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arXiv 2021年

作者： Ni, Yuqing Wu, Junfeng Li, Li Shi, Ling Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong College of Control Science and Engineering Zhejiang University Hangzhou China College of Electronics and Information Engineering Tongji University Shanghai China

In this paper we focus on the dynamic state estimation which harnesses a vast amount of sensing data harvested by multiple parties and recognize that in many applications, to improve collaborations between parties, the estimation procedure must be designed with the awareness of protecting participants’ data and model privacy, where the latter refers to the privacy of key parameters of observation models. We develop a state estimation paradigm for the scenario where multiple parties with data and model privacy concerns are involved. Multiple parties monitor a physical dynamic process by deploying their own sensor networks and update the state estimate according to the average state estimate of all the parties calculated by a cloud server and security module. The paradigm taps additively homomorphic encryption which enables the cloud server and security module to jointly fuse parties’ data while preserving the data privacy. Meanwhile, all the parties collaboratively develop a stable (or optimal) fusion rule without divulging sensitive model information. For the proposed filtering paradigm, we analyze the stabilization and the optimality. First, to stabilize the multi-party state estimator while preserving observation model privacy, two stabilization design methods are proposed. For special scenarios, the parties directly design their estimator gains by the matrix norm relaxation. For general scenarios, after transforming the original design problem into a convex semi-definite programming problem, the parties collaboratively derive suitable estimator gains based on the alternating direction method of multipliers (ADMM). Second, an optimal collaborative gain design method with model privacy guarantees is provided, which results in the asymptotic minimum mean square error (MMSE) state estimation. Finally, numerical examples are presented to illustrate our design and theoretical findings. Copyright © 2021, The Authors. All rights reserved.

关键词： State estimation

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Drying control system for persimmon based on single-chip computer and PID 20

Drying control system for persimmon based on single-chip com...

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Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligence

作者： Lieping Zhang Liu Tang Huihao Peng Xuan Fang Xiao-Guang Yue College of Mechanical and Control Engineering Guilin University of Technology Guilin Guangxi China Department of Computer Science and Engineering School of Sciences European University Cyprus Nicosia Cyprus

ISBN: (纸本)9781450376457

The traditional drying method of dried persimmons is laborious and time-consuming. Moreover, the finished product rate of dried persimmons is significantly low. According to the characteristics and requirements of the dried persimmon drying process, a drying control system for persimmon based on single-chip microcomputer and PID is proposed in this paper. In the control system, the single-chip STC89C52 is used as the master controller, the PID controller is used to regulate the temperature of the persimmon drying room, the LCD1602 liquid crystal screen is used to monitor the drying process in real time, and the buzzer is used to alarm when the temperature exceeds the limit. The results of simulation experiment show that, compared with the traditional drying method of dried persimmon, the designed drying control system for dried persimmon can conveniently set the parameters of drying time, temperature, etc. The control system not only realizes real-time control, detection and display of the dried persimmon drying process but also improves the drying efficiency and quality of dried persimmons.

关键词： control system

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Adaptive Decentralized Output-Constrained control of Single-Bus DC Microgrids

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 424-432页

作者： Jiangkai Peng Bo Fan Jiajun Duan Qinmin Yang Wenxin Liu the Smart Microgrid and Renewable Technology (SMRT) Research Laboratory Department of Electrical and Computer Engineering Lehigh University the State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University the GEIRI North America

A single-bus DC microgrid can represent a wide range of applications. control objectives of such systems include high-performance bus voltage regulation and proper load sharing among multiple distributed generators(DGs) under various operating conditions. This paper presents a novel decentralized control algorithm that can guarantee both the transient voltage control performance and realize the predefined load sharing percentages. First, the output-constrained control problem is transformed into an equivalent unconstrained one. Second, a two-step backstepping control algorithm is designed based on the transformed model for bus-voltage regulation. Since the overall control effort can be split proportionally and calculated with locally-measurable signals, decentralized load sharing can be realized. The control design requires neither accurate parameters of the output filters nor load measurement. The stability of the transformed systems under the proposed control algorithm can indirectly guarantee the transient bus voltage performance of the original system. Additionally, the high-performance control design is robust, flexible, and reliable. Switch-level simulations under both normal and fault operating conditions demonstrate the effectiveness of the proposed algorithm.

