Applied Mechanics and Materials Vols. 490-491

Abstract: Improved and extended level set framework with a novel iso-neigborhood concept. In the new framework, driving forces are determined by the iso-neighborhood rather than only by some exterior field outside the propagating fronts. This hybrid driving forces make the propagation of the active contour more robust. And furthermore the new framework will be very flexible to various kinds of images by defining different type of sampling algorithm in the iso-neighborhood.

Abstract: Biometric identification has advanced vastly since many decades ago. It became a blooming area for research as biometric technology has been used extensively in areas like robotics, surveillance, security and others. Face technology is more preferable due to its reliability and accuracy. By and large, face detection is the first processing stage that is performed before extending to face identification or tracking. The main challenge in face detection is the sensitiveness of the detection to pose, illumination, background and orientation. Thus, it is crucial to design a face detection system that can accommodate those problems. In this paper, a face detection algorithm is developed and designed in LabVIEW that is flexible to adapt changes in background and different face angle. Skin color detection method blending with edge and circle detection is used to improve the accuracy of face detected. The overall system designed in LabVIEW was tested in real time and it achieves accuracy about 97%.

Abstract: Software Reliability Growth Models (SRGMs) provide techniques to predict future failure behavior from known characteristics of the software testing work. However, in some cases, software developers did not have sufficient historical data to estimate the corresponding reliability and the expected testing cost, especially for a newly developed software project, and thus the results obtained from analytical models may not be reliable. In such situations, Bayesian analysis is a reasonable approach to additionally take expert's opinions into account for better decision making. In this paper, we utilized Yamada Delayed S-shaped Model with Bayesian analysis in predicting software reliability and expected testing costs to determine an optimal release time for software systems. Besides, the failure process of software are assumed to be drawn from a non-homogeneous Poisson process (NHPP), and the parameters of the proposed model are assumed to be mutually independent and Gamma distributed. Finally, a numerical example is given to verify the effectiveness of the proposed approach, and the sensitive and risk analyses are performed in light of the numerical example.

Abstract: Grinding mill power consumption is one of the largest operations, grinding process of economic indicators for the beneficiation great influence. Grinding system has a complex mechanism, strong coupling, the process, many factors, nonlinear, large delay and time-varying characteristics, so the effect of conventional control methods are not ideal. In this paper, the dynamic optimization using extreme optimization algorithm, to achieve reasonable control of the mill load, the mill operating at optimum load point, thereby improving mill efficiency, improve mill output.

Abstract: Mobile robot vision system based on image information on environment, to make it automatic separation from obstacles and achieve precise mathematical description of obstacles, we construct detection model which combined by the frame difference method and background subtraction for target detection, comprehensive utilization of the main idea of three frame difference image method, the background subtraction and frame difference method combined to complement each other, thereby overcoming each other's weaknesses and improving the effect of target detection, experiment results show that this method can effectively improve the efficiency of target detection.

Abstract: This study presents a driver identification system using voice analysis for a vehicle security system. The structure of the proposed system has three parts. The first procedure is speech pre-processing, the second is feature extraction of sound signals, and the third is classification of driver voice. Initially, a database of sound signals for several drivers was established. The volume and zero-crossing rate (ZCR) of sound are used to detect the voice end-point in order to reduce data computation. Then the Auto-correlation Function (ACF) and Average Magnitude Difference Function (AMDF) methods are applied to retrieve the voice pitch features. Finally these features are used to identify the drivers by a General Regression Neural Network (GRNN). The experimental results show that the development of this voice identification system can use fewer feature vectors of pitch to obtain a good recognition rate.

Abstract: Top-N queries are employed in a wide range of applications to obtain a ranked list of data objects that have the highest aggregate scores over certain attributes. The threshold algorithm (TA) is an important method in many scenarios. However, TA is effective only when the ranking function is monotone and the query point is fixed. In the paper, we propose an approach that alleviates the limitations of TA-like methods for processing top-N queries. Based on p-norm distances as ranking functions, our methods utilize the fundamental principle of Functional Analysis so that the candidate tuples of top-N query with a p-norm distance can be obtained by the Maximum distance. We conduct extensive experiments to prove the effectiveness and efficiency of our method for both low-dimensional (2, 3 and 4) and high-dimensional (25,50 and 104) data.

Abstract: In recently years, Electrical Impedance Tomography caused concern for people in the areas such as non-destructive testing and structural health monitoring. This paper took the disk structure composed of carbon fiber felt for example, designed and built the experiment platform for Electrical Impedance Tomography in a couple of different ways: one was traditional and the other was distributed acquisition system based on wireless sensor network. The imaging result showed that the data collected from wireless distributed system were more accurate and efficient.

Abstract: The due date set is affected by the coordination and cooperation between enterprise and customers and due date requirement and sensitivity degreed of different types of customers are different. In order to analyze more complex situations, the problem and its model including two vendors, one manufacturers and consumers is studied in the paper. Firstly, the model is built and analyzed in view of the vendors, and the optimal due date is obtained after calculation. The analysis results show that there is not any relationship between the order function and the demand quantity, the demand quantity does not affect the order period. Secondly, the model is built and analyzed in view of the manufacturer, and the optimal due date is obtained after calculation, and the inventory cost coefficient is calculated. The analysis results show that the inventory cost arising from delay in delivery is actually proportional function about the overall demand for A. Finally, the model is built and analyzed in view of the coordination between the manufacturer and the vendors, and the optimal due date is obtained after calculation, and the maximum of whole supply chain profit is calculated. According to the benefit analysis, the whole profit is indeed bigger than separate operation.

Abstract: Regional gravity anomaly provides important information for the study of regional geologic structure and the division of tectonic units. The major concern of the geophysicists is how to obtain the detailed data of regional gravity using reliable methods. In this study, curvelet transform was used for the first time in the multi-scale analysis of data of regional gravity. As one method for multi-scale geometric analysis, curvelet transform overcomes the limitations of wavelet transform in representing the high-dimensional singularities such as edges and contours. When processing of the data of regional gravity, the curvelet transform can more effectively present the detailed information. In this study, the data of synthetic model was respectively used for the experiments based on the wrapping algorithm of second-generation curvelet transform in combination with translation of cycle, iterative operation and Monte Carlo strategy for threshold adjustment. The experimental results show that the curvelet transform is efficient in multiscale separation for the data of regional gravity. This technique provides reference to the application of relevant multiscale and multi-orientational transform methods in the processing of data of gravity.