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arXiv 2024年

作者： Guo, Liping Wang, Jimin Zhao, Yanlong Zhang, Ji-Feng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

This paper addresses the optimal state estimation problem for dynamic systems while preserving private information against an adversary. To dominate the adversary’s estimation accuracy about private information in the mean square error (MSE) sense, the Cramér-Rao lower bound (CRLB) is employed to evaluate privacy level. The problem is formulated as a constrained optimization, which minimizes the MSE of the state estimate with a constraint on privacy level, achieving a trade-off between privacy and utility. To solve the constrained optimization problem, an explicit expression for CRLB is first provided using the information inequality. To overcome the increasing sizes of the involved matrices over time, a low-complexity approach is then proposed to achieve online calculation for CRLB, significantly reducing computational complexity. Next, the optimization problem is relaxed to a semi-definite programming problem, and a relaxed solution is provided. Finally, a privacy-preserving state estimation algorithm with low complexity is developed and proved to achieve differential privacy. Two illustrative examples, including a practical case of building occupancy, demonstrate the effectiveness of the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Parallel Treasury for TRUE DAOs: Model, Indicators and Mechanism*

Parallel Treasury for TRUE DAOs: Model, Indicators and Mecha...

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control Conference (ANZCC), Australian and New Zealand

作者： Sangtian Guan Juanjuan Li Wenwen Ding Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

In response to concerns over the centralization tendency in the decentralized autonomous organizations (DAOs), TRUE autonomous organizations and operations (TAOs or TRUE DAOs) have been proposed recently. TAOs aim at spreading equitable value distribution and democratized decision-making, distinguishing them from their DAOs counterparts. This study focuses on the treasury within TAOs, which acts as a central fund pool and a crucial element in the decentralized economy (DeEco) system. First, against a backdrop of potential black swan events and other long-tail unforeseen challenges, a reference model for the intelligent treasury management of TAOs is proposed. Then, an evaluation system, namely VALID, is presented with metrics including verifiability, anti-volatility, legitimacy, inclusiveness, and decentralization. Furthermore, a novel parallel treasury management mechanism is proposed to demonstrate a virtual-real interactive closed-loop management and control paradigm of the treasury, thereby fostering the formulation and development of DeEco. This research provides a comprehensive perspective on intelligent treasury management of TAOs and their role in sustainable advancement of DeEco.

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Dynamic Identification for a Manipulator Model based on Stribeck Friction using the Quasi-Newton Iterative Method

Dynamic Identification for a Manipulator Model based on Stri...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiao, Feng Zhang, Feilong Han, Bing Zhang, Hualiang Shenyang University of Technology School of Information Science and Engineering Shenyang110870 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China School of Mechanical Engineering Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China

ISBN: (纸本)9798350325706

The performance of dynamic control is intimately tied to modeling accuracy. However, traditional estimation methods and friction models, such as the least squares method and the Coulomb plus viscous model, fail to reflect the actual characteristics accurately. Particularly, the linear nature of the Coulomb plus viscous model overlooks the nonlinear static features of joint friction at slower velocities. To improve the rationality of the model structure, we integrate the Stribeck friction model into the Coulomb plus viscous model. However, introducing such nonlinearities compromises the applicability of the least squares method. As a countermeasure, we propose a new strategy that combines the least square method and the Quasi-Newton iterative method to identify the parameters of the modified nonlinear model. Additionally, the design of the excitation trajectory is critical to achieve high identification accuracy. We utilized the inverse of the smallest singular value of the observation matrix as the objective function. By minimizing it with the interior point method, we generate the excitation trajectory well-suited to stimulate dynamic characteristics. Then we leverage the discrepancies between the measured and estimated torques to assess the precision of the dynamic parameters of the manipulator. Remarkably, our proposed algorithm reduces the mean absolute error of the estimated torque by over 20.40%. Finally, an experiment of the industrial manipulator by hand guiding grab and drag is performed and shows that the proposed approach can provide the manipulator with comprehensive torque compensation. © 2023 IEEE.

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The Model of Managing a Group of UAVs by a Single Operator for Tasks Requiring Increased Attention

The Model of Managing a Group of UAVs by a Single Operator f...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Mikhail Khachumov Vyacheslav Khachumov Intelligent Control Laboratory Ailamazyan Program Systems Institute of RAS Pereslavl-Zalessky Russia RUDN University Federal Research Center "Computer Science and Control" of RAS Moscow Russia

The problem of optimizing the load on an operator of unmanned aerial vehicles (UAVs), which performs real-time tasks of researching and monitoring territories in an unstable environment is considered. Working load depends on the intensity of the information exchange between onboard and ground control stations and is determined by the agreed limit that leads to a limitation in the number of managed vehicles. As a mathematical model of the multi-functional control system, we use a closed-loop queuing system, which allows to establish coordination among its major elements (onboard computing complex, ground control station and operator) and reduce the loss of service requests. The latter is one of the main requirements to functioning of the system during emergencies. Of considerable interest are optimization statements for the problems of distribution of functions between elements and choosing the load modes. We gave formulations of some optimization problems in a general form and made appropriate conclusions. It is assumed that as an applied task, the problem of monitoring fire sites in forest areas can be considered, which requires increased attention from the operator to process information coming to the ground control station.

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Large-Scale Hyperspectral Image Clustering Using Contrastive Learning

Large-Scale Hyperspectral Image Clustering Using Contrastive...

