Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades,there is still a lack of progress in information systems dynamics(ISD),owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems.The value of ISD lies in its ability to guide the design,development,application,and evaluation of largescale information system-of-systems(So Ss),just as mechanical dynamics theories guide mechanical systems engineering.This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space,improving a mathematical theory for information measurement,and proposing a dynamic configuration model for information systems.In this way,it establishes a basic theoretical framework for ISD.The proposed theoretical methodologies have been successfully applied and verified in the Smart Court So Ss Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization.The proposed ISD provides an innovative paradigm for the analysis,design,development,and evaluation of large-scale complex information systems,such as electronic government and smart cities.

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.

Active control of turbulence for drag reduction based on the detection of near-wall streamwise vortices by wall information

The spatial relations between the measurable wall quantities （streamwise shear stress τwx, spanwise shear stress τwz, and pressure fluctuations Pw） and the near-wall streamwise vortices （NWSV） are investigated via direct numerical simulation （DNS） databases of fully developed turbulent channel flow at a low Reynolds number. In the stan- dard turbulent channel flow, the results show that all the wall measurable variables are closely associated with the NWSV. But after applying a stochastic interference, the relation based on τwx breaks down while the correlations based on Pw and τwz are still robust. Hence, two wall flow quantities based on Pw and τwz are proposed to detect the NWSV. As an appli- cation, two new control schemes are developed to suppress the near-wall vortical structures using the actuation of wall blowing/suction and obtain 16 % and 11% drag reduction, respectively.

Information rough communication based on both-branch fuzzy set

The concept of rough communication based on both-branch fuzzy set is proposed, in which the loss of information may exist, for each agent there has a different language and can not provide precise communication to each other. The method of information measure in a rough communication based on both-branch fuzzy set is proposed. By using some concepts, such as ｜α｜-both-branch rough communication cut, the relation theorem between rough communication based on both-branch fuzzy concept and rough communication based on classical concept is obtained. Finally, an example of rough communication based on both-branch fuzzy set is given.

Difference of Visual Information Metric Based on Entropy of Primitive

Image sparse representation is a method of efficient compression and coding of image signal in the process of digital image processing.Image after sparse representation,to enhance the transmission efficiency of the image signal.Entropy of Primitive(EoP)is a statistical representation of the sparse representation of the image,which indicates the probability of each base element.Based on the EoP,this paper presents an image quality evaluation method-Difference of Visual Information Metric(DVIM).The principle of this method is to evaluate the image quality with the difference between the original image and the distorted image.The comparative experiments between DVIM&PSNR&SSIM are carried out.It was found that there was a great improvement in the image quality evaluation of geometric changes.This method is an effective image quality evaluation method,which overcomes the weakness of other quality evaluation methods for geometrically changing images to a certain extent,and is more consistent with the subjective observation of the human eye.

Application of Wireless Network Positioning Technology Based on GPS in Geographic Information Measurement

Based on the analysis of the advantages and disadvantages of GPS positioning system in practical application,this paper proposes the combination of wireless network positioning technology and GPS positioning system to overcome the low accuracy of GPS positioning system in the case of occlusion.This paper introduces in detail the principle of the application of wireless network positioning technology based on GPS positioning system in geographic information measurement,and illustrates its practical application in production by taking coal mine positioning as an example.

Shannon Entropy as a Measurement of the Information in a Multiconfiguration Dirac-Fock Wavefunction

Discrete Shannon entropy is applied to describe the information in a multiconfiguration Dirac Fock wavefunction. The dependence of Shannon entropy is shown as enlarging the configuration space and it can reach saturation when there are enough configuration state wavefunctions to obtain the convergent energy levels; that is, the calculation procedure in multiconfiguration Dirae Fock method is an entropy saturation process. At the same accuracy level, the basis sets for the smallest entropy are best able to describe the energy state. Additionally, a connection between the sudden change of Shannon information entropies and energy level crossings along with isoelectronic sequence can be set up, which is helpful to find the energy level crossings of interest in interpreting and foreseeing the inversion scheme of energy levels for an x-ray laser.

