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Chinese Automation Congress (CAC)

作者： Longyun Chi Xujun Guan Xiaorong Shen Hai Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

The naturalness of the panoramic image is an important criterion for evaluating the result of image stitching. When the overlapping region of the image has a low-texture area, the alignment is hard to accomplish due to incorrect and inadequate feature point matching pairs, and the salient structures are easily destroyed due to the unreasonable position of the optimal seam. To solve the problem, we use line segment matching information to assist the alignment of low-texture regions and use superpixel segmentation regions to filter the unreliable point correspondences. An improved seam-finding algorithm is finally used to blend images together. The experimental results demonstrate that the proposed method can get more natural stitching results.

关键词： Image segmentation Strain Feature extraction Information filters Matched filters Optimization

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Fully linear graph convolutional networks for semi-supervised learning and clustering

arXiv

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arXiv 2021年

作者： Cai, Yaoming Zhang, Zijia Cai, Zhihua Liu, Xiaobo Ding, Yao Ghamisi, Pedram School of Computer Science China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China Xi'an Research Institute of High Technology Xi'an710000 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria

This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closedform solution with a decoupled procedure, resulting in a generalized linear framework and making it easier to implement, train, and apply. We show that (1) FLGC is powerful to deal with both graph-structured data and regular data, (2) training graph convolutional models with closed-form solutions improve computational efficiency without degrading performance, and (3) FLGC acts as a natural generalization of classic linear models in the non-Euclidean domain, e.g., ridge regression and subspace clustering. Furthermore, we implement a semisupervised FLGC and an unsupervised FLGC by introducing an initial residual strategy, enabling FLGC to aggregate long-range neighborhoods and alleviate over-smoothing. We compare our semi-supervised and unsupervised FLGCs against many state-of-the-art methods on a variety of classification and clustering benchmarks, demonstrating that the proposed FLGC models consistently outperform previous methods in terms of accuracy, robustness, and learning efficiency. The core code of our FLGC is released at https://***/AngryCai/FLGC. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

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Deep-learning based optimal PMU placement and fault classification for power system

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Expert systems with Applications 2025年 292卷

作者： Xin Lei Zhen Li Huaiguang Jiang Samson S. Yu Yu Chen Bin Liu Peng Shi School of Future Technology South China University of Technology Panyu District Guangdong China School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China School of Engineering Deakin University 75 Pigdon Rd Waurn Ponds Victoria 3216 Australia College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China School of Electrical and Electronic Engineering University of Adelaide 5005 SA Australia

Phasor measurement units (PMUs) are vital for power grid monitoring, yet their high cost restricts widespread adoption. PMU measurement data is also crucial for fault analysis in power systems. However, existing research seldom explores the interplay between optimal PMU placement (OPP) and fault analysis, impeding advancements in grid economy and security. This study introduces a perception-driven, deep learning-based optimization approach that integrates OPP, multi-task learning, and fault data augmentation. First, deep reinforcement learning optimizes PMU placement, balancing cost-effectiveness with observability requirements. Next, multi-task learning, enhanced by Bayesian optimization, improves fault classification efficiency using PMU data. Finally, pre-trained models paired with k -means clustering augment fault data, boosting classification accuracy. Extensive simulations across four IEEE standard test systems validate the proposed method’s effectiveness.

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Antitumor Effects of Cds-Ppix Adducts for Nuclear-Targeted Sonodynamic Therapy

SSRN

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SSRN 2023年

作者： Xue, Juanjuan Jia, Muyue Sang, Shengbo Li, Huaqian Wang, Xiaojuan Guo, Li Duan, Qianqian Xi, Yanfeng Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China Department of Pathology Shanxi Cancer Hospital Shanxi Hospital Affiliated to CancerHospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Shanxi Taiyuan030013 China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of Orthopaedics Second Hospital of Shanxi Medical University Taiyuan030001 China Department of Pathology Shanxi Tumor Hospital Taiyuan030013 China

To achieve high-efficiency treatment to diseases, accurate delivery of drugs with a carrier into the specific organelles is of great importance. The nucleus is the ultimate target for a large number of drugs and direct delivery of drugs into nucleus can greatly improve the therapeutic treatment. Whereas, in sonodynamic therapy (SDT), the fact that most drug delivery systems can only deliver drugs to the cytoplasm of the cells and cannot act directly on the nucleus. In this paper, nucleolus-targeted carbondots (CDs) are prepared. Also, the conjugate of CDs and PpIX is synthesized by amide cross-linking and then release singlet oxygen to kill cancer cells under ultrasound radiation. The resulting CDs-PpIX nanoparticles showed significant nuclear targeting properties, and greatly improved the efficiency of acoustic dynamics treatment, as the nucleus exhibited poor tolerance to reactive oxygen species (ROS) generated during acoustic dynamics treatment. In addition, ROS as an extracellular signal can activate the p38MAPK protein pathway, which is the main mechanism of ultrasound killing of cancer cells. Our results demonstrate the great potential of CDs-PpIX in targeting cell nuclei for acoustic dynamics treatment. © 2023, The Authors. All rights reserved.

