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.

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.

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.