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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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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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High Energy Efficient In-Situ Storage computing Macro for Multiply-Accumulate Operations

SSRN

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SSRN 2024年

作者： Peng, Chunyu Yan, Shengyuan Ding, Huayi Wang, Yana Lu, Wenjuan Dai, Chenghu Li, Xin Hu, Wei Wu, Xiulong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China School of Integrated Circuits Anhui University Hefei230601 China

This study presents a fully digital CIM macro featuring a novel self-write-back 12T cell. This bitcell is capable of performing Boolean logic operations and autonomously writing back results into the in-situ cell, circumventing the need for peripheral circuits. It enables in-situ logic operations, such as AND, and facilitates a 4-bit×4-bit MAC operation through an adder tree and shift-and-add process, thereby enhancing energy efficiency and throughput while minimizing area overhead. Further, we propose an 18T full adder structure with lower delay and power consumption than current state-of-the-art full adders. The proposed CIM macro, designed and simulated using a 28 nm CMOS process with a 16 Kb static random access memory (SRAM), demonstrates promising results. At VDD = 0.9 V, the logic operation energy consumption is 31.71 fJ/bit with an energy efficiency of 31.53 TOPS/W, and at VDD = 0.6 V, it decreases to 8.85 fJ/bit with an efficiency of 112.99 TOPS/W. © 2024, The Authors. All rights reserved.

关键词： Static random access storage

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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Deform U-Net: Unsupervised Deformable 3D Biomedical Image Registration Network

Deform U-Net: Unsupervised Deformable 3D Biomedical Image Re...

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Artificial intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yahu Yao Rong Lu Jun Wu Zaiyang Tao Lei Qu Key Laboratory of Computational Intelligence and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University (AHU) Hefei China Anhui University (AHU) Refei China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

We can focus on the challenges posed by traditional registration algorithms and the limitations of U-Net-based architectures in capturing sufficient semantic information due to their shallow coding layers with small receptive fields. We want to explore how newer architectures or modifications to existing ones could address these limitations. Inspired by introduced deformable convolutional networks, we plan to fuse deformable convolution into our proposed network structure. Building on its success in 2D target detection, we apply deformable convolution to 3D image registration networks. The Deform U-Net proposed in this paper can compensate for the shortcomings of existing registration networks by obtaining more semantic information with unsupervised training without Introduction of additional features. We conducted evaluations on two publicly available human brain MRI, specifically LPBA40 and IBSR18. On the LPBA40 and ISBR18 datasets, the DSC reached 68.82, 62.30, respectively. The experimental results show that the Deform U-N et image registration method outperforms the VoxelMorph image registration method.

关键词： Training Seminars Image registration Three-dimensional displays Convolution Architecture Semantics

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Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation

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Journal of Beijing Institute of Technology 2020年第1期29卷 110-119页

作者： Wenxia Bao Yaping Yang Dong Liang Ming Zhu Key Laboratory Intelligent Computing and Signal Processing of the Ministry of Education Anhui UniversityHefei 230039China

A multi-residual module stacked hourglass network(MRSH)was proposed to improve the accuracy and robustness of human body pose *** network uses multiple hourglass sub-networks and three new residual *** the hourglass sub-network,the large receptive field residual module(LRFRM)and the multi-scale residual module(MSRM)are first used to learn the spatial relationship between features and body parts at various *** the improved residual module(IRM)is used when the resolution is *** final network uses four stacked hourglass sub-networks,with intermediate supervision at the end of each hourglass,repeating high-low(from high resolution to low resolution)and low-high(from low resolution to high resolution)*** network was tested on the public datasets of Leeds sports poses(LSP)and MPII human *** experimental results show that the proposed network has better performance in human pose estimation.

