Motifs comparison plays a key role in clustering of redundant motifs and mapping motifs to transcription factors from previously characterized motif databases. Most of existed algorithms decompose the similarity of two motifs into the sum of similarities of aligned positions using position-independence assumption. However it is unreasonable to compare two motifs with vast difference in length. In this paper, we present a novel features extraction method, which extracts statistical information of positions information content and pair wise nucleotide dependencies. Then we combine these two aspects of information into one uniform formula called probability similarity scoring schema (PS3). Results on simulated dataset generated from JASPAR database demonstrates that our method outperforms others, and experiments on a real dataset from human kidney tissue shows that our method finds many motifs that are not only found in human tissue but also in relevant species such as mouse and rat, which indicates that it's a possible approach for elucidating DNA motifs that employing cross-species sequence conservation.
Along with the development of information technology, data-intensive computing has become a research hotspot and it also proposed a new challenge to traditional Bayesian inference methods. It is known that, among different Bayesian inference methods, random algorithm often been regarded as a common and effective one. And the sampling method adopted in random algorithm would largely influence the efficiency of this random algorithm. Gibbs sampling method often been used in random algorithm for Bayesian inference. Taking all of this into consideration, a Bayesian inference method under data-intensive computing is developed in this paper, which first use improved Gibbs sampling method in each station to gain the suitable information, then union them together to infer the final result. The validity of this method is discussed in theory and illustrated by experiment.
Pointing at the problem that the spares consumption quota has been using the experience to develop, which makes spares application random and blind, this paper puts forward to build the reasonable lifeless-repairable spares consumption quota model. Analyze and determine the factors influencing the lifeless-repairable spares consumption, use BP neural network to predict, and use genetic algorithm to optimize the weights and thresholds of BP neural network, so that the network can obtain the global minimum point. The example shows that the model's predicted results are relatively accurate and has high practicability.
With the further market reform of electric power industry, customer-oriented marketing concept has been more and more important in electric power enterprises. After doing research on customer service data of an electric power enterprise, we pick up some key factors which might be related to customer satisfaction, and utilize a developed association rule model to mine association rules between these key factors and customer satisfaction, which reflects the close relationship between them. Furthermore, we design metrics that can reflect the changing trend of rule confidence, which helps business officers have an insight into how these factors influence customer satisfaction as time goes on, to effectively improve customer service quality and then win praise of customers. As practical experiments prove, this method can fulfill the goal very well.
The popularization of network P2P applications has brought a significant influence on people's Internet utilization behavior. In this context, this research conducted the case study at a university in Jiangsu province by analyzing the large quantity of data collected from high-performance network traffic control equipments, in order to reveal the distribution characteristics, changing pattern, and behavior characteristics of P2P applications, and clarify people's misunderstandings on P2P applications, which is an advantageous exploration on network resources.
This paper analyzes network video surveillance technology, and designs a wireless video surveillance system. A new SIP protocol is proposed for wireless network. Based on the new SIP, system interconnect is done. The video capture terminal is designed and implemented on TI DM365. Under WIMAX network, the wireless IP video surveillance system could work properly. Deployment and on site running of the system results show that it could work properly and robustly.
The movement of the head not only helps to express sign language vividly, but also has specific meaning in sign language. In the effect of context, for example, emphasize, and the different personalities, the range and speed of the movement of head will be a great change. On the analysis of the characteristic of Chinese sign language words, this article establishes a set of rules of Sign language behavior and also proposes a parametric synthesis method of changing the range of head movement which is based on thought of motion deviation mapping. And then applies it to the emphasizing the prosody model of Chinese sign language synthesis system. Experimental results show that this method will realize any emphasis prosodic expression of head movement and raise the intelligibility and sense of reality of the sign language effectively.
Building level gradient field is the important part of center fusion algorithm in wireless sensor networks. It is found that flooding method using outward diffusion gradient will cause a serious problem of power consumption. In order to save energy in wireless sensor networks, the ant colony algorithm is applied to build level gradient field, the center fusion method based on ant colony algorithm is proposed. Simulation results verify the feasibility of the method. The results show that this algorithm can significantly reduce the energy consumption of wireless sensor networks, and prolong the lifecycle of wireless sensor networks.
Text classification is an important research direction of text mining and the research of Chinese text automatic classification is also becoming a research focus of intelligent classification. Against the particularity of the Chinese text classification, this paper presents a three-dimensional vector space model on the basis of the vector space model to improve the accuracy and efficiency of text classification. Experimental results show that the accuracy rate increased to 98.8125% which proves that the algorithm is effective.
