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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Qingtao Liu Yu Cui Qi Ye Zhengnan Sun Haoming Li Gaofeng Li Lin Shao Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China Key Lab of CS&AUS Zhejiang Province College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Department of Computer Science National University of Singapore Singapore

Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep re-inforcement learning (DRL) based methods leverage human demonstrations to reduce sample complexity due to the high dimensional action space with dexterous grasping. However, less attention has been paid to hand-object interaction representations for high-level generalization. In this paper, we propose a novel geometric and spatial hand-object interaction representation, named DexRep, to capture object surface features and the spatial relations between hands and objects during grasping. DexRep comprises Occupancy Feature for rough shapes within sensing range by moving hands, Surface Feature for changing hand-object surface distances, and LocalGeo Feature for local geometric surface features most related to potential contacts. Based on the new representation, we propose a dexterous deep reinforcement learning method DexRepNet to learn a generalizable grasping policy. Experimental results show that our method outperforms baselines using existing representations for robotic grasping dramatically both in grasp success rate and convergence speed. It achieves a 93% grasping success rate on seen objects and higher than 80% grasping success rates on diverse objects of unseen categories in both simulation and real-world experiments.

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Causal Discovery from Shifted Multiple Environments 25

Causal Discovery from Shifted Multiple Environments

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Dezhi Yang Guoxian Yu Jun Wang Jinglin Zhang Carlotta Domeniconi School of Software Shandong University Jinan Shandong China School of Control Science and Engineering Shandong University Jinan Shandong China Department of Computer Science George Mason University Fairfax VA USA

ISBN: (纸本)9798400712456

A fundamental problem in many science domains is learning the causal structure of a system from observed data. The observed data canonically come from multiple environments (i.e. different times, locations, and measurements), and causal models may have unobserved shifts. Although the causal graphs can be identified by modeling the distribution changes among different environments, existing solutions can only learn causal structures when given environmental information. In contrast, we propose a causal discovery approach (CausalSME) which automatically identifies pseudo environments and unobserved distribution shifts. Specifically, CausalSME learns a causal model containing unobserved variables, which can correct the distribution shifts with mixed environments. The heart of CausalSME is a variational autoencoder that infers shifted causal effects of unobserved variables and guides the identification of environment information. It further divides the shifted samples by the identified environments to jointly learn an invariant causal model. We prove the structure identifiability of CausalSME with the causal additive model. In our extensive experiments we show that CausalSME achieves state-of-the-art performance.

关键词： adversarial learning

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Insulation condition forewarning of form-wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network

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High Voltage 2021年第2期6卷 302-313页

作者： Yalin Wang Jiandong Wu Tao Han Kiruba Haran Yi Yin Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIllinoisUSA Department of Instrument Science and Engineering Shanghai Jiao Tong UniversityShanghaiChina

Form-wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment,such as high temperature and low *** study proposes a forewarning method for insulation condition monitoring of form-wound windings based on partial discharge(PD)and deep learning neural *** PD features are extracted from the PD profile,which provides information about physics-of-failure and reflects the degree of insulation *** algorithm fusion extracted from auto-encoder and long short-term recurrent neural network is proposed to synthesize one failure precursor from these three features and make multi-time-step prediction through historical data to provide *** electrical and thermal accelerated ageing test is performed on the form-wound windings at 0.2 atm to simulate working environment of electric *** proposed method is validated on the accelerated ageing dataset and shows better prediction accuracy than some existing time-series prediction methods,indicating the advantages of the proposed ***,an on-line hardware setup using a deep learning processor is rec-ommended to implement the forewarning *** proposed approach has the potential to be widely applied to other insulation systems and contribute to work on condition monitoring.

关键词： network processor neural

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MedFILIP: Medical Fine-grained Language-Image Pre-training

arXiv

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arXiv 2025年

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of computer and control engineering Northeast Forestry University Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. The code is available in https://***/ PerceptionComputingLab/MedFILIP. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Optimized Fractional-Order PID controller for Temperature control of CSTH: A Comparative Study with PID Cascade controller

Optimized Fractional-Order PID Controller for Temperature Co...

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2025 International Conference on Energy, Power, Environment, control and Computing, ICEPECC 2025

作者： Alaswadi, Abdulaziz F. Alyazidi, Nezar M. AL-Aswadi, Fatima N. Ahmed, Amjed Abbas Department of Control and Instrumentation Engineering King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Center of Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University Al Hudaydah Yemen Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Baghdad Iraq

ISBN: (纸本)9781837243235

controlling the temperature in chemical reactors, heating furnaces, distillation columns, and reboilers for circulating heating fluids is crucial across all industries. This project is a simulation-based study where both PID-cascade and fractional order PID (FO-PID) controllers were used to control the temperature of water in the continuously stirred tank heater system. The simulation was designed by using SIMULINK. Initially, the work reproduces results from a previous study that evaluated the performance of PID cascade controller applied for controlling the temperature of continuous stirred tank heater. Building on this foundation, a novel fractional-order PID (FO-PID) controller was designed and its parameters were optimized using the Whale Optimizer Algorithm (WOA). Results showed that the FO-PID outperforms the PID-cascade controller in terms of settling time and percentage overshoot. This study demonstrates significant advancements in process innovation and optimization, supporting sustainable industrial systems. The FO-PID controller in comparison to the Cascade PID controller reduced the percentage overshoot by approximately 97%, reduced settling time by 87% and maintained comparable steady-state error. © The Institution of engineering & Technology 2025.

