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

作者： Yang, Shu-Yu Deng, Shun-Ming Zhao, Peng-Peng Li, Jin-Ping School of Information Science and Engineering University of Jinan Jinan Shandong250022 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 Five-year Jinan Shandong250022 China Chongqing Vocational Institute of Engineering Chongqing400000 China

ISBN: (纸本)9781728105512

The artificial landmark positioning technology is one of the visual positioning methods of the inspection robot. Although this technology has simple algorithm and low maintenance cost, it is difficult to be widely used due to the limitations in some special environment. For example, people are not allowed to set a large number of landmarks on the ground in some workshops. In this paper, we propose a visual self-positioning method based on the symmetrical tangential artificial landmarks. The basic idea of this method is: firstly, a small number of tangential artificial landmarks are set on the walls or machine tools on both sides, secondly, the perspective model and triangulation method are used to realize the self-positioning of the inspection robot. The experimental results show that the method is simple to operate and has high positioning accuracy, which can meet the needs of the inspection robot. © 2018 IEEE.

关键词： Machine tools

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A multigrid-reduction-in-time solver with a new two-level convergence for unsteady fractional Laplacian problems

arXiv

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arXiv 2019年

作者： Yue, Xiaoqiang Pan, Kejia Zhou, Jie Weng, Zhifeng Shu, Shi Tang, Juan Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China School of Mathematics and Statistics HNP-LAMA Central South University Changsha410083 China School of Mathematical Sciences Fujian Province University Key Laboratory of Computation Science Huaqiao University Quanzhou362021 China

The multigrid-reduction-in-time (MGRIT) technique has proven to be successful in achieving higher run-time speedup by exploiting parallelism in time. The goal of this article is to develop and analyze a MGRIT algorithm, using FCF-relaxation with time-dependent time-grid propagators, to seek the finite element approximations of unsteady fractional Laplacian problems. The multigrid with line smoother proposed in [L. Chen, R. H. Nochetto, et al., Math. Comp. 85 (2016) 2583–2607] is chosen to be the spatial solver. Motivated by [B. S. Southworth, SIAM J. Matrix Anal. Appl. 40 (2019) 564–608], we provide a new temporal eigenvalue approximation property and then deduce a generalized two-level convergence theory which removes the previous unitary diagonalization assumption on the fine and coarse time-grid propagators required in [X. Q. Yue, S. Shu, et al., Comput. Math. Appl. 78 (2019) 3471–3484]. Numerical computations are included to confirm the theoretical predictions and demonstrate the sharpness of the derived convergence upper bound. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite element method

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Smoking Behavior Detection Based on Hand Trajectory Tracking and Mouth Saturation Changes

Smoking Behavior Detection Based on Hand Trajectory Tracking...

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

作者： Lin, Zhenkai Lv, Changfeng Dou, Yimin Li, Jinping School of Information Science and Engineering University of Jinan Jinan Shandong250022 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 Five-year Jinan Shandong250022 China Shandong Senter Electronic Co. Ltd Zibo Shandong255088 China

ISBN: (纸本)9781728105512

Smoking seriously endanger people's physical and mental health. In some special places, e.g., gas stations and forest areas, smoking may cause serious accidents. In order to detect smoking behavior timely and accurately, we propose an effective and practical detection method by means of video analysis. Two basic approaches are involved in the proposed method: one is smoke detection;the other is smoking action detection. In the first approach, we detect human face by using a special open source of face detection system called SeetaFace, then segment the mouth area and calculate the corresponding grayscale and saturation, finally we can determine if sudden a change occurs around the mouth. In the second approach, there are two basic steps: firstly, we detect the skin color area based on the skin color ellipse model in YCrCb color space, then determine the initial position of the hand by using the location of the skin color area that relative to the face;secondly, track the trajectory of hand movement by using optical flow and then detect whether the hand overlap the mouth in real time. Finally, we combine the results of the preceding two steps in the second approach with the result in the first approach together and then we can determine smoking or non-smoking person. The experimental results show that the proposed method can effectively detect smoking behavior with a small training sample in real time and achieve the detection rate of 95%. © 2018 IEEE.

关键词： Smoke

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Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery

Parallel Multi-Label Propagation Based on Influence Model an...

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作者： Qiu, Qirong Guo, Wenzhong Chen, Yuzhong Guo, Kun Li, Rongrong School of Economics and Management Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

Finding communities in networks is one of the challenging issues in complex network research. We have to deal with very large networks that contain billions of vertices, which makes community discovery a computationally intensive work. Moreover, communities usually overlap each other, which greatly increases the difficulty of identifying the boundaries of communities. In this paper, we propose a parallel multi-label propagation algorithm (PMLPA) that enhances traditional multi-label propagation algorithm (MLPA) in two ways. First, the critical steps of MLPA are parallelized based on the MapReduce model to get higher scalability. Second, new label updating strategy is used to automatically determine the most valuable labels of each vertex. Furthermore, we study the improvement of PMLPA through considering the influence of vertices and labels on label updating. In this way, the importance of each label can be described with higher precision. Experiments on artificial and real networks prove that the proposed algorithms can achieve both high discovering accuracy and high scalability. © 2017 World Scientific Publishing Company.

关键词： Complex networks

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A Dynamic Community Detection Algorithm Based on Parallel Incremental Related Vertices 2

A Dynamic Community Detection Algorithm Based on Parallel In...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Guohui Li Kun Guo YuZhong Chen Ling Wu Danhong Zhu College of Mathematics and Computer Sciences Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University) Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education

ISBN: (纸本)9781509036202

One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection ***,it usually leads to high computation *** this paper,a novel algorithm based on the Map Reduce model and the label propagation progress with the strategy of incremental related vertices is proposed,which is called PLPIRV(Parallel Label Propagation and Incremental Related Vertices).Based on the communities found at the previous interval,the new algorithm adjusts the communities the incremental related vertices belong *** clustering of the whole network can be avoided by incrementally analyzing the variation of the networks,so that the time cost can be greatly *** on artificial and real datasets show that the proposed algorithm performs well on dynamic community detection.

关键词： dynamic community detection parallel computation incremental algorithm label propagation

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Distributed Representation Learning of Knowledge Graph with Diverse information

Distributed Representation Learning of Knowledge Graph with ...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Wenzhong Guo Yuanfei Dai Yiyan Chen Xing Chen Neal N. Xiong Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Northeastern State University Tahlequah OK US

Knowledge graph is a type of network structure in which nodes represent entities and edges indicate relations. However, as the network size explosively increases, the issues of data sparsity and computation inefficiency on large-scale knowledge graph become more difficult to manipulate and manage. Knowledge graph embedding, which is a representation technique of embedding entities and relations in the knowledge graph into continuous, dense, and low-dimensional semantics vector spaces to tackle these challenges and endow the model with the abilities of knowledge fusion and inference, has recently attracted much attention. In this paper, we firstly introduce the overall framework and specific idea of embedding models. We then introduce two applications that apply KG embedding, compare the performance of the methods in these applications. Finally, we summarize several challenges to overcome, and provide some prospective future research directions such as deep learning network for approaches and applications.

关键词： Semantics Computational modeling Uncertainty Gaussian distribution Euclidean distance Neural networks

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Pixels Matching in No Obvious Feature Area in Binocular Vision Based on Peripheral Feature Points

Pixels Matching in No Obvious Feature Area in Binocular Visi...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Renlong Chen Mingjun Liu Xueyan Gong Jinping Li School of Information Science and Engineering Shandong Provincial Key Laboratory of Network Based Intelligent Computing (University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Jinan China Qilu Institute of Technology Shandong Provincial Key Laboratory of Network Based Intelligent Computing (University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Jinan China

ISBN: (纸本)9781728105529

In binocular vision, the pixel matching of no obvious feature refers to the matching of pixels in the area where the gray value does not change significantly or in the area where there is no significant gradient change in the gray level. The basic idea is that the position of the matching pixels in the area with no obvious features can be determined by the peripheral feature points. First, the camera is calibrated by the chessboard calibration method, and the internal and external parameters of the camera are obtained. The distortion of the left and right images is corrected by the calibration results. Then, the SIFT algorithm is used to extract the feature points, the Euclidean distance threshold is set to determine the better matching points, and the random sample consensus (RANSAC) algorithm is used to eliminate the wrong matching points. Finally, four target matching points with the same distance and direction information are obtained, and the final target matching points are obtained by averaging the four target matching points. Experiments show that any pair of pixels in the unmarked area can be matched accurately.

关键词： Binocular vision Pixel matching with no obvious feature area SIFT RANSAC Peripheral feature points Binocular vision pixel Feature Points Sifting gray value Cameras

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous information. All rights reserved.

关键词： Semantics

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Traffic Condition Analysis Based on Users Emotion Tendency of Microblog

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2018年 650卷 299-311页

作者： Wang, Shuru Cao, Donglin Lin, Dazhen Chao, Fei Cognitive Science Department Xiamen University Xiamen China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Xiamen University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9783319669380

Analysis of traffic condition is of great significance to urban planning and public administration. However, traditional traffic condition analysis approaches mainly rely on sensors, which are high-cost and limit their coverage. To solve these problems, we propose a semi-supervised learning method which uses the social network data instead and analyzes the traffic condition based on user’s emotion tendency. First we train the Gated Recurrent Unit (GRU) model to estimate the sentiment of microblog with traffic information, then using the emotional tendency to predict whether traffic jams happen or not. In order to reduce the data annotated by manpower, we propose a new idea to employ the Conditional Generative Adversarial networks (CGAN) to generate samples which are as a supplement to the training set of GRU. Finally compared with the GRU model trained by solely the manual annotation data, our method improves the classification accuracy by 4.07%. We also use our model to predict the time and roads of traffic jams in 4 Chinese cities which is proved to be effective. © 2018, Springer International Publishing AG.

关键词： Generative adversarial networks

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Sentiment Analysis Model Based on Structure Attention Mechanism

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2018年 650卷 17-27页

作者： Lin, Kai Lin, Dazhen Cao, Donglin Cognitive Science Department Xiamen University Xiamen China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Xiamen University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9783319669380

Since the long short-term memory (LSTM) network is a sequential structure, it is difficult to effectively represent the structural level information of the context. Sentiment analysis based on the original LSTM causes a problem of structural level information loss, and its capacity to capture the context information is finite. To address this problem, we proposed a novel structure-attention-based LSTM as a hierarchical structure model. It may capture relevant information in the context as much as possible. We propose HM (ht matrix) to storage the structural information of the context. Furthermore, we introduce the attention mechanism to realize vector selection. Compared with the original LSTM and normal attention-based sentiment classification methods, our model obtains a higher classification precision. It is proved that the structure-attention-based method proposed in this study has an advantage in capturing the potential semantic structure. © 2018, Springer International Publishing AG.

关键词： Long short-term memory

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