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arXiv 2020年

作者： Zhu, Haiping Shan, Hongming Zhang, Yuheng Che, Lingfu Xu, Xiaoyang Zhang, Junping Shi, Jianbo Wang, Fei-Yue The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201210 China The GRASP Laboratory University of Pennsylvania PhiladelphiaPA United States The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The University of Chinese Academy of Sciences Beijing100049 China

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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The potential of prostate gland radiomic features in identifying the Gleason score

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 144卷 105318-105318页

作者： Gong, Lixin Xu, Min Fang, Mengjie He, Bingxi Li, Hailin Fang, Xiangming Dong, Di Tian, Jie Northeastern Univ Coll Med Shenyang 110016 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Nanjing Med Univ Wuxi Peoples Hosp Imaging Ctr Wuxi 214023 Jiangsu Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Beijing 100191 Peoples R China Jinan Univ Zhuhai Precis Med Ctr Zhuhai Peoples Hosp Zhuhai 519000 Peoples R China

Background: Gleason score (GS) is one of the most critical predictors of diagnosing prostate cancer (PCa). The prostate gland, including both lesions and their microenvironment, may contain more comprehensive information about the PCa. We aimed to investigate the potential of prostate gland radiomic features in identifying Gleason scores (GS) < 7, = 7, and > 7. Methods: We retrospectively examined preoperative magnetic resonance imaging (MRI) results, clinical data, and postoperative pathological findings from 489 PCa patients. The three-dimensional (3D) and two-dimensional (2D) radiomic features were extracted from the manually segmented 3D prostate gland and its maximum 2D layer on MRI, respectively. Significant features were selected, and sequence signatures were then developed via multi-class linear regression (MLR) accordingly. Subsequently, 2D and 3D radiomic models were constructed by applying MLR to the combination of the sequence signatures, respectively. The stability of the significant features was discussed by their average ranking in the other 30 random cohorts. Based on our distance matrix algorithm, we generated different regions of interest to simulate the manual segmentation biases and discuss the model's tolerance to them. Results: Our 2D model reached a C-index of 0.728 and an average area under the receiver operating characteristic curve of 0.794 in the validation cohort. The corresponding key features were stable, with an average ranking of the top 8.352% in 30 random cohorts, and the model could tolerate a segmentation boundary deviation of 2 mm without significant performance degradation. Conclusion: 2D prostate-gland-MRI-based radiomic features showed stable potential in identifying GS.

关键词： MRI Radiomics Prostate cancer Gleason score Gland segmentation

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Scheduling of Autonomous Mining Trucks: Allocation Model Based Tabu Search Algorithm Development

Scheduling of Autonomous Mining Trucks: Allocation Model Bas...

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IEEE Intelligent Transportation systems Conference (ITSC)

作者： Zhang, Xiaotong Chen, Long Ai, Yunfeng Tian, Bin Cao, Dongpu Li, Lingxi Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Univ Chinese Acad Sci Artificial Intelligence Dept Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Univ Waterloo Waterloo Cognit Autonomous Driving CogDr Lab Waterloo ON N2L 3G1 Canada Indiana Univ Purdue Univ Transportat & Autonomous Syst Inst TASI 723 West Michigan StSL 160 Indianapolis IN 46202 USA Indiana Univ Purdue Univ Dept Elect & Comp Engn Purdue Sch Engn & Technol 723 West Michigan StSL 160 Indianapolis IN 46202 USA

ISBN: (纸本)9781728191423

Transport activities in open-pit mines contribute to a large portion of total energy consumption. Most truck scheduling studies still take a traditional approach to autonomous mining trucks, which does not fully exploit the strengths of the autonomous trucks that can perform more accurate speed control compared to conventional manned trucks. In this paper, a mixed-integer programming model is developed for joint optimization of autonomous truck trips and speed to reduce fuel consumption. To solve the integrated scheduling model, a new tabu search algorithm is proposed, which consists of two stages, where an allocation model is formulated in the upper stage to identify the optimal truck flow, and in the lower stage a tabu search algorithm is developed initialized with the guidance of optimal truck flow. A coal mine in Inner Mongolia is used for example to analyze and verify the effectiveness of the proposed model and solution techniques.

