In this paper, an combined evaluation process to evaluate the value of substation was proposed. Firstly, a substation function index system was established with consideration of the function structure and features of substation. Secondly, the Analytic Hierarchy Process (AHP) and entropy method were separately used to calculate the subjective and objective weights of the function indexes. Thirdly, the sum of squares of deviations method and Particle Swarm Optimization (PSO) method were used to get the function indexes’ combined weights. At last, the evaluation model was formed by function index compared with the cost index. The substation design data were used to test the evaluation model, the results showed it was feasible.
{"title":"Substation Design Optimization Research Based on PSO and Value Index Method","authors":"Jinchao Li, H. Yang","doi":"10.12733/JICS20105571","DOIUrl":"https://doi.org/10.12733/JICS20105571","url":null,"abstract":"In this paper, an combined evaluation process to evaluate the value of substation was proposed. Firstly, a substation function index system was established with consideration of the function structure and features of substation. Secondly, the Analytic Hierarchy Process (AHP) and entropy method were separately used to calculate the subjective and objective weights of the function indexes. Thirdly, the sum of squares of deviations method and Particle Swarm Optimization (PSO) method were used to get the function indexes’ combined weights. At last, the evaluation model was formed by function index compared with the cost index. The substation design data were used to test the evaluation model, the results showed it was feasible.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126211631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A kind of risk evaluation method based on Analytic Hierarchy Process (AHP) and improved BP neural network is proposed in this paper. Importance degree of risk evaluation indexes is sorted by using the analytic hierarchy process and the risk evaluation index system is simplified, and then the simplified index acts as the input of improved BP neural network to evaluate the risk. This method simplifies the network model and improves the performance and calculation accuracy of the network. The risk of the power grid project is evaluated based on this method, indicating that this method is feasible and effective for the risk evaluation of the power grid project.
{"title":"Risk Evaluation of Power Grid Project Based on Analytic Hierarchy Process and Improved BP Neural Network","authors":"Shuguo Zhang","doi":"10.12733/JICS20105585","DOIUrl":"https://doi.org/10.12733/JICS20105585","url":null,"abstract":"A kind of risk evaluation method based on Analytic Hierarchy Process (AHP) and improved BP neural network is proposed in this paper. Importance degree of risk evaluation indexes is sorted by using the analytic hierarchy process and the risk evaluation index system is simplified, and then the simplified index acts as the input of improved BP neural network to evaluate the risk. This method simplifies the network model and improves the performance and calculation accuracy of the network. The risk of the power grid project is evaluated based on this method, indicating that this method is feasible and effective for the risk evaluation of the power grid project.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129661322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cheng Zhong, Hang Su, Yifan Ding, Qing-tao Zeng, Xiaochun Jia
In smart grid, one of the important issues that should be solved is how to establish a reasonable and efficient data collection routing mechanism to adapt to the characteristics of smart grid. Compared with traditional routing algorithms a different point is that the major risk of power communication data collection is not sudden congestion caused by bursty traffic, but too much data streams converged at critical nodes. To solve this problem, this paper proposes a routing algorithm based on balanced tree to overcome network congestion, thereby ensuring the reliability of data collecting. First, the algorithm has established a mathematical model for power communication network. The routing metric model that constituted by queue length and buffer capacity of network nodes, together with the balanced tree algorithm, are used to establish routing algorithm. At the end, the difference between the two algorithms were compared in the MATLAB environment. The experimental results show that RA-DBMM algorithm can effectively solve the problem of data congestion problems, so as to improve reliability and throughput of the electric power communication data acquisition network.
