Evolving Information Filtering for PersonalizedInformation Service

Information filtering (IF) systems are important for personalized information service. However, most current IF systems suffer from low quality and long training time. In this paper, a refined evolving information filtering method is presented. This method describes user's information need from multi-aspects and improves filtering quality through a process like natural selection. Experimental result shows this method can shorten training time, improve filtering quality, and reduce the relevance between filtering results and training sequence.

Improving the precision of the keyword-matching pornographic text filtering method using a hybrid model

With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applications which can block or filter such information are used. Approaches in those systems can be roughly classified into two kinds: metadata based and content based. With the development of distributed technologies, content based filtering technologies will play a more and more important role in filtering systems. Keyword matching is a content based method used widely in harmful text filtering. Experiments to evaluate the recall and precision of the method showed that the precision of the method is not satisfactory, though the recall of the method is rather high. According to the results, a new pornographic text filtering model based on reconfirming is put forward. Experiments showed that the model is practical, has less loss of recall than the single keyword matching method, and has higher precision.

ADAPTIVE AND ACTIVE COMPUTING PARADIGM FOR PERSONALIZED INFORMATION SERVICE IN DISTRIBUTED HETERONGEOUS ENVIRONMENT

To solve the problem that traditional pull based information service can’t meet the demand of long term users getting domain information timely and properly, an adaptive and active computing paradigm (AACP) for personalized information service in heterogeneous environment is proposed to provide user centered, push based higsh quality information service timely in a proper way, the motivation of which is generalized as R 4 Service: the right information at the right time in the right way to the right person, upon which formalized algorithms framework of adaptive user profile management, incremental information retrieval, information filtering, and active delivery mechanism are discussed in details. The AACP paradigm serves users in a push based, event driven, interest related, adaptive and active information service mode, which is useful and promising for long term user to gain fresh information instead of polling from kinds of information sources.

DGPS/INS Data Fusion for Land Navigation

The interest for land navigation has increased for the recent years. With the advent of the Global Position System (GPS) we have now the ability to determine the absolute position anywhere on the globe. The problem is that the GPS systems work well only in open environments with no overhead obstructions and they are subject to large unavoidable errors when the reception from some of the satellites are blocked. This occurs frequently in urban environments, forests and tunnels. GPS systems require at least four “visible” satellites to maintain a good position fix. In many situations in which higher level of accuracy is required, the navigation cannot be achieved by GPS alone. This paper discusses the design of a reliable multisensor fusion algorithm using GPS and Inertial Navigation System in order to decrease the implementation cost of such systems on land vehicles. The major contribution of this paper is in the definition of the possible developments and research axes in land navigation.

Maneuvering Target Tracking Algorithm Based on Muti-paramter Sequential Extended Kalman Filter

Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial velocity obtained in the signal processing are introduced into the measurement vector by coordinate transformation.In order to solve the problem of high nonlinearity of the radial acceleration,radial velocity and the state vector,a new algorithm of multi-parameter sequential extended Kalman filter( MSEKF) is proposed.The tracking performance of this algorithm is tested and compared with the other tracking algorithms.It is shown that the proposed algorithm outperforms these algorithms in strong and weak maneuvering environments.

Uncalibrated visual servoing design for competitive networked robots

In this paper,a method with an eye-in-hand configuration is developed to hit targets during visual tracking for the TLS(Tele-Light Saber) game.It is not necessary to calibrate camera parameters and predict the trajectory of the moving object.Firstly,the expression of the image Jacobian matrix for the eye-in-hand configuration is proposed,and then an update law is designed to estimate the image Jacobian online.Furthermore,a control scheme is presented and the Lyapunov method is employed to prove asymptotic convergence of image errors.No assumption for the moving objects is needed.Finally,both simulation and experimental results are shown to support the approach in this paper.

