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arXiv 2021年

作者： Qing, Zhiwu Huang, Ziyuan Wang, Xiang Feng, Yutong Zhang, Shiwei Jiang, Jianwen Tang, Mingqian Gao, Changxin Ang, Marcelo H. Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology ARC National University of Singapore Tsinghua University Alibaba Group

This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop. The goal of our task is to locate the start time and the end time of the action in the long untrimmed video, and predict action category. We adopt sliding window strategy to generate proposals, which can better adapt to short-duration actions. In addition, we show that classification and proposals are conflict in the same network. The separation of the two tasks boost the detection performance with high efficiency. By simply employing these strategy, we achieved 16.10% performance on the test set of EPIC-KITCHENS-100 Action Detection challenge using a single model, surpassing the baseline method by 11.7% in terms of average mAP. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Switched High-Gain Based Interval Observer Design for Harmonic Oscillator System

Switched High-Gain Based Interval Observer Design for Harmon...

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Chinese control Conference (CCC)

作者： Tianhang Ming Xiaoling Wang Bo Liu Housheng Su College of Automation & AI Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing China College of Information Engineering Minzu University of China Beijing P. R. China Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Huazhong University of Science and Technology Wuhan P. R. China

In this paper, we revisit the interval observer design problem of harmonic oscillator system with unknown but bounded external disturbance and measurement noise. With the bounding information of the external disturbance and measurement noise, a high-gain interval observer is firstly defined and constructed for the harmonic oscillator system to realize the interval estimation on the state and the robust control of the system. Then, a switched high-gain interval observer is proposed to reduce the influence of the measurement noise for a tighter interval width on the state estimation. Finally, some numerical simulations are given to verify the theoretical results.

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Capacity optimization and energy dispatch strategy of hybrid energy storage system based on proton exchange membrane electrolyzer cell

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ENERGY CONVERSION AND MANAGEMENT 2022年 272卷

作者： Zhao, Dongqi Xia, Zhiping Guo, Meiting He, Qijiao Xu, Qidong Li, Xi Ni, Meng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Intelligent Control Educ Minist Wuhan Hubei Peoples R China Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Kowloon Hong Kong Peoples R China Shenzhen Huazhong Univ Sci Technol Res Inst Shenzhen Guangdong Peoples R China

The introduction of proton exchange membrane electrolyzer cells into microgrids allows renewable energy to be stored in a more stable form of hydrogen energy, which can reduce the redundancy of battery energy storage system and the abandonment of wind and photovoltaic energy. However, most studies of energy dispatch strategies for microgrids only focus on the costs without considering the long-life operation. Therefore, in this study, the proposed capacity optimization method first ensures that the optimized distributed energy capacity can meet the user demand even in the most impoverished meteorological conditions. Moreover, the optimal efficiency and operating conditions of the electrolyzer corresponding to the reference power are determined through the artificial neural network, thereby realizing efficient hydrogen production. Subsequently, the multi -objective energy dispatch strategy is analyzed and designed, considering both low-cost and long-life operations. Compared with the economical energy dispatching strategy, the multi-objective energy dispatching strategy only increases the average daily dispatching cost by 0.055 $, however, reduces the volatility indicator of the elec-trolyzer by 49 %, which is beneficial to the sustainable operation of the electrolyzer. Furthermore, the required electrolyzer capacity is also reduced by 17.5 % by suppressing the power fluctuation of the electrolyzer. This study can provide useful information for understanding the energy dispatch strategy in hydrogen-electric coupling microgrids.

关键词： High temperature proton exchange membrane electrolyzer cell Capacity optimization Economic dispatch strategy Multi -objective dispatch strategy Hydrogen energy Long -life operation

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The computation power of spiking neural P systems with polarizations adopting sequential mode induced by minimum spike number

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NEUROCOMPUTING 2020年 401卷 392-404页

作者： Wu, Tingfang Pan, Linqiang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Inst Artificial Intelligence Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

