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A Tetrahedral Spectral Element Method for Thermo-Mechanical Analysis of Electronic Devices

IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Zhang, Shuai Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

For the coupled thermo-mechanical simulation of complex electronic devices, we propose a Tetrahedral Spectral Element Method (TSEM), which truly combines the high accuracy of spectral methods with the strong geometric adaptability of Finite Element Methods (FEMs). The TSEM employs a collapse transformation method and successfully converts the orthogonal Gauss-Lobatto-Legendre (GLL) basis functions from a hexahedron to a tetrahedral reference element. This ensures numerical accuracy and stability for arbitrary high-order interpolation. We also generate non-equidistant GLL spectral interpolation nodes for tetrahedral elements through Warp&Blend transformation, enabling direct nodal numerical integration calculation through Vandermonde matrices. This approach replaces the classical Gaussian quadrature in the FEM, not only significantly enhancing the computational efficiency of element matrix but also avoiding potential numerical dissipation and dispersion associated with Gaussian integration, thus achieving rapid convergence. In the TSEM thermo-mechanical coupling, we further construct a separate solver based on the implicit Newmark-β method, which realizes unconditional stability for temporal integration for arbitrary time step increment. Moreover, it supports independent inversion of system matrices for temperature and stress fields, without increasing computational resource consumption due to the coupling of multi-physics equations. By quantitatively comparing with the COMSOL Multiphysics software, several typical case studies are used to demonstrate the significant advantages of the TSEM in numerical accuracy, efficiency, and stability. © 2025 IEEE. All rights reserved.

关键词： Orthogonal functions

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Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation

Exploiting Multi-Decision and Deep Refinement for Ultrasound...

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

作者： Wenjing Liu Xuanya Li Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc Beijing China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the MDM effectively addresses the issue of inconspicuous target regions in ultrasound images by combining multi-scale features and multi-receptive field self-attention to enhance the discriminative representation of features and diagnose feature points multiple times. In addition, to alleviate the problem of blurred boundaries and severe speckle noise, the DRM progressively fuses multi-scale features and makes the fused features interact with higher-level features to refine the target details step by step. Finally, we evaluate the proposed method on two publicly available datasets, namely BUSI and UDIAT. We achieve a Dice of 0.8265 and 0.8827 on the two datasets, which are at least 2% and 1.24% higher than other state-of-the-art ultrasound image segmentation methods.

关键词： Deep learning Image segmentation Visualization Ultrasonic imaging Fuses Neural networks Speckle

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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A Nonconformal SETI-DP Method for Transient Thermal Simulation

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Li, Hongyang Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

This paper proposes a novel nonconformal meshbased dual-primal spectral element tearing and interconnecting (SETI-DP) method designed for the efficient transient simulation of complex large-scale heat conduction problems utilizing a domain decomposition method (DDM). This method allows for the division of complex, large-scale problems into smaller subdomain problems. By applying Robin-type transmission conditions at the subdomain interfaces, rapid convergence of the global interface iterative solution is achieved for arbitrary non-conformal subdomain problems. This non-conformal DDM approach supports independent meshing of subdomains, eliminating the need to generate a globally continuous mesh for large-scale problems, thereby significantly enhancing meshing efficiency. In addition, this non-conformal meshing strategy enables high-contrast spatial discretization and mixed-order spectral interpolation between different subdomains. This significantly increases meshing flexibility, minimizes the number of degrees of freedom (DoF) in multi-scale problems, and reduces computational costs. In addition, by developing a subdomain-coupled time iteration scheme, the global corner problem of residual transfer is explicitly given, which greatly improves the versatility and flexibility of the algorithm. Finally, to demonstrate the accuracy, efficiency, and robustness of our algorithm, we conduct benchmark tests on several representative examples. © 2011-2012 IEEE.

关键词： Domain decomposition methods

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Hierarchical Graph Convolutional Networks With Latent Structure Learning for Mechanical Fault Diagnosis

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IEEE/ASME Transactions on Mechatronics 2023年第6期28卷 3076-3086页

作者： Zhong, Kai Han, Bing Han, Min Chen, Hongtian Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Shanghai Ship and Shipping Research Institute State Key Laboratory of Navigation and Safety Technology Shanghai200135 China Dalian University of Technology Key Lab. of Intelligent Contr. and Optimization for Indust. Equipment of the Ministry of Education Dalian116024 China University of Alberta Department of Chemical and Materials Engineering EdmontonABT6G 2V4 Canada

Industrial sensor signals are essentially non-Euclidean graph structures due to the interplay between process variables;thus, graph convolutional networks (GCNs) have been widely studied and applied. However, most of the existing GCN-based methods may suffer from two drawbacks: 1) it is difficult to characterize multiple interactions among nodes and 2) the input graph constructed from the original data may contain errors and missing edges, which will degenerate the fault diagnosis performance. To address the abovementioned issues, this article designs a hierarchical GCN with latent structure learning for industrial fault diagnosis, which can organize hierarchical networks to collaboratively improve the quality of latent graph structure, and enhanced diagnostic performance can be guaranteed. First, a high-quality updated graph is formed by incorporating the original graph with the new graph in the graph constructing layer, which can not only eliminate the adverse effects of noise and outliers but also characterize the multiple interactions among nodes. Then, the updated graph is fed into the multilayer GCN layer for better feature learning and enhances the node representation through intra- and inter-layer convolutional operations simultaneously. After that, the produced node embeddings are used to guide the latent structure learning process for optimal graph. Finally, the proposed method is verified in both the simulated and real industrial processes. The experimental results demonstrate that the new approach has better fault diagnosis accuracy and practicability than state-of-the-art methods. © 1996-2012 IEEE.