{"title":"Balanced Tree Based Data Collection Algorithm in Smart Grid","authors":"Cheng Zhong, Hang Su, Yifan Ding, Qing-tao Zeng, Xiaochun Jia","doi":"10.12733/JICS20105426","DOIUrl":"https://doi.org/10.12733/JICS20105426","url":null,"abstract":"In smart grid, one of the important issues that should be solved is how to establish a reasonable and efficient data collection routing mechanism to adapt to the characteristics of smart grid. Compared with traditional routing algorithms a different point is that the major risk of power communication data collection is not sudden congestion caused by bursty traffic, but too much data streams converged at critical nodes. To solve this problem, this paper proposes a routing algorithm based on balanced tree to overcome network congestion, thereby ensuring the reliability of data collecting. First, the algorithm has established a mathematical model for power communication network. The routing metric model that constituted by queue length and buffer capacity of network nodes, together with the balanced tree algorithm, are used to establish routing algorithm. At the end, the difference between the two algorithms were compared in the MATLAB environment. The experimental results show that RA-DBMM algorithm can effectively solve the problem of data congestion problems, so as to improve reliability and throughput of the electric power communication data acquisition network.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124707008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The existing collaborative recommendation algorithms suffer from lower recommendation precision due to the problem of data sparsity. To solve this problem, we propose a novel collaborative recommendation algorithm which integrates the probabilistic matrix factorization and neighbor models. We first propose a method to calculate the similarity between users or items based on the probabilistic matrix factorization model and construct a natural exponential function to compute the weighted similarity. Then we devise a collaborative recommendation algorithm to make recommendations for the target user, which dynamically adjusts the recommendation results for user- and item-based models by the balance adjustment factor. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of prediction accuracy.
{"title":"A Novel Collaborative Recommendation Algorithm Integrating Probabilistic Matrix Factorization and Neighbor Model","authors":"Hongtao Yu, Lisha Dou, Fuzhi Zhang","doi":"10.12733/JICS20105604","DOIUrl":"https://doi.org/10.12733/JICS20105604","url":null,"abstract":"The existing collaborative recommendation algorithms suffer from lower recommendation precision due to the problem of data sparsity. To solve this problem, we propose a novel collaborative recommendation algorithm which integrates the probabilistic matrix factorization and neighbor models. We first propose a method to calculate the similarity between users or items based on the probabilistic matrix factorization model and construct a natural exponential function to compute the weighted similarity. Then we devise a collaborative recommendation algorithm to make recommendations for the target user, which dynamically adjusts the recommendation results for user- and item-based models by the balance adjustment factor. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of prediction accuracy.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124793808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Adaptive RRT Based on Dynamic Step for UAV Path Planning","authors":"Na Lin","doi":"10.12733/JICS20105520","DOIUrl":"https://doi.org/10.12733/JICS20105520","url":null,"abstract":"","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128342186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the measurement precision, transmission delay and so on, we could only obtain uncertainty position information of moving objects. Spatial uncertainty trajectory data is uncertain in location of mobile objects. It is leading to the challenge of modeling uncertainty trajectory data and mining usable knowledge about movement pattern. In this paper, we propose a two-stages dynamic division method to dealing with the spatial uncertainty trajectory. The approach presents the notions of adjacent boundary cells and shared cells, and merges these cells into basic cells through distance and density membership degree. A comprehensive performance study on synthetic datasets shows that the proposed method in both effectiveness and scalability.
{"title":"Spatial Uncertainty Trajectory Dataset Mining Based on Two-stages Dynamic Division ⋆","authors":"Wang Liang, Mei Wang, He Hucheng","doi":"10.12733/JICS20105632","DOIUrl":"https://doi.org/10.12733/JICS20105632","url":null,"abstract":"Due to the measurement precision, transmission delay and so on, we could only obtain uncertainty position information of moving objects. Spatial uncertainty trajectory data is uncertain in location of mobile objects. It is leading to the challenge of modeling uncertainty trajectory data and mining usable knowledge about movement pattern. In this paper, we propose a two-stages dynamic division method to dealing with the spatial uncertainty trajectory. The approach presents the notions of adjacent boundary cells and shared cells, and merges these cells into basic cells through distance and density membership degree. A comprehensive performance study on synthetic datasets shows that the proposed method in both effectiveness and scalability.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124128079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An optimization method of the NMF algorithm initialization is proposed. This optimization method can be easily integrated with the existing initialization methods of NMF algorithm. The strategy is based on the geometric interpretation of NMF, in the convex hull, the intersection point between the connect of two points and the corresponding boundary of probability simplex, is used to update the initial basis vectors corresponding point in the matrix, so that the base vector matrix extended, and it can better contain the original matrix. Many numerical examples show that, compared with the original initialization, this method can obtain better results.
