Advanced Materials Research Vols. 756-759

Abstract: This paper presents a novel method to extract edge lines from point clouds of these eroded, rough fractured fragments. Firstly, a principal component analysis based method is used to extract feature points, followed by clustering of these feature points. Secondly, a local feature lines fragment is constructed for each cluster and afterwards a smooth and noise pruning process for each local feature lines fragment. Thirdly, these separated local feature lines fragments are connected and bridged in order to eliminate the gaps caused by the eroded regions and construct completed global feature lines. Fourthly, a noise pruning process is performed. The output of this method is completed, smoothed edge feature lines. We illustrate the performance of our method on a number of real-world examples.

Abstract: This paper discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation using the root multiple signal classification (MUSIC) algorithm in a bistatic multiple input and multiple output (MIMO) radar. The proposed algorithm gets the estimation of DOA and DOD via computing the roots of polynomials and it avoids the spectral peak searching in the conventional MUSIC algorithm. Thus the Root-MUSIC algorithm has much lower computational load. Simulation results illustrate our proposed algorithm has better angle estimation performance than the conventional algorithms.

Abstract: Considering the uncertainty of recovery and remanufacturing productivity rates in the reverse logistics remanufacturing process, we describe it by adopting the discrete scenarios of non-probabilistic and establish a multi-objective remanufacturing reverse logistics LRP robust model based on the NPRO.Lingo10 is used to solve the specific example, and then compared with the optimization values of objective function under the corresponding certain environment. The results verify the robustness of the model.

Abstract: The method of Principal Component Analysis (PCA) needs to convert image matrix to high-dimensional column vector used in feature extraction. The 2-dimensional PCA (2DPCA) offsets disadvantages of PCA. However, 2DPCA compresses image along the rows or columns only, the number of features is still large. In order to solve the above problems, bidirectional 2DPCA was used to compress image matrix along row and column meanwhile, then use PCA reduce the number of computations and feature dimensions. Three kinds of ground static military targets images acquired by SAR were used as the experimental data. The experimental result shows that, the method of SAR image recognition presented by this paper reduced the dimensions of feature matrix and raised the recognition rate.

Abstract: The bidirectional 2DPCA (two-dimensional principal component analysis) method for SAR images recognition, can compress the columns and rows of images matrix and reduce the number of feature dimensions. However, it fails to use high order statistics information of image data, neglects the nonlinearity correlation between pixels. Therefore, this paper presents the method combined bidirectional 2DPCA with KPCA (Kernel Principal Component Analysis). This method not only compresses the dimensions of images data, but develops the superiority of KPCA in describing correlation between many pixels. Experimental results show that: this method can decrease calculated amount and raise recognition rate of SAR target effectively.

Abstract: Current advanced Fuzzing technique can only implement vulnerability mining on a single vulnerable statement each time, and this paper proposes a new multi-dimension Fuzzing technique, which uses niche genetic algorithm to generate test cases and can concurrently approach double vulnerable targets with the minimum cost on the two vulnerable statements each time. For that purpose, a corresponding mathematical model and the minimum cost theorem are presented. The results of the experiment show that the efficiency of the new proposed Fuzzing technique is much better than current advanced Fuzzing techniques.

Abstract: Based on traditional MFCC feature, this paper suggests a new kind of speech signal feature: CMFCC by introducing the method of nonlinear properties. Simulation results indicate that the method has a strong robust to noise and is able to enhance the recognition rate under low SNR.

Abstract: Feature extraction and the classifier design were the crucial parts for image recognition of insect pests on agriculture field crops. The hardware of the detection device for insect pests included the trapping, stunning and buffering unit, the even illumination unit, the scattering and transporting unit, and the image vision unit. The seven morphological features from binary images of the insect pests were extracted and normalized, such as area, perimeter, and complexity. The standard vector model library and the membership functions were established based on the feature mean and the feature standard deviation of the nine species of pests. The fuzzy decisions were analyzed based on the fuzzy principle of the minimum and maximum membership degree. The results showed that the fuzzy classifier could identify the nine species of pests that harmed seriously, and the recognition accuracy was over 86%.

Abstract: The context quantization forsource based on the modified K-means clustering algorithm is present in this paper. In this algorithm, the adaptive complementary relative entropy between two conditional probability distributions, which is used as the distance measure for K-means instead, is formulated to describe the similarity of these two probability distributions. The rules of the initialized centers chosen for K-means are also discussed. The proposed algorithm will traverse all possible number of the classes to search the optimal one which is corresponding to the shortest adaptive code length. Then the optimal context quantizer is achieved rapidly and the adaptive code length is minimized at the same time. Simulations indicate that the proposed algorithm produces better coding result than the result of other algorithm.

Abstract: The study constructs a stop schedule model for high-speed railway. A greedy-based algorithm is proposed and the problem is divided into 3 stages to decrease its difficulty to an acceptable extent. Some greedy principles are applied and series of optimal models are constructed to solve the problem. The result of the sample shows that the models and algorithms in the study are effective.