关键词： Velocity control Production Programming Search problems Scheduling Real-time systems Resource management

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Stripe artifact elimination based on nonsubsampled contourlet transform for light sheet fluorescence microscopy

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JOURNAL OF BIOMEDICAL OPTICS 2016年第10期21卷 106005-106005页

作者： Liang, Xiao Zang, Yali Dong, Di Zhang, Liwen Fang, Mengjie Yang, Xin Arranz, Alicia Ripoll, Jorge Hui, Hui Tian, Jie Chinese Acad Sci Key Lab Mol Imaging 95 Zhongguancun East Rd Beijing 100190 Peoples R China State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci 80 Zhongguancun East Rd Beijing 100190 Peoples R China Ctr Mol Biol Severo Ochoa Calle Nicolas Cabrera 1 Madrid 28049 Spain Univ Carlos III Madrid Dept Bioengn & Aerosp Engn Escuela Politecn Super Ave Univ 30 Madrid 28911 Spain Hosp Gregorio Maranon Inst Invest Sanitaria Expt Med & Surg Unit Calle Dr Esquerdo 46 Madrid 28007 Spain

Stripe artifacts, caused by high-absorption or high-scattering structures in the illumination light path, are a common drawback in both unidirectional and multidirectional light sheet fluorescence microscopy (LSFM), significantly deteriorating image quality. To circumvent this problem, we present an effective multidirectional stripe remover (MDSR) method based on nonsubsampled contourlet transform (NSCT), which can be used for both unidirectional and multidirectional LSFM. In MDSR, a fast Fourier transform (FFT) filter is designed in the NSCT domain to shrink the stripe components and eliminate the noise. Benefiting from the properties of being multiscale and multidirectional, MDSR succeeds in eliminating stripe artifacts in both unidirectional and multidirectional LSFM. To validate the method, MDSR has been tested on images from a custom-made unidirectional LSFM system and a commercial multidirectional LSFM system, clearly demonstrating that MDSR effectively removes most of the stripe artifacts. Moreover, we performed a comparative experiment with the variational stationary noise remover and the wavelet-FFT methods and quantitatively analyzed the results with a peak signal-to-noise ratio, showing an improved noise removal when using the MDSR method. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： light sheet fluorescence microscopy nonsubsampled contourlet transform stripe artifact

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The Emerging Intelligent Vehicles and Intelligent Vehicle Carriers Collaborative systems

The Emerging Intelligent Vehicles and Intelligent Vehicle Ca...

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IEEE Symposium on Intelligent Vehicle

作者： Zhipeng Shen Chao Huang Hailong Huang Yutong Wang Fei-Yue Wang Abbas Jamalipour Duc Truong Pham Ljubo Vlacic Andrey V. Savkin Department of Aeronautical and Aviation Engineering the Hong Kong Polytechnic University Hong Kong Department of Industrial and Systems Engineering the Hong Kong Polytechnic University Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China School of Electrical and Information Engineering University of Sydney Sydney Australia Department of Mechanical Engineering University of Birmingham Birmingham UK Institute of Intelligent & Integrated Systems and the School of Engineering and Built Environment Griffith University Brisbane Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

关键词： Intelligent vehicles Collaboration Charging stations Boosting

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A novel CNN model for fine-grained classification with large spatial variants

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Junpeng Wang Yanfeng Lu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China

Convolutional Neural Networks (CNN) have achieved great performance in many visual tasks. However, CNN models are sensitive to samples with large spatial variants, especially severe in fine-grained classification task. In this paper, we propose a novel CNN model called ST-BCNN to solve these problems. ST-BCNN contains two functional CNN modules: Spatial Transform Network (STN) and Bilinear CNN(BCNN). Firstly, STN module is used to select key region in input samples and get it spatially modified. Since the adoption of STN will cause an information loss phenomenon called boundary loss, we design a brand-new IOU loss method to solve it. We make a theoretical analysis of the IOU loss method. Secondly, to discover discriminative features for fine-grained classification task, BCNN module is applied. BCNN interacts CNN features from different channels to produce more discriminative bilinear features than fully connected features of CNN. ST-BCNN works by reducing irrelevant spatial states and producing fine-grained features. We evaluate our model on 3 public fine-grained classification datasets with large spatial variants: CUB200-2011, Fish100 and UAV43. Experiments show that the IOU loss method can reduce boundary loss and make STN module output spatial transformed image appropriately. Our proposed ST-BCNN model outperforms other advanced CNN models on all three datasets.

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Boundedness of Solutions for a Bouncing Ball Model with Quasiperiodic Potential

SSRN

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SSRN 2022年

作者： Li, Denghui Zhang, Xiaoming Liu, Xianbin Xie, Jianhua Grebogi, Celso School Of Mathematics And Statistics Hexi University Gansu Zhangye734000 China State Key Lab of Mechanics and Control for Mechanical Structures College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China School Of Mechanics And Aerospace Engineering Southwest Jiaotong University Sichuan Chengdu610031 China Institute For Complex Systems Mathematical Biology King’S College University Of Aberdeen AberdeenAb24 3Ue United Kingdom

In this work, we consider the model of a free falling ball on a wall that is elastically reflected. The wall is supposed to move in the vertical direction according to a given quasiperiodic function. Based on the invariant curve theorem of smooth quasiperiodic twist map, we prove the boundedness of all solutions and the existence of quasi-periodic solutions for the system. © 2022, The Authors. All rights reserved.

