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Chinese Control and Decision Conference, CCDC

作者： Qiang Ren Hang Gao Hailei Ren Baoran An Tianyou Yun Xiaoli Wang Institute of Computer Application China Academy of Engineering Physics Mianyang State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha Institute of Fluid Physics China Academy of Engineering Physis MianYang China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

High-power laser facility plays an import role both in scientific research and military application, and the occurred faults are usually transient. In this paper, a transient fault diagnosis algorithm based on feature classification is proposed, and the algorithm has been applied in the high-power laser facility. The method extracts the features of input data with an autoencoder network. And based on the extracted features, a multi-layer full connection neural network is adopted for fault diagnosis. By comparing the results with those obtained by traditional fault diagnosis methods, the performance of the algorithm has been verified.

关键词： Feature extraction Fault diagnosis Circuit faults Neural networks Principal component analysis Kernel Transient analysis

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Enhanced gear fault diagnosis via heterodyne downconversion: Theoretical verification and optimized ultrasonic signal acquisition

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Measurement 2025年 256卷

作者： Jingchao Zhang Shaoqun Zhang Yixue Dong Chen Li Guoqian Jiang Yingwei Li Xiaoli Li School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao 066004 China Intelligent Control System R&D Department Computer Application Technology Research Institute China Ordnance Industry Beijing 100089 China School of Electrical Engineering Yanshan University Qinhuangdao 066004 China School of Automation Science and Engineering South China University of Technology Guangzhou 510641 China Pazhou Lab Guangzhou 510330 China

Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its practical application is constrained by the requirement for extremely high sampling rates. Heterodyne downconversion offers a potential solution by translating ultrasonic spectral components to lower frequencies, though its effectiveness in preserving diagnostic features remains insufficiently validated. This study conducts a theoretical analysis of the influence of heterodyne downconversion on ultrasonic signal characteristics and proposes an optimized heterodyne-based ultrasonic acquisition system with enhanced charge amplification and frequency conversion circuits. Experimental evaluations using a gear fault test platform demonstrate that the downconverted signals preserve the envelope spectral features of the original ultrasonic signals. Furthermore, comparative analyses indicate that the proposed method achieves superior fault detection sensitivity compared to conventional vibration-based techniques, particularly under high rotational speeds. These findings validate the feasibility and diagnostic advantages of the proposed heterodyne-based approach for efficient and accurate ultrasonic condition monitoring.

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Multispectral Pan-sharpening via Dual-Channel Convolutional Network with Convolutional LSTM Based Hierarchical Spatial-Spectral Feature Fusion

arXiv

引用

arXiv 2020年

作者： Wang, Dong Bai, Yunpeng Li, Ying School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xian China School of Computing and Information Systems University of Melbourne VIC3010 Australia

Multispectral pan-sharpening aims at producing a high resolution (HR) multispectral (MS) image in both spatial and spectral domains by fusing a panchromatic (PAN) image and a corresponding MS image. In this paper, we propose a novel dual-channel network (DCNet) framework for MS pan-sharpening. In our DCNet, the dual-channel backbone involves a spatial channel to capture spatial information with a 2D CNN, and a spectral channel to extract spectral information with a 3D CNN. This heterogeneous 2D/3D CNN architecture can minimize causing spectral information distortion, which typically happens in conventional 2D CNN models. In order to fully integrate the spatial and spectral features captured from different levels, we introduce a multi-level fusion strategy. Specifically, a spatial-spectral CLSTM (S2-CLSTM) module is proposed for fusing the hierarchical spatial and spectral features, which can effectively capture correlations among multi-level features. The S2-CLSTM module attaches two fusion ways: the intra-level fusion via bi-directional lateral connections and inter-level fusion via the cell state in the S2-CLSTM. Finally, the ideal HR-MS image is recovered by a reconstruction module. Extensive experiments have been conducted at both simulated lower scale and the original scale of real-world datasets. Compared with the state-of-the-art methods, the proposed DCNet achieves superior or competitive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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A short-term output power prediction model of wind power based on deep learning of grouped time series

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European Journal of Electrical Engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and Engineering technology Association. All rights reserved.

关键词： Wind power

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Corrigendum to “Weak multi-label learning with missing labels via instance granular discrimination” [Inform. Sci. 594 (2022) 200–216]

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Information Sciences 2023年 619卷 1-1页

作者： Anhui Tan Xiaowan Ji Jiye Liang Yuzhi Tao Wei-Zhi Wu Witold Pedrycz School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 PR China School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi PR China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 PR China Department of Electrical and Computer Engineering University of Alberta Edmonton T6R 2V4 AB Canada

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From digitized whole-slide histology images to biomarker discovery: A protocol for handcrafted feature analysis in brain cancer pathology

Brain-X

引用

Brain-X 2025年第2期3卷

作者： Lu, Xuanjun Ying, Yawen Chen, Jing Chen, Zhiyang Wu, Yuxin Prasanna, Prateek Chen, Xin Jing, Mingli Liu, Zaiyi Lu, Cheng School of Electronic Engineering Xi'an Shiyou University Shaanxi Xi'an China Department of Medical Research Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangdong Guangzhou China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Guangzhou China School of Clinical Dentistry University of Sheffield Sheffield United Kingdom Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA United States Department of Computer Science & Informatics Emory University Atlanta GA United States Department of Biomedical Informatics Stony Brook University Stony Brook NY United States Department of Radiology School of Medicine Guangzhou First People's Hospital South China University of Technology Guangdong Guangzhou China

