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Dynamic Self-Triggered Control for Nonzero-Sum Games of Unknown Nonlinear Constrained systems via Generalized Fuzzy Hyperbolic Models

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3506-3518页

作者： Liu, Fan Su, Hanguang Zhang, Huaguang Luo, Biao Wang, Jiawei Northeastern University School of Information Science and Engineering Shenyang110004 China Northeastern University School of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Central South University School of Automation Changsha410083 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China

In this article, a novel adaptive dynamic programming algorithm is devised to handle the multiplayer nonzero-sum (NZS) games of completely unknown nonlinear systems, subject to state and input constraints under the dynamic self-triggered mechanism. Initially, in order to eliminate the demand for system dynamics, a generalized fuzzy hyperbolic model based identifier is established, which only relies on input-output data. Then, the equivalent transformation of the reconstructed system is implemented by virtue of barrier functions. With the aid of the nonquadratic utility function, the Hamilton-Jacobi equation of the NZS game is derived. After that, an adaptive critic scheme with experience replay is employed to acquire the Nash equilibrium solution. Furthermore, a novel dynamic self-triggered rule is proposed with the dead-zone operation, which not only significantly reduces source consumption but also overcomes the implementation difficulty of monitoring hardware in the event-triggered mechanism. Moreover, the stability of the system and the uniform ultimate boundedness of the critic weights are guaranteed. Ultimately, two simulation examples are given to validate the feasibility of the developed method. © 2025 IEEE.

关键词： Adaptive algorithms

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Dynamic thermal performance of cryogenic liquid hydrogen heating from gas-liquid stratification to supercritical under a constant heat flux condition: A two-step simulation

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International Journal of Hydrogen Energy 2024年 136卷 1242-1250页

作者： Zhang, Liang Wang, Erzhuo Shi, Jianfeng Institute of Thermal Science and Power Systems College of Energy Engineering Zhejiang University Zhejiang Hangzhou 310027 China Institute of Process Equipment Zhejiang University Zhejiang Hangzhou 310027 China Engineering Research Center of High-Pressure Process Equipment and Safety Ministry of Education 310027 China

The dynamic thermal performance was significant for safety storage of cryogenic liquid hydrogen in tank. In this work, a numerical simulation was presented to investigate the thermal performance of cryogenic liquid hydrogen in a C-type tank heating from gas-liquid stratification to supercritical state. A two-step simulation using separate model was applied. The results show that a leading rise of absolute pressure was observed due to the uneven temperature distribution in the gas-liquid stratification state. Accordingly, the rise of heat flux will enhance the non-uniformity of temperature distribution and reduce the safety storage time. Meanwhile, the effects of heat flux and thermodynamic state on the safety storage time were discussed. Further, the effects of identification of critical state boundary on the accuracy of the two-step simulation were analyzed. It was found that the boundary condition when local supercritical state occurs was better than other two conditions in this work. © 2024 Hydrogen Energy Publications LLC

关键词： Cryogenic liquid hydrogen Dynamic thermal performance Heat flux Safety storage time Supercritical hydrogen

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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A Player Position Tracking Method Based on a Wide-Area Pan-Tilt-Zoom Video 12

A Player Position Tracking Method Based on a Wide-Area Pan-T...

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Proceedings of the 12th International Conference on Sport sciences Research and Technology Support, icSPORTS 2024

作者： Yamagishi, Shunzo Xie, Chun Shishido, Hidehiko Kitahara, Itaru Master's and Doctoral Program in Intelligent Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Center for Computational Sciences University of Tsukuba Ibaraki Japan Faculty of Science and Engineering Department of Information Systems Engineering Soka University Tokyo Japan

ISBN: (纸本)9789897587191

This paper proposes a method to estimate the posture of an athlete moving on a vast field in a sporting event using a pan-tilt-zoom camera. In order to estimate the posture of an athlete on a sports field from a dynamic video sequence, our method extracts image features to search correspondence points among successive frames and computes the homography transformation matrix that compensates for changes the camera parameters (e.g., angle of view and posture). The effectiveness of this method is qualitatively verified using MLB TV broadcast video. The accuracy and error factors are also quantitatively verified by CG simulation. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Pan-Tilt-Zoom Camera Player Tracking Sports Analytics Video processing Wide-Area

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Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

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Chinese Journal of Chemical engineering 2024年第8期72卷 85-94页

作者： Yujing Zhao Qilei Liu Jian Du Qingwei Meng Liang Sun Lei Zhang State Key Laboratory of Fine Chemicals Frontiers Science Center for Smart Materials Oriented Chemical EngineeringInstitute of Chemical Process Systems EngineeringSchool of Chemical EngineeringDalian University of TechnologyDalian 116024China Ningbo Institute of Dalian University of Technology Ningbo 315016China Shenzhen Shuli Tech Co. LtdShenzhen 518126China Department of Physics City University of Hong KongTat Chee AvenueKowloonHong Kong SARChina

Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug design framework, Drug CAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking,binding pose metadynamics(BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitro activity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitro studies assessing inhibitor activities have confirmed that the compound EN300-331859shows potential Factor Xa inhibition, with an IC_(50)value of 34.57 μmol·L^(-1). Through in silico molecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.

