Aircraft Trajectory Prediction Based on Modified Interacting Multiple Model Algorithm

In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual’s mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.     

Differentially Private Top-k Items Based on Least Mean Square——Take E-Commerce Platforms for Example

User preference data broadly collected from e-commerce platforms have benefits to improve the user’s experience of individual purchasing recommendation by data mining and analyzing,which may bring users the risk of privacy disclosure.In this paper,we explore the problem of differential private top-k items based on least mean square.Specifically,we consider the balance between utility and privacy level of released data and improve the precision of top-k based on post-processing.We show that our algorithm can achieve differential privacy over streaming data collected and published periodically by server provider.We evaluate our algorithm with three real datasets,and the experimental results show that the precision of our method reaches 85%with strong privacy protection,which outperforms the Kalman filter-based existing methods.     

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).     

A Recommendation Mechanism for Web Publishing Based on Sentiment Analysis of Microblog

Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data(LOD) with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70%-89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users’ requirement greatly.     

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

This paper describes a building subsidence deformation prediction model with the self-memorization principle.According to the non-linear specificity and monotonic growth characteristics of the time series of building subsidence deformation,a data-based mechanistic self-memory model considering randomness and dynamic features of building subsidence deformation is established based on the dynamic data retrieved method and the self-memorization equation.This model first deduces the differential equation of the building subsidence deformation system using the dynamic retrieved method,which treats the monitored time series data as particular solutions of the nonlinear dynamic system.Then,the differential equation is evolved into a difference-integral equation by the self-memory function to establish the self-memory model of dynamic system for predicting nonlinear building subsidence deformation.As the memory coefficients of the proposed model are calculated with historical data,which contain useful information for the prediction and overcome the shortcomings of the average prediction,the model can predict extreme values of a system and provide higher fitting precision and prediction accuracy than deterministic or random statistical prediction methods.The model was applied to subsidence deformation prediction of a building in Xi’an.It was shown that the model is valid and feasible in predicting building subsidence deformation with good accuracy.     

Fuzzy clustering method for Web user based on pages classification

A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the site. then computes fuzzy degree of cross page through aggregating on data of Web tog. After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits. and derives the fuzzy similarity matrix from the interest vectors for the Web users. Finally. it gets the clustering result through the fuzzy clustering method. The experimental results show the effectiveness of the method.     

Web multimedia information retrieval using improved Bayesian algorithm

The main thrust of this paper is application of a novel data mining approach on the log of user‘ s feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author‘ s expression and the user‘ s understanding and expectation. User spacemodel was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the au-thors‘ proposed algorithm was efficient.     

Context-aware recommendation in mobile environments: An approach based on interest resonance

The recommendation system can efficiently solve the information overload in mobile Internet. Thus, how to effectively utilize context information to improve the accuracy of recommendation becomes the research focus in the field. This article puts forward a novel approach to realize the context-aware recommendation in mobile environments. It first gets users’ interest resonance with a hash-based interest resonance mining algorithm. Then, it calculates the association degree between the user and the item and then predicts the user’s rating on the item. Finally, it comprehensively figures out the recommending index. Moreover, this article also designs a personal recommendation model for the users and provides relevant decision-making coefficients. Experiments have demonstrated that our approach is superior to the traditional ones (RMP, RSTE, MD and BBBs) in both performance and efficiency.     

Design and implementation of highway distress and preventive measures decision support system based on WebGIS 

A highway distress and preventive measures decision support system based on WebGIS and J2EE architecture were studied, and the key issues for the system are presented. Such issues as spatial data and attribute data organization, technology of enhancing user experience, database connection pool were investigated. The feasibility of the theories and methods were tested through the system.     

Processing Time Prediction Method Based on SVR in Semiconductor Manufacturing

The prediction problem of the actual value of the dynamic parameters in the simulation model in semiconductor manufacturing was discussed. Considering the fact that the default value of processing time of one certain equipment in the simulation model was not the same as its actual value. a general data driven prediction model of the processing time was built based on support vector regression （SVR） , with the utilization of manufacturing information in manufacturing execution system （MES）. The processing time of one certain equipment was highly related to the status of the equipment itself and the wafers being processed. To uncover the relationship of the processing time with the information of historical products. process flow. technical standard of silicon wafers and manual intervention. data were extracted from MES and used to build a prediction model. This model was employed on an ion implantation equipment as a case. and the effectiveness of the proposed method was shown by comparing with other approaches.     

A Novel Personalized Web Search Model

A novel personalized Web search model is proposed. The new system, as a middleware between a user and a Web search engine, is set up on the client machine. It can learn a user＇s preference implicitly and then generate the user profile automatically. When the user inputs query keywords, the system can automatically generate a few personalized expansion words by computing the term-term associations according to the current user profile, and then these words together with the query keywords are submitted to a popular search engine such as Yahoo or Google. These expansion words help to express accurately the user＇s search intention. The new Web search model can make a common search engine personalized, that is, the search engine can return different search results to different users who input the same keywords. The experimental results show the feasibility and applicability of the presented work.     

Accurate tracking control in LOM application

The fabrication of accurate prototype from CAD model directly in short time depends on the accurate tracking control and reference trajectory planning in (Laminated Object Manufacture) LOM application. An improvement on contour accuracy is acquired by the introduction of a tracking controller and a trajectory generation policy. A model of the X-Y positioning system of LOM machine is developed as the design basis of tracking controller. The ZPETC (Zero Phase Error Tracking Controller) is used to eliminate single axis following error, thus reduce the contour error. The simulation is developed on a Maltab model based on a retrofitted LOM machine and the satisfied result is acquired.     

