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International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Jianghan Hu Song Lian Simon Hu Cristoforo Demartino Gaoang Wang Xin Liu Yongfu Li Claudio Roncoli Der-Horng Lee Zhejiang University/University of Illinois at Urbana-Champaign Institute (ZJU-UIUC Institute) Haining China College of Civil Engineering and Architecture Zhejiang University Hangzhou China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Hangzhou China Department of Procurement Management China National Aviation Fuel Group Ltd Beijing Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunication Chongqing China Department of Built Environment Aalto University Espoo Finland

With the breakthrough of connected and autonomous vehicles (CAVs) technology, vehicle trajectories can be collected by various sensors installed on CAVs continuously and sent to the traffic control center for operation and management. However, the trajectory data collected by CAVs may contain incomplete part owing to sensor limitation, thus hindering the data availability. To address this issue, we propose a CNN-based generative model for reconstructing multiple vehicle trajectories in multi-lane traffic scenarios using hybrid detection data from both CAVs and fixed sensors. Specifically, we generate missing trajectories using a Generative Adversarial Network (GAN) architecture with spatio-temporal features extracted by Convolutional Neural Networks (CNNs). The performance of the method is examined on a simulated arterial by assessing the mean absolute error (MAE) of the reconstructed data. The results indicate our method is robust even at a low CAV penetration rate.

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Small Object Detection with Improved CenterNet

Small Object Detection with Improved CenterNet

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Jie Xu Yalan Ye Ziqi Liu Fanjun Meng Dashun Zhang Ming Sun School of Automation Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China The Fifty-fifth Research Institute of China North Industries Group Corporation Limited China

ISBN: (纸本)9781665428781

Detection of small objects is a notorious challenge due to their properties of low resolution and less information. Most existing works aim for general object detection while ignoring the impact of the object size, resulting in high missed detection rate of small objects, making it difficult to apply object detection in real scenarios such as autonomous driving. The implementation of an efficient detection algorithm for small objects is thus worth discussing. In this paper, we propose a small object detection framework with improved CenterNet. Specifically, the proposed framework consists of four parts. The super-resolution (SR) subnetwork enhances the information contained in small objects by generating an SR version of the original image. The future pyramid subnetwork fuses different levels of features thereby retaining semantic information of small objects. The center localization header and the size regression header are combined to detect objects via prediction of object center and object size. In this way, the performance of object detection is improved especially on small objects. We have conducted comprehensive experiments on MS COCO benchmark, and the experimental results have proved the effectiveness of our framework, especially in the detection of small objects better than existing methods.

关键词： Heating systems Location awareness Fuses Conferences Semantics Superresolution Object detection

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Unobtrusive and Non-Invasive Human Activity Recognition using Kinect Sensor

Unobtrusive and Non-Invasive Human Activity Recognition usin...

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd International Conference on

作者： Shalini Nehra Jagdish Lal Raheja Control & Automation Group Cyber Physical System Area CSIR-Central Electronics Engineering Research Institute Pilani India

ISBN: (数字)9781728149998

ISBN: (纸本)9781728150000

The main objective of this paper is to present a depth imaging human activity recognition system that is basically used to monitor and detect normal activities of the person in the indoor environment. The Proposed system runs in real-time and is capable of detecting moving falls, sitting fall accurately and robustly without taking into account any false positive activities. Human activity recognition with high accuracy is a huge challenge in the research world. Fall detection is an important technology in health care and elderly person activity monitoring. The proposed method is feasibility and workability are illustrated throughout the experimental result that shows the perfect human tracking and activity detection. System works well with low lighting conditions; lighting does not affect the accuracy of detecting activities. Moreover, the proposed method is able to avoid false positive as: lying down, retrieve something from the floor.

关键词： Skeleton Floors Activity recognition Feature extraction Legged locomotion Hidden Markov models Acceleration

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HATSDF SLAM – Hardware-accelerated TSDF SLAM for Reconfigurable SoCs

HATSDF SLAM – Hardware-accelerated TSDF SLAM for Reconfigur...

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European Conference on Mobile Robots (ECMR)

作者： Marc Eisoldt Marcel Flottmann Julian Gaal Pascal Buschermöhle Steffen Hinderink Malte Hillmann Adrian Nitschmann Patrick Hoffmann Thomas Wiemann Mario Porrmann Computer Engineering and Autonomous Robotics Groups at the Institute of Computer Science Osnabrück University Osnabrück Germany German Research Center for Artificial Intelligence (DFKI) Niedersachsen Lab Plan-Based Robot Control Group Osnabrück Germany

Simultaneous Localization and Mapping (SLAM) is one of the fundamental problems in autonomous robotics. Over the years, many approaches to solve this problem for 6D poses and 3D maps based on LiDAR sensors or depth cameras have been proposed. One of the main drawbacks of the solutions found in the literature is the required computational power and corresponding energy consumption. In this paper, we present an approach for LiDAR-based SLAM that maintains a global truncated signed distance function (TSDF) to represent the map. It is implemented on a System On Chip (SoC) with an integrated FPGA accelerator. The proposed system is able to track the position of a Velodyne VLP-16 LiDAR in real time, while maintaining a global TSDF map that can be used to create a polygonal map of the environment. We show that our implementation delivers competitive results compared to state-of-the-art algorithms while drastically reducing the power consumption compared to classical CPU or GPU-based methods.