Spiking neural P systems (SN P systems) refer to a type of distributed parallel neural computation model in the membrane computing framework. In this work, a new type of SN P system is examined, i.e., spiking neural P systems with polarizations (PSN P systems), by taking inspiration from the polarized cell membrane of a neuron. For the PSN P system, the firing condition of rules is the neuron-associated polarization. This work focuses on examining the computation power of the PSN P systems adopting the sequential mode induced by the minimum spike number, where at a computation step, one (resp., all) of neurons that hold the minimum spike number within the neurons which can fire will fire, i.e., the min-sequentiality strategy (resp., min-pseudo-sequentiality strategy). We prove that PSN P systems adopting the min-sequentiality strategy or min-pseudo-sequentiality strategy are Turing universal as the number generating devices. These results indicate the computation power of PSN P systems is robust regarding their strategies of sequentiality. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Bio-inspired computing Membrane computing Spiking neural P system Universality

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An Approach for National-Scale Accessibility Assessment and Vulnerability Analysis of Integrated Multimodal Transportation Systems

SSRN

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SSRN 2021年

作者： Zhang Ouyang, Min Sun, Wenjing Hong, Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

A series of ambitious accessibility-oriented policies have been launched in the world, which center around improving accessibility of the integrated multimodal transportation systems including road, airline, and high-speed rail. The multimodal accessibility assessment is one of the "basic" analyses for the design and implementation of these policies. This paper models the integrated road, high-speed rail, airline, and walking system (IRHAWS) as a four-layer network with the consideration of their interdependencies. An algorithm is then developed to accurately assess accessibility based on the four-layer network with the consideration of daily schedule of high-speed trains and flights, where the accessibility between two nodes is defined as the minimum travel time given a departure time. The proposed approach enables the time-varied accessibility assessment of both off-network and on-network areas at a national or global scale, and is applied to map the multimodal accessibility to 363 city centers in China and analyze the optimal travel modes for on-network travels. Based on the proposed accessibility assessment approach, this paper also investigates the vulnerability of the Chinese integrated multimodal transportation system under the extreme storm event recently occurred in Zhengzhou (July 2021). The findings in this work provide insightful suggestions to plan the road, high-speed rail, and airline systems as a whole. © 2021, The Authors. All rights reserved.

关键词： Railroad transportation

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Pricing decisions in a dual-channel green supply chain with product customization

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JOURNAL OF CLEANER PRODUCTION 2020年 247卷

作者： Wang, Jian Jiang, Huijuan Yu, Mingzhu Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Coll Management Inst Big Data Intelligent Management & Decis Shenzhen 518060 Guangdong Peoples R China

This paper studies a dual-channel closed-loop supply chain consisting of one manufacturer and one retailer. Product customization is taken into consideration. The manufacturer sells customized products through his online direct sale channel and sells standard products through both an online direct sale channel and a traditional retail channel. In the reverse supply chain, three modes of collecting used products are considered: via the manufacturer, via the retailer or via both the manufacturer and the retailer simultaneously (joint collecting mode). Three pricing models are proposed accordingly to analyze the optimal decisions of supply chain members. Through model analysis and computational studies, we find that the retailer collecting mode is most effective for the overall supply chain. The price of the customized product is almost not affected by the collecting modes. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Closed-loop supply chain Dual-channel Pricing Customization Remanufacturing

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2021年第1期9卷 1-12页

作者： Tan, Wenjun Liu, Pan Li, Xiaoshuo Liu, Yao Zhou, Qinghua Chen, Chao Gong, Zhaoxuan Yin, Xiaoxia Zhang, Yanchun Northeastern Univ Minist Educ Key Lab Intelligent Comp Med Image Shenyang 110189 Peoples R China Shenyang Aerosp Univ Dept Comp Sci Shenyang 110136 Liaoning Peoples R China Tianjin Univ Technol Key Lab Complex Syst Control Theory & Applicat Tianjin 300384 Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Victoria Univ Coll Engn & Sci Ctr Appl Informat Melbourne Vic 8001 Australia

