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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Yu, Chang Lu, Xiaoqing Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University School of Electrical Engineering and Automation Wuhan430000 China

ISBN: (纸本)9798350316339

Microgrids subjected to secondary cooperative control encounter significant challenges, including operational constraints and clock drifts, adversely affecting their stability and efficiency. This paper provides conditions that assure optimal microgrid performance in both transient and steady-state scenarios, focusing on the effects of clock drifts and fluctuations in load. Furthermore, we introduce a novel approach for designing secondary control parameters, specifically engineered to minimize steady-state discrepancies attributable to clock drifts while ensuring adherence to standards for transient operations. Comprehensive experimental validations corroborate the effectiveness of our proposed solutions. © 2024 IEEE.

关键词： Microgrids

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A Multitask Learning Approach for Chinese National Instruments Recognition and Timbre Space Regression 9th

A Multitask Learning Approach for Chinese National Instrumen...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Xu, Shenyang Jiang, Yiliang Li, Zijin Sun, Xiaoheng Li, Wei Central Conservatory of Music Beijing100031 China School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Musical instrument recognition is an essential task in the domain of music information retrieval. So far, most existing research are focused on western instruments. In this research, we turn to Chinese national instruments recognition. First, a dataset containing 30 Chinese national instruments is created. Then, a well-designed end-to-end Convolutional Recurrent Neural Network is proposed. Moreover, we combine instrument recognition with instrument timbre space regression using a multitask learning approach to improve performances of both tasks. We conduct experiments in instrument recognition and timbre space regression to evaluate our model and multitask learning approach. Experimental results show that our proposed model outperforms previous algorithms, and the multitask approach can further improve the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Music

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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Extracting Variable-Depth Logical Document Hierarchy from Long Documents:Method,Evaluation,and Application

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Journal of Computer Science & technology 2022年第3期37卷 699-718页

作者： Rong-Yu Cao Yi-Xuan Cao Gan-Bin Zhou Ping Luo Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China WeChat Search Application Department Tencent Holdings Ltd.Beijing 100080China Peng Cheng Laboratory Shenzhen 518066China

In this paper,we study the problem of extracting variable-depth"logical document hierarchy"from long documents,namely organizing the recognized"physical document objects"into hierarchical *** discovery of logical document hierarchy is the vital step to support many downstream applications(e.g.,passage-based retrieval and high-quality information extraction).However,long documents,containing hundreds or even thousands of pages and a variable-depth hierarchy,challenge the existing *** address these challenges,we develop a framework,namely Hierarchy Extraction from Long Document(HELD),where we"sequentially"insert each physical object at the proper position on the current *** whether each possible position is proper or not can be formulated as a binary classification *** further improve its effectiveness and efficiency,we study the design variants in HELD,including traversal orders of the insertion positions,heading extraction explicitly or implicitly,tolerance to insertion errors in predecessor steps,and so *** for evaluations,we find that previous studies ignore the error that the depth of a node is correct while its path to the root is *** such mistakes may worsen the downstream applications seriously,a new measure is developed for a more careful *** empirical experiments based on thousands of long documents from Chinese financial market,English financial market and English scientific publication show that the HELD model with the"root-to-leaf"traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.972,6,0.729,1 and 0.957,8 in the Chinese financial,English financial and arXiv datasets,***,we show that the logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval *** summary,we conduct a systematic study on this task in terms of methods

关键词： logical document hierarchy long documents passage retrieval

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Active Learning with Core-Set Sampling and Scale-Sensitive Loss for 3D Object Detection

Active Learning with Core-Set Sampling and Scale-Sensitive L...

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

作者： Dejun Zhang Xiaowei Lin Benxin Yi Yiqi Wu Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Information Systems Technology and Design Pillar Singapore University of Technology and Design

Deep learning-based 3D object detectors often require large-scale labeled 3D datasets, which can be expensive to annotate. To tackle this issue, we introduce a core-set sampling strategy within an active learning framework, selecting highly informative data from a data pool to reduce reliance on such datasets. Additionally, we introduce a scale-sensitive loss function into the 3D object detector to mitigate the disparities in the influence of small and large objects on model learning, thereby enhancing the accuracy of small object detection. Our experiments on the KITTI dataset demonstrate that our method achieves comparable results to the baseline using only 20% of the data. Notably, our approach outperforms the baseline in small object detection, with an 8% accuracy improvement for pedestrians and a 4% improvement for cyclists. The source code of the proposed method is available at https://***/djzgroup/al-cs-ssl.

