Earticle

Manufacturers of ship industry always have more than one supplier evaluation index systems, which are still not standard in China. Based on the principle of observation, through the theoretical research and field investigation, we build a supplier evaluation index system made up of 21 tertiary indicators of ship industry. We adopt the optimal combination weight which composes of G1, G2, entropy value weight and deviation to investigate the case study. We find that the lead indicators of ship industry to choose suppliers are technology design, research and development, technical service and support, competitive price and quick response ability.

This paper presents a computationally efficient graphics processing unit (GPU) implementation of a reliable fault diagnosis method using two-dimensional (2D) representation of vibration signals. The fault diagnosis method first converts time-domain vibration signals into 2D gray-level images to exploit texture information from the converted images. Then, the global dominant neighborhood structure (GNS) map is utilized to extract texture features by averaging local neighborhood structure (LNS) maps of central pixels. In addition, the principle component analysis (PCA) algorithm is employed to select only the most dominant features. Finally, the selected features are used as inputs to a one-against-all multi-class support vector machine (OAA-MCSVM) to identify each fault of the induction motor. Despite the fact that the 2D fault diagnosis methodology shows satisfactory classification accuracy, its computational complexity limits its use in real-time applications. To accelerate the 2D fault diagnosis method, this paper utilizes an NVIDIA GeForce GTX 580 GPU, where all tasks are executed in parallel. The experimental results indicate that the proposed GPU-based approach achieves about 118.5 faster operation than the equivalent sequential CPU implementation while maintaining 100% classification accuracy.

General warning method of electronic banks generates based on clear detection of relevant data. However, this method is mostly a reflection of electronic banks’ risks in the past which is a manifestation of historical data that can only be able to analyze problems that have already occurred but can not give a better future anticipation. Therefore early warning system established in this paper is based on the data detection system and we use an optimized gray early warning method to predict the indicators of the risk of electronic banks. Based on the idea of using descending cumulation to change traditional gray 1-AGO sequence, we use weaken buffer operators to deal with the original data sequence and then use genetic algorithm to estimate a and b--important parameters of background values. Eventually the optimized gray GM (1,1) model prediction method generate and it can effectively forecast the risk profile of electronic banks. Then analyze the results, further read and dig the hidden meaning, give a series of practical conclusions for the development, risk prevention and control of electronic banks. Decision making depends on the degree of risk in the future. Then we can take effective measures to cope with the arrival of electronic bank crisis that may arise.

The macro-femto overlaid LTE-Advanced networks have been drawing many attentions from mobile operators with their capability of extending coverages and supporting higher data rates. Effective and efficient resource allocation schemes must be preceded in order to deploy this overlaid cellular network successfully. This paper proposes the adaptive resource management scheme which categorizes the entire time-frequency resource blocks of the overlaid cellular network into the dedicated and the shared one, and allocates these resources stage by stage on the basis of user location and user-required data rate in order to expand the user accommodation capacity. Moreover, it enables to share loads evenly in the overlaid cellular network by performing cross-tier handovers from the macrocell to the femtocell so as to maximize the total packet throughput to a certain degree. We used a simulation to evaluate the effectiveness of our scheme with the performance measure of the outage probability and total packet throughput.

To utilize the geometry structure information and similarity information within the neighborhood surrounding a spatio-temporal interest point for human action recognition task, we employ the axes of a regular polyhedron as a reference locating system, and build a novel local feature named polyhedron neighborhood feature (PNF). Then, to reduce quantization error in the coding stage, locality-constrained linear coding method is used to encode the obtained PNFs. Next, multi-temporal-scale PNFs (MPNFs) are created for handling the problem of various action speeds. In classification, support vector machine (SVM) based on linear kernel is used as classifier taking time consumption into account. The experiments on the KTH and UCF sports datasets show that the recognition system based on PNFs achieves better performance than the competing local spatio-temporal feature-based human action recognition methods.

Cognitive Radio Networks (CRNs) has been considered as a key technology for future wireless communications and mobile computing. Localization of primary user is crucial in enabling several key capabilities in CRNs. In this paper, we present a survey of representative methods dealing with user localization and location privacy preservation issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around localization and location privacy preservation in cognitive radio network. This survey is intended to help researchers in quickly understanding existing works and challenges, and possible improvements to bring.

We can take the e-Learning using smart devices on information society is that well-formed internet environment. LMS (Learning Management System) supports mobile video lectures. The proposed system designs that instructors can set up lectures without the professional computer related technologies, and the interaction between instructors and learners becomes easy. This system focuses on helping the interaction between instructors and learners made easier by using a web browser in the mobile device based on web app, anytime and anywhere learners want to use the system.

Accurate audio segmentation has recently received increasing attention for its applications in automatic indexing, content analysis and information retrieval. Hence, this paper proposes a highly accurate audio segmentation methodology using a genetic algorithm-based approach to adapting and optimizing segmentation window lengths. Specifically, this paper analyzes the parameter sequence of the root-mean-square values of an input audio stream with optimal sliding window (or segmentation window) lengths found and adapted by a genetic algorithm. In addition, this paper determines whether an audio-cut occurs or not by utilizing the parameter sequences as inputs of a support vector machine. Experimental results indicate that the proposed approach achieves 100.00% and 98.69% in the average precision and recall rates of segmentation performance, respectively.