New multi-layer data correlation algorithm for multi-passive-sensor location system

Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Study on the Monitoring Malfunction of Water Pollution during Drought or Flood Period and Low-carbon and High-value Methodology--A Case Study of the Correlation Test of Water,Soil and Gas Pollution in Xiangxiang County

Based on the low-carbon and high-value methodology of chemical ecology and chemical informatics,combining theory and methods,taking saving,environmental protection,low carbon,high production,high value and circulation as values and aims,the relationship between human and land as a basis,ecosystem as a center,overall control as a goal and agricultural ecological engineering as a mean,environmental pollution detection,as one of bottlenecks for agricultural products and food security,should be solved firstly;through the field survey in dry years from 2009 to 2010 when drought and flood were frequent and the frequency of drought was higher than that of flood,plus the determination of surface water flow and water quantity in a small typical river basin,the correlation of local water,soil and gas in the county could be found,and the transfer of monitoring focus from water environment to atmospheric environment was possible and necessary.The study would promote the quantitative research on the correlation among water,soil and gas,and the results were in accordance with the conclusions of related studies.

ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

Observation and Modeling of Geostationary Orbit Electron Energization Induced by Enhanced Dayside Whistler-Mode Waves

We provide correlated observations of enhanced dayside whistler-mode waves and energetic electron acceleration collected by the CLUSTER and GOES satellites during the 23～24 September 2001 storm. Energetic （〉0.6 MeV） electron fluxes are found to increase significantly during the recovery phase and the main phase, by a factor of～50 higher than the prestorm level. These high electron fluxes occur when strong dayside whistler-mode waves are present. Two-dimensional （2D） numerical simulations are carried out and the results demonstrate that the dayside whistler-mode wave can contribute to such enhancements in electron flux within 24 h, consistent with the observation.

A New Approach for Dispersion Parameters

This paper presents a new approach to identify and estimate the dispersion parameters for bivariate, trivariate and multivariate correlated binary data, not only with scalar value but also with matrix values. For this direction, we present some recent studies indicating the impact of over-dispersion on the univariate data analysis and comparing a new approach with these studies. Following the property of McCullagh and Nelder [1] for identifying dispersion parameter in univariate case, we extended this property to analyze the correlated binary data in higher cases. Finally, we used these estimates to modify the correlated binary data, to decrease its over-dispersion, using the Hunua Ranges data as an ecology problem.

Correlation analysis of combined and separated effects of wing deformation and support system in the CAE-AVM study

The Chinese Aeronautical Establishment（CAE） Aerodynamic Validation Model（AVM）is a dual-purpose test geometry dedicated to verify the aerodynamic performance of a conceptual intercontinental jet aircraft and to provide a dataset for CFD software validation. To this end, a scaled model of the AVM was tested in the High-Speed Tunnel（HST） of the German-Dutch Wind-tunnels（DNW） with special test consideration and instrumentation. For complementary analysis of experimental results, specific CAE-AVM geometries are analyzed using a CAE inhouse CFD code. The specific geometries consist of a baseline aircraft, an aircraft with a deformed wing shape, and an aircraft with both a deformed wing shape and a representation of the model support system used in the wind tunnel. Detailed analysis of numerical and experimental results is presented; both the combined and individual attributions of wing deformation and support system interference on wing pressure distributions and longitudinal aerodynamic characteristics are summarized.

Correlation analysis between characteristics under gastroscope and image information of tongue in patients with chronic gastritis

OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under gastroscopy.RESULTS:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/macula.There are a total of 9 strong correlation rules.CONCLUSIONS:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.The conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue.

A new modeling scheme for powered parafoil unmanned aerial vehicle platforms: Theory and experiments

A novel framework is established for accurate modeling of Powered Parafoil Unmanned Aerial Vehicle(PPUAV). The model is developed in the following three steps: obtaining a linear dynamic model, simplifying the model structure, and estimating the model mismatch due to model variance and external disturbance factors. First, a six degree-of-freedom linear model, or the structured model, is obtained through dynamic establishment and linearization. Second, the data correlation analysis is adopted to determine the criterion for proper model complexity and to simplify the structured model. Next, an active model is established, combining the simplified model with the model mismatch estimator. An adapted Kalman filter is utilized for the real-time estimation of states and model mismatch. We finally derive a linear system model while taking into account of model variance and external disturbance. Actual flight tests verify the effectiveness of our active model in different flight scenarios.