关键词： Chemical product design Mathematical programming method Deep learning Binding affinity Factor Xa inhibitor

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Modulated Degradation Rates of Bone Mineral-Like Calcium Phosphate Glass to Support the Proliferation and Osteogenic Differentiation of Bone Marrow-Derived Stem Cells

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Journal of Bionic engineering 2024年第4期21卷 1960-1974页

作者： Lizhe He Yuye Huang Jiafei Gu Xiaoling Liu Jun Yin Xiang Gao Department of Neurosurgery The First Affiliated Hospital of Ningbo UniversityNingbo315010China Center for Medical and Engineering Innovation The First Affiliated Hospital of Ningbo UniversityNingbo315010China The State Key Laboratory of Fluid Power Transmission and Control Systems Key Laboratory of 3D Printing Process and Equipment of Zhejiang ProvinceSchool of Mechanical EngineeringZhejiang UniversityHangzhou310028China Faculty of Science and Engineering University of Nottingham Ningbo ChinaNingbo315100China

With an elemental composition similar to bone mineral,and the ability to release phosphorus and calcium that benefit bone regeneration,Calcium Phosphate Glass(CPG)serves as a promising component of bone tissue engineering ***,the degradation of CPG composites typically results in increased acidity,and its impact on bone-forming activity is less *** this work,we prepared 3D-printed composite scaffolds comprising CPG,Poly-ε-caprolactone(PCL),and various Magnesium Oxide(MgO)*** the MgO content effectively suppressed the degradation of CPG,maintaining a physiological pH of the degradation *** the degradation of CPG/PCL scaffolds resulted in upregulated apoptosis of Rat Bone Marrow-derived Stem Cells(rBMSC),scaffolds containing MgO were free from these negative impacts,and an optimal MgO content of 1 wt%led to the most pronounced osteogenic differentiation of *** work demonstrated that the rapid degradation of CPG impaired the renewability of stem cells through the increased acidity of the surrounding media,and MgO effectively modulated the degradation rate of CPG,thus preventing the negative effects of rapid degradation and supporting the proliferation and osteogenic differentiation of the stem cells.

关键词： Phosphate glass Polymer composites 3D printing Bone tissue engineering Scaffolds

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Effect of Retained Austenite on Corrosion Behavior of Ultrafine Bainitic Steel in Marine Environment

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Acta Metallurgica Sinica(English Letters) 2023年第5期36卷 717-731页

作者： Xian Zhang Li Gong Yanpeng Feng Zhihui Wang Miao Yang Lin Cheng Jing Liu Kaiming Wu Hubei Province Key Laboratory of Systems Science in Metallurgical Process The State Key Laboratory of Refractories and MetallurgyCollaborative Innovation Center for Advanced SteelsWuhan University of Science and TechnologyWuhan430081China School of Chemistry Chemical Engineering and Life SciencesWuhan University of TechnologyWuhan430070China

In this study,the impact of retained austenite on corrosion initiation,propagation,and resistance of ultrafine bainitic steels in marine environments based on the one-step and two-step bainitic isothermal transformation was investigated using a combination of theoretical calculations and experimental *** to the results,the microstructure of ultrafine bainitic steels was composed of parallel arranged bainite ferrite(BF)and retained austenite(RA).The fraction of the block RA was significantly reduced,whereas the amount of the film RA increased through the two-step bainitic *** the initial stage of corrosion,micro-galvanic effects occurred between the multiphases due to the difference in nobility,leading to the selective dissolution of BF sheaves,which is adjacent to the block RA in the one-step bainitic ***,uniformly distributed film RA in the two-step bainitic steel acted as a barrier against corrosion *** electrochemical measurements and neutral salt spray tests revealed a relatively lower corrosion rate for the two-step bainitic steel,indicating a higher corrosion resistance than the one-step bainitic *** corrosion products layer mainly consisted ofα-FeOOH,γ-FeOOH,and Fe_(3)O_(4),which were stable and favorable for the formation of a protective rust layer.

