STATISTICAL PROPERTY OF THRESHOLD-CROSSING FOR ZERO-MEAN-VALUED, NARROW-BANDED GAUSSIAN PROCESSES

Based on a comprehensive discussion of the calculation method for the threshold-crossing statistics of zero mean valued, narrow banded Gaussian processes of various practical engineering problems, including the threshold-crossing probability, average number of crossing events per unit time, mean threshold-crossing duration and amplitude, a new Simple numerical procedure is proposed for the efficient evaluation of mean threshold-crossing duration. A new dimensionless parameter, called the threshold-crossing intensity, is defined as a measure of the threshold-crossing severity, which is equal to the ratio of the product of average number of crossing events per unit time and mean threshold-crossing duration and amplitude over the threshold. It is found, by the calculation results for various combinations of stochastic processes and different thresholds, that the threshold-crossing intensity, irrelevant of the threshold and spectral density of the process, is dependent only on the threshold-crossing probability.

基于高阶次Cross Sensor处理的波束形成方法

为得到鲁棒性、高分辨波束形成,提出了一种高阶次Cross Sensor处理方法。该方法依据阵元间协方差矩阵同一斜对角线上不同元素具有相同相位差的特点,对协方差矩阵进行M阶次Cross Sensor处理后可虚拟出近（2^M-1）（N-1）个虚拟阵元（N为原始阵元数）。虚拟阵元的增加可扩大线阵有效孔径,降低波束形成主瓣宽度,提高方位分辨率。在Cross Sensor处理过程中,该方法对协方差矩阵同一斜对角线上不同元素进行了叠加运算,可进一步削弱噪声对波束形成的影响,提高了波束形成鲁棒性。数值仿真和海上试验结果表明,该方法既能有效降低波束形成主瓣宽度,提高方位分辨率,又可削弱方位历程图干扰背景。相比已有的逆波束形成,该方法具有较好的方位分辨率;相比已有的基于AR模型的高分辨逆波束形成,该方法对最低门限信噪比的要求较低,方位估计均方误差较小。

Autonomous process collaboration framework for C4ISR system interoperation

Interoperability plays an important role in the joint command, control, communication, computer, intelligence, surveillance, reconnaissance(C4 ISR) operations. Coordinating and integrating operational processes to fulfill a common mission are challenged by the ever-changing battlefield and hence requires a cross-organizational process management that produces an autonomous, flexible and adaptable architecture for collaborative process evolution. The traditional business process collaboration pattern is based on the predefined "public-view" perspective and cannot meet the requirement of the joint task operations. This paper proposes a flexible visibility control mechanism and a dynamic collaboration framework for modeling and generating collaborative processes. The mechanism allows collaborators to define a set of visibility rules to generate different views of the private processes for different collaborations, which gives a great flexibility for the collaboration initiator to decide on an appropriate collaboration pattern. The framework supports collaborators to dynamically and recursively add a new process or even a new organization to an existing collaboration. Moreover, a formal representation of the processes and a set of generation algorithms are provided to consolidate the proposed theory.

RESEARCH ON THE ALGORITHM OF BINARY IMAGE CROSS-CORRELATION FOR UNSTEADY FLOW FIELD MEASUREMENT

The algorithm of Binary Image Cross-Correlation (BICC) was developed to measure the unsteady flow field. A vortex flow field was used to test the algorithm by numerical simulation. The results show that BICC is an effective algorithm for particle identification from consecutive images, the accurate velocity vector field can be obtained. The real velocity field in a valve chamber was measured by BICC in this study. From the full-field velocity information, the pressure and vorticity fields were also extracted by post-processing. (Edited author abstract) 6 Refs.

Detrended cross-correlation analysis of electroencephalogram

In the paper we use detrended cross-correlation analysis （DCCA） to study the electroencephalograms of healthy young subjects and healthy old subjects. It is found that the cross-correlation between different leads of a healthy young subject is larger than that of a healthy old subject. It was shown that the cross-correlation relationship decreases with the aging process and the phenomenon can help to diagnose whether the subject＇s brain function is healthy or not.

CROSS-SPECTRAL MOMENT AND SVD METHODS ESTIMATING THE PARAMETERS OF CLOSE SINUSOIDS IN COLORED NOISE

The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the modern auto-spectral estimation method is introduced into cross-spectral estimation in this paper, meanwhile the cross-correlation based Yule-Walker equation is proposed theoretically and the moment and singular-value decomposition (SVD)) algorithms for cross-spectral estimation have been developed. Finally, a numerical example is given for comparing the presented methods with the well-known Cadzow’s SVD method.

