Performance of CMIP6 models in simulating the dynamic sea level:Mean and interannual variance

本研究采用卫星测高数据与第六次国际耦合模式比较计划(CMIP6)海平面动力进行对比,重点针对40S-40N地区的动力海平面(DSL),评估了模式对其平均态与年际变率的综合模拟能力,结果表明,对于DSL平均态的模拟,模式与观测结果非常吻合,模式之间的差异较小.其中,副热带北大西洋是模拟偏差和模式间差异较为显著的区域,对于DSL年际变率的模拟,模式之间保持较高的一致性,但是,模式与观测结果存在明显差异,模式普遍低估了DSL的年际方差;其中,误差大值区域出现在副热带西边界流附近,模式分辨率会影响CMIP6对中小尺度海洋过程的重现能力,这可能是导致CMIP6历史模拟出现误差的原因之一.

基于mean-variance的服务集群负载均衡方法

大量并发请求任务进行分配时,负载调度机制是通过最小化响应时间及最大化节点利用率实现网络中节点的负载均衡,在基于遗传算法的负载均衡算法中,适应度函数设计对服务集群负载均衡效率产生重要的影响。对此提出了一种基于mean-variance的服务集群负载均衡方法对适应度函数进行优化,采用投资组合选择模型mean-variance进行最小化响应时间,以得到每个服务器资源利用率的权重,从而获得最优的分配组合,进而提高适应度函数的准确性和有效性。在不同服务环境下与其他模型进行比较,仿真结果表明,本文的负载均衡算法在节点利用率和响应时间方面使服务集群得到了更好的均衡。

摩擦市场条件下的Mean-Variance-Skewness模型

在投资组合选择模型中考虑了资产收益率分布中正的偏度水平,并通过引入一些市场摩擦因素建立了摩擦市场条件下的Mean-Variance-Skewness模型.提出了一个新的遗传算法加速其搜索收敛过程,解决了该模型的计算复杂性问题.在该模型框架内对交易费用和税收等市场摩擦因素进行了敏感性分析.研究证明资产收益率分布的偏度水平是与投资者的决策相关的,市场摩擦因素对投资者的决策行为也有直接的影响.因此,考虑摩擦市场条件下基于正偏度水平偏好的最优投资组合模型对投资者有很强的实践指导价值.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

MARKOV-MODULATED MEAN-VARIANCE PROBLEM FOR AN INSURER

In this paper, we consider an insurance company which has the option of investing in a risky asset and a risk-free asset, whose price parameters are driven by a finite state Markov chain. The risk process of the insurance company is modeled as a diffusion process whose diffusion and drift parameters switch over time according to the same Markov chain. We study the Markov-modulated mean-variance problem for the insurer and derive explicitly the closed form of the efficient strategy and efficient frontier. In the case of no regime switching, we can see that the efficient frontier in our paper coincides with that of [10] when there is no pure jump.

基于Mean-Variance-CVaR准则的保险公司最优资产配置与再保险策略

本文研究了连续时间下保险公司基于均值-方差-CVaR准则选择最优资产配置和再保险策略的问题.我们运用鞅方法求解优化问题并得到了相应的显示解.基于数值模拟,我们分析了在不同参数值下最优财富、资产配置和再保险策略随市场条件变化而变化的趋势.

Portfolio Choice under the Mean-Variance Model with Parameter Uncertainty

Assuming the investor is uncertainty-aversion,the multiprior approach is applied to studying the problem of portfolio choice under the uncertainty about the expected return of risky asset based on the mean-variance model. By introducing a set of constraint constants to measure uncertainty degree of the estimated expected return,it built the max-min model of multi-prior portfolio,and utilized the Lagrange method to obtain the closed-form solution of the model,which was compared with the mean-variance model and the minimum-variance model; then,an empirical study was done based on the monthly returns over the period June 2011 to May 2014 of eight kinds of stocks in Shanghai Exchange 50 Index. Results showed,the weight of multi-prior portfolio was a weighted average of the weight of mean-variance portfolio and that of minimumvariance portfolio; the steady of multi-prior portfolio was strengthened compared with the mean-variance portfolio; the performance of multi-prior portfolio was greater than that of minimum-variance portfolio. The study demonstrates that the investor can improve the steady of multi-prior portfolio as well as its performance for some appropriate constraint constants.