关键词： Ultrafine bainitic steel Retained austenite Marine environment Corrosion behavior First principle modeling

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Enhanced carbon capture with motif-rich amino acid loaded defective robust metal-organic frameworks

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Nano Research 2024年第3期17卷 2004-2010页

作者： Qi-Ye Ju Jia-Jia Zheng Li Xu Hai-Yan Jiang Zi-Qian Xue Lu Bai Yang-Yang Guo Ming-Shui Yao Ting-Yu Zhu State Key Laboratory of Multi-phase Complex Systems CAS Key Laboratory of Green Process and EngineeringBeijing Key Laboratory of Ionic Liquids Clean ProcessInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China Laboratory of Theoretical and Computational Nanoscience National Center for Nanoscience and TechnologyChinese Academy of SciencesBeijing 100190China School of Rare Earths University of Science and Technology of ChinaHefei 230026China Ganjiang Innovation Academy Chinese Academy of SciencesGanzhou 341000China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijing 100049China Institute for Integrated Cell-Material Sciences Kyoto UniversityInstitute for Advanced StudyKyoto UniversityYoshidaUshinomiya-choSakyokuKyoto 606-8501Japan

The use of metal-organic frameworks(MOFs)as solid adsorption materials for carbon capture is promising,but achieving efficient and reversible adsorption with a balance of capacity and selectivity for carbon dioxide(CO_(2))over N_(2) remains a *** take full advantage of the strong channel traffic and robustness of MOFs with relatively small pores,it is highly necessary to employ a defect-engineering strategy to construct a broader channel structure that can facilitate the loading of functional motif-rich amino acids(AAs).This strategy can greatly enhance the CO_(2) adsorption performance of *** this study,motif-rich amino acids are loaded into the defective and robust porous frameworks via combined defect-engineering and post-synthetic *** defective Zr/Hf-MOF-808s modified with AAs,especially for the 18 mol%4-nitroisophthalic acid,generated defective products allowing for the loading of L-serine(L-Ser).This modification resulted in a significant improvement in both the adsorption capacity(248%improvement at 298 K,100 kPa)and the selectivity of CO_(2)/N_(2) using the ideal adsorbed solution theory(IAST),with the selectivity increasing to 120.55 and 38.27 at 15 and 100 kPa,respectively,while maintaining good cycling *** functional theory(DFT)simulation,CO_(2) temperature-programmed desorption(CO_(2)-TPD),and in situ Fourier transform infrared spectroscopy(FTIR)were further employed to have a better understanding of the enhanced CO_(2) adsorption ***,unlike the AAs loaded pristine MOF-808s that showed the best CO_(2) adsorption capacity with the loading of short and small glycine(Gly),the broadened channel size in our work enables the loading of functional motif-rich L-serine,which brings more active binding sites,improving CO_(2) adsorption.

关键词： carbon capture and storage metal-organic framework(MOF) amino acids(AAs) defect-engineering motif-rich

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A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial processes Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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The manipulation of rectifying contact of Co and nitrogen-doped carbon hierarchical superstructures toward high-performance oxygen reduction reaction

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Carbon Energy 2024年第9期6卷 338-349页

作者： Jing Li Tingyu Lu Yu Fang Guangyao Zhou Mingyi Zhang Huan Pang Jun Yang Yawen Tang Lin Xu Jiangsu Key Laboratory of New Power Batteries School of Chemistry and Materials ScienceJiangsu Collaborative Innovation Center of Biomedical Functional MaterialsNanjing Normal UniversityNanjingJiangsuChina College of Science Jinling Institute of TechnologyNanjingJiangsuChina Key Laboratory for Photonic and Electronic Bandgap Materials Ministry of EducationSchool of Physics and Electronic EngineeringHarbin Normal UniversityHarbinHeilongjiangChina School of Chemistry and Chemical Engineering Yangzhou UniversityYangzhouJiangsuChina State Key Laboratory of Multiphase Complex Systems Center of MesoscienceInstitute of Process EngineeringChinese Academy of SciencesBeijingChina

Rational design and construction of oxygen reduction reaction(ORR)electrocatalysts with high activity,good stability,and low price are essential for the practical applications of renewable energy conversion devices,such as metal-air *** modification through constructing metal/semiconductor Schottky heterointerface represents a powerful strategy to enhance the electrochemical ***,we demonstrate a concept of Schottky electrocatalyst composed of uniform Co nanoparticles in situ anchored on the carbon nanotubes aligned on the carbon nanosheets(denoted as Co@N-CNTs/NSs hereafter)toward *** experimental findings and theoretical simulation testify that the rectifying contact could impel the voluntary electron flow from Co to N-CNTs/NSs and create an internal electric field,thereby boosting the electron transfer rate and improving the intrinsic *** a consequence,the Co@N-CNTs/NSs deliver outstanding ORR activity,impressive long-term durability,excellent methanol tolerance,and good performance as the air-cathode in the Zn-air *** design concept of Schottky contact may provide the innovational inspirations for the synthesis of advanced catalysts in sustainable energy conversion fields.

关键词： Co-based electrocatalysts oxygen reduction reaction rectifying contact Zn-air batteries

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