基于Skewness纹理的北京城市化区域提取

北京地区部分农作物和城市化区域的光谱属性接近,仅仅利用光谱信息无法有效地提取北京的城市化区域。提出了利用Skewness纹理区分农作物和城市化区域,结合光谱和高程信息可以有效提取城市化区域。经过大量的样本检验表明,引入Skewness特征能够大幅度提高分类精度。

Skewness of subsurface ocean temperature in the equatorial Pacific based on assimilated data

The skewness of subsurface temperature anomaly in the equatorial Pacific Ocean shows a significant asymmetry between the east and west. A positive temperature skewness appears in the equatorial eastern Pacific, while the temperature skewness in the western and central Pacific is primarily negative. There is also an asymmetry of the temperature skewness above and below the climatological mean therrnocline in the central and western Pacific. A positive skewness appears below the thermocline, but the skewness is negative above the thermocline. The distinctive vertical asymmetry of the temperature skewness is argued to be attributed to the asymmetric temperature response to upward and downward thermocline displacement in the presence of the observed upper-ocean vertical thermal structure. Because of positive （negative） second derivative of temperature with respect to depth below （above） the thermocline, an upward and a downward shift of the thermocline with equal displacement would lead to a negative temperature skewness above the thermocline but a positive skewness below the thermocline. In the far eastern equatorial Pacific, the thermocline is close to the base of the mixed layer, the shape of the upper-ocean vertical temperature profile cannot be kept. Positive skewness appears in both below the thermocline and above the thermocline in the far eastern basin. Over the central and eastern Pacific, the anomalies of the subsurface waters tend to entrain into the surface mixed layer （by climatological mean upwelling） and then affect the SST. Hence, the positive （negative） subsurface skewness in the far eastern （central） Pacific may favor positive （negative） SST skewness, which is consistent with the observational fact that more La Nina （EI Nino） occur in the central （eastern） Pacific. The present result implies a possible new paradigm for EI Nino and La Nina amplitude asymmetry in the eastern Pacific.

SKEWNESS FACTOR OF TURBULENT VELOCITY DERIVATIVE

Using nonequilibrium statistical mechanics closure method, it is shown that the skewness factor of the velocity derivative of isotropic turbulence ap- proaches a constant -0.515 when the Reynolds number is very high, which is in agree- ment with the DNS (direct numerical simulation) result of Vincent and Meneguzzi (1991).

A skew–normal mixture of joint location, scale and skewness models

Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models： a skew-normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation-Maximization （EM） algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index （BMI） data are presented.

The role of velocity derivative skewness in understanding non-equilibrium turbulence

The turbulence governed by the Navier-Stokes equation is paramount in many physical processes.However,it has been considered as a challenging problem due to its inherent nonlinearity,non-equilibrium,and complexity.Herein,we review the connections between the velocity derivative skewness Sk and the non-equilibrium properties of turbulence.Sk,a reasonable candidate for describing the non-equilibrium turbulence,which varies during the non-equilibrium procedure.A lot of experimental or numerical evidences have shown that the perturbation of energy spectrum,which associated with the excitation of large scales,results in an obvious variation of Sk,and Sk is a negative value in this rapid energy decay process.The variation of positive Sk is closely related to the perturbation of transfer spectrum,and this corresponds to the backward energy transfer process.In addition,the skewness characterizes the production(or reduction)rate of enstrophy due to vortex stretching(or compression).Using the transport equation of turbulent energy dissipation rate and enstrophy,it is possible to establish a theoretical connection between skewness and the non-equilibrium turbulence.It is expected that this work could trigger the rapid advancement of the future studies of non-equilibrium turbulence,and also the improvement of turbulence models.

A Statistical Power Comparison of the Kolmogorov-Smirnov Two-Sample Test and the Wald Wolfowitz Test in Terms of Fixed Skewness and Fixed Kurtosis in Large Sample Sizes

