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arXiv 2024年

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University China College of Control Science and Engineering Zhejiang University China Institute for AI Industry Research Tsinghua University China Department of Computer Science and Technology Tsinghua University China

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqizju/MOPD. © 2024, CC BY.

关键词： Intelligent robots

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A Class of Perimeter Defense Strategy via Relative Time and Maximum Matching

A Class of Perimeter Defense Strategy via Relative Time and ...

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Chinese Automation Congress (CAC)

作者： Chengqian Yang Ziyi Du Lixiong Lin Guangtong Xu Bomin Huang School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China Department of Precision Instrument Tsinghua University Beijing China School of Ocean Information Engineering Jimei University Xiamen China Huzhou Institute Zhejiang University Huzhou China College of Computer Engineering Jimei University Xiamen China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper studies a class of perimeter defense problems for large-scale multi-robot systems. A perimeter defense strategy is designed via relative time calculations and maximum matching methods. The strategy is simple, effective, and easy to implement for large-scale perimeter defense problems. Some simulations are made to verify the effectiveness of the strategy.

关键词： Automation Scalability Kinematics Complexity theory Multi-robot systems

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PDDM: IP Image Design via Diffusion Model

PDDM: IP Image Design via Diffusion Model

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Data Science and computer Application (ICDSCA), IEEE International Conference on

作者： Mingbo Yao Haiyi Wang Shuxiang Yang School of Mathematics and Statistics Shandong University of Technology Zibo China College of Computer and Control Engineering Northeast Forestry University Harbin China Faculty of Computational Mathematics and Cybernetics Shenzhen MSU-BIT University Shenzhen China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Traditional methods and models, while capable of generating high-quality images, often require substantial human resources and time, limiting their feasibility for large-scale applications. To address these challenges, this paper proposes the Pose-Directed Diffusion Model. Through optimized training of this module, the model in this paper significantly reduces training time. Additionally, the model in this paper integrates the Pro-controlNet module to enhance fine control during the image generation process. Importantly, this paper constructs a new CCC Image Collection dataset, featuring precise classifications, detailed annotations, and high-resolution images. The experimental results on the CCC Image Collection dataset demonstrate that our model performs excellently in terms of evaluation metrics such as MSE, PSNR, and SSIM. It shows significant improvement in training effectiveness on the original dataset, with outstanding performance both in terms of absolute error measurement and visual perception by the human eye.

关键词： Training Adaptation models Visualization Accuracy Image synthesis Computational modeling Process control Diffusion models Data models IP networks

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Adjusted and controllability Properties of Memristor Rucklidge System

Adjusted and Controllability Properties of Memristor Rucklid...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Zang, Hongyan Huang, Lili Zhang, Shourong Joseph, Annie Anak Fu, Haiyan Lei, Tengfei Qilu Institute of Technology Collaborative Innovation Center of Memristive Computing Application Jinan250200 China Universiti Malaysia Sarawak Faculty of Engineering Kota Samarahan Sarawak Malaysia College of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan250200 China Shandong Vanform High Energy Physics Technology Co. Ltd. Jinan250100 China Universiti Malaysia Sarawak Department of Electrical and Electronics Kota Samarahan Sarawak Malaysia College of Computer and Information Engineering Qilu Institute of Technology Jinan250200 China Jinan250200 China

ISBN: (纸本)9798350316230

There are many models of memristors, but there are no reports on exponential memristors. In this paper, a three- dimension memristor Rucklidge system with exponential memristor is proposed by introducing exponential function and constructing exponential memristor. The dynamic behavior of the system is analyzed by means of Lyapunov exponent and bifurcation diagram. The complex attractor coexistence and amplitude modulation characteristics of the system are found. The internal variable of memristor is introduced into the second two-dimensional equation as a feedback term, and a conditional symmetric system is constructed for the three-dimensional memristor system, which can realize the polarity control of output signal. By discussing the dynamic characteristics of exponential memristor chaotic system and the control mechanism of amplitude modulation, bias and conditional symmetry, the complex dynamic characteristics of exponential function memristor are revealed, which lays a foundation for its application in the field of secure communication. © 2023 IEEE.