Hematoxylin and eosin (H&E)-stained histopathological slides contain abundant information about cellular and tissue morphology and have been the cornerstone of tumor diagnosis for decades. In recent years, advancements in digital pathology have made whole-slide images (WSIs) widely applicable for diagnosis, prognosis, and prediction in brain cancer. However, there remains a lack of systematic tools and standardized protocols for using handcrafted features in brain cancer histological analysis. In this study, we present a protocol for handcrafted feature analysis in brain cancer pathology (PHBCP) to systematically extract, analyze, model, and visualize handcrafted features from WSIs. The protocol enabled the discovery of biomarkers from WSIs through a series of well-defined steps. The PHBCP comprises seven main steps: (1) problem definition, (2) data quality control, (3) image preprocessing, (4) feature extraction, (5) feature filtering, (6) modeling, and (7) performance analysis. As an exemplary application, we collected pathological data of 589 patients from two cohorts and applied the PHBCP to predict the 2-year survival of glioblastoma multiforme (GBM) patients. Among the 72 models combining nine feature selection methods and eight machine learning classifiers, the optimal model combination achieved discriminative performance with an average area under the curve (AUC) of 0.615 over 100 iterations under five-fold cross-validation. In the external validation cohort, the optimal model combination achieved a generalization performance with an AUC of 0.594. We provide an open-source code repository (GitHub website: https://***/XuanjunLu/PHBCP) to facilitate effective collaboration between medical and technical experts, thereby advancing the field of computational pathology in brain cancer. © 2025 The Author(s). Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical technology.

关键词： brain cancer pathology handcrafted features protocol whole-slide images

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Coexistence of Photoelectric Conversion and Storage in van der Waals Heterojunctions

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Physical Review Letters 2021年第21期127卷 217401-217401页

作者： Yucheng Jiang Anpeng He Run Zhao Yu Chen Guozhen Liu Hao Lu Jinlei Zhang Qing Zhang Zhuo Wang Chen Zhao Mingshen Long Weida Hu Lin Wang Yaping Qi Ju Gao Quanying Wu Xiaotian Ge Jiqiang Ning Andrew T. S. Wee Cheng-Wei Qiu Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou Jiangsu 215009 People’s Republic of China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 China State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences 500 Yu Tian Road Shanghai 200083 China School of Materials Science and Engineering Shanghai University Shanghai 200444 China Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA School for Optoelectronic Engineering Zaozhuang University Shandong 277160 China Vacuum Interconnected Nanotech Workstation Suzhou Institute of Nano-tech and Nano-Bionics (SINANO) Suzhou Jiangsu 215123 People’s Republic of China Department of Physics National University of Singapore Singapore 117551 Singapore

Van der Waals (vdW) heterojunctions, based on two-dimensional (2D) materials, have great potential for the development of ecofriendly and high-efficiency nanodevices, which shows valuable applications as photovoltaic cells, photodetectors, etc. However, the coexistence of photoelectric conversion and storage in a single device has not been achieved until now. Here, we demonstrate a simple strategy to construct a vdW p−n junction between a WSe2 layer and quasi-2D electron gas. After an optical illumination, the device stores the light-generated carriers for up to seven days, and then releases a very large photocurrent of 2.9 mA with bias voltage applied in darkness; this is referred to as chargeable photoconductivity (CPC), which completely differs from any previously observed photoelectric phenomenon. In normal photoconductivity, the recombination of electron-hole pairs occurs at the end of their lifetime; in contrast, infinite-lifetime photocarriers can be generated and stored in CPC devices without recombination. The photoelectric conversion and storage are completely self-excited during the charging process. The ratio between currents in full- and empty-photocarrier states below the critical temperature reaches as high as 109, with an external quantum efficiency of 93.8% during optical charging. A theoretical model developed to explain the mechanism of this effect is in good agreement with the experimental data. This work paves a path toward the high-efficiency devices for photoelectric conversion and storage.

关键词： Electrical conductivity Photoconductivity Solid-solid interfaces Two-dimensional electron gas Van der Waals systems

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Fine-tuning by curriculum learning for non-autoregressive neural machine translation

arXiv

引用

arXiv 2019年

作者： Guo, Junliang Tan, Xu Xu, Linli Qin, Tao Chen, Enhong Liu, Tie-Yan Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Microsoft Research

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Curricula

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Illuminating Recommendation by Understanding the Explicit Item Relations

引用

Journal of computer Science & technology 2018年第4期33卷 739-755页

作者： Qi Liu Hong-Ke Zhao Le Wu Zhi Li En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027 China School of Computer and Information Hefei University of Technology Hefei 230009 China School of Software Engineering University of Science and Technology of China Hefei 230051 China

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

关键词： recommender system item relation recommendation interpretability

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Synchronous Motion Generation of Multiple Continuum Robots Based on a Jacobian-Estimation Strategy

Synchronous Motion Generation of Multiple Continuum Robots B...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Ning Tan Ruikun Hu Yuyang Wu Xinyu Zhang Fenglei Ni Zhenglong Sun School of Computer Science and Engineering Sun Yat-sen University Guangzhou PR China State Key Laboratory of Robotics and Systems (HIT) Harbin PR China Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education (East China Normal University) Shanghai PR China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9781665405362

In this paper, a novel model-free control scheme is proposed for synchronous motion control of multiple continuum robots. The control problem is formulated as two convex optimization routines in an adaptive and model-free closed-loop framework. The proposed approach is heading to address cooperative task execution for multiple continuum robots. The model-free feedback control guarantees the high portability for a wide range of continuum robots. Simulation studies are conducted on two kinds of tasks, which are cooperative task execution with two robots and simultaneously tracking control with three robots. Besides, the superiority of proposed approach is validated through comparisons with other methods. Simulation results prove the high accuracy and efficiency of this model-free feedback control scheme.

关键词： Jacobian matrices Adaptation models Tracking Simulation Conferences Convex functions Feedback control

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