With the rapid development of the network technology and information age, library information digitization is particularly important. Personalized service is an important part of the library digitalization, which can ...
详细信息
With the rapid development of the network technology and information age, library information digitization is particularly important. Personalized service is an important part of the library digitalization, which can make library work more effectively. Based on the clustering algorithm, the push service of personalized resources in library was studied. In this paper, the basic principle of clustering algorithm was expounded;the clustering analysis method was analyzed;based on this, the library database was analyzed;a library of personalized resources push system was constructed. Through system testing and evaluation, it was proved that the system can realize the push service to the user's personalized resources.
Computer aided process planning (CAPP) is an important bridge between computer aided design (CAD) and computer aided manufacturing (CAM) in computer integrated manufacturing environment. Operation sequence generation ...
详细信息
Computer aided process planning (CAPP) is an important bridge between computer aided design (CAD) and computer aided manufacturing (CAM) in computer integrated manufacturing environment. Operation sequence generation is one of the most difficult tasks in CAPP. The aim of operation sequencing in CAPP is to determine the best order of machining operations with minimal manufacturing cost while satisfying all the precedence constraints. This paper presents a proposed method for optimizing operation sequence using modified clustering algorithm. The key concept of method is that the precedence constraints are firstly checked for selecting all possible next operations of the last operation in the sequence and their traveling costs are compared to choose the optimal feasible operation which has the minimum traveling cost in the sequence. Then, all operation sequences are calculated the total traveling cost for obtaining the optimal sequence result. Because of removing all unfeasible sequences at the beginning of procedure and selecting the optimal operation into sequence in each step, the time can be significantly reduced. The capability and performance of the proposed method are demonstrated in three specific case studies. The comparisons show that the proposed method can solve the problem in much lesser computational time while generating more alternate optimal feasible sequences than previous algorithms.
clustering algorithm has been widely used in refined oil marketing strategy system, but there are certain shortcomings in sales productivity and diversity means. Thereby, it is necessary to further improve refined oil...
详细信息
clustering algorithm has been widely used in refined oil marketing strategy system, but there are certain shortcomings in sales productivity and diversity means. Thereby, it is necessary to further improve refined oil marketing strategy system based on clustering algorithm. A unified strategic framework was proposed in the paper, which could make marketing strategy options gain financial returns on the basis of trading, and combine the change of company client assets with customer life cycle. The life cycle and purchase frequency data of all the customers in the industry were summarized, and a set of fast and effective refined product marketing strategy system was designed, which improved the efficiency of sales.
With the rapid development of all aspects of our country, there are numerous data in each field. How to deal with the data quickly and effectively is the problem we are facing currently. Based on this, research on the...
详细信息
With the rapid development of all aspects of our country, there are numerous data in each field. How to deal with the data quickly and effectively is the problem we are facing currently. Based on this, research on the large data particle clustering algorithm based on the minimum variance response was carried out in this paper. First, the concept of minimum variance response was introduced, and then large data particle clustering algorithm was explained. Ultimately, the construction of large data particle clustering algorithm based on the minimum variance response was described in detail. The results indicate that the large data particle clustering algorithm based on the minimum variance response can effectively meet the requirements of data search and mining.
The aim of this study is to present a new spatial clustering process for time series data. It has become an important and demanding application when the data involves chronological long time series and huge datasets. ...
详细信息
The aim of this study is to present a new spatial clustering process for time series data. It has become an important and demanding application when the data involves chronological long time series and huge datasets. A great challenge in clustering is to achieve an optimal solution in searching similarity along the series. Furthermore, it also involves a very large-scale data analysis. Unfortunately, the existing clustering time series algorithms have become impractical since data do not scale properly for longer time series. The performance of the clustering algorithm gets even worse if it relies on actual data and many clustering algorithms are often faced with conflict in handling high dimensional data. In the case of spatial time series, the problem can be solved by unsupervised approaches rather than supervised classification, with appropriate preprocessing techniques to transform the actual data. The unsupervised solution using time series clustering algorithms is capable to extract valuable information and identify structure in complex and massive datasets as spatial time series. Therefore, a clustering algorithm by introducing data transformation using X-means data splitting is proposed to investigate the spatial homogeneity of time series rainfall data. The hierarchical clustering was used to demonstrate the similarity once the data was divided into training and testing sets. The proposed algorithm is compared with five types of data transformation techniques, namely mean and median in monthly data and the rest is in daily data such as binary, cumulative and actual values. Results indicate that data transformation using X-means data splitting in hierarchical clustering outperformed other transformation techniques and more consistent between training and testing datasets based on similarity measures.
