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A Non-Parametric Scheme for Identifying data Characteristic Based on Curve Similarity Matching

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1424-1437页

作者： Quanbo Ge Yang Cheng Hong Li Ziyi Ye Yi Zhu Gang Yao School of Automation Nanjing University of Information Science&TechnologyJiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent SystemsNanjing University of Information Science&Technologyand the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing 210044China School of Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China China Classification Society Nantong Office Nantong 226006China Chinese Flight Test Establishment Xi’an 710089China School of Mathematics Tsinghua UniversityBeijing 100084China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China IEEE

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity *** the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and *** com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

关键词： Curve similarity matching Gaussian-like noise non-parametric scheme parzen window.

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Low-loss Zero-index Waveguides and Devices

Low-loss Zero-index Waveguides and Devices

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Dong, Tian Dai, Tianxiang Chen, Ye Liu, Yueyang Liu, Hancheng Wang, Yiting Ma, Anqi Hu, Haifeng Xu, Lihua Zhao, Le Chu, Weiguo Peng, Chao Wang, Jianwei Li, Yang State Key Laboratory of Precision Measurement Technology and Instrument Department of Precision Instrument Tsinghua University Beijing100084 China State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics and Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Nanofabrication Laboratory National Center for Nanoscience and Technology Beijing100190 China

We report flexible zero-index waveguides and devices whose loss is two orders of magnitude lower than the state of the art, enabling phase-error-free high-dense photonic integrated circuits for classical and quantum i... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Harvesting random embedding for high-frequency change-point detection in temporal complex systems

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National science Review 2022年第4期9卷 92-104页

作者： Jia-Wen Hou Huan-Fei Ma Dake He Jie Sun Qing Nie Wei Lin Research Institute of Intelligent Complex Systems Fudan University Centre for Computational Systems Biology Institute of Science and Technology for Brain-Inspired IntelligenceFudan University School of Mathematical Sciences Soochow University Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine School of Mathematical Sciences and Shanghai Center for Mathematical Sciences Fudan University Department of Mathematics Department of Developmental and Cell Biologyand NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of California Shanghai Key Laboratory for Contemporary Applied Mathematics LNMS (Fudan University)and LCNBI (Fudan University) State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceInstitutes of Brain ScienceFudan University

Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and model-free method to achieve the mission based merely on the time-series data recorded from real-world systems. Here, we develop such a model-free approach, named temporal change-point detection(TCD), and integrate both dynamical and statistical methods to address this important challenge in a novel way. The proposed TCD approach, basing on exploitation of spatial information of the observed time series of high dimensions, is able not only to detect the separate change points of the concerned systems without knowing, a priori, any information of the equations of the systems, but also to harvest all the change points emergent in a relatively high-frequency manner, which cannot be directly achieved by using the existing methods and *** effectiveness is comprehensively demonstrated using the data from the representative complex dynamics and real-world systems from biology to geology and even to social science.

关键词： temporal systems time series change-point detection complex dynamical systems

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A Canonical Correlation Analysis-Based Approach for Battery State of Charge Estimation

A Canonical Correlation Analysis-Based Approach for Battery ...

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Chinese Control and Decision Conference, CCDC

作者： Zichuan Ni Ying Yang Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

This brief presents a novel method based on canonical correlation analysis (CCA) and particle filter (PF) for battery state of charge (SOC) estimation. More specifically, CCA is adopted to provide a universal way for battery SOC estimation with PF for error correction. Technically, the input data are first mapped to polynomial terms before training to find the intrinsic features. Afterwards, ℓ 2 -norm regularization is further added to the loss function in order to prevent overfitting. Finally, the SOC estimation result from CCA training is combined with the Coulomb counting model and updated by PF for error correction. Experimental results using battery testing data at different testing profiles and temperatures show the high accuracy of estimation and robustness to wrong initial guess. In particular, compared with linear regression, support vector regression, and neural network, the error of the proposed method is reduced to 1.47%.

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Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy 1

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International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Hai, Tao Zhou, Jincheng Olatunji, Timothy A. Ajoboh, Oluwakemi A. Chen, Lee Iwendi, Celestine Omeoga, Nkechi Sinha, Anurag School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guizhou Guiyang550025 China School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun558000 China School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom Manchester Metropolitan University Manchester United Kingdom Department of Computer Science and Information Technology IGNOU New Delhi India

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

To improve the accuracy in the detection of diabetes-type diagnosis using Random Forest Algorithm and Support Vector Method. A dataset on diabetes was collected on ***. Support vector method analysis was performed on the data using kernel as linear, poly, and rbf with random state = 42 and test size = 0.2. The Random Forest Algorithm with n_estimators = 100 has the same accuracy as the support vector method with the kernel as rbf. When it comes to type diagnosis for a patient having diabetes, the Random Forest Algorithm and Support Vector Method with kernel rbf can be used, unlike another kernel of the Support Vector Method. The dataset was also visualized using power. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Radial basis function networks

