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第40届中国控制会议

作者： Jiaqi Fan Tianjiao Huo Zhenqi Wei Jiachen Wang Xin Li Bingzhao Gao State Key Laboratory of Automotive Simulation and Control Jilin University College of Computer Science and Technology Jilin University Clean Energy Automotive Engineering Center Tongji University

With the development of autonomous driving vehicle target detection technology, multi-target detection in complex environment has gradually become the focus of attention. In this paper, based on the YOLOv4 algorithm, the target detection problem is carried out on the images from different time periods in the training and test set. Firstly, we train the unsupervised CycleGAN model to get some synthetic images at night. Secondly, these synthetic night images combined with the images collected in the sunny weather are input to train the YOLOv4 detector and then the final model is used to detect the targets at night. The experimental results show that the detection accuracy of our model for car, person and traffic sign is improved by 3.65%, 4.29% and 5.42% respectively when compared to the baseline model, that is, only training the images from the daytime and testing the targets during the night. In addition, the recall values of these three targets are all increased by more than 5%,which indicates that our model reduces the missing detection rate. The above experimental results have fully demonstrated the effectiveness of the combination of supervised and unsupervised algorithm for target detection cross different time periods. And the proposed model greatly improves the safety of the autonomous driving vehicles, which is of great significance for target detection in the road.

关键词： YOLOv4 CycleGAN Cross Domain Detection Transfer Learning Unsupervised Algorithm

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Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds

arXiv

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arXiv 2022年

作者： Liu, Qi Liu, Yiyun Su, Honglei Yuan, Hui Hamzaoui, Raouf The College of Electronic Information Qingdao University Qingdao266071 China The School of Electrical and Computer Engineering New York University United States The School of Control Science and Engineering Shandong University Ji’nan250061 China The School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom

With the wide applications of colored point cloud in many fields, point cloud perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages. However, the existing point cloud quality assessments ignore the mechanism of human visual system (HVS) which has an important impact on the accuracy of the perceptual quality assessment. In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed. The PKT-PCQA merges local features from neighboring regions and global features extracted from graph spectrum. Taking into account the HVS properties, the spatial and channel attention mechanism is also considered in PKT-PCQA. Besides, inspired by the hierarchical perception system of human brains, PKT-PCQA adopts a progressive knowledge transfer to convert the coarse-grained quality classification knowledge to the fine-grained quality prediction task. Experiments on three large and independent point cloud assessment datasets show that the proposed no reference PKT-PCQA network achieves better of equivalent performance comparing with the state-of-the-art full reference quality assessment methods, outperforming the existed no reference quality assessment network. Copyright © 2022, The Authors. All rights reserved.

关键词： Visual communication

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A Systematic Survey of control Techniques and Applications in Connected and Automated Vehicles

arXiv

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arXiv 2023年

作者： Liu, Wei Hua, Min Deng, Zhiyun Meng, Zonglin Huang, Yanjun Hu, Chuan Song, Shunhui Gao, Letian Liu, Changsheng Shuai, Bin Khajepour, Amir Xiong, Lu Xia, Xin School of Automotive Studies Tongji University Shanghai218074 China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130025 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Civil and Environmental Engineering University of California Los AngelesCA90095 United States School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Vehicle and Mobility Tsinghua University Beijing100084 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooON Canada

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Differential Diagnostic Value of Machine Learning–Based Models for Embolic Stroke

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Clinical and Applied Thrombosis/Hemostasis 2023年 29卷 10760296231203663-10760296231203663页

作者： Kuo, HsunYu Liu, Tsai-Wei Huang, Yo-Ping Chin, Shy-Chyi Ro, Long-Sun Kuo, Hung-Chou Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Neurology Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Institute of Electrical and Electronics Engineers Taipei Taiwan Institution of Engineering and Technology Taipei Taiwan Fellow of Chinese Automatic Control Society Taipei Taiwan Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan

Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.). The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke. © The Author(s) 2023.