In this study, the statistical powers of Kolmogorov-Smimov two-sample （KS-2） and Wald Wolfowitz （WW） tests, non-parametric tests used in testing data from two independent samples, have been compared in terms of fixed skewness and fixed kurtosis by means of Monte Carlo simulation. This comparison has been made when the ratio of variance is two as well as with equal and different sample sizes for large sample volumes. The sample used in the study is： （25, 25）, （25, 50）, （25, 75）, （25, 100）, （50, 25）, （50, 50）, （50, 75）, （50, 100）, （75, 25）, （75, 50）, （75, 75）, （75, 100）, （100, 25）, （100, 50）, （100, 75）, and （100, 100）. According to the results of the study, it has been observed that the statistical power of both tests decreases when the coefficient of kurtosis is held fixed and the coefficient of skewness is reduced while it increases when the coefficient of skewness is held fixed and the coefficient of kurtosis is reduced. When the ratio of skewness is reduced in the case of fixed kurtosis, the WW test is stronger in sample volumes （25, 25）, （25, 50）, （25, 75）, （25, 100）, （50, 75）, and （50, 100） while KS-2 test is stronger in other sample volumes. When the ratio of kurtosis is reduced in the case of fixed skewness, the statistical power of WW test is stronger in volume samples （25, 25）, （25, 75）, （25, 100）, and （75, 25） while KS-2 test is stronger in other sample volumes.

A Tale of Two“Skewness”:Managerial Epidemic Experience,Probability Weighting and Financial Market Stability

Under probability weighting,entrepreneurs with skewness preference tend to seek rightskewed and avoid left-skewed risks.We show that Chinese firms managed by CEOs with professional epidemic experience,i.e.,who previously experienced the outbreak of SARS during their tenure as high level executives,have a lower stock price crash risk measured by the negative skewness of stock prices in subsequent periods.In particular,those firms intentionally avoid stock price crashes by adopting more conservative strategies in decisionmaking.Overall,we provide the first evidence on the unintended effect of entrepreneurs'subjective judgments of the probabilities of disease outbreaks on financial market stability.These have long-term implications for the financial system.

SKEWNESS OF RETURN DISTRIBUTION AND COEFFICIENT OF RISK PREMIUM

The skewness of the return distribution is one of the important features of the security price.In this paper,the authors try to explore the relationship between the skewness and the coefficient ofrisk premium.The coefficient of the risk premium is estimated by a GARCH-M model,and the robustmeasurement of skewness is calculated by Groeneveld-Meeden method.The empirical evidences forthe composite indexes from 33 securities markets in the world indicate that the risk compensationrequirement in the market where the return distribution is positively skewed is virtually zero,andthe risk compensation requirement is positive in a significant level in the market where the returndistribution is negative skewed.Moreover,the skewness is negatively correlated with the coefficient ofthe risk premium.

Variable Selection in Joint Location, Scale and Skewness Models of the Skew-Normal Distribution

Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and(or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes,and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.

Skewness of generalized Petersen graphs and related graphs

The skewness of a graph G is the minimum number of edges in G whose removal results in a planar graph. In this paper, we determine the skewness of the generalized Petersen graph P（4k, k） and hence a lower bound for the crossing number of P（4k, k）. In addition, an upper bound for the crossing number of P（4k, k） is also given.

Diophantine equations and Fermat's last theorem for multivariate(skew-)polynomials

Fermat’s Last Theorem is a famous theorem in number theory which is difficult to prove.However,it is known that the version of polynomials with one variable of Fermat’s Last Theorem over C can be proved very concisely.The aim of this paper is to study the similar problems about Fermat’s Last Theorem for multivariate(skew)-polynomials with any characteristic.

A Design of Modular Interior Ferrite Magnet Fluxswitching Linear Motor for Track Transport

A novel topology of modular ferrite magnet fluxswitching linear motor(FMFSLM)use for track transport is presented in this paper,which enables more ferrite magnets to be inserted into the primary iron core.The motor has a significant low-cost advantage in long-distance linear drive.The proposed FMFSLM’s structure and working principle were introduced.Further,the thrust force expression of the motor was established.The thrust force components triggering thrust force ripple were investigated,and their expressions can be obtained according to the inductances’Fourier series expressions.Resultantly,the relationship between the harmonics of thrust force and that of self-and mutual inductances was revealed clearly.Based on the relationship,a skewed secondary should be practical to reduce the thrust force ripple.Thus,the effect of employing a skewed secondary to the proposed FMFSLM was investigated,and an optimized skewing span distance was determined.Finite element analysis(FEA)was conducted to validate the exactness of the theoretical analysis.The simulation results indicate that the strategy of suppressing thrust force ripple has a significant effect.Meanwhile,the motor maintains a good efficiency characteristic.The results of the prototype experiment are in good agreement with FEAs,which further verifies the proposed modular interior FMFSLM’s practicability.