关键词： Convolution

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A Method to Verify Internal Parameters of Camera Calibration in World Coordinate System 6

A Method to Verify Internal Parameters of Camera Calibration...

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6th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2023

作者： Xing, Zheng Wang, Qingchun Li, Yingsong Qi, Junwei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9798350399059

Aiming at the problem that reprojection error (RE) cannot reflect the camera calibration accuracy intuitively, this paper proposes a method to verify the camera calibration results in world coordinate system. Firstly, the checkerboard calibration target is photographed with the camera under test, and the internal parameters and distortion parameters of the camera are calculated by using the pinhole model and the Kannala-Brandt (KB) fisheye model, respectively. Then, the same camera is used to shoot two targets in the field of view (FOV), and the pixel coordinates of the two target points are detected from the image. Thirdly, the inverse projection model is used to calculate the distance between the two objects and the angle between the camera and the two objects, and the real distance and angle are compared. The experimental results show that the average distance errors of the pinhole model and the KB fisheye model are 0.34% and 0.35%, respectively, and the angle errors are 0.40% and 0.12%, respectively, which proves that the verification method can effectively characterize the calibration accuracy of the camera. © 2023 IEEE.

关键词： Calibration

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Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Clustered Temporal Patch Transformer Network via Deep Local Feature Learning for Industrial Soft Sensor Applications

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IEEE Sensors Journal 2025年

作者： Xiao, Lin Wang, Pingping Fang, Yijing Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

Accurate estimation of key quality indexes is critical for achieving optimal control in industrial processes. However, fluctuations in operating conditions, coupled with process lags, lead to multimodal data distributions and time-dependent correlations. These dynamics create fixed characteristic patterns within operating modes while propagating changes with delays, increasing the complexity of quality soft sensor modeling. To address these challenges, this paper proposes a novel Clustered Temporal Patch Transformer (CTPT-Former) for soft sensor modeling in industrial processes. CTPT-Former perceives coarse-grained process variability by identifying distinct operational modes in multimodal data. Each modal data is subsequently partitioned into patch units to enhance local feature extraction and capture fine-grained temporal dynamics. Futhermore, a time-dependent self-attention mechanism (CTPT-SA) is developed to strengthen the ability of model to uncover local and temporal correlations within and across modes, alleviating the complexities introduced by operational dependencies and spatial continuity of materials. Experimental results on real industrial datasets validate the superiority of the proposed approach compared to existing state-of-the-art methods. © 2001-2012 IEEE.

关键词： Model predictive control

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The Problems and Analysis of Artificial Intelligence Specialty Construction in Universities Under the Present Situation of Artificial Intelligence Development 8th

The Problems and Analysis of Artificial Intelligence Special...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Fu, Weina Liu, Shuai Institute of Information Science and Engineering Hunan Normal University Hunan Changsha410081 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha410081 China

ISBN: (纸本)9783031211638

China’s independent innovation ability in the field of artificial intelligence is a key link to occupy the commanding heights of future science and technology and talent competition. The cultivation of artificial intelligent talents is crucial to promote the development of the artificial intelligent industry. There is a large demand for artificial intelligence talents in the market, but the development of artificial intelligence in colleges and universities is still faced with many problems, such as the construction of related disciplines and majors has just started, the strength of artificial intelligence teachers in colleges and universities is weak, the talent training is mainly based on postgraduate education and the education system is not perfect. This paper analyzes the current cultivation situation of artificial intelligence major in undergraduate colleges and vocational colleges, points out the shortcomings in the construction of artificial intelligence major, and gives some suggestions. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Personnel training

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Food computing for Nutrition and Health

Food Computing for Nutrition and Health

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International Conference on Data Engineering Workshops

作者： Shuqiang Jiang Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing University of Chinese Academy of Sciences Beijing

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

Dietary intake is the main source of human nutrition and thus an important material foundation for maintaining human health. Food computing applies computational approaches to intelligently obtain dietary categories, ingredients, and nutrient intake from dietary data, and then provides dietary recommendations to users based on various dietary factors. Therefore, it can achieve intelligent dietary management to assist in solving human health issues. For its significant impact on human health, it has received more attention from both academia and industry, and has made rapid development, especially with the booming Artificial Intelligence (AI). This talk will first introduce the definition, methods, tasks and applications of food computing. Then we will discuss its various tasks, ranging from vision-based multi-granularity food analysis, multimodal recipe analysis, food knowledge graph construction and dietary recommendation. Finally, we will point out future research directions on food computing, such as large food language/multimodal models and the emerging interdisciplinary field AI4Food from the synergy between AI and food science. We expect that this talk will provide the researchers with a more comprehensive understanding of the field, and provide perspectives for the next wave of development in this rapidly growing field.

关键词： Industries Conferences Computational modeling Knowledge graphs Data engineering Distance measurement Artificial intelligence

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