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Flexible Safety Systems for Smart Manufacturing

Flexible Safety Systems for Smart Manufacturing

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Dieter Etz Thomas Frühwirth Wolfgang Kastner Institute of Computer Engineering Automation Systems Group TU Wien Research Department Austrian Center of Digital Production Wien

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Smart manufacturing is realizing the idea and potential of Industry 4.0 in reality. An essential part of smart manufacturing are production facilities that dynamically adapt to changing production needs. This brings completely new challenges to functional safety systems, which are mandatory for the protection of man, machine, and environment. Currently, functional safety systems are designed and certified in a static way. The safety design and the configuration are derived from the risk assessment which is performed during the design of a machine. Once the system is put into operation, the safety configuration is not changed anymore. This approach constitutes an impediment to flexibility which smart manufacturing production facilities require *** paper proposes the design of a self-organizing safety system with the objective to assist an engineer who operates a smart manufacturing facility by discovering all safety-related devices and generating automatically a suitable safety configuration. This configuration will be deployed to the system automatically after adaptation and validation by the safety engineer. The proposed self-organizing safety system, simplifies the safety configuration in a dynamically changing environment. Consequently, it would reduce engineering efforts and decrease machine downtime which improves profitability.

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Scheme for Turn-to-Turn Fault Protection of Stepped controllable Shunt Reactor control Winding 5th

Scheme for Turn-to-Turn Fault Protection of Stepped Controll...

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International Top-Level Forum on engineering Science and Technology Development Strategy and 5th PURPLE MOUNTAIN FORUM on Smart Grid Protection and control, PMF2020

作者： Zhu, Liwei Wu, Tonghua Dai, Wei Zhang, Kunxian Zhu, Xiang Ni, Saisai State Key Laboratory of Smart Grid Protection and Control Nanjing Jiangsu211106 China Nari Group Corporation Nanjing Jiangsu211106 China NARI School of Electrical and Automation Engineering Nanjing Normal University Nanjing Jiangsu210023 China State Grid Gansu Electric Power Company Lanzhou Gansu730000 China Electric Power Research Institute of State Grid Gansu Electric Power Company Lanzhou Gansu730000 China

ISBN: (纸本)9789811597459

This paper analyzes the negative-sequence network diagram of stepped controllable shunt reactor (SCSR) in case of turn-to-turn short-circuit fault and turn-to-ground fault of the SCSR control winding, as well as the line fault out of the SCSR area and open-phase running of the system based on the "T"-type equivalent circuit, thus deducing the discriminant of the negative-sequence power direction. The paper also configures the negative-sequence power directional protection for the SCSR control winding and derives the relationship of negative-sequence current between line side winding and control winding. The occurrence of fault is identified by the negative-sequence current of the control winding and the position of fault is determined by the negative-sequence power direction angle. For small turn-to-turn fault, the paper proposes a negative-sequence current amplitude adaptive starting threshold detection method based on the capacity to determine the starting threshold of the negative-sequence current. It also puts forward a negative-sequence phase angle smoothing method based on the dynamic mean to deal with the large phase angle fluctuations of the negative-sequence voltage during the small turn-to-turn short-circuit. The simulation results verify that the method can judge the turn-to-turn fault of the SCSR control winding reliably and effectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Equivalent circuits

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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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Immunometabolism and potential targets in severe COVID-19 peripheral immune responses

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Asian Journal of Pharmaceutical Sciences 2021年第6期16卷 665-667页

作者： Shanshan Wang Xian Zeng Yali Wang Yuzong Chen Chaoxin Wang Dongzhi Zhuoma Fengying Zhang Yufen Zhao Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences Institute of Drug Discovery TechnologyNingbo UniversityNingbo 315211China Department of Biological Medicines&Shanghai Engineering Research Center of Immunotherapeutics Fudan University School of PharmacyShanghai 201203China Bioinformatics and Drug Design Group Department of PharmacyNational University of Singapore117543Singapore Department of Computer Science Kansas State UniversityManhatten KS 66506USA Medical College Tibet University36 Jiangsu RoadLhasa 850011 China Key Lab of Agricultural Products Processing and Quality Control of Nanchang City Jiangxi Agricultural UniversityNanchang 330045China

In a recent single cell transcriptomic investigation of the peripheral immune responses of the patients with severe COVID-19[1],no substantial expression of pro-inflammatory cytokines has been identified in the peripheral monocytes and lymphocytes,while these peripheral immune cells exhibit phenotypes of heterogeneously expressed interferon-stimulated genes in certain COVID-19 *** genes may contribute to the inhibitory response to the viral entry,translation,replication and *** remain regarding why the peripheral cytokine production is constrained,what additional roles these peripheral immune cells may play in COVID-19 pathophysiology,and which potential targets can be explored for facilitating COVID-19 therapeutics.

关键词： metabolism interferon lymphocytes

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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