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

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作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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A Review of Spiking Neural Network Research in the Field of Bearing Fault Diagnosis 1

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Wang, Yusen Wang, Hongjun Xie, Long Ge, Henglin Zhou, Mingyang Chen, Tao Shi, Yuxing School of Mechanical and Electrical Engineering Beijing Information Science & Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education BISTU Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception and Control BISTU Beijing100192 China

ISBN: (数字)9783031694837

ISBN: (纸本)9783031694820

In view of the bearing fault diagnosis problem, in order to better deal with the temporal data, extract nonlinear features, enhance the model robustness and adaptability, and realize efficient distributed computing, the spiking neural network is introduced to improve the accuracy and efficiency in the process of bearing fault diagnosis. In recent years, after a series of studies, the spiking neural network has developed in supervised learning, unsupervised learning, semi-supervised learning, and some other derived methods, such as reinforcement learning and transfer learning, for its applicable scenarios with different advantages. From the perspective of learning principle, method advantages and practical applications, we introduced several spiking neural network for bearing fault diagnosis, discussed the advantages, disadvantages and applicable scenarios of each learning method, discussed the application prospect of spiking neural network for bearing fault diagnosis, and proposed the spiking neural network for bearing fault diagnosis in future research, so as to provide reference for subsequent research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Self-supervised learning

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Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting

arXiv

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arXiv 2025年

作者： Yan, Zhiying Liang, Yiyuan Cai, Shilv Zhang, Tao Zhong, Sheng Yan, Luxin Zou, Xu Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China Nanyang Technological University Singapore

Semantic 4D Gaussians can be used for reconstructing and understanding dynamic scenes, with temporal variations than static scenes. Directly applying static methods to understand dynamic scenes will fail to capture the temporal features. Few works focus on dynamic scene understanding based on Gaussian Splatting, since once the same update strategy is employed for both dynamic and static parts, regardless of the distinction and interaction between Gaussians, significant artifacts and noise appear. We propose Dual-Hierarchical Optimization (DHO), which consists of Hierarchical Gaussian Flow and Hierarchical Gaussian Guidance in a divide-and-conquer manner. The former implements effective division of static and dynamic rendering and features. The latter helps to mitigate the issue of dynamic foreground rendering distortion in textured complex scenes. Extensive experiments show that our method consistently outperforms the baselines on both synthetic and real-world datasets, and supports various downstream tasks. Project Page: https://***/DHO. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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Mongolian Medicine Named Entity Recognition via Dictionary-Based Synonym Generalization 9

Mongolian Medicine Named Entity Recognition via Dictionary-B...

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9th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2023

作者： Qin, Si Bao, Feilong Dulamragchaa, Uuganbaatar Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Huhhot Inner Mongolia010021 China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Huhhot Inner Mongolia010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Huhhot Inner Mongolia010021 China Institute of Mathematics and Digital Technology Mongolian Academy of Sciences Ulaanbaatar999097 Mongolia

ISBN: (纸本)9798350304428

The task of named entity recognition in Mongolian medicine poses challenges due to the diversity and complexity of terminology and herbal names. Variations in names used across different literature sources and regions hinder accurate recognition, as traditional dictionary-based methods often fail to encompass all synonyms and variants. To address this issue, this paper presents a lexicon-based synonym generalization method. By leveraging lexical resources in Mongolian, this approach extends the synonyms of common herbs and terms, incorporating additional synonyms and related words into the named entity recognition system. Consequently, the system becomes capable of recognizing a wider range of names and variants. In this study, the authors construct the Mongolian Medicine Named Entity Recognition Datasets (MMNER), comprising five entity types and 44,968 entities. The datasets are developed using the proposed synonym generalization framework. Subsequently, a series of deep learning models are employed to conduct experimental comparisons on the MMNER datasets. The experimental results demonstrate that by enhancing synonym coverage, the recognition of Mongolian medicine entities in text achieves a significantly improved accuracy, with an F1 value of 93.66%. These findings signify a noteworthy enhancement in the performance of Mongolian medicine named entity recognition. © 2023 IEEE.

关键词： Deep learning

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