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A systematic framework of constructing surrogate model for slider track peeling strength prediction

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Science China Technological Sciences 2024年第10期67卷 3261-3274页

作者： DONG XingJian CHEN Qian LIU WenBo WANG Dong PENG ZhiKe MENG Guang State Key Laboratory of Mechanical system and Vibration Shanghai Jiao Tong University KEIPER Seating Mechanisms Co. Ltd. department of Industrial Engineering and management Shanghai Jiao Tong University

Peeling strength can comprehensively reflect slider track safety and is crucial in car seat safety assessments. Current methods for determining slider peeling strength are primarily physical testing and numerical simulation. However, these methods encounter the potential challenges of high costs and overlong time consumption which have not been adequately addressed. Therefore, the efficient and low-cost surrogate model emerges as a promising solution. Nevertheless, currently used surrogate models suffer from inefficiencies and complexity in data sampling, lack of robustness in local model predictions, and isolation between data sampling and model prediction. To overcome these challenges, this paper aims to set up a systematic framework for slider track peeling strength prediction, including sensitivity analysis, dataset sampling, and model prediction. Specifically, the interpretable linear regression is performed to identify the sensitivity of various geometric variables to peeling strength. Based on the variable sensitivity, a distance metric is constructed to measure the disparity of different variable groups. Then, the sparsity-targeted sampling(STS) is proposed to formulate a representative dataset. Finally, the sequentially selected local weighted linear regression(SLWLR) is designed to achieve accurate track peeling strength prediction. Additionally, a quantitative cost assessment of the supplementary dataset is proposed by utilizing the minimum adjacent sample distance as a mediator. Experimental results validate the efficacy of sequential selection and the weighting mechanism in enhancing localization ***, the proposed SLWLR method surpasses similar approaches and other common surrogate methods in terms of prediction performance and data quantity requirements, achieving an average absolute error of 3.3 kN in the simulated test dataset.

关键词： slider track peeling strength surrogate model sensitivity analysis data sampling local weighted linear regression

Damping Characteristic Analysis and Optimization of Wind-thermal-bundled Power Transmission by LCC-HVDC systems

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Journal of Modern Power systems and Clean Energy 2024年第1期12卷 299-312页

作者： Liwen Zheng Shiying Ma the Power system department China Electric Power Research InstituteBeijing 100192China

With the rapid development of renewable energy,wind-thermal-bundled power transmission by line-commutated converter based high-voltage direct current(LCC-HVDC)systems has been widely *** dynamic interaction mechanisms among permanent magnet synchronous generators(PMSGs),synchronous generators(SGs),and LCC-HVDC system become *** deal with this issue,a path analysis method(PAM)is proposed to study the dynamic interaction mechanism,and the damping reconstruction is used to analyze the damping characteristic of the ***,based on the modular modeling,linearized models for the PMSG subsystem,the LCC-HVDC subsystem,and the SG subsystem are ***,based on the closed-loop transfer function diagram of the system,the disturbance transfer path and coupling relationship among subsystems are analyzed by the PAM,and the damping characteristic analysis of the SG-dominated oscillation mode is studied based on the damping *** with the PAM,the small-signal model of the system is obtained and eigenvalue analysis results are ***,the effect of the control parameters on the damping characteristic is analyzed and the conclusions are verified by time-domain ***,the penalty functions of the oscillation modes and decay modes are taken as the objective function,and an optimization strategy based on the Monte Carlo method is proposed to solve the parameter optimization *** simulation results are presented to validate the effectiveness of the proposed strategy.

关键词： Wind-thermal-bundled power transmission line-commutated converter based high-voltage direct current(LCC-HVDC) path analysis method(PAM) damping characteristic analysis Monte Carlo method

Multiscale spatiotemporal meteorological drought prediction:A deep learning approach

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Advances in Climate Change Research 2024年第2期15卷 211-221页

作者： Jia-Li ZHANG Xiao-Meng HUANG Yu-Ze SUN department of Earth system Science Ministry of Education Key Laboratory for Earth System ModellingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related *** utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological ***,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time *** address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple *** formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis *** indicators span time scales of approximately 30 d and one ***-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future *** focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal *** model's performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought *** results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of *** of the four drought indicators accurately delineates specific facets of the meteorological drought ***,three representative drought events,namely flash drought,sustained drought and severe drought,

关键词： Meteorological drought Spatiotemporal prediction Multiscale Swim transformer Deep learning

Toward Human-centered XAIin Practice:A survey

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Machine Intelligence Research 2024年第4期21卷 740-770页

作者： Xiangwei Kong Shujie Liu Luhao Zhu department of Data Science and management Engineering School of ManagementZhejiang UniversityHangzhou 310058China

Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI *** AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human ***,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI *** first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of *** addition,a taxonomy of XAI methods is developed for visual computing with analysis of method *** clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in *** we check the availability of open-source XAI tools for stakeholders'*** survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.

