The New Neutrosophic Double and Triple Exponentially Weighted Moving Average Control Charts

The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.

Parameters optimization for exponentially weighted moving average control chart using generalized regression neural network

As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA （MEWMA） control charts, in which the optimal parameter pair （ λ, k） or （ λ, h ） was searched by using the generalized regression neural network （GRNN）. The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length （ARLo） and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.

A new cellular automata model of traffic flow with negative exponential weighted look-ahead potential

With the development of traffic systems, some issues such as traffic jams become more and more serious. Efficient traffic flow theory is needed to guide the overall controlling, organizing and management of traffic systems. On the basis of the cellular automata model and the traffic flow model with look-ahead potential, a new cellular automata traffic flow model with negative exponential weighted look-ahead potential is presented in this paper. By introducing the negative exponential weighting coefficient into the look-ahead potential and endowing the potential of vehicles closer to the driver with a greater coefficient, the modeling process is more suitable for the driver’s random decision-making process which is based on the traffic environment that the driver is facing. The fundamental diagrams for different weighting parameters are obtained by using numerical simulations which show that the negative exponential weighting coefficient has an obvious effect on high density traffic flux. The complex high density non-linear traffic behavior is also reproduced by numerical simulations.

Generalized Weighted Exponential-Gompertez Distribution

Statistical analysis of lifetime data is a significant topic in social sciences, engineering, reliability, biomedical and others. We use the generalized weighted exponential distribution, as a generator to introduce a new family called generalized weighted exponential-G family, and apply this new generator to provide a new distribution called generalized weighted exponential gombertez distribution. We investigate some of its properties, moment generating function, moments, conditional moments, mean residual lifetime, mean inactivity time, strong mean inactivity time, Rényi entropy, Lorenz curves and Bonferroni. Furthermore, in this model, we estimate the parameters by using maximum likelihood method. We apply this model to a real data-set to show that the new generated distribution can produce a better fit than other classical lifetime models.

Degenerations of rational Bézier surface with weights in the form of exponential function

Rational Bezier surface is a widely used surface fitting tool in CAD. When all the weights of a rational B@zier surface go to infinity in the form of power function, the limit of surface is the regular control surface induced by some lifting function, which is called toric degenerations of rational Bezier surfaces. In this paper, we study on the degenerations of the rational Bezier surface with weights in the exponential function and indicate the difference of our result and the work of Garcia-Puente et al. Through the transformation of weights in the form of exponential function and power function, the regular control surface of rational Bezier surface with weights in the exponential function is defined, which is just the limit of the surface. Compared with the power function, the exponential function approaches infinity faster, which leads to surface with the weights in the form of exponential function degenerates faster.

WEIGHTED PSEUDO ALMOST-PERIODIC SOLUTIONS OF SHUNTING INHIBITORY CELLULAR NEURAL NETWORKS WITH MIXED DELAYS

In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprising different discrete and distributed time delays. Some sufficient conditions are given for the existence and the global exponential stability of the weighted pseudo almost-periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper complement the previously known ones. Finally, an illustrative example is given to demonstrate the effectiveness of our results.

Image Quality Stability of Whole-body Diffusion Weighted Imaging

Objective To assess the reproducibility of whole-body diffusion weighted imaging(WB-DWI) technique in healthy volunteers under normal breathing with background body signal suppression.Methods WB-DWI was performed on 32 healthy volunteers twice within two-week period using short TI inversion-recovery diffusion-weighted echo-planar imaging sequence and built-in body coil.The volunteers were scanned across six stations continuously covering the entire body from the head to the feet under normal breathing.The bone apparent diffusion coefficient(ADC) and exponential ADC(eADC) of regions of interest(ROIs) were measured.We analyzed correlation of the results using paired-t-test to assess the reproducibility of the WB-DWI technique.Results We were successful in collecting and analyzing data of 64 WB-DWI images.There was no significant difference in bone ADC and eADC of 824 ROIs between the paired observers and paired scans(P>0.05).Most of the images from all stations were of diagnostic quality.Conclusion The measurements of bone ADC and eADC have good reproducibility.WB-DWI technique under normal breathing with background body signal suppression is adequate.

