Earticle

Green technology innovation is the critical path for enterprises to achieve sustainable development. However, corporate green technology innovation in most cases is affected and constrained by multiple factors. It is featured by multi-factor, multi-target, multi-layer and fuzzy uncertainty, etc. Thus it is of great importance to measure and analyze the performance of corporate green technology innovation. To this end, this paper aims to study the performance measurement of corporate green technology innovation and proposes a performance analysis model based on extension theories for corporate green technology innovation. First, the model analyzed relevant factors that influence performance measurement analysis of corporate green technology innovation. Then, based on the analysis, it established the performance measurement analysis index system of corporate green technology innovation. Meanwhile, the model tried to normalize the different types of measurement analysis indexes after which corresponding classical fields and controlled fields were obtained. It then calculated the extension distances and the extension dependencies between different measurement analysis indexes as well as the classical fields and the controlled fields. The size of the extension dependency revealed the ability levels of corporate green technology innovation. Lastly, the model was tested by specific cases.

For multi-relational data mining, efficiency is always a focus of research. The main bottleneck to improve the efficiency of the algorithm is hypothesis space. This paper presents the improved multi-relational decision tree algorithm, MRDTL-2, whose efficiency is improved. First, the tuple ID propogation technology is applied to the multi-relational decision tree algorithm. Secondly, under user’s guide, when a data item is greater than the transmitting threshold, set the null relationship Ra. And transmit the primary key, the background attributes, the class label to Ra, then Ra involves in other multi-relational decision tree algorithms instead of the background relations. Finally, the paper has carried on the experiments to verify the improved multi-relational decision tree algorithm MRDTL-2.

Particle swarm optimization is the optimization technique motivated by swarm intelligence and aims to find the best solution in the swarm. Aging leader and challengers with Particle swarm optimization (ALC-PSO) is a population based optimization method which introduced the concept of aging and challenger generation in the PSO technique. This variant of PSO has been successful in preventing premature convergence of PSO and maintaining swarm diversity. In this paper, we briefly reviewed the inertia weight parameter and its strategies in PSO and experimentally analyzed the effect of inertia weight strategies on ALC-PSO performance. Comparison is drawn between PSO and ALC-PSO based on these strategies. Results are obtained using five different benchmark functions.

Aiming at the situation of two coherent sources, their bearings are estimated of high resolution by single acoustic vector-hydrophone. The pressure and particle-velocity channels of single acoustic vector-hydrophone can be considered as an array at one point with special spatial directivity. The reception model of two coherent sources is gained and the signal covariance matrix of single acoustic vector-hydrophone is decorrelated by the method of phasic smoothing, then the performances of different high-resolution algorithms are compared. The computer simulation indicates that a single vector hydrophone can realize two coherent sources DOA estimating and the method of vector optimization robust beamforming (VORB) gives the best DOA estimating result.

The purpose of this paper is to provide a model for communication in large scale projects in Big Data environment. Given the fact that Big Data is going to be part of every business organization very soon, this study aims to present a new communication management strategy in order to accommodate the change in technology of data base storage systems. The complex large projects always emerge with different modes and have different attributes for processing the data. In this model different data sources of a large scale project are connected to the enterprise data ware house using Information Inference Framework (IIF). This new approach may provide an enhanced way of communication management for activities in large scale projects.

Reliability is an essential performance in GPS navigation system. Therefore, satellite fault detection is considered as one of the most important functions in GPS receivers. For the declining problem of the ability of fault detection for the traditional GPS satellite fault detection algorithm under the condition of small fault, a new GPS satellite fault detection algorithm based on wavelet analysis is proposed. The raw pseudorange measurements and the positioning data information are transformed by wavelet analysis and the data jumping point can be detected and identified through the different wavelet scales, and the satellite fault could be detected. Two kinds of GPS satellite fault detection methods are given in detail, and the advantages and disadvantages of them are compared. Validated by the real collected raw data from the GPS receiver, the results show that the wavelet analysis method can detect smaller mutations in a sequence of parameters, and the feasibility and effectiveness of applying the wavelet analysis algorithm in satellite fault detection for GPS receiver are verified.

In view of fault diagnosis of the ship generator , the paper proposes improved fault diagnosis method of ship generator ,which is Optimized Neural Network based on Multi-population Chaos Genetic Algorithm. The results prove that the method effectively solves low precision,slow constringency and local minimum of neural network and improves global search ability, optimizes the rate and precision of fault diagnosis. The method has a certain application prospect for the ship power system generator fault diagnosis.

Opinion leader is an authority person who has great influence in BBS. Their linguistic behavior has a huge impact on net citizen’s behavior and thought. In this paper, we propose an algorithm called OLRA (Opinion Leader PageRank Algorithm) based on topic-field to identify opinion leaders in the Internet forum. In the algorithm the closeness degree factor and sentiment analyses are taken into consideration. Meanwhile, these two authority values are defined as the weight of links among users. The data is collected from a number of posts on Tianya forum which is a famous forum in China. In the experiment, the algorithm is compared with Interest-based PageRank algorithm, online time Algorithm, and Experience-based Algorithm, the result shows that the OLRA algorithm can identify opinion leaders than others in the Internet forum effectively.

Automatic image annotation (AIA) is an active topic of research in computer vision and pattern recognition. In the last two decades, large amount of researches on AIA have been proposed, mainly including classification-based methods and probabilistic modeling methods. As one of the most common methods for AIA, support vector machine (SVM) has been widely applied in the multimedia research community, especially for image classification, image annotation and retrieval. However, compared with various SVM methods and their corresponding applications in the literature, there is almost no review research and analysis about SVM related studies. So the current paper, to start with, elaborates the basic principles of SVM. Followed by it summarizes SVM with applications to image annotation from three aspects of SVM ensemble for AIA, SVM with mixture of kernels for AIA and hybrid SVM for AIA respectively. In addition, SVM exploited in several other applications are also briefly reviewed. Finally, we end this paper with a summary of some important conclusions and highlight the potential research directions of SVM in automatic image annotation for the future.