On Weighted Possibilistic Mean,Variance and Correlation of Interval-valued Fuzzy Numbers

In this paper, the concept of weighted possibilistic mean of interval- valued fuzzy number is first introduced. Further, the notions of weighted possibilistic variance, covariance and correlation of interval-valued fuzzy numbers are presented. Meantime, some important properties of them and relationships between them are studied.

On the Mean Difference Variance in Random Samples of Student’s Variables

The purpose of this paper is to obtain the expression of the sample mean difference variance of the Student’s distributive model. In the 2007 the study of the mean difference variance, after some decades, was resumed by Campobasso [1]. Using the Nair’s [2] and Lomnicki’s general results [3], he obtained the variance of sample mean difference for different distributive models (Laplace’s, triangular, power, logit, Pareto’s and Gumbel’s model). In addition he extended the knowledge comparing to the ones already known for the other distributive model (normal, rectangular and exponential model).

A Mean-variance Problem in the Constant Elasticity of Variance(CEV) Model

In this paper, we focus on a constant elasticity of variance （CEV） modeland want to find its optimal strategies for a mean-variance problem under two constrainedcontrols： reinsurance/new business and investment （no-shorting）. First, aLagrange multiplier is introduced to simplify the mean-variance problem and thecorresponding Hamilton-Jacobi-Bellman （HJB） equation is established. Via a powertransformation technique and variable change method, the optimal strategies withthe Lagrange multiplier are obtained. Final, based on the Lagrange duality theorem,the optimal strategies and optimal value for the original problem （i.e., the efficientstrategies and efficient frontier） are derived explicitly.

Bounds for Goal Achieving Probabilities of Mean-Variance Strategies with a No Bankruptcy Constraint

We establish, through solving semi-infinite programming problems, bounds on the probability of safely reaching a desired level of wealth on a finite horizon, when an investor starts with an optimal mean-variance financial investment strategy under a non-negative wealth restriction.

Goal Achieving Probabilities of Mean-Variance Strategies in a Market with Regime-Switching Volatility

In this paper, we establish properties for the switch-when-safe mean-variance strategies in the context of a Black-Scholes market model with stochastic volatility processes driven by a continuous-time Markov chain with a finite number of states. More precisely, expressions for the goal-achieving probabilities of the terminal wealth are obtained and numerical comparisons of lower bounds for these probabilities are shown for various market parameters. We conclude with asymptotic results when the Markovian changes in the volatility parameters appear with either higher or lower frequencies.

Estimation of Population Variance Using the Coefficient of Kurtosis and Median of an Auxiliary Variable under Simple Random Sampling

In this study we have proposed a modified ratio type estimator for population variance of the study variable y under simple random sampling without replacement making use of coefficient of kurtosis and median of an auxiliary variable x. The estimator’s properties have been derived up to first order of Taylor’s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. Empirical studies have been done using real populations to demonstrate the performance of the developed estimator in comparison with the existing estimators. The proposed estimator as illustrated by the empirical studies performs better than the existing estimators under some specified conditions i.e. it has the smallest Mean Squared Error and the highest Percentage Relative Efficiency. The developed estimator therefore is suitable to be applied to situations in which the variable of interest has a positive correlation with the auxiliary variable.

General Classes of Variance Estimators in Simple Random Sampling Using Multi-auxiliary Variables

Srivastava and Jhajj [ 1 6] proposed a class of estimators for estimating population variance using multi auxiliary variables in simple random sampling and they utilized the means and variances of auxiliary variables. In this paper, we adapted this class and motivated by Searle [13], and we suggested more generalized class of estimators for estimating the population variance in simple random sampling. The expressions for the mean square error of proposed class have been derived in general form. Besides obtaining the minimized MSE of the proposed and adapted class, it is shown that the adapted classis the special case of the proposed class. Moreover, these theoretical findings are supported by an empirical study of original data.

