Applied Mechanics and Materials Vols. 411-414

Abstract: In the modern wars under the condition of informationalization, radar signals are generally characterized by repetitive patterns in time, so it is one important task of Radar Warning Receiver (RWR) to estimate the Pulse Repetition Interval (PRI) of all radar signals by intercepting and identifying the mixed radar signals. Because each transmitted radar signal are arbitrary in general. From the view of statistic, the transmitted radar pulse train from different radars is mutual independent. So the constituted model of RWR and radar transmitters accord with blind source separation which can separate mixed signals into pure signals. A deinterleaving method using blind source separation for estimating the PRI of each radar signals is proposed. The implementation architecture of the proposed method is given. Finally, computer simulations show the proposed method can gain good performance for the estimation of PRI of radar signals.

Abstract: In view of the existing coal gangue interface identification technology use ray method that it is not applicable to the working face what it dose not contain radioactive elements in the roof or contain low amounts of radioactive elements, and the detection range of radar detection is lesser, the signal attenuation is relatively serious, so putting forward a kind of coal gangue interface recognition based on Mel frequency cepstrum coefficient of MFCC. The method using coal gangue were put down in the process of coal gangue on the difference of the characteristics of the acoustic signal recognition, first of all, using Mel frequency cepstrum coefficient will after denoising coal gangue acoustic signal transformation to the frequency domain processing, extracting the 32 d characteristic parameters of coal and gangue acoustic signal;The experimental results show that the method can accurately identify the coal gangue down state.

Abstract: Cloud computing provides means of increasing the capacity or adding the capabilities which releases the heavy data user. It provides gigantic storage for data and faster computing to the customers on the internet. It transfers database and application software from the data owner to the cloud where management and maintenance of data take place. Security of data in cloud is one of the major issues which acts as an obstacle in the development of cloud computing. In this paper, an efficient model is proposed to protect the data in the process of transferring data to the cloud and get the data from the cloud. We take many precautions and measures to guarantee the security of data. To shield owners data from the malicious third party, RSA is used to encrypt data to cipher text. Because it is difficult to search data from the encrypted data, we take the technique of index the document by the keyword and then encrypt the index and send index with encrypted original data. To check the integrity of data, digital signature is taken to identify modifications of data. This article also introduces the concrete the underlying datacenter structure named Megastore and how Megastore functions seamlessly width owners while owners store data and retrieve data from the underlying datacenter. Megastore stores fine-gained partitions of data into different datacenters and the partitioning allows us to synchronously replicate each write to across wide area with reasonable latency and support seamless failover between different datacenters.

Abstract: The novel method that singular value decomposition (SVD) is combined with ensemble empirical mode decomposition (EEMD) is proposed because of the mode mixing in empirical mode decomposition (EMD). The first step of this method is to reduce the random noise in fault signal by the SVD, and then does EEMD to restrain the mode mixing effectively. Finally, the intrinsic mode function (IMF) is done for envelope demodulation and as a result, the fault feature is extracted successfully. The implementation process was analyzed by simulation signal and this method has been successfully applied to in inner race and outer race of rolling bearing fault diagnosis. The results show that this method can extract the fault information of rolling bearing effectively and realize the precise fault diagnosis.

Abstract: Presents and analyzes the typical association rules mining algorithm and it lack, then introduces several optimization algorithms and development direction.

Abstract: An Optimized Pruning-based Outlier Detecting algorithm is proposed based on the density-based outlier detecting algorithm (LOF algorithm). The calculation accuracy and the time complexity of LOF algorithm are not ideal, so two steps are taken to reduce the amount of calculation and improve the calculation accuracy for LOF algorithm. Firstly, using cluster pruning technique to preprocess data set, at the same time filtering the non-outliers based on the differences of cluster models to avoid the error pruning of outliers located at the edge of clusters, different cluster models are output by inputing multiple parameters in the DBSCAN algorithm. Secondly,optimize the query process of the neighborhood (neighbor and k-neighbor). After pruning, local outlier factors are calculated only for the data objects out of clusters. Experimental results show that the algorithm proposed in this paper can improve the outlier detection accuracy, reduce the time complexity and realize the effective local outlier detection.

Abstract: Market competition is the competition for customers. By adopting customer segmentation model, decision makers can effectively identify valuable customers and then develop effective marketing strategy. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial centroids. An improved K-means algorithm is presented,with which K value of clustering number is located according to the clustering objects distribution density of regional space,and it uses centroids of high-density region as initial clustering center points. The proposed method makes the algorithm more effective and efficient, so as to gets better clustering with reduced complexity.

Abstract: In this study, we propose EWMA control charts to monitor two dependent process stages with attribute data. The detection ability of the EWMA control charts is compared to those of Shewhart attribute control charts and cause selecting control chart by different correlation. Numerical example and simulation study show that the EWMA control charts have better performance compared to Shewhart attribute control charts and cause selecting control charts.

Abstract: This paper studied the pricing of variance swap derivatives under the multi-factor stochastic volatility models by Monte Carlo simulation. Control variate technique was well used to reduce the variance of the simulation effectively. How to choose the high efficient control variate was also contained. Then the numerical results show the high efficiency of the speed up method. The pricing structure in the paper is also applicable for the valuation of other types of variance swaps and other financial derivatives under multi-factor models.

Abstract: In this paper, trend relational algorithm and grey-fuzzy clustering method are presented. A new algorithm for analysis the trend relational between a set of key point data with a set of prototypes is proposed. Trend relational grade can be used to divide cluster by fuzzy method. The researched results can open new prospects for the development and application of systems methodology to clustering.