关键词： DC microgrids decentralized control paralleled converters output constraint

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Cross-heterogeneous Domain Authentication Scheme Based on Blockchain

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 92-100页

作者： Jing Liu Yixin Liu Yingxu Lai Rongchen Li Siyu Wu Sami Mian College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Shaanxi Key Laboratory of Network and System Security Xi’an 710071China Science and Technology on Information Assurance Laboratory Beijing 100072China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Fan Gongxiu Honors College Beijing University of TechnologyBeijing 100124China University of Pittsburgh PittsburghPA 15260USA

With the rising popularity of the Internet and the development of big data technology,an increasing number of organizations are opting to cooperate across domains to maximize their *** organizations use public key infrastructure to ensure security in accessing their data and ***,with the continuous development of identity-based encryption(IBE)technology,small-and medium-sized enterprises are increasingly using IBE to deploy internal authentication *** solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process,we propose using blockchain technology to establish a reliable cross-domain authentication *** the distributed and tamper-resistant characteristics of the blockchain,we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on *** model does not change the internal trust structure of each authentication domain and is highly ***,on the premise of ensuring security,the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate,thereby improving the authentication *** developed prototype exhibits generality and simplicity compared to previous methods.

关键词： public key infrastructure identity-based encryption cross-domain authentication blockchain

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GNNBoost: Accelerating sampling-based GNN training on large scale graph by optimizing data preparation

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Journal of Systems Architecture 2025年 167卷

作者： Yujuan Tan Yan Gan Zhaoyang Zeng Zhuoxin Bai Lei Qiao Duo Liu Kan Zhong Ao Ren Chongqing University College of Computer Science Chongqing China Beijing Institute of Control Engineering Beijing China Chongqing University School of Big Data & Software Engineering Chongqing China

Graph Neural Networks (GNNs) have successfully extended deep learning from traditional Euclidean spaces to complex graph structures. Sampling-based GNN training has been widely adopted for large-scale graphs without compromising accuracy. However, the graph irregularity results in imbalanced sampling workloads, making it challenging for existing GNN systems to effectively utilize GPU resources for graph sampling. Additionally, in GNN systems where both topology and feature caches are enabled, differences in characteristics and purposes of cache data complicate the allocation of GPU memory for these two caches with minimal overhead. To address these challenges, we propose GNNBoost, a framework designed to accelerate GNN training. GNNBoost consists of two key innovations. First, GNNBoost introduces a degree-oriented sampling schedule that groups training vertices based on their degrees and applies tailored sampling strategies to balance GPU workloads and improve sampling performance. Second, GNNBoost develops a low-overhead cache space allocation mechanism that accurately determines the optimal cache sizes for graph topology and features across different workloads, minimizing both space and time overheads. We conduct a comprehensive evaluation of GNNBoost through various GNN models and large graph datasets, demonstrating that it significantly outperforms existing GNN training systems.

关键词： Cache Graph neural networks Sample-based GNN training

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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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A Novel Sparse Adaptive Filter for Suppressing Impulsive Disturbance in Audio Signals

SSRN

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SSRN 2024年

作者： Zhou, Lei Liu, Hongqing Gan, Lu Zhou, Yi Niedźwiecki, Maciej Truong, Trieu-Kien School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Lab of Mobile Communications Technology and Intelligent Speech and Audio Research Lab Chongqing University of Posts and Telecommunications Chongqing400065 China College of Engineering Design and Physical Science Brunel University LondonUB8 3PH United Kingdom Department of Automatic Control Faculty of Electronics Telecommunications and Computer Science Gdansk University of Technology Gdansk80-233 Poland Department of Information Engineering I-Shou University 84001 Taiwan