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2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021

作者： Cai, Yaoming Liu, Yan Zhang, Zijia Cai, Zhihua Liu, Xiaobo 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

Unsupervised hyperspectral image (HSI) classification is an important but challenging task in the hyperspectral processing community. Despite great success, previous HSI clustering approaches belong to offline clustering which is often performed in a transductive scheme, thus failing to generalize to large-scale and unseen scenes. In this paper, we propose an online and deep clustering model for large-scale HSI clustering, termed spectral-spatial contrastive clustering (SSCC). Specifically, SSCC performs contrastive learning based on a series of semantic-preserving spectral-spatial augmentation to simultaneously maximize the intra-class agreement and inter-class variation, which are implemented by an instance-level contrastive loss and a cluster-level contrastive loss, respectively. The SSCC model is trained in an end-to-end fashion with minibatch, allowing it to efficiently handle large-scale HSIs. We assess the performance of SSCC on real HSI and show that SSCC significantly advances the state-of-the-art results with 8.41% improvement on accuracy. © 2021 CEUR-WS. All rights reserved.

关键词： Spectroscopy

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Traditional Chinese medicine diagnosis and treatment based on systematics

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iLIVER 2023年第4期2卷 181-187页

作者： Zhanhong Wang Danli Wang Wen Liu Zhongxia Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Hospital of Traditional Chinese Medicine Capital Medical UniversityBeijing 100010China Department of Nutrition Fifth Medical CenterPLA General HospitalBeijing 100039China

Background:Traditional Chinese Medicine(TCM)is a well-established medical system with a long ***,the overall concept and systematic thinking in TCM are not comprehensively understood and applied in its inheritance and *** This study aims to provide a basic theory for TCM diagnosis using systematics as the guiding *** modern scientific and technological achievements,we aim to explore a new TCM diagnosis ***:We analyzed previous studies on TCM diagnosis and treatment,and reviewed clinical research on TCM diagnosis and treatment from the viewpoint of *** propose a new process model based on systematics for TCM diagnosis and *** is a generalized model that summarizes the process of“establishing an image to express meaning”.Results:The proposed model was implemented in the clinical practice of *** monitored the detailed treatment process of patients in the Department of Liver Diseases at Beijing *** patient underwent a treatment program that lasted 1 year and 45 days,consisting of 12 iterative cycles,each guided by the proposed diagnostic model and tailored to the patient's evolving *** case study validates the effectiveness of the proposed model in the diagnosis and treatment of liver disease in *** therapeutic efficacy has been validated through the examination of both TCM indicators and Western medical auxiliary *** these,the TCM indicators consist of 2 components:tongue diagnosis and pulse ***,the Western medical auxiliary indicators encompass a range of assessments,including whole blood cell analysis,professional liver function examination,a series of liver function assessments,a high-sensitivity hepatitis B pentathlete test,as well as color Doppler ultrasound evaluations of the liver,bile duct,pancreas,spleen,and assessments of liver elasticity,among other related examination *** conclusion,it is evident that the syndrome of l

关键词： Traditional Chinesemedicine Systematics Diagnosis and treatment model Liver disease

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Design and application of a one-stop mobile application sharing platform for steel enterprises

Design and application of a one-stop mobile application shar...

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第42届中国控制会议

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz 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 Department of Electrical and Computer Engineering University of Alberta

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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Data-Driven Model for Detection,Isolation and Accommodation of Faulty Angle of Attack Sensor Measurements in Fixed Winged Aircraft

Data-Driven Model for Detection,Isolation and Accommodation ...

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第34届中国控制与决策会议

作者： Bemnet Wondimagegnehu Mersha Hongbin Ma School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Ethiopian Airlines' Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10,2019,at Ejere Town,south of Addis Ababa.A faulty angle of attack(AOA) sensor was the cause of the *** airplane accidents have been linked to faulty AOA sensors in the *** majority of the AOA sensor fault detection,isolation,and accommodation(SFDIA) literature relied on linear model-driven techniques,which are not suitable when the system's model is uncertain,complex,or *** multilayer perceptron(MLP) models have been employed in datadriven models in the literature and the effectiveness of deep learning-based data-driven models has not been *** this work,a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is *** proposed model uses a deep learning-based recurrent neural network(RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement,faulty total velocity measurement,and faulty pitch rate *** residual analysis with a fixed threshold is used to detect and isolate faulty AOA *** proposed and benchmark models are trained with the adaptive momentum estimation(Adam) *** show that the proposed model effectively detects,isolates,and accommodates faulty AOA measurements when compared to other data-driven benchmark *** method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.

关键词： Data-driven modeling Intelligent transportation systems Faulty angle of attack measurement Sensor fault Detection Isolation and Accommodation(SFDIA) and Recurrent neural network

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scHi-CSim:a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking

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Journal of Molecular Cell Biology 2023年第1期15卷 27-40页

作者： Shichen Fan Dachang Dang Yusen Ye Shao-Wu Zhang Lin Gao Shihua Zhang School of Computer Science and Technology Xidian UniversityXi’an 710071China School of Automation Northwestern Polytechnical UniversityXi’an 710072China NCMIS CEMSRCSDSAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeĳing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeĳing 100049China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunming 650223China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual ***,high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream ***,we developed a single-cell Hi-C simulator(scHi-CSim)that generates high-fidelity data for ***-CSim merges neighboring cells to overcome the sparseness of data,samples interactions in distance-stratified chromosomes to maintain the heterogeneity of single cells,and estimates the empirical distribution of restriction fragments to generate simulated *** demonstrated that scHi-CSim can generate high-fidelity data by comparing the performance of single-cell clustering and detection of chromosomal high-order structures with raw ***,scHi-CSim is flexible to change sequencing depth and the number of simulated *** showed that increasing sequencing depth could improve the accuracy of detecting topologically associating *** also used scHi-CSim to generate a series of simulated datasets with different sequencing depths to benchmark scHi-C clustering methods.

关键词： single-cell Hi-C data simulator high-fidelity single-cell Hi-C clustering distance-stratified sampling benchmarking

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