The COVID-19 coronavirus has spread rapidly around the world and has caused global panic. Chest CT images play a major role in confirming positive COVID-19 patients. The computer aided diagnosis of COVID-19 from CT images based on artificial intelligence have been developed and deployed in some hospitals. But environmental influences and the movement of lung will affect the image quality, causing the lung parenchyma and pneumonia area unclear in CT images. Therefore, the performance of COVID-19's artificial intelligence diagnostic algorithm is reduced. If chest CT images are reconstructed, the accuracy and performance of the aided diagnostic algorithm may be improved. In this paper, a new aided diagnostic algorithm for COVID-19 based on super-resolution reconstructed images and convolutional neural network is presented. Firstly, the SRGAN neural network is used to reconstruct super-resolution images from original chest CT images. Then COVID-19 images and Non-COVID-19 images are classified from super-resolution chest CT images by VGG16 neural network. Finally, the performance of this method is verified by the pubic COVID-CT dataset and compared with other aided diagnosis methods of COVID-19. The experimental results show that improving the data quality through SRGAN neural network can greatly improve the final classification accuracy when the data quality is low. This proves that this method can obtain high accuracy, sensitivity and specificity in the examined test image datasets and has similar performance to other state-of-the-art deep learning aided algorithms.

关键词： COVID-19 Computer aided diagnosis Chest CT images Super-resolution images Convolutional neural network

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A Novel Nonconvex Low-Rank Tensor Completion Approach for Traffic Sensor Data Recovery From Incomplete Measurements

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Chen, Xiaobo Wang, Kaiyuan Li, Zuoyong Zhang, Yu Ye, Qiaolin Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Shandong Peoples R China Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China Minjiang Univ Coll Comp & Control Engn Fujian Prov Key Lab Informat Proc & Intelligent Co Fuzhou 350121 Peoples R China Lehigh Univ Dept Bioengn Bethlehem PA 18015 USA Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China

Complete traffic sensor data are considered to be one of the critical ingredients for intelligent transportation systems (ITSs). However, traffic measurements prevalently suffer from inevitable and ubiquitous missing values (MVs). Current missing data completion algorithms are difficult to leverage the global low-rank property and the fine-grained spatial-temporal structure simultaneously. To circumvent this problem, this work presents a novel collaborative nonconvex low-rank spatial-temporal data tensor completion model that can take full advantage of the inherent spatial-temporal characteristics of traffic measurement data. First, the tensor Schatten p-norm, as an effective nonconvex surrogate of rank function, is used to exploit the global multidimensional correlation of traffic data. Then, we present an elastic net self-representation method and utilize an autoregression model in order to simultaneously capture the self-similarity and the temporal continuity of traffic data acquired in the same sensor network. By integrating the above elements in a unified nonconvex learning model, our method can explore the inherent structure of traffic data from the viewpoints of both global multidimensional correlation and fine-grained spatial and temporal dependency. Then, in view of the general framework of the alternating directions method of multipliers (ADMMs), an efficient iterative algorithm is designed to solve our model. Besides, to optimize the parameter combination of the model, a Bayesian optimization (BO)-based parameter selection algorithm is developed, which avoids manual parameter adjustment. Extensive experiments and analyses on two real-world traffic datasets are carried out. The results demonstrate the advantages of our model under diverse missing patterns and missing ratios.

关键词： Alternating directions method of multipliers (ADMMs) low-rank tensor completion (LRTC) spatial-temporal correlation traffic sensor networks

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A method of VR-EEG scene cognitive rehabilitation training

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2021年第1期9卷 1-9页

作者： Tan, Wenjun Xu, Yang Liu, Pan Liu, Chunyan Li, Yujin Du, Yanrui Chen, Chao Wang, Yuping Zhang, Yanchun Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang 110189 Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Capital Med Univ Dept Neurol Xuanwu Hosp Beijing 100053 Peoples R China Tianjin Univ Technol Key Lab Complex Syst Control Theory & Applicat Tianjin 300384 Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic 8001 Australia

Virtual reality technology can intuitively provide patients of neuropsychological diseases with an almost real environment for cognitive rehabilitation training . In this paper, virtual reality technology is used to construct specific scenes that are universal and related to MCI patients to restore and train patients' scene memory cognitive ability to help patients strengthen or gradually restore scene memory cognitive ability. The construction of virtual reality scenes with different contents such as life, environment, transportation and tourism, real-time detection is carried out in combination with EEG signals of patients in different scenes. The experimental results of the analysis of EEG signals of patients shows that memory rehabilitation training is strengthened by using specific stimulation scenes.

关键词： MCI Rehabilitation training VR EEG

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