A Scalable Method for Cross-Platform Merging of SNP Array Datasets

Single nucleotide polymorphism (SNP) array is a recently developed biotechnology that is extensively used in the study of cancer genomes. The various available platforms make cross-study validations/comparisons difficult. Meanwhile, sample sizes of the studies are fast increasing, which poses a heavy computational burden to even the fastest PC.Here, we describe a novel method that can generate a platform-independent dataset given SNP arrays from multiple platforms. It extracts the common probesets from individual platforms, and performs cross-platform normalizations and summari-zations based on these probesets. Since different platforms may have different numbers of probes per probeset (PPP), the above steps produce preprocessed signals with different noise levels for the platforms. To handle this problem, we adopt a platform-dependent smoothing strategy, and produce a preprocessed dataset that demonstrates uniform noise levels for individual samples.To increase the scalability of the method to a large number of samples, we devised an algorithm that split the samples into multiple tasks, and probesets into multiple segments before submitting to a parallel computing facility. This scheme results in a drastically reduced computation time and increased ability to process ultra-large sample sizes and arrays.

3-D RECONSTRUCTION OF SHAPE AND STRUCTURE FOR THE SPACE OBJECT FROM SERIAL CROSS-SECTIONS

A new method for reconstructing a 3-dimensional object from serial cross-sectionsis presented in this paper.The method is based on the principle of sampling and considersevery point in cross-sections as a sampling point and performs the interpolating of nonlinearfunction with these sampling points.Compared with other methods,this method has manyadvantages such as higher precision and fewer requested known sampling points.The result ofreconstruction with this method is an“entity”which involves the exterior shape and interiorconstruction information of the object simultaneously.

Improving reservoir volumetric estimations in petroleum resource assessment using discovery process models

The reservoir volumetric approach represents a widely accepted, but flawed method of petroleum play resource calculation. In this paper, we propose a combination of techniques that can improve the applicability and quality of the resource estimation. These techniques include： 1） the use of the Multivariate Discovery Process model （MDP） to derive unbiased distribution parameters of reservoir volumetric variables and to reveal correlations among the variables; 2） the use of the Geo-anchored method to estimate simultaneously the number of oil and gas pools in the same play; and 3） the crossvalidation of assessment results from different methods. These techniques are illustrated by using an example of crude oil and natural gas resource assessment of the Sverdrup Basin, Canadian Archipelago. The example shows that when direct volumetric measurements of the untested prospects are not available, the MDP model can help derive unbiased estimates of the distribution parameters by using information from the discovered oil and gas accumulations. It also shows that an estimation of the number of oil and gas accumulations and associated size ranges from a discovery process model can provide an alternative and efficient approach when inadequate geological data hinder the estimation. Cross-examination of assessment results derived using different methods allows one to focus on and analyze the causes for the major differences, thus providing a more reliable assessment outcome.

Efficient Simulation of Stationary Multivariate Gaussian Random Fields with Given Cross-Covariance

The present paper introduces a new approach to simulate any stationary multivariate Gaussian random field whose cross-covariances are predefined continuous and integrable functions. Such a field is given by convolution of a vector of univariate random fields and a functional matrix which is derived by Cholesky decomposition of the Fourier transform of the predefined cross-covariance matrix. In contrast to common methods, no restrictive model for the cross-covariance is needed. It is stationary and can also be reduced to the isotropic case. The computational effort is very low since fast Fourier transform can be used for simulation. As will be shown the algorithm is computationally faster than a recently published spectral turning bands model. The applicability is demonstrated using a common numerical example with varied spatial correlation structure. The model was developed to support simulation algorithms for mineral microstructures in geoscience.

New fast algorithm for hypercomplex decomposition and hypercomplex cross-correlation

In order to calculate the cross-correlation of two color images treated as vector in a holistic manner,a rapid vertical/parallel decomposition algorithm for quaternion is presented.The calculation for decomposition is reduced from 21 times to 4 times real number multiplications with the same results.An algorithm for cross-correlation of color images based on decomposition in time domain is put forward,in which some properties pointed out in this paper can be utilized to reduce the computational complexity.Simulation results show the effectiveness and superiority of the proposed method.

港珠澳协同背景下跨境电商物流国际竞争力提升路径研究——基于模糊层次分析法

跨境电子商务对比传统贸易方式有利于优化资源配置以及推动我国经济增长方式实现全面转变。港珠澳协同背景下,跨境电子商务在对外贸易中的比重连年保持攀升态势,是非常重要的对外贸易增长点,而跨境电商又是以物流作为支撑。由此,本文围绕港珠澳协同背景下跨境电商物流国际竞争力提升路径展开深入研究具有积极意义。

