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检索条件"任意字段=International Symposium Distributed Computing and Applications Business Engineering and Science"

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Fault Diagnosis Approach for Pneumatic Control Valves Based on Modified Expert System

Fault Diagnosis Approach for Pneumatic Control Valves Based ...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Daoming Zhang Jixing Hao Liming Chen Linna Zhou School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

This paper studies the fault diagnosis method of pneumatic control valve. Firstly, the faults characteristics of pneumatic control valves are analyzed according to the operating principle and status of pneumatic control valves; secondly, the expert experience of the fault diagnosis of pneumatic control valves is summarized, which is verified according to the operating mechanism; thirdly, a fault diagnosis approach for pneumatic control valves based on modified expert system is proposed, by combining particle swarm optimization (PSO) algorithm with expert rules. Finally, the availability and advantages of the proposed approach is verified by the designed valve experimental system platform. The results show that compared with the basic expert system-based method, the modified method improves the accuracy and reduces the false negative rate effectively.

关键词： Valves Expert systems Fault diagnosis Friction Particle swarm optimization Process control Springs

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A Discrete Random Drift Particle Swarm Optimization with Modularity in Community Detection

A Discrete Random Drift Particle Swarm Optimization with Mod...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Fen Wang Li Sun Jun Sun Qidong Chen School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

In the field of complex networks, community detection is one of important research objects. To solve the problem of poor quality and the unstable result with community structure, we propose a community detection optimization algorithm based on random drift particle swarm optimization (RDPSO) algorithm (DRDPSO-net), in which we use discrete method to update the network information. Through the discrete particle evolution process and local greedy strategy with network topology character, DRDPSO-net can obtain a better quality of community division. In addition, several representative real networks are used to verify the performance of DRDPSO-net. By comparing them across several algorithms, DRDPSO-net has more desirable value among those algorithms. Furthermore, the experimental results demonstrated that DRDPSO-net obtain a valid and steady community structure.

关键词： Optimization Heuristic algorithms Clustering algorithms Sports Dolphins Complex networks Topology

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Upgrade your network in-place with deformable convolution

Upgrade your network in-place with deformable convolution

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Wei Xi Li Sun Jun Sun School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Improving the performance of the network on is a topic that all deep learning researchers are working together. More new algorithms are proposed for different tasks. But most of these can't avoid spending a lot of time retraining the network model. Deformable convolution is a convolution structure that can extract better features of objects. This paper proposes a new method that can upgrade the standard convolution part of the network to the deformable convolution in-place, inherit the original model parameters, and reduce the time and computational resource cost for retraining. We analyzed the effects of introducing deformable convolution at different depths of the network on speed and performance. And on the detection and semantic segmentation tasks of the PASCAL VOC and COCO, a lot of experiments were carried out on our methods, and have an effective improvement.

关键词： Convolution Standards Training Feature extraction Task analysis Deformable models Two dimensional displays

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Research on items Recommendation Algorithm Based on Knowledge Graph

Research on items Recommendation Algorithm Based on Knowledg...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Pei Liu HongXing Liu ChuanLong Li School of Computer Science and Technology Wuhan University of Technology Wuhan China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Traditional recommendation systems mostly use collaborative filtering algorithm, which has problems with cold start and data sparseness. A common idea to solve these problems is to introduce some auxiliary information as input in the recommendation algorithm. The knowledge graph contains rich semantic information, which can provide potential assistance for the recommendation system. The research on the existing recommendation methods based on knowledge graphs found that these methods lacked the consideration of entity attributes information. Therefore, this paper considers attribute factors and proposes the interest modeling method the entity attributes-based in the knowledge graph, and fusions with traditional collaborative filtering algorithm to improve the recommended effect. The results show that the proposed recommended algorithm has better property than other commonly used benchmark algorithms.

关键词： Semantics Prediction algorithms Mathematical model Recommender systems Matrix decomposition Computational modeling Clustering algorithms

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Estimating Median in the Multi-sourced Heterogeneous Data Set: A distributed implementation 2

Estimating Median in the Multi-sourced Heterogeneous Data Se...

引用

2019 2nd international symposium on Big Data and Applied Statistics, ISBDAS 2019

作者： Gao, Tian Zhao, Yanyu Deng, Xinyi Li, Wenze Li, Hongjun Zhao, Xin Beijing Normal University No 18 Jinfeng Road Tangjiawan Zhuhai Zhuhai China

The continuous running of enterprise applications produces a huge volume of business data that reside in different storage and system environments and owned by different companies or organizations, which forms a typical distributed, multi-sourced heterogeneous dataset. The multi-sourced heterogeneous data set provides big potential values for official statistics. While the median is a commonly used indicator in official statistics, it is not a trivial task to estimate the median in the distributed computing environment of multi-sourced heterogeneous data set due to its mathematical nature. In this paper, we proposed a distributed method to estimate the median value for the multi-sourced heterogeneous data set. Mainly considering the different size of multi-sourced data set and unevenly distribution of their data values, we first improve the traditional interpolation based median estimation method for the multi-sourced heterogeneous data set. Then, we propose distributed implementation for the proposed median estimation method based on web service technology. Finally, we evaluate the accuracy and performance of proposed method through experimental study. CCS Concepts Information systems Data management systems Information integration Mediators and data integration. © Published under licence by IOP Publishing Ltd.