GLOBAL APPROXIMATION FOR A CLASS OF GENERALIZED MIXED EXPONENTIAL TYPE INTEGRAL OPERATORS IN POLYNOMIAL WEIGHT SPACES

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基于改进A*算法的应急救援无人机路径规划

针对救援无人机传统路径规划算法存在搜索扩展节点数多、搜索路径时间长、规划轨迹不平滑的缺点,提出了一种改进A*算法的应急救援无人机路径规划方法。采用对角线距离作为启发函数并进行指数加权,通过双向搜索策略进行搜索优化,对生成的路径点使用贝塞尔曲线进行曲线拟合。实验中设计了多种非结构化地图,对Dijkstra算法、传统A*算法和改进A*算法在搜索扩展节点数、规划路径长度和搜索路径时间三方面进行对比。实验结果表明,设计的多种地图中改进A*算法在搜索扩展节点数较Dijkstra算法和传统A*算法分别至少降低了59.82%、39.40%,在搜索路径时间分别至少降低了57.38%、33.46%,规划后的路径经过贝塞尔曲线优化后满足无人机的实际飞行要求,可以实现高效率无人机应急救援。

基于Lamb波和改进贝叶斯融合算法的CFRP边缘分层损伤分析

贝叶斯融合法在CFRP上的损伤定位分析结果比较好,但在传感器网络边缘区域会出现比较明显的定位误差。为解决原有算法对传感器网络边缘的分层损伤定位不精确的问题,分析了两种不同损伤因子构成的贝叶斯融合算法在CFRP上的损伤定位效果,验证了以ToF损伤因子形成的贝叶斯融合算法对CFRP损伤定位的可行性,以及贝叶斯融合算法对边缘分层损伤的定位精度;在以损伤因子DI所形成概率成像算法的基础上,以衰减更快的指数权重代替线性权重,将改进的概率成像算法重新代入以贝叶斯为框架的算法中形成一种新的贝叶斯融合算法。结果表明,与现有的融合重构算法相比,改进的融合重构算法至少将损伤定位精度误差减少了58%,定位误差不大于5 mm。

学习带边信息专家意见的在线投资组合策略

针对以往学习专家意见的在线投资组合策略中专家策略并未考虑有助于提高投资者收益的边信息的不足,选取在相同边信息状态下投资相同单只股票、不同边信息状态下可能投资不同单只股票的策略为专家意见,基于指数加权平均算法(EWA)提出了学习带边信息专家意见的在线投资组合策略(EWAES).然后,从理论上证明了对任何的股票价格序列该策略都能够追踪最优专家意见.最后,采用中美金融市场实际股票数据对EWAES策略进行了数值分析,结果说明了该策略的有效性.

基于MRI-DWI不同指数模型构建的列线图模型对肾透明细胞癌与肾错构瘤的鉴别诊断价值

目的:基于磁共振-弥散加权成像(MRI-DWI)不同指数构建列线图模型,探讨其对肾透明细胞癌(ccRCC)与肾错构瘤的鉴别诊断价值。方法:回顾性选择医院经病理确诊的85例ccRCC或肾错构瘤患者作为建模组,其中肾错构瘤54例,ccRCC 31例;选择经病理确诊的105例ccRCC或肾错构瘤患者作为验证组,其中肾错构瘤59例,ccRCC 46例。基于建模组数据比较肾错构瘤与ccRCC组MRI-DWI不同指数模型参数,采用多因素Logistic回归分析筛选鉴别诊断ccRCC与肾错构瘤的影响因子,并建立列线图模型进行内部验证及外部验证。结果:ccRCC组MRI-DWI参数微循环灌注相关快速表观弥散系数(ADC)(Dt)、扩散异质性指数(α)值高于肾错构瘤组,表观弥散系数(ADC)值、单纯扩散相关慢速表观弥散系数值(Dp)、灌注分数(fp)、扩散分布指数(DDC)值低于肾错构瘤组(P<0.05)。经Logistic回归分析显示,Dt、α值是鉴别诊断ccRCC与肾错构瘤的危险因子(OR>1,P<0.05),ADC值、Dp、fp、DDC值是保护因子(OR<1,P<0.05)。根据以上参数建立的列线图模型在建模组与验证组C指数分别为0.941、0.903,校准曲线与理想曲线接近;ROC曲线显示,建模组与验证组的ROC曲线下面积(AUC)分别为0.962(95%CI为0.921~1.000)、0.903(95%CI为0.846~0.961),敏感度分别为0.907、0.898,特异度分别为0.935、0.804,约登指数分别为0.842、0.702。结论:基于MRI-DWI不同模型参数构建的列线图模型具有良好的区分度、准确性,对ccRCC与肾错构瘤具有较高的鉴别诊断价值。

智能电网用电异常诊断和调控策略

为了针对智能电网不同异常用电的原因制定分类调整方法,提出了基于二次根式函数电价调整和计算定界的策略,该策略基于自动过程控制和模糊综合评价进行监控、诊断及分类调整。首先,通过指数加权移动平均值作为自动过程控制的控制变量并确定上下界来监控用电量,建立监控模型;其次,用模糊综合评价法诊断异常用电原因;最后,根据不同原因制定分类调整策略,建立电价需求响应调整模型。算例仿真结果表明,提出的基于二次根式函数电价调整策略在调整频次、残差标准误差、全社会福利和电力公司利润等方面均优于线性函数调整。应用模糊综合评价方法能有效分析异常用电原因,制定分类调整策略能消除异常并获得平稳波动用电量。

Orlicz空间中带指数权的多项式逼近

在Orlicz空间中研究了带指数权w(x)=e^(-(1-x^(2))^(-α))(α>0)的多项式逼近问题,通过引入新的光滑模和相关K-泛函,运用Hölder不等式以及相关分析技巧证明了Orlicz空间中带指数权的Jackson定理和它的弱形式,并得到了一个新的Bernstein不等式.

