Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

Generalized Sampling Series Approximation of Random Signals from Local Averages

Signals are often of random character since they cannot bear any information if they are predictable for any time t, they are usually modelled as stationary random processes .On the other hand, because of the inertia of the measurement apparatus, measured sampled values obtained in practice may not be the precise value of the signal X(t) at time tk (k∈Z), but only local averages of X(t) near tk. In this paper, it is presented that a wide (or weak ) sense stationary stochastic process can be approximated by generalized sampling series with local average samples.

Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

Increasing the Resolution and SNR of an ADC′s Measurement with a Method of Over- Sampling and Averaging

By analyzing the theory of over-sampling and averaging, the conclusion is educed that white noise accompanies the signal and the addition of each bit of resolution can be achieved via a fourfold sampling frequency. The addition of each bit will approximately increase the SNR （signal to noise ratio） to 6dB.

Evaluation of Double Average Asian Options by the Legendre Spectral Method

In this paper, the evaluation of discretely sampled Asian options was considered by numerically solving the associated partial differential equations with the Legendre spectral method. Double average options were discussed as examples. The problem is a parabolic one on a finite domain whose equation degenerates into ordinary differential equations on the boundaries. A fully discrete scheme was established by using the Legendre spectral method in space and the Crank-Nicolson finite difference scheme in time. The stability and convergence of the scheme were analyzed. Numerical results show that the method can keep the spectral accuracy in space for such degenerate problems.

Optimal Recovery of Functions on the Sphere on a Sobolev Spaces with a Gaussian Measure in the Average Case Setting

In this paper, we study optimal recovery （reconstruction） of functions on the sphere in the average case setting. We obtain the asymptotic orders of average sampling numbers of a Sobolev space on the sphere with a Gaussian measure in the Lq （S^d-1） metric for 1 ≤ q ≤ ∞, and show that some worst-case asymptotically optimal algorithms are also asymptotically optimal in the average case setting in the Lq （S^d-1） metric for 1 ≤ q ≤ ∞.

基于模态分解与SRU网络的时间序列预测

时间序列预测在工业、农业、金融及军事等领域中具有重要的应用价值。为了进一步提高预测的可靠性和准确性,构建一种基于模态分解与SRU网络的杂交预测模型。首先,针对模态个数难以确定的问题,构建基于平均样本熵来确定模态个数的自适应变分模态分解(AVMD)模型,以减少不同频率上的混叠及降低随机噪声的干扰。通过在Adam算法中引入了随机调整参数,来提高SRU网络的训练速度及增强网络跳出局部最优解的能力。最后,发展一种基于AVMD与SRU网络的杂交模型。为评估提出的预测模型的可靠性和准确性,将之与一些最新预测方法做比较。电力负荷序列的实验结果表明,所提出的杂交预测模型具有较高的准确性和可靠性。

冶金焦炭平均粒度检测中几个问题的探讨

通过对冶金焦炭平均粒度检测全过程的分析以及调研全国主要焦炭生产企业检测焦炭平均粒度的方法,特别是检测使用筛子以及中值取数等方法关键点,得到影响其检测数据的差异的因素。数据分析显示影响冶金焦炭平均粒度检测的主要原因有使用不同规格的筛子、相邻两级筛子中值取数、冶金焦炭取样地点等。文章还分析焦炭机械强与焦炭平均粒度的关系。

非参数自适应EWMA SR控制图及其变采样间隔设计

本文基于Wilcoxon符号秩(Signed Rank, SR)检验统计量,提出了一种非参数自适应指数加权移动平均(Adaptive Exponentially Weighted Moving Average, AEWMA)控制图。所提出的AEWMA SR控制图结合了非参数统计量的稳健受控性能以及自适应控制图良好的整体偏移检测特性。同时,为了提高固定采样间隔下的非参数AEWMA SR静态控制图对异常偏移的检测效率,本文进一步研究了可变采样间隔(Variable Sampling Intervals, VSI)下的非参数AEWMA SR动态控制图设计问题。使用了Markov链方法计算控制图的精确平均运行链长(Average Run Length, ARL)和平均报警时间(Average Time to Signal, ATS)等性能指标。通过仿真分析比较了VSI AEWMA SR控制图、FSI AEWMA SR控制图和VSI EWMA SR控制图的统计性能。结果表明,所提出的VSI AEWMA SR控制图能兼顾对于不同大小偏移的敏感性,且变采样间隔的动态策略能显著提高控制图的检测效率。

