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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Towards practical data alignment in production federated learning

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Frontiers of Computer Science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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WMRE: Enhancing Distant Supervised Relation Extraction with Word-level Multi-instance Learning and Multi-hierarchical Feature

WMRE: Enhancing Distant Supervised Relation Extraction with ...

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

作者： Xiang Ying Xiangchuan Xie Tianyi Xu Yue Zhao Zechen Meng Mankun Zhao College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of New Media and Communication Tianjin University Tianjin China Information and Network Center Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Distant supervised relation extraction (DSRE) obtains large amounts of data cost-effectively by aligning knowledge base with natural texts but also brings noisy data. Existing methods deal with noise through multi-instance learning (MIL) with attention. However, these approaches typically use attention at sentence-level and above, while ignoring word-level information. Intuitively, words in the same sentence are also of different importance. However, effective methods for distinguishing word-level importance differences are still lacking. Because multiplying with weighted attention leads word embeddings to be shifted in vector space. Therefore, we proposed WMRE. Specifically, WMRE concatenates the embeddings of multiple sentences. Then it uses filtering attention to remove low-importance words at different levels to extract multi-hierarchical features. In WMRE, words are the smallest units of mining information, and filtering attention distinguishes word-level importance differences while avoiding the embedding shifts. Extensive experiments on two widely used datasets show that WMRE fully utilizes word-level information and achieves state-of-the-art (SOTA) on both datasets.

关键词： Filtering Noise Knowledge based systems Feature extraction Vectors Acoustics Data mining Noise measurement Speech processing

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VarGes: Improving Variation in Co-Speech 3D Gesture Generation via StyleCLIPS

arXiv

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arXiv 2025年

作者： Meng, Ming Mu, Ke Zhu, Yonggui Zhu, Zhe Sun, Haoyu Yan, Heyang Fan, Zhaoxin School of Data Science and Media Intelligence Communication University of China China Samsung Research America United States Hainan International College Communication University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University Beijing China Hangzhou International Innovation Institute Beihang University China

Generating expressive and diverse human gestures from audio is crucial in fields like human-computer interaction, virtual reality, and animation. Though existing methods have achieved remarkable performance, they often exhibit limitations due to constrained dataset diversity and the restricted amount of information derived from audio inputs. To address these challenges, we present VarGes, a novel variation-driven framework designed to enhance co-speech gesture generation by integrating visual stylistic cues while maintaining naturalness. Our approach begins with the Variation-Enhanced Feature Extraction (VEFE) module, which seamlessly incorporates style-reference video data into a 3D human pose estimation network to extract StyleCLIPS, thereby enriching the input with stylistic information. Subsequently, we employ the Variation-Compensation Style Encoder (VCSE), a transformer-style encoder equipped with an additive attention mechanism pooling layer, to robustly encode diverse StyleCLIPS representations and effectively manage stylistic variations. Finally, the Variation-Driven Gesture Predictor (VDGP) module fuses MFCC audio features with StyleCLIPS encodings via cross-attention, injecting this fused data into a cross-conditional autoregressive model to modulate 3D human gesture generation based on audio input and stylistic clues. The efficacy of our approach is validated on benchmark datasets, where it outperforms existing methods in terms of gesture diversity and naturalness. The code and video results will be made publicly available upon acceptance: https://***/mookerr/VarGES/. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual reality

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An improved correlation of the pressure drop in stenotic vessels using Lorentz's reciprocal theorem

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Acta Mechanica Sinica 2015年第1期31卷 122-131页

作者： Chang-Jin Ji Kazuyasu Sugiyama Shigeho Noda Ying He Ryutaro Himeno Department of Modern Mechanics University of Science and Technology of China Advanced Center for Computing and Communication

A mathematical model of the human cardiovascu lar system in conjunction with an accurate lumped model for a stenosis can provide better insights into the pressure wave propagation at pathological conditions. In this study, a the oretical relation between pressure drop and flow rate based on Lorentz＇s reciprocal theorem is derived, which offers an identity to describe the relevance of the geometry and the convective momentum transport to the drag force. A voxel based simulator VFLOW VOF3D, where the vessel geome try is expressed by using volume of fluid （VOF） functions, is employed to find the flow distribution in an idealized steno sis vessel and the identity was validated numerically. It is revealed from the correlation flow in a stenosis vessel can that the pressure drop of NS be decomposed into a linear term caused by Stokes flow with the same boundary condi tions, and two nonlinear terms. Furthermore, the linear term for the pressure drop of Stokes flow can be summarized as a correlation by using a modified equation of lubrication the ory, which gives favorable results compared to the numerical ones. The contribution of the nonlinear terms to the pressure drop was analyzed numerically, and it is found that geomet ric shape and momentum transport are the primary factors for the enhancement of drag force. This work paves a way to simulate the blood flow and pressure propagation under dif ferent stenosis conditions by using 1D mathematical model.

关键词： Lorentz's reciprocal theorem Theoretical rela-tion Stenosis artery VOF method Lubrication theory

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Development of user-friendly supercomputing portal in bio research field

Development of user-friendly supercomputing portal in bio re...