关键词： Cancer cells

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Erratum to “Time-Frequency Properties of the Short-Time Linear Canonical Transform and Its Application”

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Mathematical Problems in Engineering 2022年第1期2022卷

作者： Lei Huang Qingying Sun Qian Xi Yuhu Liu Mengsheng An Zihan Zhou School of Computer Science and Technology Huaiyin Normal University Huai’an 223300 *** School of Automation Chongqing University Chongqing 400044 *** Key Laboratory of Complex System Safety and Control Ministry of Education Chongqing University Chongqing 400044 ***

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FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, droprule, and powerball adabelief

arXiv

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arXiv 2020年

作者： Shi, Zhenhua Wu, Dongrui Guo, Chenfeng Zhao, Changming Cui, Yuqi Wang, Fei-Yue Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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Publisher Correction: An open dataset for intelligent recognition and classification of abnormal condition in longwall mining

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Scientific data 2024年第1期11卷 848页

作者： Wenjuan Yang Xuhui Zhang Bing Ma Yanqun Wang Yujia Wu Jianxing Yan Yongwei Liu Chao Zhang Jicheng Wan Yue Wang Mengyao Huang Yuyang Li Dian Zhao School of Mechanical Engineering Xi'an University of Science and Technology No.58 Mid-Yanta Road Xi'an 710054 China. Shaanxi Key Laboratory of Mine electromechanical equipment intelligent Detection and control No.58 Yanta Road Xi'an 710054 China. School of Mechanical Engineering Xi'an University of Science and Technology No.58 Mid-Yanta Road Xi'an 710054 China. zhangxh@***. Shaanxi Key Laboratory of Mine electromechanical equipment intelligent Detection and control No.58 Yanta Road Xi'an 710054 China. zhangxh@***. MARCO automatic control system development Co. LTD No.20 Fenghui South Road Xi'an 710054 China.

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AFDM-SCMA: A Promising Waveform for Massive Connectivity over High Mobility Channels

arXiv

引用

arXiv 2023年

作者： Luo, Qu Xiao, Pei Liu, Zilong Wan, Ziwei Nikolaos, Thomos Gao, Zhen He, Ziming The 5G & 6G Innovation Centre University of Surrey United Kingdom The School of Computer Science and Electronics Engineering University of Essex United Kingdom The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The 5G & 6G Innovation Centre University of Surrey GuildfordGU2 7XH United Kingdom BIT Zhuhai519088 China The MIIT Key Laboratory of Complex-Field Intelligent Sensing BIT Beijing100081 China Jinan250307 China Jiaxing314019 China The Samsung Cambridge Solution Centre System LSI Samsung Electronics CambridgeCB4 0DS United Kingdom

This paper studies the affine frequency division multiplexing (AFDM)-empowered sparse code multiple access (SCMA) system, referred to as AFDM-SCMA, for supporting massive connectivity in high-mobility environments. First, by placing the sparse codewords on the AFDM chirp subcarriers, the input-output (I/O) relation of AFDM-SCMA systems is presented. Next, we delve into the generalized receiver design, chirp rate selection, and error rate performance of the proposed AFDM-SCMA. The proposed AFDM-SCMA is shown to provide a general framework and subsume the existing OFDM-SCMA as a special case. Third, for efficient transceiver design, we further propose a class of sparse codebooks for simplifying the I/O relation, referred to as I/O relation-inspired codebook design in this paper. Building upon these codebooks, we propose a novel iterative detection and decoding scheme with linear minimum mean square error (LMMSE) estimator for both downlink and uplink channels based on orthogonal approximate message passing principles. Our numerical results demonstrate the superiority of the proposed AFDM-SCMA systems over OFDM-SCMA systems in terms of the error rate performance. We show that the proposed receiver can significantly enhance the error rate performance while reducing the detection complexity. © 2023, CC BY.

关键词： Mean square error

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Vehicle detection based on point cloud intensity and distance clustering

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zhao Wang Xing Wang Bin Fang Kun Yu Jie Ma School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National key laboratory of multispectral information processing technology Huazhong University of Science and Technology Wuhan 430074 China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing 100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

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Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group

Double Deep Q-learning for Anti-saturation Attack Problem of...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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