关键词： Dynamic models

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Maximizing social power in multiple independent Friedkin-Johnsen models

Maximizing social power in multiple independent Friedkin-Joh...

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European control Conference (ECC)

作者： Lingfei Wang Yu Xing Claudio Altafini Karl H. Johansson Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering Division of Automatic Control Linköping University Linköping Sweden

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the problem of maximizing social power for a group of agents, who participate in multiple meetings described by independent Friedkin-Johnsen models. A strategic game is obtained, in which the action of each agent (or player) is her stubbornness over all the meetings, and the payoff is her social power on average. It is proved that, for all but some strategy profiles on the boundary of the feasible action set, each agent's best response is the solution of a convex optimization problem. Furthermore, even with the non-convexity on boundary profiles, if the underlying networks are given by a fixed complete graph, the game has a unique Nash equilibrium. For this case, the best response of each agent is analytically characterized, and is achieved in finite time by a proposed algorithm.

关键词： Network topology Europe Games Nash equilibrium Cost function Convex functions

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Effects of cutting speed and milling method on cutting forces, tool wear, tool life, and surface roughness in high-speed shell milling of Inconel 718 with coated carbide insert under emulsion flood cooling strategy

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International Journal of Machining and Machinability of Materials 2021年第4期23卷 307-326页

作者： Okafor, Anthony Chukwujekwu Nwoguh, Theodore Obumselu Computer Numerical Control and Virtual Manufacturing Laboratory Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology 327 Toomey Hall RollaMO65409-0050 United States

Inconel 718 use in aerospace and nuclear industries has gained wide interest due to the need to improve its machinability. This paper presents the results of experimental investigation of the effects of face milling methods and cutting speed on machinability of Inconel 718 with carbide inserts under conventional emulsion flood-cooling strategy (CEF-CS) as a benchmark for comparing alternative vegetable-oil-based MQL cooling strategy (VO-MQL-CS). The machinability parameters investigated are cutting force components, tool wear, burr formation, surface roughness, and tool life, under up and down-milling at cutting speeds of 30, 40, and 50 m/min and constant chip load. Lower cutting forces, tool wear, burr formation, surface roughness, and significant improvement in tool life and volume of material removed are achieved in down-milling over up-milling by 1,677%, 2,150% and 1,004% at 30, 40, and 50 m/min respectively. Down-milling at 40 m/min cutting speed under CEF-CS with coated carbide inserts is recommended as benchmark. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Surface roughness

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GENERAL-PURPOSE MULTI-MODAL OOD DETECTION FRAMEWORK

arXiv

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arXiv 2023年

作者： Duong, Viet Wu, Qiong Zhou, Zhengyi Zavesky, Eric Chen, Jiahe Liu, Xiangzhou Hsu, Wen-Ling Shao, Huajie Department of Computer Science College of William and Mary United States AT &T Labs United States College of Control Science and Engineering Zhejiang University China School of Computer Science Zhejiang University China

Out-of-distribution (OOD) detection identifies test samples that differ from the training data, which is critical to ensuring the safety and reliability of machine learning (ML) systems. While a plethora of methods have been developed to detect uni-modal OOD samples, only a few have focused on multi-modal OOD detection. Current contrastive learning-based methods primarily study multi-modal OOD detection in a scenario where both a given image and its corresponding textual description come from a new domain. However, real-world deployments of ML systems may face more anomaly scenarios caused by multiple factors like sensor faults, bad weather, and environmental changes. Hence, the goal of this work is to simultaneously detect from multiple different OOD scenarios in a fine-grained manner. To reach this goal, we propose a general-purpose weakly-supervised OOD detection framework, called WOOD, that combines a binary classifier and a contrastive learning component to reap the benefits of both. In order to better distinguish the latent representations of in-distribution (ID) and OOD samples, we adopt the Hinge loss to constrain their similarity. Furthermore, we develop a new scoring metric to integrate the prediction results from both the binary classifier and contrastive learning for identifying OOD samples. We evaluate the proposed WOOD model on multiple real-world datasets, and the experimental results demonstrate that the WOOD model outperforms the state-of-the-art methods for multi-modal OOD detection. Importantly, our approach is able to achieve high accuracy in OOD detection in three different OOD scenarios simultaneously. The source code will be made publicly available upon publication. Copyright © 2023, The Authors. All rights reserved.

关键词： Wood

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Two Factors that Influence Our Selection of Digital Avatars: Gender Performativity and Historical Culture 5th

Two Factors that Influence Our Selection of Digital Avatars:...

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5th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Liu, Shutang Li, Yongyu Li, Minggui Guan, Yin Jiang, Hang Jiang, Lei Faculty of Humanities and Arts Macau University of Science and Technology 999078 China Academic Affairs Office Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China Culture and Creation Department Fuzhou Polytechnic Fuzhou China

ISBN: (纸本)9789819908479

In this study, 74 college student volunteers were recruited as experimental participants, and 108 game 3D images of the Arena of Valor game were used as the selected samples. The digital avatar is presented to the subject through AR technology. Then, the participants were asked to choose five digital avatar that they would probably go on to use in the future metaverse. Finally, after excluding the effects of game familiarity and other socio-statistical variables, it was found that the gender of the selected Digital avatar was positively correlated with the actual participants’ gender, and it was further found that females were more gender specific and more aggregated in terms of Gender Expression than males, while males were less gender specific and more dispersed. This study found that although there was certain influence of historical culture on the selection aspect of the digital avatar, it was not significant. Local game characters and Japanese game characters have equally important influence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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