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1st International Conference on e-Science and Grid computing

作者： Yuasa, H Iwasaki, T Kurokawa, M Shigetani, T Horiki, T Himeno, R Advanced Center for Computing and Communication RIKEN Japan

ISBN: (纸本)0769524486

The RIKEN Super Combined Cluster (RSCC) System in RIKEN Wako Inst., started its operation in March, 2004. A web portal system called Bio Portal was developed to utilize a Linux cluster (a part of RSCC) for data analysis in the life sciences. A small SW server was then placed in RIKEN Yokohama Inst. for small analysis, and the Bio Portal integrates both RSCC and this SW server through the application of Grid technology. The aim of this portal system was to construct a simple data analysis environment for experimental researchers, and to improve performance of data analysis through the use of two differently performing computing resources. In this paper, we describe the web interface and functions of Bio Portal in detail. Later we explain RSCC, and discuss performance evaluations of the distribution patterns for BLAST jobs. Finally, we propose bioinformatics job distribution patterns for high throughput processing.

关键词： Portals

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VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1849-1861页

作者： Chunyu Qiang Wang Geng Yi Zhao Ruibo Fu Tao Wang Cheng Gong Tianrui Wang Qiuyu Liu Jiangyan Yi Zhengqi Wen Chen Zhang Hao Che Longbiao Wang Jianwu Dang Jianhua Tao School of New Media and Communication Tianjin University Tianjin China Kuaishou Technology Company Ltd. Beijing China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Institute of Automation Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Migu Culture Technology Company Ltd. Beijing China Huiyan Technology (Tianjin) Company Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Science Guangdong China

Deep learning has brought significant improvements to the field of cross-modal representation learning. For tasks such as text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), a cross-modal fine-grained (frame-level) sequence representation is desired, emphasizing the semantic content of the text modality while de-emphasizing the paralinguistic information of the speech modality. We propose a method called “Vector Quantized Contrastive Token-Acoustic Pre-training (VQ-CTAP)”, which uses the cross-modal aligned sequence transcoder to bring text and speech into a joint multimodal space, learning how to connect text and speech at the frame level. The proposed VQ-CTAP is a paradigm for cross-modal sequence representation learning, offering a promising solution for fine-grained generation and recognition tasks in speech processing. The VQ-CTAP can be directly applied to VC and ASR tasks without fine-tuning or additional structures. We propose a sequence-aware semantic connector, which connects multiple frozen pre-trained modules for the TTS task, exhibiting a plug-and-play capability. We design a stepping optimization strategy to ensure effective model convergence by gradually injecting and adjusting the influence of various loss components. Furthermore, we propose a semantic-transfer-wise paralinguistic consistency loss to enhance representational capabilities, allowing the model to better generalize to unseen data and capture the nuances of paralinguistic information. In addition, VQ-CTAP achieves high-compression speech coding at a rate of 25 Hz from 24 kHz input waveforms, which is a 960-fold reduction in the sampling rate. The experimental results demonstrate that while VQ-CTAP outperforms baseline methods in TTS and VC tasks, its performance on ASR tasks is suboptimal.

关键词： Semantics Training Representation learning Speech recognition Decoding Acoustics Speech coding Speech processing Data mining Contrastive learning

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A numerical evaluation of highly accurate multiple-precision arithmetic version of semidefinite programming solver: SDPA-GMP, -QD and -DD

A numerical evaluation of highly accurate multiple-precision...

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IEEE International Symposium on Computer-Aided Control System Design

作者： Nakata, Maho Advanced Center for Computing and Communication RIKEN Saitama 351-0198 Japan

ISBN: (纸本)9781424453542

Semidefinite programming (SDP) is an important branch of optimization and has wide range of applications: engineering, industry, chemistry, mathematics, etc. However, obtaining very accurate optimum for a semidefinite programming is difficult in general, especially for ill-posed ones. In this paper, we evaluated numerically highly accurate SDP solvers;SDPA-GMP, - QD and -DD, which employ multiple-precision arithmetic and mainly developed by our group. We applied to some problems from SDPLIB benchmark which contains some ill-posed problems as well. The SDPA-GMP, and -QD solved problems very accurately, whereas SDPA failed or obtained inaccurate optima. The SDPA-DD may be used for compensation for accuracy and speed. We also investigated the convergence behaviors which agreed well what theories indicated. © 2010 IEEE.

关键词： SDPA-DD SDPA-GMP SDPA-QD arithmetic convergence of numerical methods convex programming highly accurate multiple-precision arithmetic version ill-posed problems numerical evaluation semidefinite programming

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NUMERICAL AND EXPERIMENTAL STUDY OF LURE MOVING IN WATER

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Transactions of Nanjing University of Aeronautics and Astronautics 2013年第3期30卷 257-259页

作者： Wataru Sekiguchi Naoya Takahashi Shigeho Noda Ryutaro Himeno Graduate School of Engineering Tokyo Denki University Department of Engineering Tokyo Denki University Advanced Center for Computing and Communication

The motion of a lure in water is investigated experimentally and *** lure motion in water of apassing water tank is observed,and the periodic motion is *** the Fourier analysis,it is found that the frequency with the largest amplitude in the lateral direction depends on the lip width of the *** understand the lure dynamics,a numerical simulation of the flow field around the lure is *** shape is measured using an X-ray computer tomography and converted into a voxel *** visualization,it is found that vortex sheds from its lip correspond to the vibration frequency of the lure.

关键词： fishing lure passing water channel voxel model X-ray computer tomography computational fluid dynamics(CFD)

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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