Electromagnetism, Blood Flow and Coagulation

There is a lot of uncertainty in the theory of hemodynamics. The amount of work need to displace the blood in the systemic circulation, exceeds the work done by the left ventricle. With this, blood recovers increased flow resistance between the heartbeats with the Womersley number alterations in the rhythm of the accompanying electrocardiogram (ECG). Viscoelastic transformation is heavily expressed in coagulation. There must be a relationship between the ECG and blood transient flow resistance. The influence of the electromagnetic field on blood coagulation was studied. Venous blood was affected by the oscillated electromagnetic field (500 - 5000 Hz), with the square wave input signal in 25 healthy individuals (15 males, 10 females in the age 18 - 57 years). Electromagnetic irradiation (EMI) time of the sample 3 - 10 min. Hypocoagulation in normal blood samples was revealed (decreased quantity of Platelets up to 10 - 23 × 109/L, Prothrombin index up to 9% - 10%, Fibrinogen concentration up to 0.20 - 0.21 g/L) and thrombolysis after the blood stasis. Ac electric field from the myocardial depolarization initiates electroacoustic phenomena. An emerging repulsing electromagnetic force acts on the red blood cells (RBC) and in addition to the pulse pressure from the heart, promotes blood motion and viscoelastic changes. The alterations of the blood inertial and elasticity, in addition to hemodynamics, are facilitated by the magnetic features of the hemoglobin. The external electromagnetic signal can manage the blood coagulation process, including thrombolysis.

Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.

Autonomous orbit determination using epoch-differenced gravity gradients and starlight refraction

Autonomous orbit determination via integration of epoch-differenced gravity gradients and starlight refraction is proposed in this paper for low-Earth-orbiting satellites operating in GPSdenied environments. Starlight refraction compensates for the significant along-track position error that occurs from only using gravity gradients and benefits from integration in terms of improved accuracy in radial and cross-track position estimates. The between-epoch differencing of gravity gradients is employed to eliminate slowly varying measurement biases and noise near the orbit revolution frequency. The refraction angle measurements are directly used and its Jacobian matrix derived from an implicit observation equation. An information fusion filter based on a sequential extended Kalman filter is developed for the orbit determination. Truth-model simulations are used to test the performance of the algorithm, and the effects of differencing intervals and orbital heights are analyzed. A semi-simulation study using actual gravity gradient data from the Gravity field and steady-state Ocean Circulation Explorer（GOCE） combined with simulated starlight refraction measurements is further conducted, and a three-dimensional position accuracy of better than 100 m is achieved.

Event-triggered cooperative target tracking in wireless sensor networks

Since the issues of low communication bandwidth supply and limited battery capacity are very crucial for wireless sensor networks, this paper focuses on the problem of event- triggered cooperative target tracking based on set-membership information filtering. We study some fundamental properties of the set-membership information filter with multiple sensor measurements. First, a sufficient condition is derived for the set-membership information filter, under which the boundedness of the outer ellipsoidal approximation set of the estimation means is guaranteed. Second, the equivalence property between the parallel and sequential versions of the setmembership information filter is presented. Finally, the results are applied to a 1D eventtriggered target tracking scenario in which the negative information is exploited in the sense that the measurements that do not satisfy the triggering conditions are modelled as set-membership measurements. The tracking performance of the proposed method is validated with extensive Monte Carlo simulations.

A review of real-time multi-GNSS precise orbit determination based on the filter method

Stable and reliable high-precision satellite orbit products are the prerequisites for the positioning services with high performance.In general,the positioning accuracy depends strongly on the quality of satellite orbit and clock products,especially for absolute positioning modes,such as Precise Point Positioning(PPP).With the development of real-time services,real-time Precise Orbit Determination(POD)is indispensable and mainly includes two methods:the ultra-rapid orbit prediction and the real-time filtering orbit determination.The real-time filtering method has a great potential to obtain more stable and reliable products than the ultra-rapid orbit prediction method and thus has attracted increasing attention in commercial companies and research institutes.However,several key issues should be resolved,including the refinement of satellite dynamic stochastic models,adaptive filtering for irregular satellite motions,rapid convergence,and real-time Ambiguity Resolution(AR).This paper reviews and summarizes the current research progress in real-time filtering POD with a focus on the aforementioned issues.In addition,the real-time filtering orbit determination software developed by our group is introduced,and some of the latest results are evaluated.The Three-Dimensional(3D)real-time orbit accuracy of GPS and Galileo satellites is better than 5 cm with AR.In terms of the convergence time and accuracy of kinematic PPP AR,the better performance of the filter orbit products is validated compared to the ultra-rapid orbit products.