Hyperspectral imaging technology is used to sort varieties of seeds. However, the overall performance of prediction models decreases when they are used to test the same variety of seeds from different years or seasons...
详细信息
Hyperspectral imaging technology is used to sort varieties of seeds. However, the overall performance of prediction models decreases when they are used to test the same variety of seeds from different years or seasons. Prediction accuracy is susceptible to the influence of time and thus depends on the training set used to build the model. In this study, a model updating procedure of hyperspectral imaging data for classification of maize seeds using a clustering algorithm was proposed to maintain the accuracy and robustness of the model. A total of 2000 seeds of four typical maize varieties grown in China in three different years were used for classification based on a least-squares support vector machine classifier. After determining and applying the model parameters, the updated model achieved an overall accuracy rate of 98.3%, which is higher than the 84.6% accuracy obtained using the non-updated model. The accuracy rate of the updated model was 94.8% when testing with the Kennard-Stone algorithm, which is commonly used for selecting datasets. The proposed model updating method can successfully update seed data for cross-year model building and thus improve the overall accuracy for predicting of maize seeds harvested in different seasons.
Cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-m...
详细信息
Cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-means algorithm was adapted for directional data. In the literature, several methods have been used for the clustering of directional data. Due to the use of trigonometric functions in these methods, clustering is performed by approximate distances. As opposed to other methods, the FCM4DD uses angular difference as the similarity measure. Therefore, the proposed algorithm is a more consistent clustering algorithm than others. The main benefit of FCM4DD is that the proposed method is effectively a distribution-free approach to clustering for directional data. It can be used for N-dimensional data as well as circular data. In addition to this, the importance of the proposed method is that it would be applicable for decision making process, rule-based expert systems and prediction problems. In this study, some existing clustering algorithms and the FCM4DD algorithm were applied to various artificial and real data, and their results were compared. As a result, these comparisons show the superiority of the FCM4DD algorithm in terms of consistency, accuracy and computational time. Fuzzy clustering algorithms for directional data (FCM4DD and FCD) were compared according to membership values and the FCM4DD algorithm obtained more acceptable results than the FCD algorithm. (C) 2016 Elsevier Ltd. All rights reserved.
Techniques for analyzing genome sequences in high performance environments to predict the function and structure of a protein have been developing. The function of a protein is determined by its characteristics and th...
详细信息
Techniques for analyzing genome sequences in high performance environments to predict the function and structure of a protein have been developing. The function of a protein is determined by its characteristics and the sequence pattern, and a protein is classified as belonging to a family according to its genealogy and structure. This study determines the protein family of unknown proteins by analyzing the sequence database of the proteins, which is classified using a clustering algorithm. The analysis of the experimental clustering results verified that, by applying the proposed pf_cluster algorithm, the protein family of new proteins can be found using their sequence information.
Wireless sensor network (WSNs) have been used to achieve seamless, energy efficient, reliable and low-cost remote monitoring and control in many applications. As the energy of sensor nodes are limited, a solid energy-...
详细信息
ISBN:
(纸本)9781509041831
Wireless sensor network (WSNs) have been used to achieve seamless, energy efficient, reliable and low-cost remote monitoring and control in many applications. As the energy of sensor nodes are limited, a solid energy-efficient algorithm is necessary to improve the energy efficient in heterogeneous WSNs. In this paper, we propose an improved distributed energy efficient clustering algorithm (IDEEC) for heterogeneous WSNs. IDEEC considers the multi-level energy model. We simplify the probability threshold, improve the cluster head selection probability and optimize the estimation of the average energy of network. Simulation results confirm the performance supremacy of IDEEC compared to current clustering protocols in terms of stability period, number of messages, mean and variance of cluster heads (CHs), furthermore, IDEEC takes the minimum running time, which makes it easier to be applied in reality.
In order to improve the spectrum utilization rate of Device-to-Device (D2D) communication, we study the hybrid resource allocation problem, which allows both the resource reuse and resource dedicated mode to work simu...
详细信息
ISBN:
(纸本)9781538620625
In order to improve the spectrum utilization rate of Device-to-Device (D2D) communication, we study the hybrid resource allocation problem, which allows both the resource reuse and resource dedicated mode to work simultaneously. Meanwhile, multiple D2D devices are permitted to share uplink cellular resources with some designated cellular user equipment (CUE). Combined with the transmission requirement of different users, the optimized resource allocation problem is built which is a NP-hard problem. A heuristic greedy throughput maximization (HGTM) based on clustering algorithm is then proposed to solve the above problem. Numerical results demonstrate that the proposed HGTM outperforms existing algorithms in the sum throughput, CUEs SINR performance and the number of accessed D2D deceives.
暂无评论