A customer behavior analysis algorithm based on swarm intelligence is proposed. Firstly, customer consumption patterns are randomly projected on a plane. Then, clustering analysis is processed by a clustering method b...
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A customer behavior analysis algorithm based on swarm intelligence is proposed. Firstly, customer consumption patterns are randomly projected on a plane. Then, clustering analysis is processed by a clustering method based on swarm intelligence with different swarm similarity coefficients. Finally, the clustering customer groups with various consume characteristics are collected from the plane by a recursive algorithm. A parallel strategy is also proposed. It improves the scalability of the algorithm. The data of telecom mobile customer consumption are used in the experiment. The results are comp.red with the results obtained by other clustering methods such as k-means algorithm and self-organizing maps algorithm. The comp.rison shows that this customer behavior analysis algorithm based on swarm intelligence meets the demands of customer clustering and classifying of customer relationship management. Especially, on the aspect of master customer analysis and one to one sell analysis, the algorithm shows the advantages of visualization, self-organization and clusters with distinct characteristics.
An idea of virtual information source and the formation of data comp.ession, based on a virtual information source is put forward and a general virtual information source Y of long character bunch of 0 and 1 is set up...
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An idea of virtual information source and the formation of data comp.ession, based on a virtual information source is put forward and a general virtual information source Y of long character bunch of 0 and 1 is set up. Then the model of the virtual information source Y is established by neural network and the lossless data comp.ession based on virtual information source is constructed with the model and an integer function. The experiments show that the comp.ession ratio achieved maybe is 3-1. The data comp.ession embodies the idea of modeling of a virtual information source. It differs from the old entropy coding(lossless) and can comp.ess some data which are comp.essed by the old entropy coding. Some examples of data comp.ession of high comp.ession ratio and hifi can be obtained by combined wavelet coding with the comp.ession method.
Web testing is an important method to ensure the quality of Web applications. Since the Web applications have the characters of distributed, dynamic, interactive, hypermedia, there are numerous of contents needing tes...
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Web testing is an important method to ensure the quality of Web applications. Since the Web applications have the characters of distributed, dynamic, interactive, hypermedia, there are numerous of contents needing test and the testing tasks are very tedious. So automatic or semi-automatic testing is needed in order to improve the testing efficiency. In this paper, the Agent technology is chosen to assist the testing tasks. Firstly the characters of Web application testing are deeply analyzed for explaining the necessity of adapting the Agent technology. Furthermore the relevant contents to the Agent technology such as the realization methods and application scenes are presented, and the feasibility of applying Agent into the Web testing is also analyzed. Next, the architecture and methods in the Web application testing based on Agent are given, and three scenes are presented at the same time, i.e. applying Agent to the performance testing and usability testing for Web applications along with the automatic execution and monitor for test cases based on Agent. Due to the characters of autonomy, cooperation and learning, Agent is used to the Web performance testing, usability testing and the executing process. So the testing effect can be improved obviously, and the automatic and intelligent level of testing can be enhanced at the same time.
Dynamic network-based decision systems search the information in the distributed databases and provide an appropriate solution for the design-supplier-manufacturing planning problem using evolutionary algorithms. The ...
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Dynamic network-based decision systems search the information in the distributed databases and provide an appropriate solution for the design-supplier-manufacturing planning problem using evolutionary algorithms. The paper focuses on the development of a performance model to support such enterprise-level decision-making in network based scalable systems. Generalized Stochastic Petri Nets (GSPNs) are introduced to characterize network traffic and evolutionary algorithms. The network traffic model is based on the hyperexponential transition for analytical tractability. The algorithm model transforms the execution of the program into a stochastic activity net. The performance evaluation of the system can be explored in two directions: first, analyze and reconfigure the network connection for a specific algorithm, and second, given the network configuration, predict the performance of the algorithm. The results show that transient analysis is more important than steady-state analysis in the heavy-tailed network traffic. The paper also comp.res performance of the algorithms under different network configurations.
Data exceptions often reflect potential problems or dangers in the management of corporation. Analysts often need to identify these exceptions from large amount of data. A recent proposed approach automatically detect...
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Data exceptions often reflect potential problems or dangers in the management of corporation. Analysts often need to identify these exceptions from large amount of data. A recent proposed approach automatically detects and marks the exceptions for the user and reduces the reliance on manual discovery. However, the efficiency and scalability of this method are not so satisfying. According to these disadvantages, the optimization is investigated to improve it. A new method that pushes several constraints into the mining process is proposed. By enforcing several user-defined constraints, this method first restricts the multidimensional space to a small constrained-cube and then mines exceptions on it. Experimental results show that this method is efficient and scalable.
