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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Interpretable foundation models as decryptors peering into the Earth system

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The Innovation 2024年第5期5卷 21-22页

作者： Chenyu Li Danfeng Hong Bing Zhang Tianjun Liao Naoto Yokoya Pedram Ghamisi Min Chen Lizhe Wang Jon Atli Benediktsson Jocelyn Chanussot Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Electronic Electricaland Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China Academy of Military Sciences Beijing 100000China Graduate School of Frontier Sciences The University of TokyoChiba 277-8561Japan Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology09599 FreibergGermany Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China School of Computer Science China University of GeosciencesWuhan 430078China Faculty of Electrical and Computer Engineering University of Iceland102 ReykjavikIceland University Grenoble Alpes InriaCNRSGrenoble INPLJK38000 GrenobleFrance

THE EARTH SYSTEM AMID THE BIG DATA *** processes of the Earth system drive interactions between energy,matter,and life,and a comprehensive understanding of their full evolutionary trajectory is critical for sustainable human *** modeling primarily relies on a set of theoretical equations to simulate dynamic process such as the carbon-nitrogen cycle,solar radiation dynamics,and terrestrial ecosystem dynamics.1 Despite the extensive modeling experience of Earth scientists,the rapid advancement of Earth observation techniques has led to a significant increase in the volume of databases,with data accumulating daily or even *** has exacerbated the conflict between the capacity for data collection and utilization for big Earth ***,there is an urgent need to enhance the intelligent processing and analysis of big Earth data.

关键词： dynamics. system foundation

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Object Detection in 20 Years: A Survey

arXiv

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arXiv 2019年

作者： Zou, Zhengxia Chen, keyan Shi, Zhenwei Guo, Yuhong Ye, Jieping The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The School of Computer Science Carleton University OttawaONK1S 5B6 Canada The Alibaba Group Hangzhou310030 China

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today's object detection technique as a revolution driven by deep learning, then back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This paper extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century's time (from the 1990s to 2022). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed-up techniques, and the recent state-of-the-art detection methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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An active learning approach for reducing annotation cost in skin lesion analysis

arXiv

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arXiv 2019年

作者： Shi, Xueying Dou, Qi Xue, Cheng Qin, Jing Chen, Hao Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Imsight Medical Technology Co. Ltd. Shenzhen China Centre for Smart Health School of Nursing Hong Kong Polytechnic University Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved remarkable performance on this challenging task, however, heavily relying on large-scale labelled datasets. In this paper, we present a novel active learning framework for cost-effective skin lesion analysis. The goal is to effectively select and utilize much fewer labelled samples, while the network can still achieve state-of-the-art performance. Our sample selection criteria complementarily consider both informativeness and representativeness, derived from decoupled aspects of measuring model certainty and covering sample diversity. To make wise use of the selected samples, we further design a simple yet effective strategy to aggregate intra-class images in pixel space, as a new form of data augmentation. We validate our proposed method on data of ISIC 2017 Skin Lesion Classification Challenge for two tasks. Using only up to 50% of samples, our approach can achieve state-of-the-art performances on both tasks, which are comparable or exceeding the accuracies with full-data training, and outperform other well-known active learning methods by a large margin. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost effectiveness

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Deterministic Pilot Design for MIMO OFDM System Based on Compressed Sensing

Deterministic Pilot Design for MIMO OFDM System Based on Com...

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2012 IEEE 14th International Conference on Communication technology

作者： Nan Jing Weihong Bi Lin Wang School of Information Science and Engineering Yanshan University The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

ISBN: (纸本)9781467321006

We consider the problem of pilot design for sparse multipath channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in this *** order to reconstruct MIMO sparse channel,the prevalent compressed sensing (CS) theory is *** to random pilot construction,a deterministic pilot de- sign method is exploited to form the measurement matrix by deterministically extracting some rows of Fourier matrix that satisfies Restricted Isometry Property (RIP).Two families of continuous functions,namely f1 -SL0 and f 2-SL0,are investi- gated in this paper to approximate A 0norm for MIMO sparse channel *** results suggest that the de- terministic pilot design scheme performs as well as the random one at both low and high signal noise ratio (SNR).Moreover,f1 - SL0 and f 2-SL0 outperform A 1-dantzig algorithm with faster running time and better anti-noisy performance.

