Applied Mechanics and Materials Vols. 380-384

Abstract: t is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference. The algorithm proposed in this paper has a certain practical value.

Abstract: Cooperative spectrum sensing is a key technology to tackle the challenges such as fading or hidden terminal problem in local spectrum sensing of cognitive radio system. Conventional cooperative method can improve the detection performance in some sense, but increase overhead of control channel. In order to reduce the overhead, a new cooperative spectrum sensing algorithm based on confidence level is proposed. In this algorithm, the maximum-eigenvalue-based detection scheme is carried out to obtain the local spectrum detection and the detection probability and false alarm probability of each secondary user are used to estimate the reliability of the sensing decision. The test statistic of the secondary users with high reliability are chosen and sent to fusion center. Then weighted factors of chosen secondary users are derived from creditability values, and the global decision is made by weighted fusion at fusion center. The simulation results show that the proposed algorithm improves the detection probability in the guarantee of the false-alarm probability close to 0 and saves half of the overhead in the control channel.

Abstract: The search of the attribute reduction algorithm of rough set in incomplete decision table is a research hot spot. Though analysis of the advantages and disadvantages of the existing attribute reduction algorithms,we put forward a definition of relative discernibility matrix base on the positive area. Then we compute the tolerance class with the the idea of cardinal number sorting method, giving a quick heuristic algorithm of attribute reduction with theconditional entropy and relative discernibility matrix, which of the time complexity is in the worst case. The test result shows that the algorithm can obtain an attribute reduction efficiently.

Abstract: This paper presents an optimization method about multiple evaluation function with hybrid particle swarm with constraints on the base of an optimization algorithm of hybrid particle swarm, which is used to solve the problem of multi-agent collaboration in the rescue simulation system. The optimization process uses a variety of evaluation function and also calculates the constraint relationship among the evaluation functions on the particle iterative process in order to obtain multi-objective optimization results that meet multiple conditions. The method is suitable for the collaborative problem among a variety of heterogeneous agents, which presents the collaboration among heterogeneous agents through constraints. The method proves to be effective in the practical application of the rescue simulation system.

Abstract: The theme of The Sound and the Fury is a constancy theme in the literature world. It is embodied in the declination of the Compson family. It is also a theme that produced in the transformation of the entire world from the traditional to the modern. The text reveals the nature of the tragedy through Sartre Libertarian, Heidegger's phenomenology of death, Fromm and Marx's theory of alienation. The principal component analysis is used to analyze the tragedy in the wor that produced in man's own survival predicament process of eigenvalues and eigenvectors. Simultaneously it analyzes the coupling relationship of the influence factors of tragedy. We can discover the true meaning of tragedy related to the Existential of death. To a certain extent, we can provide a theoretical basis and technical support for the appreciation and literary value of The Sound and the Fury.

Abstract: Parameter Identification is the key technology in measurement-based load modeling. In order to identify parameters in power system ,the differential method which is based on the multiple curves fitting and interpolated method are compared in the paper. Numerical results illustrate that the differential method can improve the accuracy for load modeling parameter identifications.

Abstract: The international oil price has fluctuated in a relatively large amplitude fluctuation in recent years, so the accurately prediction of the crude oil price is very important for a country and a company. There are a lot of means to forecast the trend of things, but if the problem is uncertain and the information is lacking, grey system theory (GST) is an efficient method. In this work we forecast the international crude oil price by using the grey system theory and creating a MATLBA program to achieve it. In order to improve the prediction accuracy, we modified the prediction results.

Abstract: This paper presents a probabilistic data stream clustering method P-Stream. An effective clustering algorithm called P-Stream for probabilistic data stream is developed in this paper for the first time. For the uncertain tuples in the data stream, the concepts of strong cluster, transitional clusters and weak cluster are proposed in the P-Stream. With these concepts, an effective strategy of choosing candidate cluster is designed, which can find the sound cluster for every continuously arriving data point. In this paper, we systematically defined the dataspace, the uncertain data, and proposed a updated algorithm of queries on uncertain data based on Effective Clustering Algorithm.

Abstract: In order to solve the problem of ignoring the correlation between class labels, this paper describes a new method for multi-label classification based on the frequent item sets to classify an unseen instance on the basis of its k nearest neighbors ( MLFI-KNN). For each unseen instance, MLFI-KNN takes its k-nearest neighbors in the training set and counts the number of occurrences of each label in this neighborhood, and then utilizes the FP-growth algorithm to obtain the frequent item sets between the labels that these neighboring instances include, in order to determine the predicted label set. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach as compared to some existing well-known methods.

Abstract: The computation of JND is very complex, which makes it difficult to embed it into integrated circuits. To solve this problem, Haar-DWT based JND model is exploited and its corresponding pipeline architecture is developed in this paper. To evaluate its performance, the architecture is modeled with hardware description language, and implemented by SMIC 0.18um technology. The area of JND core is 42052 gates, which is significantly smaller than the full band JND based architecture. From the experiment results, the system goes on well at 161 MHz and achieves 78% time saving compared with the full band JND based architecture.