Reciprocal Cloaking Algorithm for Spatial K-Anonymity

Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. Due to the nature of spatial queries, location-based service (LBS) needs the user position in order to process requests. On the other hand, revealing exact user locations to LBS may pinpoint their identities and breach their privacy. Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. However, existing reciprocal methods rely on a specialized data structure. In contrast, a reciprocal algorithm was proposed using existing spatial index on the user locations. At the same time, an adjusted median splits algorithm was provided. Finally, according to effectiveness (i.e., anonymizing spatial region size) and efficiency (i.e., construction cost), the experimental results verify that the proposed methods have better performance. Moreover, since using employ general-purpose spatial indices, the proposed method supports conventional spatial queries as well.     

Community-Based User Domain Model Collaborative Recommendation Algorithm

Collaborative Filtering (CF) is a commonly used technique in recommendation systems. It can promote items of interest to a target user from a large selection of available items. It is divided into two broad classes: memory-based algorithms and model-based algorithms. The latter requires some time to build a model but recommends online items quickly, while the former is time-consuming but does not require pre-building time. Considering the shortcomings of the two types of algorithms, we propose a novel Community-based User domain Collaborative Recommendation Algorithm (CUCRA). The idea comes from the fact that recommendations are usually made by users with similar preferences. The first step is to build a user-user social network based on users’ preference data. The second step is to find communities with similar user preferences using a community detective algorithm. Finally, items are recommended to users by applying collaborative filtering on communities. Because we recommend items to users in communities instead of to an entire social network, the method has perfect online performance. Applying this method to a collaborative tagging system, experimental results show that the recommendation accuracy of CUCRA is relatively good, and the online time-complexity reduces to O.n/.     

Determination of the controlled landing trajectory of Chang’E-1 satellite and the coordinate analysis of the landing point on the Moon

Based on the tracking observations of radio ranges and VLBI delays of Chang’E-1 (CE-1) satellite during the controlled landing on the Moon on March 1, 2009, the landing trajectory and the coordinates of the landing point are determined by positioning analysis. It is shown that the landing epoch (the emission epoch of the last signal) of CE-1 satellite on the Moon was at UTC8h13m6.51s. The lunar longitude, latitude and surface height of the landing point in the lunar primary axes frame are respectively 52.27...     

Practical Encrypted Key Agreement Using Passwords

0 IntroductionWith rapid development on the Internet a user Bob caneasily access the networkto get some services fromaservice provider ,or toretrieve his sensitive private data storedinthe server previously.In that case,he has to establish se-cure session keys with remote servers . To verify the identity(IDfor short) of a user manyreal systems use password-basedauthenticated key exchange protocol . The fundamental prob-lems with passwords come from the fact that most users’passwords are dra…     

Research on Bayesian Network Based User＇s Interest Model

It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing users＇ interest models and some basic questions of users＇ interest （representation, derivation and identification of users＇ interest）, a Bayesian network based users＇ interest model is given. In this model, the users＇ interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users＇ interested and not interested documents are used to train the Bayesian network. Compared to the simple model, this model has the following advantages like small space requirements, simple reasoning method and high recognition rate. The experiment result shows this model can more appropriately reflect the user＇s interest, and has higher performance and good usability.     

Empirical Research on User Acceptance of Mobile Searches

The mobile search, a combination of a web search engine and a mobile communication system, is viewed as the most influential application in the 3G era. Therefore, mobile search service providers are eager to know which factors most influence user acceptance of mobile searches. Based on the characteristics of mobile searches and a review of previous information technology acceptance research, this study integrates the task technology fit model and the unified theory of acceptance and use of technology model to develop a mobile search acceptance model and empirically tests this model. This study finds that, for mobile searches, the performance expectancy, social influence, and perceived cost all significantly influence use intention and the performance expectancy increases with the increasing user’s experience and higher task technology fit degree. The effort expectancy is found to not affect the use intention of mobile searches and the users’ gender does not have a significant moderating effect on the use intention. The results are then used to develop suggestions for mobile search providers to promote their application and development.     

Simulation of Emergency Evacuation in Virtual Reality

A virtual reality system was developed to simulate emergency evacuations during fires. The spreading of the flame and smoke in the virtual fire was modeled based on numerical fire simulations, so that the conditions are similar to real life. A multi-grid, multi-base-state database model was used to overcome the disadvantages of traditional smoke spreading simulations. Textured images and particle systems provide visualization of the flame and smoke. The system immerses the user in a virtual environment with detailed interactions between the users and the virtual environment. The system can show which evacuation methods are effective for building safety evaluations.     

Impact of public name resolving services on Akamai

To enhance user perceived performance,Akamai＇ s content distribution network （CDN） utilizes the domain name system （DNS） effectively to redirect users to close-by content replicas over short time scale.The use of DNS redirection has brought a significant advantage to Akamai＇ s CDN.However,the reliance on DNS also poses a fundamental threat to Akamai＇ s content distribution model.In particular,systematic evaluation and quantification illustrates the impact of recent emerging public DNS resolving services on Akamai＇ s CDN,including the degradation of corresponding user perceived performance and the benefit damnification of collaborating internet services providers （ISPs） that host Akamai＇ s edge servers.The measurement demonstrates that a considerable penetration of public DNS resolving services （e.g.,OpenDNS and GoogleDNS） effectively corrupts the Akamai＇ s serving model,namely the large-scale server distribution and quick DNS redirection.