关键词： atrial fibrillation cancer-associated thrombosis data augmentation differential diagnosis diffusion-weighted imaging machine learning

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YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection

arXiv

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arXiv 2024年

作者： Tang, Xiaoyu Chen, Xingming Cheng, Jintao Wu, Jin Fan, Rui Zhang, Chengxi Zhou, Zebo The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China The School of Physics South China Normal University Guangdong Guangzhou510000 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The School of Aeronautics Astronautics University of Electronic Science and Technology of China and Aircraft swarm intelligent sensing and cooperative control Key Laboratory of Sichuan Province Chengdu610097 China The National Laboratory on Adaptive Optics Chengdu610209 China

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network’s feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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On the Convergence of Least Squares Estimator for Nonlinear Autoregressive Models

On the Convergence of Least Squares Estimator for Nonlinear ...

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第40届中国控制会议

作者： Zhaobo Liu Chanying Li College of Computer Science and Software Engineering of Shenzhen University Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

This paper is concerned with the least squares estimator for a basic class of nonlinear autoregressive models, whose outputs are not necessarily to be ergodic. Several asymptotic properties of the least squares estimator have been established under mild conditions. These properties suggest the strong consistency of the least squares estimates in nonlinear autoregressive models which are not divergent.

关键词： Nonlinear Autoregressive Models Strong Consistency Least Squares Harris Recurrent

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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors

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Heliyon 2025年第1期11卷 e41409页

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Nouraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Information Technology Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Radiation Oncology Research Center (RORC) Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm. We integrated CT image features with dosimetric and clinical data subsequently, ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31 % of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision was able to predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024 The Authors

关键词： Computed tomography Machine learning Radiation-induced optic neuropathy Radiomics Visual evoked potential

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

引用

SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Open-source dataset for control-oriented modelling in diesel engines

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science China(Information sciences) 2019年第7期62卷 223-224页

作者： Jinghua ZHAO Sitong ZHOU Yunfeng HU Mingjun JU Ruixue REN Hong CHEN State Key Laboratory of Automotive Simulation and Control Jilin University Department of Control Science and Engineering Jilin University Computer College Jilin Normal University Changchun FAW Sihuan Engine Manufacture Co. Ltd

In this study, an experimental dataset of a diesel engine under the European transient cycle (ETC)is demonstrated and opened for academic *** dataset with 7 dimensional features is collected from the advanced diesel engine manufactured by Changchun FAW Sihuan Engine Manufacture Co., *** experimental data of diesel engines is very important for system modeling and identification,

关键词： SOI Open-source dataset for control-oriented modelling in diesel engines

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Formation control for mobile robot using fuzzy - PI controller

Formation control for mobile robot using fuzzy - PI controll...

引用

2020 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2020

作者： Lee, Min-Fan Ricky Willybrordus, H.P.M. Sukamto Hassen, Sharfiden Nugroho, Asep National Taiwan University of Science and Technology Taipei Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan National Taiwan University of Science and Technology College of Graduate Institute of Automation and Control Taipei Taiwan National Taiwan University of Science and Technology Graduate Institute of Automation and Control Taipei Taiwan

ISBN: (纸本)9781728198231

Protecting soldiers in the long march of platoon formation is crucial mission in the military operation. Autonomous ground mobile robots can be deployed to carry out such kind of the mission. The primary task is maintaining robot position automatically based on the movement of soldier in a platoon formation. The GPS is employed to determine the soldier current pose. This information is used as an input to create a dynamic convex hull around a platoon. The Proportional-Integral (PI) controller is applied to control each robot so it can move in a desired trajectory. The fuzzy logic control (FLC) is involved to tune the gain of PI controller to optimize the performance. Three protective robots and nine soldiers are used to evaluate the algorithm in simulation. The proposed algorithm will provide a platoon soldiers with optimal protection encirclement and enhance their safety. The simulation results show good performance using the proposed controller. © 2020 IEEE.

关键词： Mobile robots

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