关键词： Artificial intelligence(AI)application explainable AI(XAI) human-centered design visual computing medical diagnosis.

Novel Cs-Mg-Al mixed oxide with improved mobility of oxygen species for passive NOx adsorption

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Green Energy & Environment 2024年第10期9卷 1581-1591页

作者： Yimeng Yin Chizhong Wang Lei Qiu Xing Li Feilin Zhao Jie Yu Jinchi Han Huazhen Chang School of Environment and Natural Resources Renmin University of China department of Earth system Science Tsinghua University

The development of passive NOxadsorbers with cost-benefit and high NOxstorage capacity remains an on-going challenge to after-treatment technologies at lower temperatures associated with cold-start ***,Cs1Mg3Al catalyst prepared by sol±gel method was cyclic tested in NOxstorage under 5 vol%*** 100°C,the NOxstorage capacity (1219μmol g-1) was much higher than that of Pt/Ba O/Al2O3(610μmol g-1).This provided new insights for non-noble metal catalysts in low-temperature passive *** addition of Cs improved the mobility of oxygen species and thus improved the NOxstorage *** XRD,XPS,IR spectra and in situ DRIFTs with NH3probe showed an interaction between CsOxand AlOxsites via oxygen species formed on Cs1Mg3Al *** improved mobility of oxygen species inferred from O2-TPD was consistent with high NOxstorage capacity related to enhanced formation of nitrate and additional nitrite species by ***,the addition of Mg might improve the stability of Cs1Mg3Al by stabilizing surface active oxygen species in cyclic experiments.

关键词： Passive NO x adsorber Low-temperature NO x storage Cesium Oxygen mobility

Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

Energy exchange between charged relativistic fluids in f(T) gravity

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Communications in Theoretical Physics 2024年第10期76卷 108-120页

作者： Z Yousaf U A Khokhar Nasser Bin Turki T Suzuki department of Mathematics University of the Punjab Quaid-i-Azam Campus department of Mathematics College of Science King Saud University department of Regional management Faculty of Management Fukushima College

This study focuses on the effects of a polytropic fluid on a charged gravitational source within f(T) gravity, where T is the torsion scalar. We investigate how the electromagnetic field affects the flow of energy in spherically symmetric and static celestial objects that contain relativistic fluids. By using the gravitational decoupling technique, we analyze the effects of polytropic fluid on the dynamics of the gravitational source, accompanied by the matching of the interior geometry with an exterior at the hypersurface Σ. Finally, with the help of the Tolman ⅡV solution, we observe the conduct of energy conditions with the existence of charge using f(T)field equations and got the intended outcomes.

关键词： anisotropy complexity energy exchange hydrodynamics self-gravitating systems

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness-China,2014-2019

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China CDC weekly 2024年第26期6卷 629-634页

作者： Xuan Han Jiao Yang Yan Luo Dazhu Huo Xuya Yu Xuancheng Hu Ling Xin Liuyang Yang Hualei Xin Ting Zhang Zhongjie Li Weizhong Yang School of Population Medicine and Public Health Chinese Academy of Medical Sciences(CAMS)&Peking Union Medical CollegeBeijingChina School of Health Policy and management Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina department of management science and information system Faculty of Management and EconomicsKunming University of Science and TechnologyKunming CityYunnan ProvinceChina

Introduction:This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage(ILI%)across regions in *** aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness ***:In this study,data on ILI%and Baidu Index were collected from 30 provincial-level administrative divisions(PLADs)spanning April 2014 to March *** Baidu Index was categorized into Overall Index,Ordinary Index,Prevention Index,Symptom Index,and Treatment Index based on search query *** lagged correlation between the Baidu Index and ILI%was examined through the cross-correlation function(CCF)***:Correlating the Baidu Overall Index of 30 PLADs with ILI%revealed CCF values ranging from 0.46 to 0.86,with a median lag of 0.5 *** analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%,with median lags of−9 and−0.5 days,respectively,compared to 0 and 3 days for the Ordinary and Treatment *** median lag days between the Baidu Index and the ILI%were earlier in the northern PLADs compared to the southern ***:The Prevention and Symptom Indexes show promising predictive capabilities for influenzalike illness epidemics.

关键词： utilizing illness Baidu

Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz department of Computer Science and Technology Tongji UniversityShanghaiChina Project management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina department of Electrical and Computer Engineering AlbertaEdmontonCanada system Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory