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A Novel Fault Propagation Path Identification Algorithm Using Parent Nodes Filter

A Novel Fault Propagation Path Identification Algorithm Usin...

引用

第六届中国计算机学会大数据学术会议

作者： Shuanzhu Sun Bei Song Chunlei Zhou Xiaolong Qi Hao Wang Jiangsu Frontier Electric Technology Co.Ltd. Nanjing211102China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing210046China

Fault diagnosis techniques based on probabilistic graphical models are often used for uncertain information *** them,Bayesian network,an effective tool which has strong characteristics of practicality and applicability,is used *** identify fault propagation paths in industrial systems with large volume data,a novel BN-based identification algorithm using parent nodes filter is *** all possible combinations of parents,values of specific symptom child node with the maximum conditional probability are estimated using decomposition of conditional probability as well as Dichotomy after BN *** the most probable cause is determined by comparing estimations with the observed *** layer-by-layer analysis,fault propagation paths in the network can be finally *** results have demonstrated that the novel approach is capable of identifying fault paths effectively,which provides higher adaptability and faster speed.

关键词： Bayesian network Fault propagation path Parent nodes filter The maximum conditional probability

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An Optimized Artificial Bee Colony based Parameter Training Method for Belief Rule-Base

An Optimized Artificial Bee Colony based Parameter Training ...

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第六届中国计算机学会大数据学术会议

作者： Man-Na SU Zhi-Jian FANG Shao-Zhen YE Ying-Jie WU Yang-Geng FU College of Mathematics and Computer Science Fuzhou University

In order to solve the problems of poor portability,complex implemen-tation,and low efficiency in the traditional parameter training of the Belief rule-base,an artificial bee colony algorithm combined with Gaussian disturbance op-timization was introduced,and a novel Belief rule-base parameter training method was *** the light of the algorithm principle of the artificial bee colony,the honey bee colony search formula and the cross-border processing method were improved,and the Gaussian disturbance was employed to prevent the search from falling into a local *** parameter training was imple-mented in combination with the constraint conditions of the Belief *** fitting the multi-peak function and the leakage detection experiment of oil pipe-lines,the experimental error were compared with the traditional and existing pa-rameter training methods to verify its effectiveness.

关键词： Belief rule-base (BRB) Artificial bee colony algorithm Swarm In-telligence Parameter optimization model

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A Vertex-Centric Graph Simulation Algorithm for Large Graphs

A Vertex-Centric Graph Simulation Algorithm for Large Graphs

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第六届中国计算机学会大数据学术会议

作者： Jingdong Li Jin Li Xiaoling Wang Shanghai Key Laboratory of Trustworthy Computing East China Normal University ShanghaiChina School of Software Yunnan University650000 KunmingChina

Graph simulation as a well studied model of graph pattern matching problem,has been adopted to reduce the complexity and meet the need of novel applications such as mining potential associations between users in online social *** recent years,graph processing frameworks such as Pregel bring in a vertex-centric,Bulk Synchronous Parallel(BSP)programming model for processing massive data graphs and achieve encouraging ***,developing efficient vertexcentric algorithms for graph simulation model is very challenging,because this problem does not naturally align with a vertex-centric programming *** paper presents novel distributed algorithms based on the vertex-centric programming model for graph *** the same time,considering the enormous cost of the message passing and the algorithm complexity of the pattern matching in the processing of the massive data graph,the part of message passing in the algorithm is optimized to reduce the communication *** experimentally verify the effectiveness and efficiency of these algorithms,using real-life massive data graph.

关键词： vertex-centric graph simulation optimization

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Multi-constrained Dominate Route Queries in Time-Dependent Road Networks

Multi-constrained Dominate Route Queries in Time-Dependent R...

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第六届中国计算机学会大数据学术会议

作者： Fangjun Luan Qi Li Keyan Cao Liwei Wang Department of Information and Control Engineering Shenyang Jianzhu University110168ShenyangChina

With the rapid development of location-based services,there is more and more personalized demand for route *** existing studies on route queries on time-dependent network to find the optimal path,for example: shortest route,highest scoring route,***,in practical application,users will want to be satised with the constraint and evaluate the good routes to make a choice,for example,users want to look for the well-evaluation route through bank,café shop,restaurant in ordered within 3 hours for *** on this requirements,a new location-based service is proposed in this paper,which is called time-dependent and dominated route query(MTDDR).In order to solve the MTDDR problem,three algorithms are designed in this paper,which are respectively the precise algorithm BSL algorithm,the time-dependent es-timation algorithm TDER algorithm,a heuristic FTDR algorithm,while the design further reduces the pruning strategy of the search ***-ing the real network data set on OpenStreetMap,to test the validity of three algorithms under different parameters.

关键词： Time-dependent network Route query Dominate route

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Facial Components Based Representation for Caricature Face Recognition

Facial Components Based Representation for Caricature Face R...

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第六届中国计算机学会大数据学术会议

Caricature face recognition is an interesting but also a hard task for studying owing to the huge exaggeration between two quite different face modalities,photos and ***,we propose a new representation for recognition which is fused by the representation learned from photos,caricatures and generated *** generated face contains 4 main facial ***,caricatures and generated faces are sent to Photo ResNet,Caricature ResNet and Generated Face ResNet to learn specific *** the learned three presentation are sent a fully connected *** adopt softmax loss and centerloss for training which can reduce the distance of *** test the performance of our proposed representation,we build a new dataset for caricature face recognition,which consists of 259 subjects,with 6490 caricatures and 8143 *** dataset we build is the biggest available caricature *** basic methods are used for caricature face *** test the discrimination of our proposed representation,two more groups of experiments are fulfilled,including searching photos according to the selected caricature(CTP)and searching caricatures according to the selected photo(PTC),and our proposed method performs better than other convolutional neural network based representations.

关键词： caricature face recognition generated face facial components centerloss caricature dataset

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Multi-class Weather Classification Fusing Weather Dataset and Image Features

Multi-class Weather Classification Fusing Weather Dataset an...

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第六届中国计算机学会大数据学术会议

作者： Shan Wang Yidong Li Wenhua Liu Beijing Jiaotong University Beijing 110000China

Weather classification is getting more and more attractive because it has many potential applications,such as visual systems and intelligent transportation,especially in ***,the researches about it were based weather dataset are designed to be executed in clear weather *** it is challenging tasks due to the diversity of weather cues and lack of discriminate ***,in addition to the image features,real-time weather factors is needed to provide additional *** existing weather classification methods involve many pre-processing techniques,for instance sky detection and boundary detection,which makes the model highly rely on the performance of the aforementioned ***,in this paper,we propose a novel method for multi-class weather *** newly constructed multi-class weather dataset contains 5K images which has more adverse weather *** improve the discrimination of image representation and decrease the influence of pre-processing,our approach combines the real-time weather data with the image feature as the final feature vector to identify different *** experiments on our own dataset,we demonstrate that the proposed framework achieves superior performance compared to the state-of-the-art methods.

关键词： Multi-class weather classification Dark channel Real-time weather dataset

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A Multi-scale Dehazing Network with Transmission Range Stretching

A Multi-scale Dehazing Network with Transmission Range Stret...

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第六届中国计算机学会大数据学术会议

作者： Jingjing Yao Xinzhe Han Gang Long Wen Lu Xidian University UniversityXi'an 710071China University of Chinese Academy of Sciences Beijing 100049China

Image dehazing has become a significant research area in recent ***,the traditional dehazing algorithms based on statistics priors cannot adaptive to various conditions of natural hazy *** those algorithms based on Data-driven learning such as some dehazing networks for estimating transmission almost have the problem that the range of the estimated transmission is too narrow for those haze images where hazy density changes *** in this paper,we present a novel Dehazing Network to learn the relationship between the hazy image and its corresponding transmission *** uses jump connection and the layer of Multi-scale features fusion to obtain more feature related to haze density and use both max pooling and average pooling which in turn remove some details of the transmission map and make the gained transmission map more ***,we also propose a linear stretching algorithm based on dark channel prior to extent the transmission *** experimental result demonstrate that proposed algorithm achieves favorable result against existing dehazing algorithms on both synthetic images and natural images.

关键词： Dehazing Network Dark Channel Prior Adaptively Stretching the Transmission Range

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Multiple Meta Paths Combined for Vertex Embedding in Heterogeneous Networks

Multiple Meta Paths Combined for Vertex Embedding in Heterog...

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第六届中国计算机学会大数据学术会议

作者： Tong Wu Chaofeng Sha Xiaoling Wang Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghaiChina Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200433China

In the real-world many complex systems exist in the form of heterogeneous *** we all know,heterogeneous networks consist of various types of vertices and relations,so it is difficult to deal directly with data *** present,although many state-of-the-art methods of network representation learning have been developed,these methods can only deal with homogeneous networks or lose information when han-dling heterogeneous *** order to compensate for the weakness of the previous methods,we propose a multiple meta paths combined em-bedding(MMPCE)model to represent the heterogeneous *** method can automatically obtain the low-dimensional vector representa-tion of vertices and preserve the rich semantic and structural information in the *** conduct experiments on two real world *** experimental results demonstrate the efficacy and efficiency of the pro-posed method in heterogeneous network mining *** to the previous method,our model can cover a wider range of semantic infor-mation and be more exible and scalable.

关键词： network embedding vertices embedding heterogeneous representation learning heterogeneous information embedding

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A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure

A Semi-Supervised Deep Network Embedding Approach Based on t...

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第六届中国计算机学会大数据学术会议

作者： Wenmao Wu Zhizhou Yu Jieyue He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information IntegrationSoutheast UniversityNanjing 210096China

Network embedding is a very important task to represent the high-dimensional network in a low-dimensional vector space,which aims to capture and preserve the network *** existing network embedding methods are based on shallow ***,the actual network structure is so complicated that the shallow model can not obtain the high-dimensional nonlinear features of the network *** recently proposed unsupervised deep learning model of network representation learning ignores the labels *** overcome these problems,in this paper,we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding(SLLDNE).SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using SVM classifier as a supervised component to improve the ability of ***,we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural *** experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.

关键词： network embedding deep learning semi-supervised network analysis reconstruction

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PowerHash:A Hash Grouping Scheme by Leveraging Power-Law Properties of Data

PowerHash:A Hash Grouping Scheme by Leveraging Power-Law Pro...

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第六届中国计算机学会大数据学术会议

作者： Xun Wei Xiaowang Kong Yanfeng Zhang Northeastern University Liaoning 110819China Beijing Baidu Netcom Science and Technology Co. Ltd.3.Northeastern University Liaoning110819China

We study the GroupBy implementation scheme widely used in distributed systems and *** GroupBy operation partitions a set of out-of-order records into *** to the massive data size,many I/O efficient grouping schemes that exploit external memory have been *** this paper,we observe that the group sizes of input data exhibit power-law property and the grouping schemes' performance varies a lot for data with different group *** indexing-filling approach prefers data with big group size,while the partitioned hash approach prefers data with small group *** on this observation,we propose a hybrid approach,PowerHash,which invokes different grouping schemes for different *** group size information is approximately estimated by the count-min sketch so that the big groups and small groups can be distinguished from each *** a given memory budget,our results show that PowerHash can improve the performance by 1.25-2 times over the existing GroupBy implementations.

关键词： key value grouping power-law distribution GroupBy hashtable

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