Ground-Based Cloud Using Exponential Entropy/Exponential Gray Entropy and UPSO

Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast.Thus safety of aircraft taking off and landing and air flight can be guaranteed.Thresholding is a kind of simple and effective method of cloud classification.It can realize automated ground-based cloud detection and cloudage observation.The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect.Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation.In view of the above-mentioned problems,multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization(UPSO)are proposed.Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy.In addition,exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster.Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy.In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection,the UPSO algorithm is adopted.It can find the optimal thresholds quickly and accurately.As a result,the real-time processing of segmentation of groundbased cloud image can be realized.The experimental results show that,in comparison with the existing groundbased cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy,the proposed methods can extract the boundary shape,textures and details feature of cloud more clearly.Therefore,the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved.

A New Exponential Fuzzy Entropy of Order-(α,β)and its Application in Multiple Attribute Decision-Making Problems

Fuzzy entropy is an important concept to measure the fuzzy information.Measure of fuzziness of a fuzzy set is the measure of its fuzziness.In the present communication,we have defined an exponential fuzzy entropy of order-(α,β).Besides establishing the validity of the proposed measure,we have also discussed some of its properties.At last,we have given the application of the proposed measure in multiple attribute decision-making problems.In this section,we have considered two cases for the weights of attributes:One is the case when weights are completely unknown to us,and the other is the case when weights are partially known to us.

On thermodynamic self-consistency of generic axiomatic-nonextensive statistics

Generic axiomatic-nonextensive statistics introduces two asymptotic properties,to each of which a scaling function is assigned.The first and second scaling properties are characterized by the exponents c and d,respectively.In the thermodynamic limit,a grand-canonical ensemble can be formulated.The thermodynamic properties of a relativistic ideal gas of hadron resonances are studied,analytically.It is found that this generic statistics satisfies the requirements of the equilibrium thermodynamics.Essential aspects of the thermodynamic self-consistency are clarified.Analytical expressions are proposed for the statistical fits of various transverse momentum distributions measured in most-central collisions at different collision energies and colliding systems.Estimations for the freezeout temperature（T_（ch）） and the baryon chemical potential（μ_b） and the exponents c and d are determined.The earlier are found compatible with the parameters deduced from Boltzmann-Gibbs（BG） statistics（extensive）,while the latter refer to generic nonextensivities.The resulting equivalence class（c,d） is associated with stretched exponentials,where Lambert function reaches its asymptotic stability.In some measurements,the resulting nonextensive entropy is linearly composed on extensive entropies.Apart from power-scaling,the particle ratios and yields are excellent quantities to highlighting whether the particle production takes place（non）extensively.Various particle ratios and yields measured by the STAR experiment in central collisions at 200,62.4 and 7.7 GeV are fitted with this novel approach.We found that both c and d 〈 1,i.e.referring to neither BG-nor Tsallis-type statistics,but to（c,d）-entropy,where Lambert functions exponentially rise.The freezeout temperature and baryon chemical potential are found comparable with the ones deduced from BG statistics（extensive）.We conclude that the particle production at STAR energies is likely a nonextensive process but not necessarily BG or Tsallis type.