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18th International Conference on Intelligent systems and Knowledge Engineering, ISKE 2023

作者： Li, Yixin Wu, Weijie Luo, Xintao Zheng, Minjia Zhang, Yinning Peng, Bo Grid Planning & Research Center of Guangdong Power Grid Co. Ltd System Planning Research Department Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350318401

In traditional machine learning, model training usually involves training the model from scratch using the entire dataset. However, this approach often leads to catastrophic forgetting, i.e., the model rapidly and significantly forgets previously learned knowledge while training on samples from new tasks or new data distributions, when dealing with streaming data. Incremental learning, a particular machine learning paradigm, has drawn widespread attention as it allows the model to continuously learn from new data without forgetting the previously acquired knowledge. This paper comprehensively reviews existing incremental learning methods. The main contents of this survey include: the scenarios of incremental learning, a systematic review of incremental learning, a summary of performance evaluation metrics, and a discussion of open problems and future research directions in the area of incremental learning. © 2023 IEEE.

关键词： Machine learning

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Customized Online Trajectory Planning for Planetary Pinpoint Landing

Customized Online Trajectory Planning for Planetary Pinpoint...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Dai, Fa-An Gan, Qingzhong Zhang, Ziyou Wang, Lu Chen, Xi Zhao, Qianchuan Tsinghua University Department of Automation Beijing China Shanghai Aerospace Control Technology Institute Department of Control System of Launch Vehicle Shanghai China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Planetary pinpoint landing is a key issue in the aerospace field, and the purpose of powered-descent guidance is to make the vehicle fly to the target point according to a planned trajectory and realize vertical landing. The purpose of this article is to realize a customized and efficient powered-descent guidance solver for online trajectory planning. Considering the actual engineering needs, the powered-descent guidance model is summarized and improved, and the original model is transformed into a second-order cone programming model by lossless convexification, linearization, discretization, scaling and other methods. By analyzing the characteristics of the landing problem, the general interior point method is improved. By using historical trajectory information to realize warm start, the efficiency of the powered-descent guidance solver is greatly improved. Considering the influence of aerodynamic drag, the vacuum powered descent guidance solver and atmospheric powered descent guidance solver are realized respectively, which have good performance in different landing environments. © 2022 IEEE.

关键词： Second-order cone programming

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Consensus Control of Multi-Agent systems Using Fault-Estimation-in-the-Loop:Dynamic Event-Triggered Case

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1440-1451页

作者： Yamei Ju Derui Ding Xiao He Qing-Long Han Guoliang Wei Shanghai Key Lab of Modern Optical System the Department of Control Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Automation BNRistTsinghua UniversityBeijing 100084China College of Science University of Shanghai for Science and TechnologyShanghai 200093China

The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems(MASs)in the presence of faults.A dynamic event-triggered protocol(DETP)by adding an auxiliary variable is utilized to improve the utilization of communication ***,a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation(FC)is adopted to realize the demand of reliability and safety of addressed ***,a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the l_(2)-l_(∞)constraint by employing the variance analysis and the Lyapunov stability ***,the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way,***,a simulation result is employed to verify the usefulness of the proposed design framework.

关键词： Consensus control dynamic event-triggered protocol(DETP) fault compensation(FC) fault estimation multi-agent systems(MASs)

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Internet of Things-based pregnancy tracking and monitoring service

Internet of Things-based pregnancy tracking and monitoring s...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Ivanov, Rosen Yordanov, Stanimir Dinev, Dinko Technical University - Gabrovo Department Computer System and Technologies Gabrovo Bulgaria Technical University - Gabrovo Department Automation Information and Control Technology Gabrovo Bulgaria

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

This paper presents the architecture and implementation of a service for pregnancy tracking and monitoring. The main goal of the service is to analyze the behavior of pregnant women in order to proactively decide to notify medical staff when symptoms are detected that are risky for the normal pregnancy. This is achieved by (1) providing the necessary pregnancy-related information for each of the gestational weeks (nutrition, physical activity, normal and risk symptoms, necessary screening tests, etc.), (2) analysis of physical activity of pregnant women, (3) measurement and analysis of basic biological indicators using a wireless sensor network (pulse oximeter, human body temperature, biopotential channel to obtain electrocardiogram (ECG) and bioimpedance channel to measure respiration), and (4) receiving push notifications about important events related to the pregnancy;scheduled medical examinations;risk factors;and messages from the obstetrician under whose supervision the pregnant woman is. The service has a distributed architecture - it uses multiple microservices. The communication between clients (mobile app), wireless sensor network and microservices is realized through a message broker. Microservices use its own MongoDB databases hosted in the Azure cluster. Experiments were conducted to prove the feasibility of the service on simulated wireless sensor network. © 2022 IEEE.

关键词： Wireless sensor networks

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Distributed computations for large-scale networked systems using belief propagation

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Journal of automation and Intelligence 2023年第2期2卷 61-69页

作者： Qianqian Cai Zhaorong Zhang Minyue Fu School of Automation Guangdong University of TechnologyGuangzhou 510006China Department of Applied Mathematics Hong Kong Polytechnic UniversityHong Kong Special Administrative Region of China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China

This paper introduces several related distributed algorithms,generalised from the celebrated belief propagation algorithm for statistical *** algorithms are suitable for a class of computational problems in largescale networked systems,ranging from average consensus,sensor fusion,distributed estimation,distributed optimisation,distributed control,and distributed *** expressing the underlying computational problem as a sparse linear system,each algorithm operates at each node of the network graph and computes iteratively the desired *** behaviours of these algorithms are discussed in terms of the network graph topology and parameters of the corresponding computational problem.A number of examples are presented to illustrate their *** introduced is a message-passing algorithm for distributed convex optimisation.

