PI sheet, then a thick Cu sheet was friction stir spot welded onto the chemical-plated PI. Under the appropriate thermo-mechanical conditions, Cu sheet beneath the stir zone was bonded atomically with the chemical-plated Cu layer, transferring both vertically downward force and horizontally shearing torque to the Cu materials approaching to the Cu/PI interface. A strong “mortise and tenon” interlocking was thus generated at the Cu/PI interface owing to the plastic deformation of Cu and the constraint from PI. Simultaneously, the released CO and NO₂ gases from PI, along with the thermal environment, oxidized the Cu surface at the Cu/PI interface, forming a nanoscale Cu₂O layer. This enabled chemical bonding between the functional group (-C=O) on the PI's surface and the Cu₂O oxide on the Cu surface. Both “mortise and tenon” interlocking and chemical bonding were finally generated in-situ during the welding process, thereby playing a remarkable effect on improving the mechanical performance of Cu/PI joints.

关键词： Thermosetting polyimide Copper "mortise and tenon" interlocking Chemical bonding

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/digitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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CM-UNet++: A Multi-Level Information Optimized Network for Urban Water Body Extraction from High-Resolution Remote Sensing Imagery

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Remote Sensing 2025年第6期17卷 980-980页

作者： Cai, Jiangchen Tao, Liufeng Li, Yang National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan 430074 China School of Computer Science China University of Geosciences Wuhan 430074 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resource Shenzhen518000 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan 430074 China School of Geophysics and Geomatics China University of Geosciences Wuhan 430074 China Anhui Urban & Rural Planning and Design Institute Co. Ltd. Hefei230022 China

Urban water bodies are crucial in urban planning and flood detection, and they are susceptible to changes due to climate change and rapid urbanization. With the development of high-resolution remote sensing technology and the success of semantic segmentation using deep learning in computer vision, it is possible to extract urban water bodies from high-resolution remote sensing images. However, many urban water bodies are small, oddly shaped, silted, or spectrally similar to other objects, making their extraction extremely challenging. In this paper, we propose a neural network named CM-UNet++, a combination of the dense-skip module based on UNet++ and the CSMamba module to encode different levels’ information with interactions and then extract global and local information at each level. We use a size-weighted auxiliary loss function to balance feature maps of different levels. Additionally, features beyond RGB are incorporated into the input of the neural network to enhance the distinction between water bodies and other objects. We produced a labeled urban water extraction dataset, and experiments on this dataset show that CM-UNet++ attains 0.8781 on the IOU (intersection over union) metric, which indicates that this method outperforms other recent semantic segmentation methods and achieves better completeness, connectivity, and boundary accuracy. The proposed dense-skip module and CSMamba module significantly improve the extraction of small and spectrally indistinct water bodies. Furthermore, experiments on a public dataset confirm the method’s robustness. © 2025 by the authors.

关键词： Semantic Segmentation

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Considering Multi-Scale Data for Continuous Traffic Prediction Using Adaptive Multi-Agent system

Considering Multi-Scale Data for Continuous Traffic Predicti...

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International Conference on Intelligent Transportation

作者： Ha Nhi Ngo Elsy Kaddoum Matej Cabecauer Erik Jenelius Anaïs Goursolle Continental Digital Services France Toulouse France Cooperative Multi-Agent System team IRIT Computer Science Research Institute of Toulouse Paul Sabatier University Toulouse France Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden data scientist at Continental Digital Service France Toulouse France

Accurate traffic prediction is essential for effective traffic management and planning. However, traffic prediction models are challenged by various factors such as complex spatiotemporal dependencies in traffic data. Recently, researchers have explored the new approach known as stream analysis since it can continuously update models by capturing new behaviors from the traffic data stream. However, applying this approach specifically raises the question about the balance between model complexity and model flexibility for dynamic updates. ADRIP - Adaptive multi-agent system for DRIving behaviors Prediction proposed in [1], [2] has combined the dynamic clustering and the multi-agent system approach to solve this challenge. This system has been applied to predict traffic dynamics at the road segment level. In this paper, we aim to extend ADRIP to complete its functionality for traffic prediction at the network level. Experiments for multi-scale traffic data are conducted to compare extended ADRIP with well-known clustering models.

