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arXiv 2023年

作者： Teng, Siyu Hu, Xuemin Deng, Peng Li, Bai Li, Yuchen Yang, Dongsheng Ai, Yunfeng Li, Lingxi Xuanyuan, Zhe Zhu, Fenghua Chen, Long BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon 999077 Hong Kong The School of Computer Science and Information Engineering Hubei University Wuhan430062 China The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body China The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China University of Chinese Academy of Sciences Beijing100049 China The School of Public Management/Emergency Management Jinan University Guangzhou510632 China Indianapolis United States Guangdong provincial key lab of IRADS BNU-HKBU United International College Zhuhai519087 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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An Intelligent Wireless Displacement Sensor for Landslide Monitoring and Early Warning

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IOP Conference Series: Earth and Environmental science 2021年第7期861卷

作者： Xing Zhu Hewei Xi Zhaoqing He Liu Yang State Key Laboratory Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu 610059 China College of Computer Science and Cyber Security Chengdu Univeristy of Technology Chengdu 610059 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610059 China

Landslide poses a great threat to the safety of local communities, engineering construction and operation. Accurate monitoring and successful early-warning of rapid landslides using conventional techniques with constant and sparse sampling strategy remains a challenge in geo-disaster research and mitigation. Sampling and uploading all sensor data to cloud imposes huge burden on the power supply and radio bandwidth, which greatly reduces the long-term stability and robustness of the wireless sensor in field. With aim to achieve the best powerful sensing ability based on limited power and bandwidth of sensor, we proposed a low-cost and energy efficient wireless crack-meter sensor integrated with intelligent computing architecture for rapid deformation monitoring. A self-adaptive data sampling and uploading algorithm based on the variation of objective physical value was implemented in the embedded microcontroller of sensor node, and only the valuable information derived from sensor data to the cloud server. Experimental results demonstrated that the intelligent sensing approach can effectively collect the crucial data at a high frequency during accelerating deformation of landslide, while only update status at several hours during non-deformation phase. Several landslide monitoring applications have proved that this intelligent sensing can significantly and effectively enhance the success rate and real-time performance of early-warning for rapid landslide.

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Preface

Synthesis Lectures on Computer Vision

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Synthesis Lectures on computer Vision 2023年 Part F825卷 v-vi页

作者： Wan, Jun Escalera, Sergio Li, Stan Z. Guo, Guodong Escalante, Hugo Jair State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Mathematics and Informatics University of Barcelona and Computer Vision Center Barcelona Spain AI Lab Westlake University Hangzhou China Department of Computer Science and Electrical Engineering West Virginia University Morgantown China Department of Computer Science Instituto Nacional de Astrofísica Optica y Electrónica Puebla Mexico

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Deep matrix factorization based on self-attention mechanism for student grade prediction

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Zongxin Guo Tieyuan Liu Liang Chang Tianlong Gu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin 541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin 541004 China

Deep learning is gradually emerging in the field of educational data mining. Aiming at student grade prediction in education data mining, a prediction model combining self-attention mechanism and deep matrix factorization (ADMFN) is proposed. It can quickly extract important features of sparse data and process complex nonlinear data. Firstly, the student and course vector corresponding to the score matrix are input into the model and projected to obtain the potential feature vector of the student (course). Then, the potential feature vectors of students (courses) are added into the self-attention mechanism to construct a multi-layer perceptron network. Finally, by constructing a bilinear pooling layer, the output vectors of rows and columns are fused to obtain the predicted value of the score. We compare the existing recommendation system baselines to evaluate our model. The experimental results show that the proposed model is effective in real dataset.

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Retraction Note: CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams

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Neural Computing and Applications 2024年第18期36卷 11073-11073页

作者： Jain, Deepak Kumar Kumar, Akshi Shrivastava, Akshat Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing China Department of Information Technology Netaji Subhas University of Technology Delhi India Department of Computer Science & Engineering Delhi Technological University Delhi India

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ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