基于正态变换的变样本容量Shewhart比例控制图设计

为了提高Shewhart型比例控制图的监控效率,将其与变样本容量策略结合,提出一种新的比例控制图(VSS Shewhart-RZ*控制图)来监控二元正态变量之比。基于正态变换方法设计了新的比例统计量Z*,以消除样本容量变化对比例控制图设计及使用的不利影响。采用马尔科夫链方法计算了该控制图的平均运行长度和平均样本容量,给出该控制图的参数优化算法。通过仿真分析研究了该控制图的统计性能,并与Shewhart比例控制图进行比较,结果表明VSS Shewhart-RZ*控制图的性能明显优于传统固定样本容量Shewhart比例控制图。

基于样本熵的港口集装箱吞吐量可预测性测度研究

港口吞吐量历史时间序列数据具有较强的随机性,而不同特征的时间序列数据的预测精度差异较大,由此产生了时间序列数据可预测性的测度问题。学术界认为,这种可预测性可以用熵进行描述。文章采用样本熵表征测度我国20个港口集装箱吞吐量时间序列数据的复杂性,然后运用自回归综合移动平均模型(ARIMA)预测港口吞吐量。结果表明,样本熵与其预测精度之间的相关性较弱,ARIMA模型对于港口生命周期处于“成长”阶段的港口或者大型港口的预测精度更好。研究结论有助于理解熵和时间序列数据可预测性之间的关系。

平均降采样多周期微分均值的旋转部件故障特征增强方法

为解决编码器的瞬时角速度(Instantaneous Angular Speed,IAS)信号中旋转部件故障特征微弱的难题,本文提出一种平均降采样多周期微分均值(Average Down⁃Sampling Multi⁃period Differential Means,ADSMPDM)的故障特征增强方法。基于IAS信号的估计特性,开展了IAS信号的平均降采样研究,验证了平均降采样具有抑制随机噪声的特性;基于平均降采样抑制随机噪声特性、降低计算成本和减小存储空间的优势,结合多周期微分均值的累积特性,提出一种ADSMPDM算法对原始IAS信号中的旋转部件故障分量进行增强处理;通过阶次谱分析揭示故障特征。采用仿真数据和实验数据进行验证分析,并与快速谱峭度、可调整多点优化最小熵反卷积、离散随机分离和谱幅值调制算法进行对比,验证了ADSMPDM算法增强旋转部件故障特征的有效性和优势。

基于代客充电模式的电动汽车充电站布局优化

当新能源汽车用户自行前往充电站充电,将会产生时间成本和等待焦虑。而代客充电服务能够有效解决这些问题,但是其服务成本受到充电站布局与社区用户分布等因素的影响。为探究充电站布局对代客充电服务模式的影响,本文建立基于充电时间的等待焦虑程度量化模型,引入代客充电服务比率量化用户的服务选择行为,同时考虑用户需求的随机性,以最小化充电站建设成本与用户前往充电站充电的行驶成本、最大化代客充电业务利润为优化目标,建立多目标两阶段随机混合整数规划模型,并使用Epsilon约束法与抽样平均近似方法进行求解。本文在对模型及其参数进行数值分析的基础上,以合肥市瑶海区为背景设计案例研究,发现在充电站建设数目一定的前提下,通过改变充电站布局可以提升代客充电业务利润,但同时也会导致用户充电行驶成本的升高。

多路音频传感信号低功耗实时采集方法设计

由于多路音频存在信号混合噪声干扰,以单路无线传感器网络(Wireless Sensor Networks, WSN)节点为基础的采集过程,淡化了不同路线上的噪声特征,以单一阈值为基础降噪,存在采集偏差大、丢包率高等问题,提出基于WSN节点的多路音频信号低功耗实时采集方法。利用五元麦克风声源定位模型,确定音频信号声源的方位、距离和角度等信息,引入传感器节点,引入静音区检测的多路降噪法,通过调整静音区幅度值为0达到对音频信号不同线路的降噪目的;通过计算合适的采样时间点和平均采样频率,实现多路音频信号低功耗采集。在对比实验测试中,所提方法取得的音频信号值最接近实际值,并且丢包率最高仅为2.5%,最高功耗仅为0.62 W,该方法有效提高了信号的准确性。

Gibbs Sampler方法在考生写作能力的贝叶斯估计中的应用

本文将同族ＴＥＳＴ模型（Ｊｏｒｅｓｋｏｇ，１９７１）一般化之后作为作文考试数据的模型，并应用ＧｉｂｂｓＳａｍｐｌｅｒ方法对考生的作文能力进行贝叶斯估计。通过具体实例分析，说明了这种方法的估计是合理的，得到的结果是有效的。