{"title":"An Optimal Method for the Initialization of Non-negative Matrix Factorization (NMF) ⋆","authors":"Hao Xie, Juan Qiu, Chuanlin Zhang","doi":"10.12733/JICS20105569","DOIUrl":"https://doi.org/10.12733/JICS20105569","url":null,"abstract":"An optimization method of the NMF algorithm initialization is proposed. This optimization method can be easily integrated with the existing initialization methods of NMF algorithm. The strategy is based on the geometric interpretation of NMF, in the convex hull, the intersection point between the connect of two points and the corresponding boundary of probability simplex, is used to update the initial basis vectors corresponding point in the matrix, so that the base vector matrix extended, and it can better contain the original matrix. Many numerical examples show that, compared with the original initialization, this method can obtain better results.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115697177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Classifying performance evaluation is one of open problems in data mining and machine learning fields. We note that nearly all the existing evaluation measures ignore the predicted probabilities which are greatly significant in the process of classifiers’ evaluation. In this paper, we construct a weighted confusion matrix to reflect the information on predicted probabilities. In addition, based on the weighted confusion matrix, traditional evaluation measures, such as accuracy, precision, recall, F-measure, are redefined to taking predicted probabilities into account. Finally, properties of the re-written evaluation measures are investigated. Experimental results show that the re-defined evaluation measures are superior to traditional ones in terms of discrimination.
{"title":"Classification of Evaluation Measurement Taken Predicted Scores into Account","authors":"T. Ding, Xiong-fei Li","doi":"10.12733/JICS20105555","DOIUrl":"https://doi.org/10.12733/JICS20105555","url":null,"abstract":"Classifying performance evaluation is one of open problems in data mining and machine learning fields. We note that nearly all the existing evaluation measures ignore the predicted probabilities which are greatly significant in the process of classifiers’ evaluation. In this paper, we construct a weighted confusion matrix to reflect the information on predicted probabilities. In addition, based on the weighted confusion matrix, traditional evaluation measures, such as accuracy, precision, recall, F-measure, are redefined to taking predicted probabilities into account. Finally, properties of the re-written evaluation measures are investigated. Experimental results show that the re-defined evaluation measures are superior to traditional ones in terms of discrimination.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123054721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiming Niu, Feng Liu, Shaoyong Guo, Liang Han, Chun Zhang
Software defined network is applied to improve the transmission efficiency. In this environment, how to optimize the distribution of network traffic is solved in this paper. Firstly, a dual optimization problem is proposed to improve software defined network throughout and alleviate the congestion. Secondly, the algorithm is designed with dual theory in hybrid software defined network. At last, we use network throughout as evaluation index with different topology structure and network traffic in simulation.
{"title":"A Dual Optimization Mechanism for Traffic Engineering in the Hybrid SDN","authors":"Qiming Niu, Feng Liu, Shaoyong Guo, Liang Han, Chun Zhang","doi":"10.12733/JICS20105579","DOIUrl":"https://doi.org/10.12733/JICS20105579","url":null,"abstract":"Software defined network is applied to improve the transmission efficiency. In this environment, how to optimize the distribution of network traffic is solved in this paper. Firstly, a dual optimization problem is proposed to improve software defined network throughout and alleviate the congestion. Secondly, the algorithm is designed with dual theory in hybrid software defined network. At last, we use network throughout as evaluation index with different topology structure and network traffic in simulation.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124794411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To learn the distribution of outcomes from previously ambiguity information for multi-attribute decision making, a novel associative Evidential Chains (ECs)-based fusion reasoning method was proposed. The convex combination of probabilistic beliefs from multiple refined ECs were induced by the proposed model based on multiple criteria linear programming. Its solution for the query case has varied flexibility with the similarity matrix derived from the historical ECs. Results of the applications with the benchmark cardiac diagnostic data sets verify that the proposed method is effective and interpretable.
{"title":"Ambiguity Multi-attribute Decisions with Evidential Chains-based Fusion Reasoning Method","authors":"Jian-min Shen","doi":"10.12733/JICS20105635","DOIUrl":"https://doi.org/10.12733/JICS20105635","url":null,"abstract":"To learn the distribution of outcomes from previously ambiguity information for multi-attribute decision making, a novel associative Evidential Chains (ECs)-based fusion reasoning method was proposed. The convex combination of probabilistic beliefs from multiple refined ECs were induced by the proposed model based on multiple criteria linear programming. Its solution for the query case has varied flexibility with the similarity matrix derived from the historical ECs. Results of the applications with the benchmark cardiac diagnostic data sets verify that the proposed method is effective and interpretable.","PeriodicalId":213716,"journal":{"name":"The Journal of Information and Computational Science","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127671210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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