The paper analysed the mobile node, home agent, and foreign agent of mobile IP network firstly, some key technique, such as mobile IP network basical principle, protocol work principle, agent discovery, registration, and IP packet transmission, were discussed. Then a network simulation model was designed, validating the characteristic of mobile IP network, and some advantages, which were brought by mobile network, were testified. Finally, the conclusion is gained: mobile IP network could realize the expectation of consumer that they can communicate with others anywhere.

Timely identification of defective modules improves both software quality and testing efficiency. A software metrics-based ensemble k-NN algorithm is proposed for software defect prediction. Firstly,a set of base k-NN predictors is constructed iteratively from different bootstrap sampling datasets. Next,the base k-NN predictors estimate the software module independently and their individual outputs are combined as the composite result. Then,an adaptive threshold training approach is designed for the ensemble to classify new software modules. If the composite result is greater than the threshold value, the software module is recognized as defective,otherwise as normal. Finally,the experiments are conducted on NASA MDP and PROMISE AR datasets. Compared with a widely referenced defect prediction approach,the results show the considerable improvements of the ensemble k-NN and prove the effectiveness of software metrics in defect prediction.

Important errors existed in the consistency problem of ground training simulation with actual space flight in rendezvous and docking mission are investigated and countermeasures are put forward.Three characteristics of errors generated from simulation models,visual perception and operation perception are studied by focusing on astronauts' perception in the loop.The influence of training simulation errors in both autonomous docking mode and manual docking mode on astronaut's handling quality in real flight is analyzed in detail based on data of training samples,and approaches of model construction,midway-amending and identification are proposed.The statistic of simulation shows that the countermeasures are applicable,and the configurable bias of time delay and handling response in simulation are effective.The analyzing of simulation errors and countermeasures has a good application value for development of training simulator and astronauts' training.

Semi-supervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. This paper explores the usage of labeled data to generate and optimize initial cluster centers for k-means algorithm. It proposes a max-distance search approach in order to find some optimal initial cluster centers from unlabeled data, especially when labeled data can't provide enough initial cluster centers. Experimental results demonstrate the advantages of this method over standard random selection and partial random selection, in which some initial cluster centers come from labeled data while the other come from unlabeled data by random selection.

QoS is essential for selecting an appropriate service from a set of services that match user’s functionality requirements. To enable this, a QoS Ontology for semantic service discovery, which provide the uniform model for QoS, is proposed in this article. With this ontology, the algorithms for QoS matchmaking and service ranking are presented. And an expanded service discovery framework is also presented.

Automatic Term Recognition(ATR),as an important task in Information Extraction and Text Mining,aims at acquiring formalized words that are not recorded in time in the glossary.In recent years,several statistical methods have made substantial progresses in this field,and emerging methods such as C-Value,NC-Value,Term-Extractor have shown great advantages on this task.However,few work has been done on the Weighted Voting algorithm which could merge those statistical metrics as a whole.In this paper,we first collect part-of-speech rules from already-known terms,then match them with pos-tagged strings to acquire candidate terms,and finally sort those terms by Weighted Voting algorithm.The experiment on literature in Electric Engineering field from IEEE2006-2007 metadata shows that the weighted voting algorithm performs better than any seperate metrics alone.

The self organizing maps(SOM) has been used as a tool for mapping high-dimensional input data into a low-dimensional feature map, which has significant advantages for text clustering applications. In this paper, a novel dynamic and adaptive SOM algorithm applied to high dimensional large scale text clustering is proposed. The characteristic feature of this novel neural network model is its dynamic architecture which grows (when the similarity between input pattern (text vector) and weight vector of the winning node is smaller than a given threshold) during its training process to find the inherent topology structure of the document set. By using unsupervised competitive learning in network, the weight vectors of the winning node and its nearest neighbors are adjusted adaptively (where learning rate is related to similarity in amended learning rule) in this algorithm. The results of the experiments indicated that the algorithm successfully improve quality of text clustering and learning speed of neural network.

This paper depicts how to construct parametric biorthogonal wavelet family via lifting scheme,and gives the complete construction of a new class of parametric biorthogonal interpolating wavelets with one parameter.The exact parameter expressions of their associated interpolating filter banks are also derived.The free parameter provides a degree of freedom to optimize the resulting wavelets,and a previously unpublished interpolating wavelet is obtained with respect to the coding gain criteria,which has binary filter coefficients and can realize a multiplication-free discrete wavelet transform(DWT).Simulations show that the new wavelet has exhibited image compression performance superior to the most widely used 9/7 tap wavelet by Cohen et al.in the field of wavelet transform coding,yet its computational complexity has decreased by more than 17%.This indicates a better tradeoff between compression performance and computational complexity.

Automatic Term Recognition (ATR) is an important task for Knowledge Acquisition, which aims at acquiring formalized words which are not recorded in time in the glossary. In recent years, several statistical methods has proved to be effective, and emerging methods such as C-value, NC-Value, TermExtractor has shown great advantages on this task. However, few works have been done on the Metric mixing algorithm that combines those metrics as a whole. In this paper, we first collect part-of-speech templates from already-known terms automatically, namely Auto-POS templates, instead of artificial regular expressions, and then we match them with POS strings to acquire candidate terms. Finally we sort those candidates by metric mixing algorithm. Experimental results on IEEE2006-2007 metadata show that the metric mixing algorithm performs better than any separate metrics alone.

