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第42届中国控制会议

作者： Langyu Li Hao Wang Zhixin Xie Zixuan Liu Wei Cui Yu Pan Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control TechnologyCollege of Control Science and Engineering Zhejiang University School of Automation Science and Engineering South China University of Technology School of Mechanical Engineering and Automation Harbin Institude of Technology

A numerical technique called Simulated Ising Annealing(SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization(QUBO) problem in combinatorial optimization,which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo(GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.

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Analysis of dominant phases inhibiting dissociation of smelting slags and the alkali depolymerization mechanism

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Rare Metals 2024年第11期43卷 6068-6080页

作者： Juan-Li Yuan Jing-Biao Liu Zhen Wu Xiang-Ying Lv Jian-Wei Du Xue-Ming Liu Hong Deng Zhang Lin School of Environment and Energy Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Resource RecyclingSouth China University of TechnologyGuangzhou 510006China School of Advanced Manufacturing School of Ocean Fuzhou UniversityQuanzhou362251China School of Chemistry and Chemical Engineering Shandong UniversityJinan250100China South China Institute of Environmental Science Ministry of EcologyandEnvironmentGuangzhou510535China College of Metallurgy and Environment Central South UniversityChangsha 410083China

This study analyzed the prevalent physicochemical phases of smelting slag from the perspective of data science and *** delineated the silicate phase as the pivotal and predominant constraining phase for the resource utilization of smelting *** intricate correlation between metallic elements and dominant phases was *** silicate phase olivine(OL)was synthesized as a paradigm to examine alkali depolymerization,unveiling the optimal conditions for such depolymerization to be an alkali to olivine molar ratio of 1:5,a reaction temperature of 700℃,and a duration of 3 *** underlying mechanism of alkali depolymerization within silicate phases was elucidated under these *** reaction mechanism of alkali depolymerization within silicate phases can be encapsulated in three sequential steps:(1)NaOH dissociation and subsequent adsorption of OH^(-)to cationic(Mg or Fe)sites;(2)disruption of cation-oxygen bonds,leading to the formation of hydroxide compounds,which then underwent oxidation;(3)Na^(+)occupied the resultant cation vacancy sites,instigating further depolymerization of the intermediate Na_(2)(Mg,Fe)SiO_(4).The articulated mechanism is anticipated to furnish theoretical underpinnings for the efficacious recuperation of metals from smelting slags.

关键词： Smelting slag key phase analysis Silicate phases Alkali depolymerization mechanism

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GPP estimation of a grassland ecosystem based on photosynthesis-hydrology coordination optimization

GPP estimation of a grassland ecosystem based on photosynthe...

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2024 China Automation Congress, CAC 2024

作者： Fu, Lijiang Tan, Jinglu Jiang, Yongnian Allakhverdiev, Suleyman I. Xu, Zhenyu Guo, Ya Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Department of Chemical & Biomedical Engineering University of Missouri ColumbiaMO United States Jiangsu Zhongnong IoT Technology Co. LTD Yixing China Timiryazev Institute of Plant Physiology RAS Moscow Russia Longcom Internet of Things Co. Ltd Hefei China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China

ISBN: (纸本)9798350368604

Accurate estimation of gross primary production (GPP, ecosystem level photosynthesis) is critical for monitoring terrestrial ecosystems. However, most of the current models used for GPP estimation are with many unknown parameters and rarely include the effects of soil water stress due to the covariation of soil moisture and air humidity. In addition, grasslands, as the most widely distributed ecosystems in the world, have simple ecosystem structure and are easily affected by soil drought. Therefore, in this paper, we applied photosynthesis-hydrology coordination optimization theory and soil water potential based on soil moisture effect to develop a combined model (PH model) for GPP estimation of grassland ecosystems and used the US-Var grassland ecosystem, one of the FLUXNET sites from January to July 2004 (the driest period in recent years) as an example to validate the model. The results show that the model can effectively track the measured GPP during soil drought period and is better than the result from another commonly used photosynthesis coordination model (P-model) which does not consider the effect of soil drought. Further, we can infer the vegetation photosynthetic capacity parameters including the maximum carboxylation capacity (Vcmax) and the maximum electron-transport capacity (Jmax) under the effect of soil drought. The result also shows that if soil drought is not considered, the photosynthetic capacity parameters of vegetation will be overestimated. These results provide evidence for the application of photosynthesis-hydrology coordination theory in the assessment of ecosystem carbon cycle under the intensified climate changes. © 2024 IEEE.

