A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Traffic Assignment Forecast Model Research in ITS

As an important role in the development of ITS, traffic assignment forecast is always the research focus. Based on the analysis of classic traffic assignment forecast models, an improved traffic assignment forecast model, multi-ways probability and capacity constraint (MPCC) is presented. Using the new traffic as- signment forecast model to forecast the traffic volume will improve the rationality and veracity of traffic as- signment forecast.

Application of Dempster-Shafer theory in mineral resource potential mapping

The Dempster-Shafer theory has been successfully applied to mineral resource potential mapping in GIS environmental. In this applied form, basic probability assignment and combined basic probability assignment are applied to measuring map pattem and map pattem combination, respectively; and the environment composed of the only two singleton sets （deposit set and non-deposit set）, is used for expressing the entire map area. For a subarea in which the certain map pattern combination exists, the combined basic probability assignment corresponding to the map pattern combination existing in this subarea, expresses the belief of inferring the subarea belonging to the deposit set from the evidence that the corresponding map pattern combination existing in the subarea. Thus, it may be served as a statistical index measuring the relative mineral resource potentials of the subarea. And it may be determined like 1） dividing the map area into a series of small equal-sized grid cells and then select the training sample set composed of the well-known grid cells or the entire grid cells; 2） estimating the basic probability assignments corresponding to each map pattern fromthe training sample set; 3） determining the map pattern combination existing in each cell, and then appling the Dempster＇s Rule of Combination to integrating the all basic probability assignments corresponding to the map patterns existing in the cell into the combined basic probability assignment. Mineral resource potential mapping with the Dempster-Shafer theory is demonstrated on a case study to select mineral resource targets. The experimental results manifest that the model can be compared with the weights of evidence model in the effectiveness of mineral resource target selection.

DETERMINGING BPA UNDER UNCERTAINTY ENVIRONMENTS AND ITS APPLICATION IN DATA FUSION

Dempster-Shafer (DS) theory of evidence has been widely used in many data fusion ap- plication systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, a new method to obtain Basic Probability Assignment (BPA) is proposed based on the similarity measure between generalized fuzzy numbers. In the proposed method, species model can be constructed by determination of the min, average and max value to construct a fuzzy number. Then, a new Radius Of Gravity (ROG) method to determine the similarity measure between generalized fuzzy numbers is used to calculate the BPA functions of each instance. Finally, the efficiency of the proposed method is illustrated by the classi- fication of Iris data.

A NEW APPROACH OF SIMULTANEOUS FAULTS DIAGNOSIS BASED ON RANDOM SETS AND DSMT

Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults' model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is pro- posed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

A generalized evidence distance

How to efficiently measure the distance between two basic probability assignments（BPAs） is an open issue. In this paper, a new method to measure the distance between two BPAs is proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method.

Attribute measure recognition approach and its applications to emitter recognition

This paper studies the emitter recognition problem. A new recognition method based on attribute measure for emitter recognition is put forward. The steps of the method are presented. The approach to determining the weight coefficient is also discussed. Moreover, considering the temporal redundancy of emitter information detected by multi-sensor system, this new recognition method is generalized to multi-sensor system. A method based on the combination of attribute measure and D-S evidence theory is proposed. The implementation of D-S reasoning is always restricted by basic probability assignment function. Constructing basic probability assignment function based on attribute measure is presented in multi-sensor recognition system. Examples of recognizing the emitter purpose and system are selected to demonstrate the method proposed. Experimental results show that the performance of this new method is accurate and effective.

Using the Dempster-Shafer Theory of Evidence to Rank Documents

Multi-source information can be utilized collaboratively to improve the performance of information retrieval. To make full use of the document and collection information, this paper introduces a new informa- tion retrieval model that relies on the Dempster-Shafer theory of evidence. Each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster＇s rule of combination, and the belief committed to the relevance is obtained. Retrieved documents are then ranked according to the belief committed to the relevance. Several basic probability as- signments are also proposed. Extensive experiments over the Text REtrieval Conference （TREC） test col- lection ClueWeb09 show that the proposed model provides performance similar to that of the Vector Space Model （VSM）. Under certain probability assignments, the proposed model outperforms the VSM by 63% in terms of mean average precision,