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Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning

arXiv

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arXiv 2023年

作者： Chen, Long Teng, Siyu Li, Bai Na, Xiaoxiang Li, Yuchen Li, Zixuan Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd United Kingdom BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The College of Mechanical and Vehicle Engineering Hunan University China The Department of Engineering University of Cambridge United Kingdom The College of Artificial Intelligence Xi’an Jiaotong University China The School of Mechanical Engineering Tsinghua University China

Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits. While previous surveys have captured progress in this field, a comprehensive and forward-looking summary is needed. Our work fills this gap through three distinct articles. The first part, a"Survey of Surveys" (SoS), outlines the history, surveys, ethics, and future directions of AD and IV technologies. The second part,"Milestones in Autonomous Driving and Intelligent Vehicles Part I: control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors" delves into the development of control, computing system, communication, HD map, testing, and human behaviors in IVs. This part, the third part, reviews perception and planning in the context of IVs. Aiming to provide a comprehensive overview of the latest advancements in AD and IVs, this work caters to both newcomers and seasoned researchers. By integrating the SoS and Part I, we offer unique insights and strive to serve as a bridge between past achievements and future possibilities in this dynamic field. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Society 5.0: Metaverse Facilitated Human-centric “5H” Services Across Cyber-Physical-Social Spaces

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IFAC-PapersOnLine 2024年第3期58卷 159-164页

作者： Xiao Wang Jun Huang QingHua Ni Zhe Wan HuaDan Xue Larry Stapleton Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China Beijing Normal University Beijing 100875 China Department of Radiology Peking Union Medical College Hospital Shuaifuyuan Beijing 100730 Dongcheng District China Brownsbarn Thomastown Co. Kilkenny Ireland R95D8P2 Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Cyber-Physical-Social systems bridge the gap between social resource organization and distribution, and give birth to Society 5.0, which represents a transformative vision aimed at realizing the human-centric paradigm for more efficient and effective sustainable development. In this paper, we discuss the decentralized paradigm in fostering human needs-driven smart services, in line with Maslow’s hierarchy of needs theory. Societies 5.0 is committed to optimizing the use of natural, artificial, and social resources by enhancing democratic participation and ensuring that decision-making processes are more transparent, reliable, and aligned with the needs of the populace in sustainable ways. Moreover, our discussion extends to how Society 5.0’s principles correlate with the United Nations Sustainable Development Goals (SDGs), providing a framework that supports health, education, and poverty reduction, among other targets. This approach also aligns with the Human Development Index (HDI), reinforcing the notion that technological progress in Society 5.0 does not overshadow but rather complements and enhances human values, paving a synergistic path towards sustainable development that is both advanced and fundamentally human-centric.

关键词： Cyber-Physical-Social systems Society 5.0 Human-Centric Services Socio-technical synergistic sustainable development

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Chat With ChatGPT on Intelligent Vehicles: An IEEE TIV Perspective

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2020-2026页

作者： Du, Haiping Teng, Siyu Chen, Hong Ma, Jiaqi Wang, Xiao Gou, Chao Li, Bai Ma, Siji Miao, Qinghai Na, Xiaoxiang Ye, Peijun Zhang, Hui Luo, Guiyang Wang, Fei-Yue Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia Hong Kong Baptist Univ Fac Sci Dept Comp Sci Hong Kong 999077 Peoples R China BNU HKBU United Int Coll Zhuhai 519087 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Univ Calif Los Angeles Samueli Sch Engn Los Angeles CA 90095 USA Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Taipa 999078 Macau Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Cambridge Dept Engn Cambridge CB2 1PZ England Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci & Technol Beijing 100876 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

This letter reports on a TIV DHW (decentralized and hybrid workshop) that explores the prospective influence of ChatGPT on research and development in intelligent vehicles. To assess the update capabilities of ChatGPT, we conducted tests involving both basic and technically relevant questions. Our preliminary testing revealed that ChatGPT's information can be updated and corrected at one time, but it may take some time for the changes to be reflected in ChatGPT's responses, so it may not always possess the latest knowledge regarding specific topics. We further discuss the prospective influence of ChatGPT on the field of intelligent vehicles, particularly possible applications of ChatGPT in areas like autonomous driving, human-vehicle interaction, and intelligent transportation systems, highlighting challenges and opportunities associated with these applications. Additionally, we address technical questions, such as the feasibility of training intelligent vehicles using the same methods as ChatGPT and the reflection of the intelligence of intelligent vehicles in the context of human-machine shared control. In conclusion, this letter presents a preliminary exploration of the potential of ChatGPT for intelligent vehicle research, from an IEEE TIV perspective, acknowledging the limitations and uncertainties of this emerging technology.

关键词： Chatbots Intelligent vehicles Uncertainty Testing Artificial intelligence Safety Autonomous vehicles Neural language programming intelligent vehicles human-vehicle interaction intelligent transportation systems

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