Measuring causality by taking the directional symbolic mutual information approach

We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is different from the conventional conceptions.Based on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual information.This measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series.In addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling delays.This method has the property of robustness,conceptual simplicity,and fast computational speed.

Detecting high-dimensional multipartite entanglement via some classes of measurements

Mutually unbiased bases, mutually unbiased measurements and general symmetric informationally complete measure- ments are three related concepts in quantum information theory. We investigate multipartite systems using these notions and present some criteria detecting entanglement of arbitrary high dimensional multi-qudit systems and multipartite sys- tems of subsystems with different dimensions. It is proved that these criteria can detect the k-nonseparability （k is even） of multipartite qudit systems and arbitrary high dimensional multipartite systems of m subsystems with different dimensions. We show that they are more efficient and wider of application range than the previous ones. They provide experimental implementation in detecting entanglement without full quantum state tomography.

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.

Modified sequential importance resampling filter

In order to deal with the particle degeneracy and impov- erishment problems existed in particle filters, a modified sequential importance resampling （MSIR） filter is proposed. In this filter, the resampling is translated into an evolutional process just like the biological evolution. A particle generator is constructed, which introduces the current measurement information （CMI） into the resampled particles. In the evolution, new particles are first pro- duced through the particle generator, each of which is essentially an unbiased estimation of the current true state. Then, new and old particles are recombined for the sake of raising the diversity among the particles. Finally, those particles who have low quality are eliminated. Through the evolution, all the particles retained are regarded as the optimal ones, and these particles are utilized to update the current state. By using the proposed resampling approach, not only the CMI is incorporated into each resampled particle, but also the particle degeneracy and the loss of diver- sity among the particles are mitigated, resulting in the improved estimation accuracy. Simulation results show the superiorities of the proposed filter over the standard sequential importance re- sampling （SIR） filter, auxiliary particle filter and unscented Kalman particle filter.

Robust Object Tracking via Information Theoretic Measures

Object tracking is a very important topic in the field of computer vision.Many sophisticated appearance models have been proposed.Among them,the trackers based on holistic appearance information provide a compact notion of the tracked object and thus are robust to appearance variations under a small amount of noise.However,in practice,the tracked objects are often corrupted by complex noises(e.g.,partial occlusions,illumination variations)so that the original appearance-based trackers become less effective.This paper presents a correntropy-based robust holistic tracking algorithm to deal with various noises.Then,a half-quadratic algorithm is carefully employed to minimize the correntropy-based objective function.Based on the proposed information theoretic algorithm,we design a simple and effective template update scheme for object tracking.Experimental results on publicly available videos demonstrate that the proposed tracker outperforms other popular tracking algorithms.

Improvement of Location Algorithm in Wireless Networks

In order to improve the accuracy of wireless network positioning,the triangulation method of wireless network positioning technology is proposed,which is based on the linear least square fitting method.It makes the observed value and the fitting value very close,effectively solves the problem of significant contradiction between the fitting result and the observed value in the principle of least square method,and can realize the accurate measurement of geographic information by wireless network positioning technology.

Oscillatory Shannon entropy in the process of equilibration of nonequilibrium crystalline systems

We present a study of the equilibration process of some nonequilibrium crystalline systems by means of molecular dynamics simulation technique. The nonequilibrium conditions are achieved in the systems by randomly defining velocity components of the constituent atoms. The calculated Shannon entropy from the probability distribution of the kinetic energy among the atoms at different instants during the process of equilibration shows oscillation as the system relaxes towards equilibrium. Fourier transformations of these oscillating Shannon entropies reveal the existence of Debye frequency of the concerned system.

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

Average coherence with respect to complementary measurements

We investigate the average coherence with respect to a complete set of complementary measurements.By using a Wigner-Yanase skew information-based coherence measure introduced in Luo and Sun(2017 Phys.Rev.A 96,022130),we evaluate the average coherence of a state with respect to any complete set of mutually unbiased measurements and general symmetric informationally complete measurements,respectively.We also establish analytically the relations among these average coherences.