关键词： sparse channel estimation,deterministic pilot design,compressed sensing,MIMO OFDM

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EPI-Guided Cost Construction Network for Light Field Disparity Estimation

EPI-Guided Cost Construction Network for Light Field Dispari...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Tun Wang Rongshan Chen Ruixuan Cong Da Yang Zhenglong Cui Fangping Li Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing P.R. China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou P.R. China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R. China

Recent learning-based light field (LF) disparity estimation methods construct cost volume by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets. They only use the visual information of SAIs and lose the geometry of LF. In this paper, we design a simple network that can cleverly integrate EPI features with cost volume to estimate the disparity. Firstly, we propose an efficient EPI extraction module to use abundant line characteristics. Secondly, we offer an EPI-Cost volume construction module that can create volume guided by the EPI line and the color consistency of images. Finally, after completing it, we adopt an inter-volume fusion module to considerably correlate the validity of EPI lines in both directions. Experimental results show the proposed method achieves state-of-the-art performance in the quantitative and qualitative evaluation of the UrbanLF-Syn dataset.

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Evaluation of Motor Training Performance in 3D virtual Environment via Combining Brain-computer Interface and Haptic Feedback

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Procedia computer Science 2017年 107卷 256-261页

作者： Hong Wu Shuang Liang Wenlong Hang Xiaolu Liu Qiong Wang Kup-Sze Choi Jing Qin School of Information Engineering Wuhan University of Technology Wuhan 430070 China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Digital Media Jiangnan University Wuxi 214122 China School of Biomedical Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Hung Hom Hong Kong

Brain-computer interfaces (BCIs) based on virtual reality (VR) mostly integrate visual and/or auditory feedback. Haptic feedbackwhich has the potential on improving the feasibility and operability of VR-based BCI systems is rarely explored in previous studies. In this article, we present a novel framework of BCI system based on both visual and haptic feedback, in which users can learn to manipulate the haptic device's stylus for motor training. The effects of motor training with and without haptic feedback are evaluated by detecting and analysing the changes of electroencephalogram (EEG). The preliminary experimental results indicate that haptic feedback may influence the modulation of the beta rhythms over left and right sensorimotor cortex during hand movements. This study can be easily replicated to evaluate the existing systems with haptic feedback and used to develop new applications for motor training.

关键词： Haptic feedback brain-computer interface (BCI) Electroencephalogram (EEG) event-related spectral perturbation (ERSP) virtual reality (VR)

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A filtering algorithm for scattered point cloud based on curvature features classification

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Journal of Information and Computational Science 2015年第2期12卷 525-532页

作者： Gu, Xiaoying Liu, Yongshan Wu, Qiong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

In order to remove the noise of three-dimensional scattered point cloud, and to smooth point cloud model effectively, an algorithm which uses different filtering strategies on different feature regions of a point cloud model is proposed. The algorithm firstly estimates the normal vectors of a point cloud model by using covariance analysis method, then estimates Gaussian curvatures of the point cloud model by using parabolic fitting method, and uses the average Gaussian curvature in the k-neighborhood as the threshold, to divide the point cloud model into flat neighborhood type regions and mutant neighborhood type regions, and, we use improved median filtering algorithm and bilateral filtering algorithm on different feature regions, so as to achieve the purpose of smoothing scattered point cloud. Experimental results show that it has a very good smoothing effect and maintains the detail features of the point cloud model, which has proved the algorithm is simple and effective. Copyright © 2015 Binary Information Press.

关键词： Three dimensional computer graphics

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