The fixed number of reference neighbors leads to mutable prediction errors and low overall accuracy for various unknown instances, according to existing k-NN methods. To address this problem, a bagging-based k-NN numeric prediction algorithm with attribute selection is proposed. Within each training procedure, a set of base k-NN predictors are built iteratively in terms of different bootstrap sampling datasets. Then, the base predictors estimate the unknown instance respectively. The combination mechanism of these individual outcomes determines the performance of this ensemble algorithm. Hence, an instance-relevant rule is proposed to calculate the composite result. The weight of each base k-NN predictor is dynamically updated with respect to the distinct features of current unknown instance. The accuracy in response to different number of base k-NN predictors is also explored. The experimental results on public datasets show the considerable improvement of k-NN numeric prediction.

Associative classification algorithms commonly have low efficiency and accuracy.A new associative classification algorithm by distilling effective rules,called ACDER,is presented.Both the remaining support and remaining confidence are defined.Then association classifier is constructed and pruned by distilling the most effective rules to ensure that there exists no any redundant and conflictive rules in the classifier.Experiment results on eight data sets show that the average accuracy of the classifier is 4.15% higher while the average number of rules in classifier is 54% lower than the CBA classification method.

Space Flight Simulator is important equipment for astronaut training on the ground. Function and implement architecture was described. Implement method and technology of simulation system,mockup system,instrument system,instructor system,vision system,acoustics system and motion system were discussed. The prospect of development was put forward.

Classification Based on Association(CBA) algorithm is one of the main methods in data mining.However,this algorithm only uses confidence parameter to process classification problem which may impact the classification accuracy.In this paper,a new improved method of CBA is presented.On the basis of CBA method,shorter rules are selected firstly for classification.Experimental results show this algorithm has higher classification accuracy and fewer numbers of classification rules than traditional ones.

According to the theory on elementary transforms of polynomial matrix,the corresponding discrete wavelet transform (DWT) of a kind of 17/11 biorthogonal wavelet with rational filter coefficients was factored into lifting steps,thereby the computational complexity was decreased remarkably. Finally,this lifting based DWT was applied to the embedded image coding,and simulations show that it exhibits image compression performance much superior to the lifting based 9/7 DWT used in JPEG 2000 standard.

The prediction accuracy of various instances can hardly be ensured by the existing k-nearest neighbor (k-NN) predictor, which works under a single k value. A combined k-NN algorithm, Bk-NN predictor, is proposed in this paper. The novel Bk-NN algorithm is used to build up a set of prediction models based on the Boosting principle, and then each model is used to predict a new instance respectively. The final predicted value of this instance is the weighted sum of these prediction values. The prediction error is reduced since the circumstance that various instances demand specific prediction models to match with is thoroughly taken into account by the Bk-NN algorithm. Moreover, the Bk-NN predictor can work well with both discrete and continuous attribute values. The experimental results on standard datasets show that, compared with the traditional k-NN predictor, the prediction accuracies of the Bk-NN predictor are improved by 6.44%-15.25%.

BACKGROUND: Software effort prediction clearly plays a crucial role in software project management.PROBLEM: In keeping with more dynamic approaches to software development it is not sufficient to only predict the whole-project effort at an early stage.Rather, the project manager must also dynamically predict the effort of different stages or activities during the software development process.This can assist the project manager to re-estimate effort and adjust the project plan, thus avoiding effort or schedule overruns.METHOD: This paper presents a method for software physical time stage-effort prediction based on grey models GM(1,1) and Verhulst.This method establishes models dynamically according to particular types of stage-effort sequences and can adapt to particular development methodologies automatically by using a novel grey feedback mechanism.RESULT: We evaluate the proposed method with a large-scale real world software engineering data set and compare it with the linear regression method and the Kalman filter method, revealing that accuracy has been improved by at least 28% and 50%, respectively.CONCLUSION: The results indicate that the method can be effective and has considerable potential.We believe that stage predictions could be a useful complement to whole-project effort prediction methods.

QoS is essential for selecting an appropriate service from a set of services that match user’s functionality requirements. To enable this, a QoS Ontology for semantic service discovery, which provide the uniform model for QoS, is proposed in this article. With this ontology, the algorithms for QoS matchmaking and service ranking are presented.

Software stage effort has the features of data starvation and uncertainty. It is difficult to use the current methods (e.g. regression) to make predictions. This paper proposes a novel prediction method,which gets the effort sequence feature— changing from the completed stage effort sequences,and gets the changing ratio threshold from historical projects by machine learning methods,then uses grey models to make predictions. The experimental results on 10 real world software engineering datasets show that,compared with linear regression method,the prediction accuracy of the proposed method has been improved by 20%~80%. This is very encouraging and indicates that the method has considerable potential.

With the rapid increase in the use of databases, missing data make up an important and unavoidable problem in data management and analysis. Because the mining of association rules can effectively establish the relationship among items in databases, therefore, discovered rules can be applied to predict the missing data. In this paper, we present a new method that uses association rules based on weighted voting to impute missing data. Three databases were used to demonstrate the performance of the proposed method. Experimental results prove that our method is feasible in some databases. Moreover, the proposed method was evaluated using five classification problems with two incomplete databases. Experimental results indicate that the accuracy of classification is increased when the proposed method is applied for missing attribute values imputation.