arXiv

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arXiv 2023年

作者： Zhong, Tianyang Zhao, Wei Zhang, Yutong Pan, Yi Dong, Peixin Jiang, Zuowei Kui, Xiaoyan Shang, Youlan Yang, Li Wei, Yaonai Yang, Longtao Chen, Hao Zhao, Huan Liu, Yuxiao Zhu, Ning Li, Yiwei Wang, Yisong Yao, Jiaqi Wang, Jiaqi Zeng, Ying He, Lei Zheng, Chao Zhang, Zhixue Li, Ming Liu, Zhengliang Dai, Haixing Wu, Zihao Zhang, Lu Zhang, Shu Cai, Xiaoyan Hu, Xintao Zhao, Shijie Jiang, Xi Zhang, Xin Li, Xiang Zhu, Dajiang Guo, Lei Shen, Dinggang Han, Junwei Liu, Tianming Liu, Jun Zhang, Tuo School of Automation Northwestern Polytechnical University Xi’an710072 China The Second Xiangya Hospital Central South University Changsha410011 China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute of Medical Research Northwestern Polytechnical University Xi’an710072 China Glasgow College University of Electronic Science and Technology of China Chengdu611731 China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Central South University Hunan Province Changsha410083 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Lingang Laboratory Shanghai200031 China Imaging Center The Second Affiliated Hospital of Xinjiang Medical University Urumuqi830000 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China Department of Radiology Xiangtan Central Hospital Xiangtan411199 China Department of Radiology Yueyang Central Hospital City Yueyang414000 China Department of Radiology The First People's Hospital of Changde City Changde415003 China Department of Radiology The First Hospital of Hunan University of Chinese Medicine Changsha410021 China Department of Radiology Huadong Hospital Affiliated to Fudan University Shanghai200040 China School of Computing University of Georgia GA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Shanghai United Imaging Intelligence Co. Ltd. China Shanghai Clinical Research and Trial Center China

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial & neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of 332,673 observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports. © 2023, CC BY.

关键词： Decision making

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工业人工智能驱动的流程工业智能制造

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engineering 2021年第9期 70-83页

作者：杨涛易新蕾卢绍文 Karl H.Johansson 柴天佑 State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

基于对流程工业特点和运行现状的分析，以及全球智能制造产业的发展，本文提出了一种流程工业智能制造新模式，即工业人工智能、工业互联网与流程工业的深度融合。本文分析了流程工业现有的由企业资源规划、制造执行系统和过程控制系统... 详细信息

基于对流程工业特点和运行现状的分析，以及全球智能制造产业的发展，本文提出了一种流程工业智能制造新模式，即工业人工智能、工业互联网与流程工业的深度融合。本文分析了流程工业现有的由企业资源规划、制造执行系统和过程控制系统组成的三层结构的发展现状，并对流程工业所采用的决策、控制和运行管理进行了总结。在此基础上，阐述了流程工业智能制造的含义，提出了人机合作的智能优化决策系统和智能自主控制系统的愿景。最后，分析了实现流程工业愿景功能所需要攻克的科学挑战和关键技术。

关键词：工业人工智能工业互联网智能制造流程工业

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Exact Differentiator with Lipschitz Continuous Output and Optimal Worst-Case Accuracy Under Bounded Noise

Exact Differentiator with Lipschitz Continuous Output and Op...

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IEEE Conference on Decision and Control

作者： Rodrigo Aldana-López Richard Seeber Hernan Haimovich David Gómez-Gutiérrez Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas (CIFASIS) CONICET-UNR Rosario Argentina Intel Tecnología de México Intel Labs Intelligent Systems Research Lab Jalisco Mexico Tecnológico Nacional de México Instituto Tecnológico José Mario Molina Pasquel y Henríquez Unidad Académica Zapopan Jalisco Mexico

The online differentiation of a signal contaminated with bounded noise is addressed. A differentiator is developed that generates a Lipschitz continuous output, is exact in the absence of noise, and provides the optimal worst-case accuracy among all possible exact differentiators when noise is present. This combination of features is not shared by any previously existing differentiator. Tuning of the developed differentiator is very simple, requiring only the knowledge of a bound for the second-order derivative of the signal. The approach consists in regularizing the possibly highly noisy output of a recently introduced linear adaptive robust exact differentiator and feeding it to a first-order sliding-mode filter designed to maintain optimal accuracy. The proposed regularization and filtering of this output allows trading the speed with which exactness is obtained for the feature of a Lipschitz continuous, hence less noisy, output. An illustrative example is provided to highlight the features of the developed differentiator.

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Removed: VitSeg: Weakly supervised vitiligo segmentation in skin image

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computerized medical imaging and graphics : the official journal of the computerized Medical Imaging Society 2020年 85卷 101779页

作者： Zhangxing Bian Siyu Xia Chao Xia Ming Shao Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA. Electronic address: jasonbian.zx@***. Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China. Electronic address: xsy@***. School of Electronics Information and Electrical Engineering ShangHai Jiao Tong University Shanghai China. Electronic address: xiabc612@***. Dept. of Computer and Information Science University of Massachusetts Dartmouth Dartmouth MA USA. Electronic address: mshao@umassd.edu.

This article has been removed: please see Elsevier Policy on Article Withdrawal (https://***/about/our-business/policies/article-withdrawal). This article has been removed at the request of the Authors and Editor-in-Chief because complete consent was not obtained by the authors in accordance with journal policy prior to publication. The authors and the journal sincerely apologize for this oversight.

关键词： Convolutional neural network Saliency propagation Skin lesion Vitiligo Weakly supervised segmentation

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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