关键词： Memristors

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Robust Length of Stay Prediction Model for Indoor Patients

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computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems:Tackling Multiplicative Noises and Missing Measurements

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1127-1138页

作者： Shaoying Wang Zidong Wang Hongli Dong Yun Chen Guoping Lu College of Science Shandong University of AeronauticsBinzhou 256603China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590 Department of Computer Science Brunel University LondonUxbridge UB83PHUK Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Automation Hangzhou Dianzi UniversityHangzhou 310018China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission *** multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary *** attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance *** this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented ***,we analyze statistical properties of augmented noises as well as high-order moments of certain random *** the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain ***,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

关键词： filtering quadratic bounds

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

A Modified Dempster Shafer Approach to Classification in Sur...

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Iranian Conference on Electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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An Economic Scheduling Method for Coordinated Integrated Energy Systems and Transportation Networks

An Economic Scheduling Method for Coordinated Integrated Ene...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Wenjie Qiao Yinghua Han Fangyuan Si Ning Zhang Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern University Shenyang China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China Department of Electrical Engineering Tsinghua University Beijing China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China

Integrated energy systems (IES) improve overall energy efficiency by synchronizing resources and networks across multiple regions, the high penetration level of electric vehicles (EVs) may pose a threat without coordinating with the IES. Thus, this paper proposes a coordinated IES and transportation network (TN) operation model toward economic scheduling, with the secure operation conditions are comprehensively considered. Specifically, the composition networks of IES and the TN are modeled separately, then a transactive relationship model is proposed to get the networks bridged. To reduce the computational intractability, several approximation and relaxation methods are utilized to transform the model into a mixed-integer-quadratic problem (MIQP). Results show that the coordinated scheduling of IES and TN can lower 4.3% (105.266k$) of consumption compared with the independent scheduling.

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haodi Zhang Yichi Wang Yifan Jian Jiahui Jiang Zhaohai Bai Lin Ma College of Computer Science and Software Engineering Shenzhen University Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Climate downscaling is crucial for detailed small- scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State- Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-of-the-art performance and provides strong support for climate downscaling and the planning of climate-related strategies.

关键词： Couplings Signal processing Encoding Robustness Spatiotemporal phenomena Planning Kernel Speech processing Forecasting Meteorology

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Light focusing in linear arranged symmetric nanoparticle trimer on metal film system

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Chinese Physics B 2022年第1期31卷 540-546页

作者： Yuxia Tang Shuxia Wang Yingzhou Huang Yurui Fang State Key Laboratory of Coal Mine Disaster Dynamics and Control and Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials College of PhysicsChongqing UniversityChongqing 400044China Applied Physics School of Computer Science and Information EngineeringChongqing Technology and Business UniversityChongqing 400067China Key Laboratory of Materials Modification by Laser Electronand Ion Beams(Ministry of Education)School of PhysicsDalian University of TechnologyDalian 116024China

Benefiting from the induced image charge on film surface,the nanoparticle aggregating on metal exhibits interesting optical *** this work,a linear metal nanoparticle trimer on metal film system has been investigated to explore the novel optical *** the electric field and surface charge distributions demonstrate the light is focused on film greatly by the nanoparticles at two sides,which could be strongly modulated by the wavelength of incident *** the influence of nanoparticle in middle on this light focusing ability has also been studied here,which is explained by the plasmon hybridization *** finding about light focusing in nanoparticle aggregating on metal film not only enlarges the novel phenomenon of surface plasmon but also has great application prospect in the field of surface-enhanced spectra,surface catalysis,solar cells,water splitting,etc.

关键词： electric field enhancement light focusing nanoparticle trimer on metal film system plasmonic hybridization

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