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MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation

MPS-AMS: Masked Patches Selection and Adaptive Masking Strat...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiangtao Wang Ruizhi Wang Biao Tian Jiaojiao Zhang Shuo Zhang Junyang Chen Thomas Lukasiewicz Zhenghua Xu State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China College of Computer Science and Software Engineering and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Institute of Logic and Computation TU Wien Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling methods are mainly utilized in natural images, and their applications in medical images are relatively lacking. Besides, their fixed high masking strategy limits the upper bound of conditional mutual information, and the gradient noise is considerable, making less the learned representation information. Motivated by these limitations, in this paper, we propose masked patches selection and adaptive masking strategy based self-supervised medical image segmentation method, named MPS-AMS. We leverage the masked patches selection strategy to choose masked patches with lesions to obtain more lesion representation information, and the adaptive masking strategy is utilized to help learn more mutual information and improve performance further. Extensive experiments on three public medical image segmentation datasets (BUSI, Hecktor, and Brats2018) show that our proposed method greatly outperforms the state-of-the-art self-supervised baselines.

关键词： Image segmentation Upper bound Image analysis Self-supervised learning Signal processing Lesions Task analysis

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MvCo-DoT: Multi-View Contrastive Domain Transfer Network for Medical Report Generation

MvCo-DoT: Multi-View Contrastive Domain Transfer Network for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ruizhi Wang Xiangtao Wang Zhenghua Xu Wenting Xu Junyang Chen Thomas Lukasiewicz State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China College of Computer Science and Software Engineering and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Institute of Logic and Computation TU Wien Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency. However, the existing medical report generation methods cannot exploit the rich multi-view mutual information of medical images. Therefore, in this work, we propose the first multi-view medical report generation model, called MvCo-DoT. Specifically, MvCo-DoT first propose a multi-view contrastive learning (MvCo) strategy to help the deep reinforcement learning based model utilize the consistency of multi-view inputs for better model learning. Then, to close the performance gaps of using multi-view and single-view inputs, a domain transfer network is further proposed to ensure MvCo-DoT achieve almost the same performance as multi-view inputs using only single-view inputs. Extensive experiments on the IU X-Ray public dataset show that MvCo-DoT outperforms the SOTA medical report generation baselines in all metrics.

关键词： Deep learning Training Semantics Reinforcement learning Signal processing Task analysis Speech processing

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New Smart Optimal Transition Molecules Structure Approach from Discrete to Continuous Based on Three-Dimensional Analysis Method

New Smart Optimal Transition Molecules Structure Approach fr...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Rhalem, Wajih Millimono, Sory Bellarbi, Larbi Aqili, Nabil Alidrissi, Najib Hamdi, Salsabil Kharmoum, Nassim Said, Asma Chaik Jidane, Said Belyamani, Lahcen Ghazal, Hassan Ezziyani, Mostafa E2SN Research Team ENSAM Mohammed V University in Rabat Rabat Morocco Moroccan Society of Digital Health Rabat Morocco Department of Surgery School of Medicine Mohammed VI University of Health Sciences Casablanca Morocco Environmental Health Laboratory Department of Research Pasteur Institute Casablanca Morocco National Center for Scientific and Technical Research Rabat Rabat Morocco Emergency Department Mohammed the Vth Military Training Hospital Mohammed V University in Rabat Rabat Morocco Computer Sciences Department Faculty of Sciences and Techniques of Tangier University of Abdelmalek Essaadi Tangier Morocco Mohammed VI Center for Research and Innovation Rabat Morocco Laboratory of Genomics Bioinformatics and Digital Health Mohammed VI University of Sciences and Health Casablanca Morocco Research Team in Biomedical Engineering and Pharmaceutical Sciences ENSAM Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031523847

To understand the relationship between a molecule’s function and its three-dimensional structure, researchers have been studying the evolution and mutations of the SARS-CoV-2 virus. This research focuses on identifying interaction areas between the viral spike and human cell receptors for developing treatments. In this work, a novel approach based on shape alignment and residue interaction region identification is proposed. Experimental studies using dynamic programming and Cubic Spline polynomial interpolation show promising results, making the approach highly effective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Coronavirus

