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Error analysis of SAV finite element method to phase field crystal model

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arXiv 2019年

作者： Wang, Liupeng Huang, Yunqing Jiang, Kai School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan Hunan411105 China Key Laboratory of Ministry of Education for Intelligent Computing and Information Processing Xiangtan University Xiangtan Hunan411105 China

In this paper, we construct and analyze an energy stable scheme by combining the latest developed scalar auxiliary variable (SAV) approach and linear finite element method (FEM) for phase field crystal (PFC) model, and show rigorously that the scheme is first-order in time and second-order in space for the L2and H1 gradient ow equations. To reduce eficiently computational cost and capture accurately the phase interface, we give a simple adaptive strategy, equipped with a posteriori gradient estimator, i.e. L2norm of the recovered gradient. Extensive numerical experiments are presented to verify our theoretical results and to demonstrate the efiectiveness and accuracy of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite element method

Exploring Multi-loss Function Based Attention Framework for Vehicle Re-Identification

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Exploring Multi-loss Function Based Attention Framework for ...

IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Wei Liu Yi Zhang Zhiming Luo Shaozi Li East China Jiaotong University Jiangxi China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yi Chang China Postdoc Center of Information and Communication Engineering Xiamen University China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Cognitive Science Department Xiamen University Fujian China

Vehicle Re-identification(Re-ID) refers to the task of matching a specific vehicle across multiple non-overlapping cameras. Precise vehicle Re-ID suffers from two main challenges: (1) different vehicle identities with a similar appearance of the same type have a subtle inter-instance discrepancy, (2) one vehicle usually has large intra-instance differences due to viewpoint and illumination variations. To address these challenges, in this paper, we proposed an Attention-based Framework for Vehicle Re-ID named AFVR, which combines the benefits of both the cross-entropy loss function and the triplet loss function. Experimental results on two popular vehicle Re-ID benchmarks demonstrate the effectiveness of our proposed method, compared with the state of the arts.

关键词： Feature extraction Cameras Training Measurement Lighting Visualization Benchmark testing

Overlapping Community Detection Based on Influence and Seeds Extension

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2019年第1期47卷 153-160页

作者： Yu, Zhi-Yong Chen, Ji-Jie Guo, Kun Chen, Yu-Zhong Xu, Qian College of Mathematics and Computer Sciences Fuzhou University FuzhouFujian350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing FuzhouFujian350116 China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing FuzhouFujian350116 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. FuzhouFujian350003 China

Community detection is a significant research direction in the research of social networks. To improve the quality of seeds selection and expansion, we propose an influence seeds extension overlapping community detection (i-SEOCD) algorithm for overlapping community detection. First, i-SEOCD uses a node influence strategy to find the seed communities with tight structures. Second, on the basis of the seed communities, we calculate the similarity among communities and their neighbor nodes. The nodes whose similarity is greater than a predefined threshold are selected. Third, the strategy of optimizing a self-adaptive function is adopted to expand the communities. Finally, the free nodes in the network are assigned to their corresponding communities in order to find out all the overlapping community structures. Experiments on the real and artificial networks show that i-SEOCD is capable of discovering overlapping communities in complex social networks efficiently. © 2019, Chinese Institute of Electronics. All right reserved.

关键词： Population dynamics

Reversible data hiding based on reducing invalid shifting of pixels in histogram shifting

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arXiv 2019年

作者： Jia, Yujie Yin, Zhaoxia Zhang, Xinpeng Luo, Yonglong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China School of Computer Science Fudan University Shanghai201203 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu Anhui240002 China

In recent years, reversible data hiding (RDH), a new research hotspot in the field of information security, has been paid more and more attention by researchers. Most of the existing RDH schemes do not fully take it into account that natural image's texture has influence on embedding distortion. The image distortion caused by embedding data in the image's smooth region is much smaller than that in the unsmooth region, essentially, it is because embedding additional data in the smooth region corresponds to fewer invalid shifting pixels (ISPs) in histogram shifting. Thus, we propose a RDH scheme based on the images texture to reduce invalid shifting of pixels in histogram shifting. Specifically, first, a cover image is divided into two sub-images by the checkerboard pattern, and then each sub-image's fluctuation values are calculated. Finally, additional data can be embedded into the region of sub-images with smaller fluctuation value preferentially. The experimental results demonstrate that the proposed method has higher capacity and better stego-image quality than some existing RDH schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

Modeling of graphene based strong coulping nanodevice

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Modeling of graphene based strong coulping nanodevice

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Xingang Ren Yong Wang Hao Ren Zhixiang Huang Xianliang Wu Guangdong Provincial Key Laboratory of Nano-Micro Materials Research Peking University Shenzhen Graduate School Shenzhen China Ministry of Education Institute of Physical Science and Information Technology Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

The monolayer graphene has received intensive attention due to its unique characteristics, such as atomic thickness, tunable Fermi level by doping. Here, we will present comprehensive studies from theoretical modeling to application of graphene based nanodevice. Our work indicates that graphene can be considered as bulk material with an effective thickness. In addition, we propose graphene as nanospacer forming a strong coupling sandwiched nanodevice, which realizes strong coupling between localized surface plasmon of metal nanoparticle and surface plasmon of planar metal film. Our work is of critical importance for graphene modeling and applications.

关键词： Graphene Plasmons Conductivity Metals Couplings Permittivity Sensors

Positioning and Pressing Elevator Button by Binocular Vision and Robot Manipulator

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Positioning and Pressing Elevator Button by Binocular Vision...

2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Fang, Tiyu Chen, Huiwu Shi, Jianjie Li, Jinping University of Jinan School of Information Science and Engineering Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan Shandong250022 China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Fiveyear Jinan Shandong250022 China

ISBN: (纸本)9781728105512

There are two key problems of anthropomorphic dual-arm robot up and down floors which are path planning and pressing buttons of elevator automatically. Our research group has solved the problem of robot path planning based on electronic map in early stage. As for the problem of pressing buttons automatically, two issues still need to be handled, including positioning elevator buttons in three-dimensional space and pressing specific button automatically by the manipulator of robot. At present, our research group has achieved the accurate detection and recognition of elevator target button in the image. Besides, for the core issue, a new method of pressing target button automatically by manipulator with binocular vision servo is proposed. First, the binocular measurement is applied to get the distance and declination relationships between robot itself and target button, then we shall find the precise position of target button in visual three-dimensional coordinate system and transform its position into the three-dimensional coordinate system which takes robot shoulder as the origin. Thus, the manipulator movement model can be established by constructing relationships between the declination of three degrees of freedom and the real-time coordinates of manipulator, which contains extension of shoulder joint, Internal rotation of shoulder joint and flexion-extension function of elbow joint, and each of them as a separate dimension. On the basis of this model, the PWM (Pulse Width Modulation) control technology is employed to make the manipulator terminal close to target button autonomously. Finally, with deviation relationships between the manipulator terminal and target button as well as the threshold which is set to modulate manipulator, the goal of pressing target button by manipulator can be achieved eventually. Furthermore, the algorithm was tested on medium-sized wheeled dual-arm robot Hawk and results obtained verify the feasibility of the method proposed above ca

关键词： Robot applications

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

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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

Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model

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Camera Abnormal Movement and Foreign Object Invasion Detecti...

2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Yu, Xiangru Cai, Fudong Dou, Yimin Li, Jinping Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105512

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region;secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence;thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm;fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence;fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image;sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of

关键词： Cameras

Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

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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