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Low-loss Zero-index Waveguides and Devices

Low-loss Zero-index Waveguides and Devices

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Dong, Tian Dai, Tianxiang Chen, Ye Liu, Yueyang Liu, Hancheng Wang, Yiting Ma, Anqi Hu, Haifeng Xu, Lihua Zhao, Le Chu, Weiguo Peng, Chao Wang, Jianwei Li, Yang State Key Laboratory of Precision Measurement Technology and Instrument Department of Precision Instrument Tsinghua University Beijing100084 China State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Nanofabrication Laboratory National Center for Nanoscience and Technology Beijing100190 China

关键词： Photonic integrated circuits

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Multi-BS Fusion Scheme for PHD Simultaneous Localization and Mapping 23

Multi-BS Fusion Scheme for PHD Simultaneous Localization and...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Du, Tao Yang, Jie Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing211189 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China Zte Corporation Shenzhen518057 China State Key Laboratory of Mobile Network and Mobile Multimedia Shenzhen518057 China

ISBN: (纸本)9798350325959

Integrated sensing and communication (ISAC) is expected to improve the energy and spectral efficiency of communication systems. Probability hypothesis density (PHD) simultaneous localization and mapping (SLAM) without explicit data association is one of the mainstream algorithms for implementing sensing function in ISAC. However, most existing research focused on multi-sensor and multi-user sensing fusion, while few studies involve multi base station (BS) fusion schemes. In this study, we introduce the virtual reference point rather than common used virtual anchor to model reflecting surface to form a consistent description of the surrounding environment among different BSs. Then, the estimation results of BSs are sequentially utilized as prior information, which is compared with arithmetic average (AA) and generalized covariance intersection (GCI) fusion algorithms. To improve the poor performance of existing schemes when filed of view (FoV) of different BSs is partial overlapped, we propose the concept of establishing global cloud map. The AA fusion algorithm is integrated into cloud fusion to improve the map estimation performance. Simulation results demonstrate that the performance of sequential fusion is superior to AA and GCI schemes when FoVs of different BSs are fully overlapped. Besides, the cloud map fusion scheme can obtain global maps even if some features are no longer in FoV, as well as improve the accuracy and stability of local maps estimation for each BS. © 2023 IEEE.

关键词： integrated sensing and communication mmWave multi-BS fusion probability hypothesis density

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Relationship graph learning network for visual relationship detection 2

Relationship graph learning network for visual relationship ...

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Li, Yanan Yu, Jun Zhan, Yibing Chen, Zhi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450383080

Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are significant considerations when predicting objects' relationships. However, most current visual relationship detection methods only exploited the correlations among objects, and the correlations among objects' relationships remained underexplored. This paper proposes a relationship graph learning network (RGLN) to explore the correlations among objects' relationships for visual relationship detection. Specifically, RGLN obtains image objects using an object detector, and then, every pair of objects constitutes a relationship proposal. All relationship proposals construct a relationship graph, in which the proposals are treated as nodes. Accordingly, RGLN designs bi-stream graph attention subnetworks to detect relationship proposals, in which one graph attention subnetwork analyzes correlations among relationships based on visual and spatial information, and the other analyzes correlations based on semantic and spatial information. Besides, RGLN exploits a relationship selection subnetwork to ignore redundant information of object pairs with no relationships. We conduct extensive experiments on two public datasets: the VRD and the VG datasets. The experimental results compared with the state-of-the-art demonstrate the competitiveness of RGLN. © 2021 ACM.

关键词： Semantics

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Dynamic State Estimation for Power System with Communication Constraint Using Event-Triggered Cubature Kalman Filter

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Journal of Beijing Institute of Technology (English Edition) 2021年第ZK期30卷 129-140页

作者： Wei, Minfeng Xu, Min Zhang, Fengdi School of Automation and Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China

Accurate dynamic state estimation plays an important role in power systems. Although various filtering algorithms, such as unscented Kalman filter (UKF) and particle filter (PF), have been proposed based on phasor measurement units (PMUs), they occupy a huge communication bandwidth without specific concern, which puts heavy burden on the communication network especially when wireless communications are widely applied in smart grids. In order to relieve this communication burden, the event-triggered cubature Kalman filter (ETCKF) is proposed based on the stochastic event-triggered schedule in this paper, which guarantees that only the measurements containing innovational information are transmitted and has advantages over other event-triggered schedules, such as the deterministic event-triggered schedule, since it can maintain the Gaussian property of the conditional distribution of the system state. Based on the developed nonlinear event-triggered schedule, the cubature Kalman filter (CKF), using the third-degree spherical-radial cubature rule, further provides more accurate estimation than UKF and has lower computational complexity than PF. The proposed filter can effectively reduce the communication rate while ensuring the accuracy of filtering. Finally, the standard IEEE 39-bus system is utilized to verify the feasibility and performance of the proposed method. © 2020 Journal of Beijing Institute of Technology

关键词： Phasor measurement units

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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