一种拖拉机核心执行机构的可靠性分析

以拖拉机转向机构为研究对象,对不确定因素下转向机构的运动精度进行分析,并利用概率学相关理论对转向机构运动误差和可靠性进行建模,同时采用一次二阶矩阵进行可靠性模型求解。结果表明:受转向机构工作过程可靠性不确定因素影响,拖拉机转向机构结构件误差最大时,其失效概率并未达到最大值。

基于机器学习的茶树DNA聚类算法

为了研究茶树基因序列的聚类问题,设计一种基于累计方差贡献率进行改进的核主成分分析(KPCA)与k均值(k-means)++聚类算法相结合的降维聚类算法(KPCA-k-means++)。将基因库数据集筛选分组后,利用k-mers算法提取基因数据的数据特征,根据累计方差贡献率的占比大于85%的标准确定降维主元个数对KPCA进行降维改进并采用k-means++算法对降维后数据聚类,通过CH(Calinski-Harabaze Index)指标和响应时间分析聚类结果。结果表明:在单独聚类、KPCA聚类、改进PCA聚类、改进KPCA聚类4种处理方式中,改进KPCA-k-means++算法在不同处理方式和不同样本数的对比下,CH指标均为最高,与未改进时相比平均高出33%。在响应时间方面,改进KPCA-k-means++算法与同样改进PCA-k-means++算法在不同聚类数和样本数的对比下响应时间均较短。改进KPCA-k-means++算法能够保证对于茶树的基因序列的聚类准确率和聚类速度,表现出极好的聚类稳定性。

基于初始质心的K-Means算法优化

由于传统K-Means聚类算法对初始质心的赋值具有随机性,使得模拟结果有着极大的波动。针对这一问题,文章采用均分法,首先将样本数据清洗,将偏差值较大的数据筛除,然后把处理后的样本数据在二维平面内均匀划分成若干等分,进行排序归纳计算出最佳初始质心用于聚类算法的初次迭代,并调用SSE等度量指标对迭代的质心进行更新,最后将样本数据划分成有意义的簇。实验结果表明,文章针对K-Means算法的优化在一定程度上减少了质心的迭代次数,节省了时间同时提高了准确率,对初始质心赋值优化具有有效性和实用性的特点。

建成环境对交叉口行人事故严重程度异质性影响

为探索建成环境及其他因素对行人事故伤害严重程度的影响,通过建立均值和方差异质性随机参数Logit模型对交叉口行人事故严重程度进行异质性分析。利用行人碰撞事故数据,从行人、驾驶员、车辆、道路、环境、时间、建成环境7个方面筛选出27个影响因素,通过弹性系数分析显著变量对事故严重程度的影响程度。结果表明:地铁站可达性为“存在”是随机参数,车辆状态为“其他”会减小该随机参数的均值,降低交叉口行人事故严重程度;方差与“下午”显著相关,此变量会使随机参数的离散程度增加,分布变宽,随机性增加;不同用地类型与交叉口类型对行人事故严重程度的影响具有显著差异,用地类型为商业区、地铁站的可达性为“存在”时,交叉口行人发生交通事故的概率与伤害严重程度降低;行人有不当行为等因素显著增加了交叉口行人事故严重程度。研究结果为制定交叉口建成环境的优化改善措施提供参考和依据。

考虑消费者双重行为的农产品绿色供应链数字化策略

为探究消费者风险规避和绿色偏好行为对绿色供应链数字化发展的双重影响,构建产品绿色度和安全水平函数,运用Stackelberg博弈模型,构建农户和农产品零售商组成的两级绿色供应链博弈模型,分析两种行为对绿色供应链数字化策略的影响,对集中决策和分散决策下供应链成员决策进行对比分析,并设计考虑奖惩的供应链契约。最后对模型进行数值仿真,验证该契约的可行性。研究发现:无论是集中决策还是分散决策,消费者规避心理总不利于绿色供应链发展,绿色偏好与之相反;相比分散决策,集中决策最优解更优;奖惩系数在合理的区间内,考虑奖惩的农产品供应链契约实现了供应链参与者的双赢,农户和农产品零售商利润均得到帕累托改善。