关键词： coordination optimization theory GPP estimation soil drought

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Frequency Domain Filtering Based Compressed Sensing Applied on Sparse-Angle CT Image Reconstruction 2022

Frequency Domain Filtering Based Compressed Sensing Applied ...

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7th International Conference on Multimedia and Image Processing, ICMIP 2022

作者： Dong, Jian Chen, Hao Yang, Xiaoxia Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China Department of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781450387408

In the process of CT scanning, multi-Angle projection data needs to be obtained from a large number of projection actions, which makes the scanned individual bear the risk of high radiation exposure. In order to solve such problems, the use of sparse projection data for CT image reconstruction is proposed as a new type of solution. The previous research can obtain good quality reconstructed images when the projection data is sparse by using the CT reconstruction technology based on the nonlinear sparsity transformation of compressed sensing. However, the heavy time loading of the image reconstruction is a practical problem that needs to be solved urgently. This study optimizes the non-linear filtering process of the regularization term of the original scheme, and proposes a novel method which replaces the original non-linear filter with a low-pass frequency domain filter. This strategy effectively utilizes the properties of low-pass frequency domain filtering in image processing. The excellent properties include high efficiency and low time complexity for image smoothing. The simulation experiment results show that in the process of CT image reconstruction using compressed sensing algorithm, the low-pass frequency domain filtering of the new scheme can greatly reduce the required time in the reconstruction of sparse projection data, and the image quality is feasibly guaranteed. © 2022 ACM.

关键词： Compressed sensing

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Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by *** improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic *** integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and *** dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference *** on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax ***,the comparative analysis is performed to validate the proposed method using the naturalistic driving *** results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

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Temperature Prediction for Stored Grain: A Multi- model Fusion Approach Based on Machine Learning

Temperature Prediction for Stored Grain: A Multi- model Fusi...

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Electronic technology and Information Science (ICETIS), International Conference on

作者： Donghao Chen Binkun Liu Key Laboratory of Grain Information Processing and Control Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

Temperature fluctuations significantly affect microorganism growth and pest activities in grain pile, precise monitoring and forecasting temperature of stored grain are essential for maintaining the quality and safety of grain storage. This paper proposes a multi-model fusion approach to predict grain temperature using historical temperature data of stored grain and meteorological data from the region. Firstly, four distinct machine learning models, namely Adaboost, decision tree, extra trees, and random forest, are fine-tuned through parameter optimization to enhance their predictive capabilities respectively; Subsequently, these optimized models are fused to form different ensemble models, which are compared for prediction accuracy to obtain the optimal fusion model. In essence, the fusion process integrates the predictions of each individual model as new feature inputs into the fusion models. Furthermore, random forest is utilized to identify the key factors influencing grain temperature, providing insights into the importance of different influencing factors. The experimental results demonstrate that the proposed fusion models can achieve higher prediction accuracy and robustness compared with the single-model prediction methods. Additionally, the analysis of feature importance also offers empirical evidence for understanding the factors influencing grain temperature.

关键词： Temperature sensors Temperature measurement Temperature distribution Humidity Predictive models Prediction algorithms Robustness

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Single-cell transcriptome analysis uncovers underlying mechanisms of acute liver injury induced by tripterygium glycosides tablet in mice

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Journal of Pharmaceutical Analysis 2023年第8期13卷 908-925页