The compiled Java class file is not really binary files, it's just a kind of in-between code. This makes it possible for hackers to decompile the java class file, and in fact the decompiled file is almost the same as the original one. So it's very hard to protect the java application program, and there's a big issue about the security of java class file. This paper treated protecting software and using more costly forms of reverse engineering as the target, and deeply researched reverse engineering and code obfuscation about defense and attack. A prototype of Code obfuscation system (JOT) based on Java byte code is realized.
In this paper, a message service system is designed and implemented based on the WCF (Windows Communication Foundation) duplex communication. The uniform message service is provided for the internet clients and the intranet clients by the two WCF bindings (wsDual Http Binding and net Tcp Binding), and the different session objects are managed by a session list. Both the online message service and the offline message service are implemented. The test results show that the difference of the response speed between the different bindings is significant, so it is necessary to build the balance between the performance and the flexibility.
In the area of Deep Web data integration, it is a key issue to efficiently and accurately build result output pattern according to uses' query. However, it is the crucial problem to improve efficiency and precision of result output pattern generating that resolving repeat pattern matching and data heterogeneity. This paper proposes the approach of Deep Web data integration oriented result output pattern building. On the basis of the conflicts and conflict resolving rules between any two data sources, the approach gives the rules for conflict integration over multi-sources. According to the result patterns and conflicts between result patterns, this paper gives the mechanism and algorithm of result output pattern building which can effectively resolve the problems of repeat pattern matching and data heterogeneity and lay a good foundation for Deep Web data merging.
Automatic target recognition (ATR) is an important issue in the military field, the topic of the ATR system is the pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recognize and classify the typical targets in the army field. The invariant features of Hu invariant moments and roundness were selected to be the input of the neural network for they have the invariance of rotation, translation and scaling. The pictures of the targets are generated by the 3-D models to improve the recognition rate for it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can implement the task of ATR system in high recognition rate and real time.
This paper is an introduction to software performance automated testing and theory. It introduces the features of Open Xml storage and SQL Server storage. Then this paper sets three state scenes and chooses different test automated tools respectively. Finally, it uses tools to monitor software performance index from these two data storage systems. Results are then analyzed, comparing the quality performance of different storage systems to the same state scene.
The BP neural network is a feed-forward network trained by backward propagation of errors algorithm, which is the most widely used neural network model, but BP neural network can't avoid the shortcoming that is strong randomness and easily converging to local minimum. In this paper, we discuss the principle, structure and realization way of BP neural network improved by genetic algorithm, which can effectively improve the performance of BP neural network. At the same time, the improved model is used in the tax case-selecting, the financial statements and tax returns of 80 enterprises are analyzed, and then the analysis result is compared with that of BP neural network and binary logistic regression analysis. The comparing analysis shows that the GA-BP neural network method can assist the case-selecting and can improve the efficiency and effect of the tax inspection.
With the characteristics that the instantaneous requencies of non-stationary signal can be given by Hilbert-Huang Transform (HHT) method, HHT modem is proposed. Combining with RAKE receivers and Reed-Solomon (RS) code, the HHT underwater acoustic communication system was built. The emulation of HHT underwater acoustic communication system was given through computer simulation. Results from processing experimental data show that the HHT underwater acoustic communication system is valid and steady in real communication environment. HHT method used in underwater acoustic communication is feasible.
This article puts forward a land-cover hierarchical classification method of TM image in loess hilly ravine area. This method selects different typological samples of ground object, analysises the statistics feature of the samples, confirms the TM image's hierarchical classification trees. The hierarchical classification trees exist more difference in the classification Node, and have higher divisibility. Based on the ground object's spectrum feature, this method put forwards band selection and feature extraction scheme according to different ground objects. This job makes the ground objects having higher divisibility in the selective bands. This article does precision evaluation with the classification results by means of confusion matrix, and compares the classification results with the results of maximum likelihood supervised classification. The land-cover classification accuracy has been increased.
The emerging cloud computing is developing into a new enable technology for service integration, making essential conditions for the appearance of public clouds, which integrate external services and provide services to third-party users. In contrary with traditional application integration, the service integrated in public clouds has to deal with a larger scale user accesses and monitor the execution of external atom services, it is necessary for Integrated Cloud Service Vendors (ICSV) to consider deployment architecture to ensure a high quality of service on user end. To solve such issues, this paper designs and proposes a deployment framework for public clouds, which constitutes business process model, cluster distribution model and node deployment model. These models are constructed to figure out and solve the deployment issues in different perspectives and become a system solution as a whole. The proposed framework aims to conduct ICSV to rationally deploy public cloud clusters, avoid potential congestion caused by single-point communication and provide reliable public cloud service to end users.
