Information uncertainty related to marked random times and optimal investment

We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous ran-dom mark added at default time.Two types of agents who have different levels of information are considered.We first make precise the insider’s information flow by using the theory of enlargement of filtrations and then obtain explicit logarith-mic utility maximization results to compare optimal wealth for the insider and the ordinary agent.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Data processing of small samples based on grey distance information approach

Data processing of small samples is an important and valuable research problem in the electronic equipment test. Because it is difficult and complex to determine the probability distribution of small samples, it is difficult to use the traditional probability theory to process the samples and assess the degree of uncertainty. Using the grey relational theory and the norm theory, the grey distance information approach, which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples, is proposed in this article. The definitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples, with their characteristics and algorithms, are introduced. The correlative problems, including the algorithm of estimated value, the standard deviation, and the acceptance and rejection criteria of the samples and estimated results, are also proposed. Moreover, the information whitening ratio is introduced to select the weight algorithm and to compare the different samples. Several examples are given to demonstrate the application of the proposed approach. The examples show that the proposed approach, which has no demand for the probability distribution of small samples, is feasible and effective.

Study of testability measurement method for equipment based on Bayesian network model

To analyze and evaluate the testability design of equipment, a testability analysis method based on Bayesian network inference model is proposed in the paper. The model can adequately apply testability information and many uncertainty information of design and maintenance process, so it can analyze testability by and large from Bayesian inference. The detailed procedure to analyze and evaluate testability for equipments by Bayesian network is given in the paper. Its modeling process is simple, its formulation is visual, and the analysis results are more reliable than others. Examples prove that the analysis method based on Bayesian network inference can be applied to testability analysis and evaluation for complex equipments.

Possibilistic entropy-based measure of importance in fault tree analysis

With respect to the subjective factors and nonlinear characteristics inherent in the important identification of fault tree analysis （FTA）, a new important measure of FTA is proposed based on possibilistic information entropy. After investigating possibilistic information semantics, measure-theoretic terms, and entropy-like models, a two-dimensional framework has been constructed by combining both the set theory and the measure theory. By adopting the possibilistic assumption in place of the probabilistic one, an axiomatic index of importance is defined in the possibility space and then the modelling principles are presented. An example of the fault tree is thus provided, along with the concordance analysis and other discussions. The more conservative numerical results of importance rankings, which involve the more choices can be viewed as “soft” fault identification under a certain expected value. In the end, extension to evidence space and further research perspectives are discussed.

Group Performance Evaluation in Universities with Entropy Method

Due to the characteristics of variable quantity and great individual difference in group evaluation,an effective entropy-based method for group performance evaluation was proposed. First,a group evaluation indexing system was built upon the real scenarios of universities. Then, a continuous ordered weighted operator was introduced to eliminate uncertainty evaluation information. By using an entropy strategy,a systematic approach to group performance evaluation was established. Finally, an illustrative example was provided,showing the practicability of the proposed methods in real applications of the efficient performance appraisal theory of colleges and universities.

Uncertainty relations based on skew information with quantum memory

Uncertainty principle is one of the most fascinating features of the quantum world.It asserts a fundamental limit on the precision with which certain pairs of physical properties of a particle,such as position and momentum,can not be simultaneously known.The uncertainty principle has attracted considerable attention since the innovation of quantum mechanics and has been investigated in terms of various types of uncertainty inequalities:in terms of the noise and disturbance,according to successive measurements,as informational recourses in entropic terms,by means of

A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product

Quantitative remote sensing product(QRSP)validation is a complex process to assess the accuracy and uncertainty independently using reference data with multiple land cover types and long-time series.A web-based system named as LAnd surface remote sensing Product VAlidation system(LAPVAS)is described in this paper,which is used to implement the QRSPs validation process automatically.The LAPAVS has two subsystems,the Validation Databases Subsystem and the Accuracy Evaluation Subsystem.Three functions have been implemented by the two subsystems for a comprehensive QRSP validation:(1)a standardized processing of reference data and storage of these data in validation databases;(2)a consistent and comprehensive validation procedure to assess the QRSPs’accuracy and uncertainty;and(3)a visual process customization tool with which the users can register new validation data,host new reference data,and readjust the validation workflows for the QRSP accuracy assessment.In LAPVAS,more than 100 GB of reference data warehoused in validation databases for 13 types of QRSPs’validation.One of the key QRSPs,land surface albedo,is selected as an example to illustrate the application of LAPVAS.It is demonstrated that the LAPVAS has a good performance in the land surface remote sensing product validation.