Two improved Elman neural networks, output-input feedback Elman network and output-hidden feedback Elman network are presented based on the Elman neural network. By using the output-input feedback Elman network as a p...
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Two improved Elman neural networks, output-input feedback Elman network and output-hidden feedback Elman network are presented based on the Elman neural network. By using the output-input feedback Elman network as a passageway of the error back propagation, a recurrent back propagation control neural network model is developed. The stability of the improved Elman neural networks is proved in the sense of Lyapunov stability theory. The optimal adaptive learning rates are obtained, which can guarantee the stable convergence of the improved Elman networks. The ultrasonic motor is simulated by using the Elman and improved Elman networks respectively. Besides simulating the speed of the ultrasonic motor successfully, some useful results are also obtained. According to the results, the different network models based on the sampling situation in the fieldwork can be chosen. Numerical results show that the recurrent back propagation control neural network controller has effectiveness for various kinds of reference speeds of the ultrasonic motor.
Agile electronics manufacturing requires integrated design, supply and manufacturing planning for modular products where suppliers and manufacturing resources are network distributed. This research is concerned with t...
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Agile electronics manufacturing requires integrated design, supply and manufacturing planning for modular products where suppliers and manufacturing resources are network distributed. This research is concerned with the modeling of such a distributed manufacturing network. The performance of network-based distributed decision systems is dominated by the network configuration and related access delays. Recent traffic measurement studies observed heavy-tails in the network traffic. Our basic approach is based on the quantile match to fit hyperexponential distributions to heavy-tailed distributions. We adopt the ON/OFF network traffic model and comp.re the fitted hyperexponential model with the heavy-tailed model. The results validate our fitting hyperexponentials. Generalized Stochastic Petri Nets (GSPNs) are introduced to analyze both the steady state and the transient behaviors of the distributed system. The results indicate that the transient analysis is more important than the steady-state analysis in network traffic modeling, since in most cases the distributed network does not reach the steads state.
A technique based on regularization method and restores image to close-to-optimal is proposed. The less the energy of the regularized residue, the better the image restoration. Based on the idea, wavelet transform is ...
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A technique based on regularization method and restores image to close-to-optimal is proposed. The less the energy of the regularized residue, the better the image restoration. Based on the idea, wavelet transform is employed to choose regularization operator qualitatively, and stochastic theory is used to calculate the expectation of the energy, by minimizing the expectation to determine regularization parameter. Qualitative analysis concludes that the regularization operator should be low-stop and high-pass, and the experimental results show that the performances of this method are better than the traditional methods and yields steadily close-to-optimal restoration.
An algorithm based on communication algorithm in the Mogent system is presented. In this algorithm, address registration and multicasting are used by means of address-book so that it is more efficient and suitable for...
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An algorithm based on communication algorithm in the Mogent system is presented. In this algorithm, address registration and multicasting are used by means of address-book so that it is more efficient and suitable for many kinds of patterns of migration and communication and has a better solution to the troubles with mobile Agent communication.
Video clip retrieval plays a critical role in the content-based video retrieval. Two major concerns in this issue are: (1) automatic segmentation and retrieval of similar video clips from video database;(2) similarity...
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Video clip retrieval plays a critical role in the content-based video retrieval. Two major concerns in this issue are: (1) automatic segmentation and retrieval of similar video clips from video database;(2) similarity ranking of similar video clips. Motivated by the maximal matching and optimal matching in graph theory, a novel approach is proposed for video clip retrieval based on matching theory. To tackle the clip segmentation and retrieval, the retrieval process is divided into two phases: shot-based retrieval and clip-based retrieval. In shot-based retrieval, a shot is temporally partitioned into several sub-shots based on motion content. The similarity among shots is measured according to the color content of sub-shots. In clip-based retrieval, candidates of similar video clips are selected by modeling the continuity of similar shots. Maximal matching based on Hungarian algorithm is then adopted to obtain the final similar video clips. To rank the similarity of the selected video clips, four different factors: visual similarity, granularity, interference and temporal order of shots are taken into consideration. These factors are modeled by optimal matching based on Kuhn-Munkres algorithm and dynamic programming. Experimental results indicate that the proposed approach is effective and efficient in retrieving and ranking similar video clips.
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