作者： Qiuyan Guo Jiangpeng Wu Qixin Wang Yuwen Huang Lin Chen Jie Gong Maobo Du Guangqing Cheng Tianming Lu Minghong Zhao Yuan Zhao Chong Qiu Fei Xia Junzhe Zhang Jiayun Chen Feng Qiu Jigang Wang State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs Artemisinin Research Centerand Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijing100700China School of Chinese Materia Medica and State Key Laboratory of Component-Based Chinese MedicineTianjin University of Traditional Chinese MedicineTianjin301617China Department of Nephrology Shenzhen Key Laboratory of Kidney DiseasesShenzhen Clinical Research Centre for GeriatricsShenzhen People's HospitalThe First Affiliated HospitalSouthern University of Science and TechnologyShenzhenGuangdong518020China College of Food Science and Engineering Institute of OceanBohai UniversityJinzhouLiaoning121013China State Key Laboratory of Infectious Disease Prevention and Control Collaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing102206China Institute of Chinese Materia Medica China Academy of Chinese Medical SciencesBeijing100700China The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine ShenzhenGuangdong518033China

Tripterygium glycosides tablet(TGT),the classical commercial drug of Tripterygium wilfordii *** been effectively used in the treatment of rheumatoid arthritis,nephrotic syndrome,leprosy,Behcet's syndrome,leprosy reaction and autoimmune ***,due to its narrow and limited treatment window,TGT-induced organ toxicity(among which liver injury accounts for about 40%of clinical reports)has gained increasing *** present study aimed to clarify the cellular and molecular events underlying TGT-induced acute liver injury using single-cell RNA sequencing(scRNA-seq)*** TGT-induced acute liver injury mouse model was constructed through short-term TGT exposure and further verified by hematoxylin-eosin staining and liver function-related serum indicators,including alanine aminotransferase,aspartate aminotransferase,alkaline phosphatase and total *** the mouse model,we identified 15 specific subtypes of cells in the liver tissue,including endothelial cells,hepatocytes,cholangiocytes,and hepatic stellate *** analysis indicated that TGT caused a significant inflammatory response in liver endothelial cells at different spatial locations;led to marked inflammatory response,apoptosis and fatty acid metabolism dysfunction in hepatocytes;activated hepatic stellate cells;brought about the activation,inflammation,and phagocytosis of liver capsular macrophages cells;resulted in immune dysfunction of liver lymphocytes;disturbed the intercellular crosstalk in liver microenvironment by regulating various signaling ***,these findings elaborate the mechanism underlying TGT-induced acute liver injury,provide new insights into the safe and rational applications in the clinic,and complement the identification of new biomarkers and therapeutic targets for liver protection.

关键词： Tripterygium glycosides tablet Acute liver injury scRNA-seq

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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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Self-feedback LSTM regression model for real-time particle source apportionment

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Journal of Environmental Sciences 2022年第4期34卷 10-20页

作者： Wei Wang Weiman Xu Shuai Deng Yimeng Chai Ruoyu Ma Guoliang Shi Bo Xu Mei Li Yue Li Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China KLMDASR Tianjin Key Laboratory of Network and Data Security TechnologyTianjin 300350China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300071China Institute of Mass Spectrometry and Atmospheric Environment Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution Jinan UniversityGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China

Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

关键词： Time series Regression Self-feedback LSTM network Particle source apportionment

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Multi-OP: A Multilevel Model for Orbital Prediction Using Multi-Feature Fusion

Multi-OP: A Multilevel Model for Orbital Prediction Using Mu...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Qinghao Chu Zhelong Wang Yu Jiang Pengrong Hou Pinliang Zhang School of Control Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Astronautic Dynamics Xi’an Satellite Control Center Xi’an China Beijing Institute of Spacecraft Environment Engineering China Academy of Space Technology Beijing China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

To address the increasing computational demands and the need for enhanced stability in the orbit prediction of large-scale resident space objects (RSOs), this paper proposes a novel method grounded in orbital dynamics, which defines and computes environmental, dynamical, and fingerprint features. A feature fusion strategy is then applied to introduce a Multi-OP model comprising a shared backbone and a prediction layer tailored for orbit prediction tasks. Specifically, the shared backbone integrates environmental and dynamical features to capture public information, thereby enhancing the model’s generalization ability. The prediction layer, in contrast, leverages fingerprint features to generate individualized orbit predictions for each RSO. Experimental results demonstrate that the Multi-OP model not only enhances the accuracy of orbit prediction but also exhibits remarkable stability.

关键词： Space vehicles Accuracy Computational modeling Information processing Fingerprint recognition Predictive models Orbits Stability analysis Intelligent control Convergence

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