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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Current-Constrained Finite-Time Control Scheme for Speed Regulation of PMSM systems With Unmatched Disturbances

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IEEE Transactions on automation Science and engineering 2025年 22卷 16877-16887页

作者： Huiming Wang Zhize Zhang Yingjie Luo Junxiao Wang Jiankun Sun Yunda Yan Xianlun Tang Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China Department of Computer Science University College London London U.K.

The article investigates the speed regulation of permanent magnet synchronous motor (PMSM) systems. Existing control methods of the non-cascade structure suffer from the drawbacks of unsatisfactory anti-disturbance performance and slow convergence rate when the system is affected by disturbances, especially unmatched disturbances. Meanwhile, the requirements of current constraint and fast dynamics cannot be effectively balanced in the single-loop structure of speed and current using traditional control methods such as the PID controller. Because large transient currents induced by fast dynamics may damage the hardware of the system. Therefore, a current-constrained finite-time control approach is proposed. Specifically, a robust finite-time control scheme is developed with the assistance of the improved finite-time observer technique. The proposed method is capable of actively suppressing both matched and unmatched disturbances in non-cascade control systems. Simultaneously, an effective penalty mechanism is established to incorporate a specific gain function into the designed controller. This approach restricts the q-axis current to a predefined safe range without solving an optimization problem. Finally, comparative experiment results indicate that the newly proposed finite-time control method outperforms the baseline control methods in terms of disturbance rejection, convergence rate, and current constraint. Note to Practitioners—This paper addresses practical challenges in controlling permanent magnet synchronous motor (PMSM) systems, such as ensuring robust disturbance rejection while limiting transient currents to protect the hardware. Traditional methods often fail to balance fast dynamics with current constraints, leading to inefficiencies and potential damage. To tackle these issues, the proposed finite-time control approach introduces a practical solution that achieves robust disturbance rejection while ensuring that transient currents remain within safe o

关键词： Convergence Observers Control systems automation Transient analysis Asymptotic stability Stability analysis Regulation Permanent magnet motors Optimization

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles

Motion Planning Integrated with Vehicle-Terrain Interactions...

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International Conference on Intelligent Transportation

作者： Jianhua Yin Xianyuan Zhan Tianying Ji Bingrong Xu Yi He Daqing Zhang Lingxi Li Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for AI Industry Research Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China School of Automation Wuhan University of Technology Wuhan China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha China Sunward Intelligent Equipment Co. Ltd. Changsha China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains.

关键词： Dynamic response Geometry Navigation Heuristic algorithms Reinforcement learning Land vehicles Hazards Planning Sensors Vehicle dynamics

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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Cross-Domain Machine Learning Approaches using Hyperspectral Imaging for Plastics litter detection

Cross-Domain Machine Learning Approaches using Hyperspectral...

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Soufyane Bouchelaghem Imad Eddine Tibermacine Marco Balsi Monica Moroni Christian Napoli Dipartimento di Ingegneria Dell’Informazione Elettronica e Telecomunicazioni (DIET) Sapienza University of Rome Rome Italy Department of Computer Automation and Management Engineering (DIAG) Sapienza University of Rome Rome Italy Dipartimento di Ingegneria Civile Edile e Ambientale Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science Italian National Research Council Rome Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa Poland

ISBN: (数字)9798350358582

ISBN: (纸本)9798350358599

Plastics detection and polymer separation are important tasks for environmental monitoring and waste recycling. The authors previously used hyperspectral data in the lower bands of the SWIR range from 900nm to 1700 nm to perform plastics classification, by applying supervised machine learning methods to data gathered by a custom-assembled stand-alone push-broom system in indoor and outdoor conditions. In this work, we explored the application of two unsupervised learning algorithms: K-Means clustering and Gaussian Mixture Models (GMM), in order to avoid the use of a manually-labeled test set, and to obtain more reliable and flexible application in different environmental conditions. The results suggest that GMM performs well indoors but poorly outside, with GMM exhibiting subtle segmentation and K-Means displaying broader, more generalist categorization. The cross-domain adaptation is difficult and models that balance granularity and generalization are needed to detect plastic in varied settings. This effort advances garbage management and recycling and establishes hyperspectral imaging in real-world environments.

关键词： Image segmentation Adaptation models Machine learning algorithms Clustering algorithms Machine learning Recycling Plastics

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Speech-Vision Based Multi-Modal AI Control of a Magnetic Anchored and Actuated Endoscope

Speech-Vision Based Multi-Modal AI Control of a Magnetic Anc...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Li, Jixiu Huang, Yisen Ng, Wing Yin Cheng, Truman Wu, Xixin Dou, Qi Meng, Helen Heng, Pheng Ann Liu, Yunhui Chan, Shannon Melissa Navarro-Alarcon, David Hang Ng, Calvin Sze Yan Chiu, Philip Wai Li, Zheng The Department of Surgery The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong The Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Surgery ChowYuk Ho Technology Centre of Innovative Medicine Li Ka Shing Institute of Health Science Multiscale Medical Robotics Center Ltd. The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665481090

In minimally invasive surgery (MIS), controlling the endoscope view is crucial for the operation. Many robotic endoscope holders were developed aiming to address this prob-lem,. These systems rely on joystick, foot pedal, simple voice command, etc. to control the robot. These methods requires surgeons extra effort and are not intuitive enough. In this paper, we propose a speech-vision based multi-modal AI approach, which integrates deep learning based instrument detection, automatic speech recognition and robot visual servo control. Surgeons could communicate with the endoscope by speech to indicate their view preference, such as the instrument to be tracked. The instrument is detected by the deep learning neural network. Then the endoscope takes the detected instrument as the target and follows it with the visual servo controller. This method is applied to a magnetic anchored and guided endoscope and evaluated experimentally. Preliminary results demonstrated this approach is effective and requires little efforts for the surgeon to control the endoscope view intuitively. © 2022 IEEE.

关键词： Endoscopy

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Non-convex potential games for finding global solutions to sensor network localization

arXiv

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arXiv 2023年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor networks

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The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

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Healthcare Analytics 2023年 4卷 100247-100247页

作者： T R, Mahesh V, Vinoth Kumar V, Dhilip Kumar Geman, Oana Margala, Martin Guduri, Manisha Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India School of Computer Science Engineering & Information Systems (SCORE) Vellore Institute of Technology (VIT) 632014 India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 600062 India Department of Computers Electronics and Automation Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava 720229 Romania School of Computing and Informatics University of Louisiana at Lafayette United States

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%. © 2023 The Author(s)

关键词： Breast cancer Class-balancing Cross-validation Diagnostic analytics Ensemble method Machine learning

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO systems Under High-Mobility Scenarios

arXiv

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arXiv 2024年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China Department of Automation Tsinghua University Beijing100084 China School of Intelligent Systems Science and Engineering Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Jinan University Zhuhai519070 China

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge- and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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