改进电机全速域无位置传感器转速控制系统研究

针对宽速电机的全速域应用场景,提出改进传统加权平均法,并利用改进后的中高速域滑模观测器法和低速域脉振高频注入法进行复合,实现精确的全速域无位置传感器控制。首先,针对滑模观测器法中符号函数导致的系统抖振,提出指数型复合函数用来替代原观测器中的符号函数;其次,针对脉振高频注入法中高频电流分量与注入信号的相位延迟,提出使用另一轴系高频电流替代注入信号;再次,针对加权平均法在过渡区域的权重分配不合理,提出新型指数型加权系数,同时为平稳过渡提出滞留缓冲带;最后,在MATLAB/Simulink仿真平台搭建基于滞留缓冲加权法的全速域转速控制系统,进行仿真验证。通过仿真结果,表明了改进后的全速域转速控制系统对转速误差和平稳过渡的优化效果。

南水北调中线总干渠水情数据智能清洗

南水北调中线总干渠水位、流量等实时水情数据受外界扰动、测量系统误差等因素影响而产生的病态水情数据将造成调度模型计算失真,甚至导致计算失败。为此,针对上下游流量数据空间上的逻辑错误和水位数据时间序列的跳变,分别建立基于粒子群优化的水量平衡模型和指数加权滑动平均模型,对病态水情数据在空间、时间上实施横向、纵向清洗处理。以穿黄节制闸至漳河节制闸间的渠段为典型研究区间,利用模型自动识别流量倒挂点,并对该渠段涉及的12座节制闸、26处分水点的流量数据进行统一修正,实现了上下游逻辑上的合理性。同时,选取研究渠段内的闫河节制闸为代表,在48 h内运行基本稳定状态下,对每2 h的闸前水位数据序列进行分析,自动识别出跳变数据并进行合理修正。结果表明:建立的模型可自动识别病态水情数据并进行智能清洗,处理后的数据能够较好地满足输水调度分析决策的需要,因此该模型具有推广应用的价值。

随机扰动下具有分布式时滞的多权重网络指数同步

研究随机扰动下具有分布式时滞的多权重网络指数同步问题.鉴于分布式时滞和随机扰动给控制系统所带来的不确定性以及多权重网络具有的灵活性和普适性,通过设计有效的控制协议,利用伊藤引理和不等式技巧,选取合适的Lyapunov函数,得到多权重网络实现指数同步的充分性条件,并通过数值仿真验证理论结果的有效性.

基于改进蜉蝣算法优化多阈值图像分割

针对图像多阈值分割中存在分割效率低、计算时间长以及精度不高等问题,提出了一种基于改进蜉蝣算法的多阈值图像分割算法。首先,在初始化阶段引入类随机采样方法中的Sobol序列,增强种群的遍历性和多样性;其次,提出一种自适应非线性惯性权重,平衡了全局与局部寻优能力,提高了算法的收敛效率,利于种群向最优解逼近;最后,采用指数熵作为计算适应度的目标函数,通过改进蜉蝣算法对图像分割的多阈值组合进行寻优,确定最优分割阈值。为了验证该改进算法的有效性,选择了伯克利图像来进行分割验证,并与其他智能算法进行比较。实验结果表明:该改进算法在分割准确性、计算时间、结构衡量指标(structure similarity index measure,SSIM)和峰值信噪比(peak signal-to-noise ratio,PSNR)上均优于对比算法,能快速有效地解决复杂多目标图像的多阈值分割问题,具有较强的工程实用性。

加指数权Lebesgue空间超齐次核积分算子搭配参数最佳的充要条件

引入超齐次函数概念,将齐次核、拟齐次核和若干非齐次核统一为超齐次核。利用权系数方法,讨论了加指数权Lebesgue空间超齐次核积分算子的有界性与算子范数问题,得到了有界算子搭配参数最佳的充分必要条件,解决了算子范数的计算问题,并给出了一些应用特例。

基于加权指数平方损失的非负稳健估计与变量选择

针对估计参数具有非负要求的回归模型,基于自适应Lasso惩罚函数和加权指数平方损失,提出一种关于非负稳健估计与变量选择的方法.该方法保证了估计参数的非负要求,而且还能进行稳健的变量选择.数值模拟结果表明该方法与其他方法相比更有效.最后,将该方法运用到上证50指数的追踪,结果证明了所提方法的稳健性和有效性.