关键词： Distributed estimation Distributed optimisation Sensor fusion Distributed algorithm

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INTRODUCING FEATURE MATCHING AND SMOOTHING LOSS: A NOVEL GAN FOR IMPROVED SAMPLE GENERATION 13

INTRODUCING FEATURE MATCHING AND SMOOTHING LOSS: A NOVEL GAN...

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13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2023

作者： Yang, Junfeng Liu, Fengchang Mi, Jinhua Yin, Shengjie Bai, Libing School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Center for System Reliability and Safety University of Electronic Science and Technology of China Chengdu China Department of SRM Design Shanghai Space Propulsion Technology Research Institute Shanghai China

ISBN: (纸本)9781839539848

Fault diagnosis of planetary gearboxes constitutes a vital element of rotating machinery. In recent years, fault diagnosis techniques for planetary gearboxes predicated on deep learning have undergone extensive research. However, in scenarios with limited samples, these deep learning methodologies can grapple with data scarcity issues, negatively impacting the model's generalization capacity and diagnostic performance. To mitigate this, Generative Adversarial Networks (GANs) have been integrated into planetary gearbox fault diagnosis to fabricate synthetic data, thereby augmenting the training dataset. Nevertheless, GANs face challenges in effectively learning local feature information from samples, leading to substantial disparities in local features between the generated data and the original data. Furthermore, the sampling of fault vibration signals from planetary gearboxes often exhibits a high frequency, with relatively smooth signal transitions, while the vibration signal samples generated by GANs often fluctuate excessively, demonstrating significant differences from the original vibration signals. To address this issue, this study introduces an enhanced GAN network that incorporates a feature attention matching mechanism in the computation of the loss function. This mechanism aims to amplify the feature similarity between the generated samples and local samples. Concurrently, a smoothness loss is introduced into the loss function to ensure the changes in the generated signal samples are more gradual. Experimental results indicate that this proposed method can generate superior quality samples, and the generated samples can achieve high accuracy on the WD-CNN diagnostic network. © The Institution of Engineering & technology 2023.

关键词： Generative adversarial networks

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Adaptive Fake Audio Detection with Low-Rank Model Squeezing

Adaptive Fake Audio Detection with Low-Rank Model Squeezing

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Zhang, Xiaohui Yi, Jiangyan Tao, Jianhua Wang, Chenglong Xu, Le Fu, Ruibo State Key Laboratory of Multimodal Artificial Intelligence System Institute of Automation Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China University of Science and Technology of China China Department of Automation Tsinghua University China School of Artificial Intelligence University of Chinese Academy of Sciences China

The rapid advancement of spoofing algorithms necessitates the development of robust detection methods capable of accurately identifying emerging fake audio. Traditional approaches, such as finetuning on new datasets containing these novel spoofing algorithms, are computationally intensive and pose a risk of impairing the acquired knowledge of known fake audio types. To address these challenges, this paper proposes an innovative approach that mitigates the limitations associated with finetuning. We introduce the concept of training low-rank adaptation matrices tailored specifically to the newly emerging fake audio types. During the inference stage, these adaptation matrices are combined with the existing model to generate the final prediction output. Extensive experimentation is conducted to evaluate the efficacy of the proposed method. The results demonstrate that our approach effectively preserves the prediction accuracy of the existing model for known fake audio types. Furthermore, our approach offers several advantages, including reduced storage memory requirements and lower equal error rates compared to conventional finetuning methods, particularly on specific spoofing algorithms. © 2023 CEUR-WS. All rights reserved.

关键词： Fake detection

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Transfer learning with a spatiotemporal graph convolution network for city flowprediction

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Frontiers of Information technology & Electronic Engineering 2025年第1期26卷 79-92页

作者： Binkun LIU Yu KANG Yang CAO Yunbo ZHAO Zhenyi XU Department of Automation University of Science and Technology of ChinaHefei 230026China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230088China

Recently,deep learning based city flow prediction has been extensively used in the establishment of *** methods are data-hungry,making them unscalable to areas lacking *** transfer learningcan use data-rich source domains to assist target domain cities in city flow prediction,the performance of existingmethods cannot meet the needs of actual use,because the long-distance road network connectivity is *** this problem,we propose a transfer learning method based on spatiotemporal graph convolution,in which weconstruct a co-occurrence space between the source and target domains,and then align the mapping of the sourceand target domains’data in this space,to achieve the transfer learning of the source city flow prediction modelon the target ***,a dynamic spatiotemporal graph convolution module along with a temporalencoder is devised to simultaneously capture the concurrent spatiotemporal features,which implies the inherentrelationship among the road network structures,human travel habits,and city bike ***,these concurrentfeatures are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence *** domain features are thereby aligned with the source domain features in the co-occurrence space by using aMahalanobis distance loss,to achieve cross-city bike flow *** proposed method is evaluated on the publicbike flow datasets in Chicago,New York,and Washington in 2015,and significantly outperforms state-of-the-arttechniques.

关键词： Transfer learning City flow prediction Spatiotemporal graph convolution

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