This work studies the sparse adaptive filter designs in the presence of impulsive disturbance for audio signal recovery. By using the sparse representation of desired signal and compressibility of impulsive disturbance, a joint sparse least mean p-norm (JSLMP) optimization, in which p-norm (1 ≤ p ≤ 2) measures the data fidelity and q-norm (0 ≤ q ≤ 1) enforces sparse solutions, is developed, termed as q-JSLMP. The filter weights update is derived based on the gradient descent, and the Adam and variable step size (VSS) are integrated to accelerate convergence and avoid potential local minima. In the special case of q = 1, namely 1-JSLMP, its convergence condition and mean square deviation (MSD) analysis are derived. Finally, the frame of application to corrupted audio signals is designed. The results from experiments demonstrate that the proposed approach outperforms other approaches in terms of objective measures on numerical simulation and speech datasets. © 2024, The Authors. All rights reserved.

关键词： Adaptive filters

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Green vehicle routing optimization based on dynamic constraint selection co-evolutionary algorithm

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Scientific Reports 2025年第1期15卷 1-18页

作者： Zhou, Lu-Jie Zhang, Hai-Fei Fu, Jun-Hao School of Automobile and Transportation Tianjin University of Technology and Education Tianjin 300222 China Nanjing Xiaozhuang University College of information engineering Nanjing China School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi 214122 China Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University) Ministry of Education Wuxi 214122 China

Aiming at the problems of single solution objective in the existing green vehicle routing optimization process and real-time speed change during vehicle travel, a multi-objective green vehicle routing problem with time window constraints in time-varying conditions(MOGVRPTW-TV) is established, then a co-evolutionary framework-based constrained multi-objective evolutionary algorithm for the solving of the model is proposed. First, to better match the actual logistics distribution, this model considers the impact of time-varying speed and capacitated variation on carbon emissions based on capacitated constraints and time window constraints. Second, a constrained multi-objective evolutionary algorithm based on the co-evolutionary framework was proposed for model solving. The algorithm treats the complete problem model as a complex task and introduces a shift crowding distance calculation that considers both individual distribution and convergence information when solving this complex task, effectively balancing the convergence and diversity of solutions. Then, a dynamic constraint selection strategy is designed for the implementation of the simple task, which only considers the effects of some constraints on the populations, and the two populations exchange information through offspring populations to achieve overall optimization. Simulations are performed on different instances and the results show that the proposed algorithm is effective in avoiding traffic congestion periods, decreasing overall distribution costs, and reducing fuel consumption and carbon emissions of vehicles. © The Author(s) 2025.

关键词： Co-evolutionary algorithm Dynamic constraint selection Logistics distribution Multi-objective optimization Time-varying property

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Planning and Tracking control of Full Drive-by-Wire Electric Vehicles in Unstructured Scenario

arXiv

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arXiv 2023年

作者： Chen, Guoying Hua, Min Liu, Wei Wang, Jinhai Song, Shunhui Liu, Changsheng The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China The School of Engineering University of Birmingham BirminghamB15 2TT United Kingdom The School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States The School of Automotive Engineering Wuhan University of Technology Wuhan430070 China The School of Automotive Studies Tongji University Shanghai218074 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Full drive-by-wire electric vehicles (FDWEV) with X-by-wire technology can achieve independent driving, braking, and steering of each wheel, providing a good application platform for autonomous driving technology. Path planning and tracking control, in particular, are critical components of autonomous driving. However, It is challenging to comprehensively design an robust control algorithm by integrating vehicle path planning in a complicated unstructured scenario for FDWEV. To address the above issue, this paper first proposes the artificial potential field (APF) method for path planning in the prescribed park with different static obstacles to generate the reference path information, where speed planning is incorporated considering kinematics and dynamic constraints. Second, two tracking control methods, curvature calculation (CC-based) and model predictive control (MPC-based) methods with the lateral dynamics model, are proposed to track the desired path under different driving conditions, in which a forward-looking behavior model of the driver with variable preview distance is designed based on fuzzy control theory. CarSim-AMESim-Simulink co-simulation is conducted with the existence of obstacles. The simulation results show that the proposed two control approaches are effective for many driving scenarios and the MPC-based path-tracking controller enhances dynamic tracking performance and ensures good maneuverability under high-dynamic driving conditions. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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