Advanced Materials Research Vol. 811

Abstract: This paper proposes a method based on the spectra response of IR detectors mounted on thermographic camera for emissivity measurement at various target surface temperatures, while the reflected temperature istaken into account, and also studies on the effect of surface roughness on the emissivity value. The emissivity (ε8-14μm) of general engineering material such as iron, stainless steel, brass, copper and aluminum obtained in this paper are in agreement with other literatures. Finally, results found that the roughness and emissivity of equipment increases with increasing of the operating time.

Abstract: The calibration algorithm for circular array in the presence of amplitude and phase error is researched deep exploiting the assistant sensors. In order to calibrate the amplitude and phase error for the circular array, in this paper, two setting methods of assitant elements are presented, i.e., assitant elements are setted on the coordinate axis and on circle. Simulation experiments results show that the two proposed setting methods are effective in the presence of amplitude and phase error for circular array, and has low computational burden and high estimation precision of angle and amplitude and phase error.

Abstract: This paper presents a real-time implementation of 3D acquisition for reading text and inspection the metallic surface based on light sectioning. A measurement is achieved with a standard low cost CMOS camera. Surface defects are modeled as deviations in the local relief from a smooth approximation of the surface. Discrete orthogonal bases are used to generate a smoothed global model of the surface structure. Modified discrete Tchebychev polynomials are used as orthogonal basis functions to perform least square approximations of the geometry. QR decomposition is used to obtain a unitary basis, minimizing the numerical effort when modeling surfaces. The result of test measurements on copper sheets in a production environment is presented to demonstrate the surface inspection. Another result is shown the readable character on the textured metallic surface after the proposed processing. A prototype system of the laser scanning instrument can be implemented in a production line as well.

Abstract: We evaluate statistical and machine learning methods for half-hourly 1-step-ahead electricity demand prediction using Australian electricity data. We show that the machine learning methods that use autocorrelation feature selection and BackPropagation Neural Networks, Linear Regression as prediction algorithms outperform the statistical methods Exponential Smoothing and also a number of baselines. We analyze the effect of day time on the prediction error and show that there are time-intervals associated with higher and lower errors and that the prediction methods also differ in their accuracy during the different time intervals. This analysis provides the foundation for construction a hybrid prediction model that achieved lower prediction error. We also show that an RBF neural network trained by genetic algorithm can achieved better prediction results than classic one. The aspect of increased transparency of networks through genetic evolution development features and granular computation is another essential topic promoted by knowledge discovery in large databases.

Abstract: In this paper an improved clustering analysis algorithm is proposed on the basis of randomly selected data in the CURE algorithm and the cluster centers setting in the K-NN algorithm. The combination of the two algorithms conquers the poor clustering accuracy in the CURE algorithm and the clustering deficiency on large data set in the K-NN algorithm. This paper first introduces the concept of clustering analysis and its real-life applications, then proceeds to describe the method of clustering analysis, and then introduces optimized CURE_KNN algorithm described in pseudo-code, at last the advantages of the algorithm are summarized.

Abstract: In this paper, based on the combination of Genetic algorithm and BP algorithm, a new algorithm is proposed in this paper. The BP operator is embedded in the genetic operation in the algorithm, the algorithm effectively assimilates the global optimization of genetic algorithm and fast convergence of BP algorithm, and it encodes the construction and the weights hybrid with real code and binary code, achieving the same step optimization of structure and weights. The simulation results show that, the new algorithm can quickly converge to the global optimal solution, but also can obtain the best approximation of weights in the network structure.

Abstract: Aiming at color images under complex background, this paper put forward a face detection algorithm based on skin color segmentation, combining the geometric characteristics. The skin region can be obtained by using skin color model and OTSU method to automatically optimize threshold segmentation image. By analyzing the characteristics of skin color region, the face position is determined by criterion of ellipse area.

Abstract: The paper introduces deburr algorithm before line trace in the course of vector quantization of binary map image, and it can receive better effect through applying for the algorithm.

Abstract: Authentication more prominent characteristics of self-control, identify the relative characteristics and identification, data analysis more difficult, phase synchronization method of this paper, a set of EEG data collected for analysis to each experimenter as a sample set for feature extraction, and then in a group of experimenters internal identification, the recognition rate up to 45%, and the use of event evoked potentials for the accuracy of the identification has a large gap, but in some less demanding recognition rate under a certain value.

Abstract: In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.