Nonadditive Skew(Anti-)commuting Maps on Operator Algebras

In this paper,we first give the general forms of skew commuting maps and skew anti-commuting maps by the Peirce decomposition on a unital ring with a nontrivial idempotent,respectively,and then,as applications,we obtain the concrete characterizations of all nonadditive skew(anti-)commuting maps on some operator algebras.

Robustness Assessment of Asynchronous Advantage Actor-Critic Based on Dynamic Skewness and Sparseness Computation: A Parallel Computing View

Reinforcement learning as autonomous learning is greatly driving artificial intelligence(AI)development to practical applications.Having demonstrated the potential to significantly improve synchronously parallel learning,the parallel computing based asynchronous advantage actor-critic(A3C)opens a new door for reinforcement learning.Unfortunately,the acceleration's influence on A3C robustness has been largely overlooked.In this paper,we perform the first robustness assessment of A3C based on parallel computing.By perceiving the policy's action,we construct a global matrix of action probability deviation and define two novel measures of skewness and sparseness to form an integral robustness measure.Based on such static assessment,we then develop a dynamic robustness assessing algorithm through situational whole-space state sampling of changing episodes.Extensive experiments with different combinations of agent number and learning rate are implemented on an A3C-based pathfinding application,demonstrating that our proposed robustness assessment can effectively measure the robustness of A3C,which can achieve an accuracy of 83.3%.

一种校准线延时差异的方法分析

阐述一种在发送端通过自发自收的方式自动校准并行数据不同PIN延时的方法,可以有效提高接收芯片Clock和Data的相位裕度,降低因并行传输信号完整性指标较差,整个系统不稳定而出现花屏,闪屏的现象。

Application of ACP Nonlinear Math in Analyzing Arithmetic and Radiation Transmission Data (Application 1 & 2) [4-21-2024, 820P] (V)

In this study, we explore the application of ACP (asymptotic curve based and proportionality oriented) Alpha Beta (αβ) Nonlinear Math to analyze arithmetic and radiation transmission data. Specifically, we investigate the relationship between two variables. The novel approach involves collecting elementary “y” data and subsequently analyzing the asymptotic cumulative or demulative (opposite of cumulative) Y data. In part I, we examine the connection between the common linear numbers and ideal nonlinear numbers. In part II, we delve into the relationship between X-ray energy and the radiation transmission for various thin film materials. The fundamental physical law asserts that the nonlinear change in continuous variable Y is negatively proportional to the nonlinear change in continuous variable X, expressed mathematically as dα = −Kdβ. Here: dα {Y, Yu, Yb} represents the change in Y, with Yu and Yb denoting the upper and baseline asymptote of Y. dβ {X, Xu, Xb} represents the change in X, with Xu and Xb denoting the upper and baseline asymptote of X. K represents the proportionality constant or rate constant, which varies based on equation arrangement. K is the key inferential factor for describing physical phenomena.

A PROJECTION NT-TYPE TEST OF ELLIPTICAL SYMMETRY BASED ON THE SKEWNESS AND KURTOSIS MEASURES

In this paper, we propose a new approach to the test of elliptical symmetry based on theprojection pursuit (P.P.) technique, the number-theoretic method, skewness and kurtosis. Thelimiting null distributions of the new statistics are derived. The powers of the tests against sixalternatives are calculated by simulation, which shows that the new t.st's are acceptable.

Severity Based Light-Weight Encryption Model for Secure Medical Information System

As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.

基于抗退化混沌系统和初等元胞自动机的动态S盒设计

S盒是多数分组密码算法的基本非线性模块,可以满足分组密码算法的混淆、扩散要求。为了提高混沌S盒的安全性,文中利用抗退化混沌系统生成S盒元素,基于初等元胞自动机生成S盒检索表的方式来生成S盒。抗退化混沌系统可以避免发生Skew Tent系统进入不动点的情况,消除低精度下系统进入短周期的现象。因为初等元胞自动机的迭代规则为二元域上的运算,且满足时空上的离散性,所以,将初等元胞自动机运用到混沌分组密码中时,不用考虑动力学退化的问题。当初等元胞自动机的迭代规则为全局混沌规则时,只要元胞个数足够,就可以保证输出的伪随机性。利用初等元胞自动机生成S盒的检索表,在保证S盒设计的混淆原则的同时还可以简化S盒的生成步骤。最后对所设计的S盒进行安全性分析对比,实验结果表明,所提方法生成的S盒具有良好的安全性,满足分组密码的混淆、扩散原则,可用于混沌分组密码算法设计中。

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.