局部平均随机过程随机场采样定理

随机信号采样定理的相关研究几十年来一直是采样定理主流方向之一,在理论和实际应用两方面都受到信息领域和数学领域的广泛关注。近年来,采样理论在随机过程和随机场的局部平均采样方面得出了一系列研究成果。首先,分析了采样定理在应用中的缺陷和局限性。其次,对近几年在随机过程、高维随机过程、随机场、时空随机场局部平均采样定理方面的发展和相关结论做了系统介绍。最后,对基于混合范数的时空随机场采样逼近、采样形式、信号形式、范数形式等问题进行了展望。结果表明:局部平均的采样方式提高了采样的精度和逼近效果,随机场局部平均采样理论在海浪参数反演和海洋污染监测方面有重要应用。特别是基于混合范数的时空随机场采样理论的研究不仅具有重要的理论价值,而且对于工程领域也有应用价值。

激光粒度仪湿法测定不同形貌超细镍粉平均粒度的研究

超细镍粉诸多物化性质随粉末形貌、粒径等参量的变化发生显著改变,因此针对超细镍粉的粒度的准确测定至关重要。确定激光粒度仪测定超细镍粉平均粒度的最佳测试条件,选用湿法测定方法,以多孔状、片状和球状超细镍粉对研究对象,研究了分散剂种类、超声时间、分散剂质量分数、遮光度及采样轮数等参数对超细镍粉平均粒径测定结果的影响。结果表明,在实验选用的6种分散剂FMES、L64、CAB、PECPM、CTAC和G-18中,添加FMES分散剂测得的超细镍粉D50值最小,而添加G-18分散剂测得的超细镍粉D50值最大,表明FMES分散剂改善了3种形貌的超细镍粉在水中的团聚,使粒度测量的准确性更高。当超声时间为8 min时,孔状超细镍粉测得的D50=4.403 μm,片状超细镍粉测得的D50=1.345 μm,球状超细镍粉测得的D50=1.289 μm。随着超声时间持续延长,3种形貌的超细镍粉测得的D50值逐渐趋于平稳,其中片状和球状形貌超细镍粉测得的D50值与电镜测试结果较为相符,而孔状超细镍粉D50值与电镜测试结果差别明显,表明激光粒度仪湿法测定不适用于孔状形貌的镍粉。采样轮数与粒度测试值具有强线性相关性,超声时间为8 min时,相邻两轮的测试结果更相近,从而确定了适宜的超声时间为8 min。综上所述,确定了片状和球状超细镍粉平均粒度的最佳测试条件为:超声时间8 min、分散剂FMES质量分数1.5%、遮光度10%—20%。在最佳测试条件下重复测试6次,片状和球状超细镍粉的平均粒度数据具有较高的准确性和重现性。

Uniform Truncation Error for Shannon Sampling Expansion from Local Averages

Let B^pΩ,1≤Р≤∞,be the set of all bounded functions in L^p（R）which can be extended to entire unctions of exponential typeΩ. The unitbrm bounds for truncation error of Shannon sampling expansion fromlocal averages are obtained for functions f∈BpΩwith the decay condition f（t）≤A/t^δ,t≠0,where A and δare positive constants. Furthermore we also establish similar results for non-bandlimit functions in Besov classes with the same decay condition as above.

Average sampling theorems for shift invariant subspaces

<正> The sampling theorem is one of the most powerful results in signal analysis. In this paper, we study the average sampling on shift invariant subspaces, e.g. wavelet subspaces. We show that if a subspace satisfies certain conditions, then every function in the subspace is uniquely determined and can be reconstructed by its local averages near certain sampling points. Examples are given.

融合注意力门控机制的大场景点云语义分割

室外大场景激光点云语义分割已成为3D场景理解、环境感知的关键性技术,在自动驾驶、智能机器人和增强现实(AR)等领域应用广泛。然而大场景的激光点云具有多目标、几何结构复杂,不同地物尺度变化大等特点,使得在稀疏的小目标点云(例如行人、摩托车等)上的分割性能较低。针对上述问题,本文提出一种融合注意力门控机制的室外点云语义分割算法,设计由注意力机制和多尺度上下文特征融合组成的注意力门控单元,提高对激光点云细粒度特征的表达,降低随机降采样过程中点云几何结构特征丢失程度,从而增强了网络对弱小目标的特征获取能力;同时设计基于共享MLP的平均池化单元,进一步简化自注意力局部特征聚合模块,有效地加速网络收敛,能高效地实现大场景点云的语义分割。本文方法在自动驾驶场景室外激光点云数据集SemanticKITTI上的实验表明,与文献RandLA-Net相比,收敛速度提升48.3%,平均交并比(mIoU)由53.9%提升至54.5%,提高0.6%,尤其是在小目标上交并比(IoU)均有明显提高,person类和motorcycle类的交并比分别提高0.8%和5.4%。