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Stochastic-Robust Planning of Networked Hydrogen-Electrical Microgrids: A Study on Induced Refueling Demand

arXiv

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arXiv 2024年

作者： Sun, Xunhang Cao, Xiaoyu Zeng, Bo Zhai, Qiaozhu Başar, Tamer Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Smart Integrated Energy Department Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

Hydrogen-electrical microgrids are increasingly assuming an important role on the pathway toward decarbonization of energy and transportation systems. This paper studies networked hydrogen-electrical microgrids planning (NHEMP), considering a critical but often-overlooked issue, i.e., the demand-inducing effect (DIE) associated with infrastructure development decisions. Specifically, higher refueling capacities will attract more refueling demand of hydrogen-powered vehicles (HVs). To capture such interactions between investment decisions and induced refueling demand, we introduce a decision-dependent uncertainty (DDU) set and build a trilevel stochastic-robust formulation. The upper-level determines optimal investment strategies for hydrogen-electrical microgrids, the lower-level optimizes the risk-aware operation schedules across a series of stochastic scenarios, and, for each scenario, the middle-level identifies the "worst" situation of refueling demand within an individual DDU set to ensure economic feasibility. Then, an adaptive and exact decomposition algorithm, based on Parametric Column-and-Constraint Generation (PC&CG), is customized and developed to address the computational challenge and to quantitatively analyze the impact of DIE. Case studies on an IEEE exemplary system validate the effectiveness of the proposed NHEMP model and the PC&CG algorithm. It is worth highlighting that DIE can make an important contribution to the economic benefits of NHEMP, yet its significance will gradually decrease when the main bottleneck transits to other system restrictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Optimization Function Selection and Its Impact on Unet’s Efficiency: A Comprehensive Analysis

Optimization Function Selection and Its Impact on Unet’s Ef...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Maghzaz, Sarah Millimono, Sory Bellarbi, Larbi Aqili, Nabil Alidrissi, Najib Dehbi, Zineb El Otmani Hamdi, Salsabil Kharmoum, Nassim Chaik, Asma Jidane, Said Belyamani, Lahcen Ghazal, Hassan Ezziyani, Mostafa Rhalem, Wajih E2SN Research Team ENSAM Mohammed V University in Rabat Rabat Morocco Moroccan Society of Digital Health Rabat Rabat Morocco Department of Surgery School of Medicine Mohammed VI University of Health Sciences Casablanca Morocco Environmental Health Laboratory Department of Research Pasteur Institute Casablanca Morocco National Center for Scientific and Technical Research Rabat Morocco Emergency Department Mohammed the Vth Military Training Hospital Mohammed V University in Rabat Rabat Morocco Computer Sciences Department Faculty of Sciences and Techniques of Tangier University of Abdelmalek Essaadi Tangier Morocco Mohammed VI Center for Research and Innovation Rabat Morocco Laboratory of Genomics Bioinformatics and Digital Health Mohammed VI University of Sciences and Health Casablanca Morocco Research Team in Biomedical Engineering and Pharmaceutical Sciences ENSAM Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031523847

Automatic segmentation of thoracic organs in lung CT images is essential in the computer-aided diagnosis (CAD) process. Several powerful models have been proposed over the last few decades to facilitate this work and set up computer-aided diagnosis systems more suited to the needs;Unet is one of them, which has been proposed explicitly for medical images. In this work, we analyzed Unet-based segmentation and experimented with the impact that the choice of optimization function can have on the performance of Unet-based models. The model is evaluated based on Kaggle’s "Chest CT-Scan images Dataset." First, we manually provide the masks associated with each image, using a set of processes such as grayscale conversion, binarization, and ROI highlighting. Then we try to predict these masks through our trained Unet network before evaluating them. Experiments show that the performance of the proposed model is higher for the case where RMSprop is the optimization function and lower for SGD. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： computerized tomography

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Guest editorial: Progress on computational techniques for electromagnetic fields and applications

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COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic engineering 2023年第1期42卷 1-1页

作者： Yang, Shiyou Sykulski, Jan Qingxin, Yang Cui, Xiang Department of Electrical Engineering Zhejiang University Hangzhou China Department of Electronics and Computer Science Southampton University Southampton United Kingdom Tianjin Key Laboratory of Advanced Electrical Engineering and Energy Technology Tianjin Polytechnic University Tianjin China State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources Research Center for Electromagnetism and Superconduction Beijing China

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