关键词： Data integration

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Model Predictive Control based Energy Collaborative Optimization Management for Energy Storage System of Virtual Power Plant

Model Predictive Control based Energy Collaborative Optimiza...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Weiguang Chang Wei Dong Lihang Zhao Qiang Yang College of Electrical Engineering Zhejiang University Hangzhou China Zhejiang Energy Research Institute Co. Ltd. Hangzhou China Jiangsu Key Construction Laboratory of loT Application Technology Wuxi Taihu University Wuxi China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

This paper presents an energy collaborative optimization management for an energy storage system (ESS) of virtual power plant (VPP) based on model predictive control (MPC). This method uses long-short term memory (LSTM) neural network to obtain the one hour-ahead forecasting information for the load, the generation of wind and photovoltaic within the jurisdiction of VPP. With the minimum economic cost of VPP as the optimization goal, the optimal scheduling is solved by an improved particle swarm optimization (PSO) algorithm in the concept of the MPC framework. Through the comparison with the conventional VPP optimization solution, the numerical results clearly demonstrated that the proposed method improves the utilization of distributed generators (DGs) and reduces the impact of prediction errors on the optimization results.

关键词： Optimal scheduling Prediction algorithms Power generation Photovoltaic systems Wind turbines Predictive models Generators

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Classification of Hyperspectral image based on superpixel segmentation and DPC algorithm

Classification of Hyperspectral image based on superpixel se...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Nian Chen Hao Zhou College of Big Data and Artificial Intelligence ChiZhou University ChiZhou China College of Computer and Information Science Southwest University ChongQing China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

In this paper, we propose an algorithm named SS_DPC for hyperspectral image classification. First, the image is segmented into hyperpixels according to spatial and spectral information, which are used as basic units for clustering instead of pixels. computing the inner product of the local density and the minimum inter_cluster distance for each unit, Density peaks clustering (DPC) algorithm sorts products in descending order and selects the globally optimal solutions as cluster centers. The following conclusions have been verified through experiments:(1)Proper quantity of superpixel (K value) can improve the consistency between clustering results and actual values effectively.(2)Image segmentation can weaken the interference of abnormal data, so the ARI values of SS_DPC, SS_K_Means are higher than that of K_Means significantly.(3)SS_DPC algorithm is much better than other clustering algorithms in precision and robustness.

关键词： Clustering algorithms Image segmentation Classification algorithms distributed databases Robustness Hyperspectral imaging Spatial resolution

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Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering

Non-negative Sparse Laplacian regularized Latent Multi-view ...

引用

international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Cong-Zhe You Hong-Hui Fan Zhen-Qiu Shu Jiangsu University of Technology Changzhou China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multiview Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.

关键词： distributed computing business

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Demonstration of quantum linear equation solver on the IBM qiskit platform

Demonstration of quantum linear equation solver on the IBM q...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Wen Ji Xiangdong Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou P.R. China Henan Key Laboratory of Network Cryptography Technology Zhengzhou P.R. China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

HHL algorithm is a basic quantum algorithm, which is mainly used to solve linear equations. It has a wide range of potential applications in science, engineering, finance, economics and other fields. Although the current scale of quantum computers is small, we can still demonstrate basic quantum algorithms. In this paper, taking the HHL quantum algorithm as an example, the quantum circuit of the HHL quantum algorithm corresponding to 4 qubits and 7 qubits is given, and the simulation verification is carried out on the IBM qiskit development platform. The results show that when the input matrix is a second-order matrix, the fidelity is very high, but when the input is a fourth-order matrix, the probability that the result is close to the real value is very high only when the sparsity of the matrix is relatively strong. However, when the sparsity of the input matrix is very weak, the result will be very different from the real value.

关键词： Mathematical model Logic gates Computers Phase estimation Manganese Machine learning algorithms Linear systems

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Dual Graph regularized NMF with Sinkhorn Distance

Dual Graph regularized NMF with Sinkhorn Distance

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Yunmeng Zhang Zhenqiu Shu Jie Zhang Congzhe You Zonghui Weng Honghui Fan Feiyue Ye School of Computer Engineering Jiangsu University of Technology Changzhou China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Many researchers have paid more attention to the application of non-negative matrix factorization (NMF) in data representation. Recently, some regularization methods can improve the performances by utilizing the data and feature manifold, simultaneously. In this work, a new method, named dual graph regularized NMF with Sinkhorn distance (DSDNMF) is presented. It not only synchronously takes the data structure and feature structure into consideration, but also measures the reconstruction error by adopting the Earth Mover's Distance (EMD) to make full use of the feature correlation. Therefore, DSDNMF can effectively explore the semantic structure information of data in contrast to traditional methods. Besides, we introduce an efficient strategy to optimize our proposed model. Comprehensive experiments on the COIL20 and PIE datasets manifest the superiority of DSDNMF.

关键词： Manifolds Databases Correlation Periodic structures Measurement Faces Clustering algorithms

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