关键词： human pose estimation residual learning image feature hourglass network

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Distributed solving Sylvester equations with fractional order dynamics

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Control Theory and Technology 2021年第2期19卷 249-259页

作者： Songsong Cheng Shu Liang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and AutomationAnhui UniversityHefei230601AnhuiChina Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China

This paper addresses distributed computation Sylvester equations of the form AX+XB=C with fractional order *** partitioning parameter matrices A,B and C,we transfer the problem of distributed solving Sylvester equations as two distributed optimization models and design two fractional order continuous-time algorithms,which have more design freedom and have potential to obtain better convergence performance than that of existing first order ***,rewriting distributed algorithms as corresponding frequency distributed models,we design Lyapunov functions and prove that proposed algorithms asymptotically converge to an exact or least squares ***,we validate the effectiveness of proposed algorithms by providing a numerical example.

关键词： Fractional order calculus Distributed optimization Sylvester equation Frequency distributed model Convergence

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Meta-feature-based Concept Evolution Detection on Feature Streams

Meta-feature-based Concept Evolution Detection on Feature St...

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6th International Conference on Intelligent computing and signal processing (ICSP)

作者： Yufeng Guo Peng Zhou Yanping Zhang Xin Jiang School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

Data in the real world is often not static but generated and processed in streams, such as real-time adjustment of device setting parameters and real-time GPS positioning data. Feature streams means the number of samples is fixed, and their features are generated and arrive individually over time. A significant challenge of learning from online streaming data is a phenomenon known as concept evolution, that the concept of the data may change over time. In the streaming feature scenario, we define meta-features as univariate statistics describing data distribution and use meta-features to capture the data distribution and statistical properties of concepts. Therefore, an efficient Meta-Feature-based Concept Evolution Detection framework on Feature Streams (MF-CED-FS) is proposed, which consists of a sliding window, meta-feature vector similarity discrimination, and a concept detection method based on a weighted bipartite graph. Extensive experiments on real-world high-dimensional datasets verify the effectiveness of MF-CED-FS.

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Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis

arXiv

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arXiv 2023年

作者： Chen, Xiuwei Chang, Xiaobin School of Artificial Intelligence Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Class incremental learning (CIL) aims to recognize both the old and new classes along the increment tasks. Deep neural networks in CIL suffer from catastrophic forgetting and some approaches rely on saving exemplars from previous tasks, known as the exemplar-based setting, to alleviate this problem. On the contrary, this paper focuses on the Exemplar-Free setting with no old class sample preserved. Balancing the plasticity and stability in deep feature learning with only supervision from new classes is more challenging. Most existing Exemplar-Free CIL methods report the overall performance only and lack further analysis. In this work, different methods are examined with complementary metrics in greater detail. Moreover, we propose a simple CIL method, Rotation Augmented Distillation (RAD), which achieves one of the top-tier performances under the Exemplar-Free setting. Detailed analysis shows our RAD benefits from the superior balance between plasticity and stability. Finally, more challenging exemplar-free settings with fewer initial classes are undertaken for further demonstrations and comparisons among the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Attention Mechanism Based Technical Phrase Extraction for Patent Text

Attention Mechanism Based Technical Phrase Extraction for Pa...

引用

6th International Conference on Intelligent computing and signal processing (ICSP)

作者： Xin Jiang Peng Zhou Shu Zhao Yufeng Guo School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

Technological innovation is becoming one of the critical factors in promoting social development all over the world. The vigorous development of patent applications in recent years provides an opportunity to reveal the inherent laws of innovation, but it also puts forward higher requirements for patent mining technology. An essential step in patent text mining is to build a technical portrait for each patent, that is, to identify the technical phrases involved, which can summarize and represent the patent from a technical perspective. Previous technical phrase extraction methods thoroughly used technical phrases' characteristics and the relationship between technical phrases. Regarding our observations, the relationship between patent texts and technical phrases is also essential. Specifically, critical technical phrases are more relevant to the patent text and can be discovered by the attention mechanism. Motivated by this, we propose an unsupervised technical phrase extraction method based on the attention mechanism named UTESC. Self-attention captures the importance of technical phrases in sentences, and cross-attention captures the relevance between technical phrases and patents. Extensive experiments and algorithm comparisons on patent datasets have proven the effectiveness of our algorithm.

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