产业共性技术跨组织合作研发激励模型研究

本文研究了一个由高校院所进行基础研究和制造企业负责后续商业开发的共性技术跨组织合作研发激励模型及其选择问题。本文将政府支持与合同激励放在同一框架,运用博弈论方法为制造企业设计产出导向单一激励模型、产出导向+过程监控激励模型及“政府支持+”综合激励模型以激发高校院所基础研究的积极性,同时解析了激励模型选择、影响因素及机理。研究表明:“政府支持+”综合激励模型是三种激励模型中的占优模型、产出导向+过程监控激励模型优于产出导向单一激励模型;双方收益分享系数具有促进和抑制高校院所认真履责行为的双重作用,且对其机会主义行为具有抑制作用,对制造企业后续商业开发行为及预期收益有“倒U型”影响;高校院所基础研究的影响系数在双方收益分享系数对制造企业后续商业开发努力的影响中发挥调节作用;政府补贴对高校院所和制造企业的合作研发行为具有明显的正向激励作用;惩罚系数可有效抑制高校院所的机会主义行为。

卓越教师经历的学习过程特征及启示——10位个案教师学习经历的跨案例研究

卓越教师培养一直在教育政策和区域教师教育工作中倍受重视,但缺乏微观层面对这一群体教师所经历的学习过程特征的探讨。基于对10位卓越教师所经历的学习过程的个案研究成果,应用跨案例研究法,采用“环境—过程—结果”(EPO)框架得到了卓越教师经历的学习过程中所呈现的特征:卓越教师在学习环境中体验到高挑战性的任务和积极的组织氛围,在学习过程中表现出高水平的内部学习动机和情感意志,学习互动以个体主动投入式为主,经历的学习内容具有多元性和差异性,学习结果更多指向来自内部的专业成就感或价值感。对卓越教师学习设计者和教师继续教育决策者提出如下建议:卓越教师的学习环境需要突出工作场域和高挑战任务特征,教师的学习过程中需要加强学习互动的设计,教师的学习结果预期亟须超越以往狭隘的知识或能力观。

面向机器人铣削加工的铣刀姿态动力特性研究

针对铣刀对机器人铣削稳定性的影响,采用回归模型与耦合子结构分析(receptance coupling substructure analysis,RCSA)方法,建立了考虑交叉耦合频响函数的不同刀具姿态动力学模型。通过设计激励试验来测量期望的频响矩阵,将实测频响矩阵与多自由度模型拟合,减少了模态参数的数量。采用多任务高斯过程(multi-task Gaussian process,MTGP)回归模型挖掘不同模态参数之间的物理相关性,与普通单任务高斯过程回归模型(single-task Gaussian process,STGP)相比,所提模型减少了MTGP所需回归模型的数量,提高了预测精度和鲁棒性。通过工业机器人的冲击试验验证了所提方法的有效性。

“实验设计与数据处理”教学设计与实践

基于成果导向教育理念,依据“实验设计与数据处理”课程所支撑的毕业要求观测点,反向设计课程目标。围绕课程目标整合教学内容和教学资源、设计教学过程、组织教学活动、评价学习成效,并基于评价结果持续改进,形成“实施-评价-反馈-改进”闭环。教学过程引入“工艺优化驱动教学”的项目式教学法,与“专业综合实验”“科研实践”等实践环节形成跨课程教学联动,以实践环节中的实验方案设计和数据处理的实际需求推动“实验设计与数据处理”课程的理论教学和课下实践,将零散的知识体系化,碎片化的应用系统化,着力提升学生实验方案设计、实验结果统计分析和模拟预测以及方案优化能力。

致孔剂对交联聚苯乙烯球合成工艺及孔结构的影响

采用分段升温的工艺,将醋酸纤维及液体石蜡作为致孔剂,通过悬浮聚合法合成了多孔交联聚苯乙烯球,研究了反应温度和反应时间对产率的影响。致孔剂的加入提高了聚合体系的黏度,调整反应的初始温度可有效提高产率。结构分析发现,多孔交联聚苯乙烯球具有微米级和纳米级共存的孔结构。

基于图像处理的行人过街安全警示系统设计与研究

为了提高行人过街的安全性和效率,避免安全事故的发生,设计一种可以实时检测行人过街行为以及车辆往来的行人过街安全警示系统。该系统采用图像处理技术和嵌入式系统技术,能够实时监测行人、车辆和交通信号灯状态,能准确快速地提醒过街行人注意出行及安全,提高城市交通秩序,有助于推进智慧城市建设和交通安全领域的技术创新,具有一定的理论和实践价值。

跨文化传播视角下多语言用户信息分享行为过程模型构建研究

[目的/意义]全球化使得个体与不同文化的接触日益频繁,掌握多种语言的个体能够借助多元文化融合的机遇,利用数字平台为中华优秀文化的交流和传播提供新的路径和方向。[方法/过程]社交媒体时代多语言用户成为中国文化跨文化传播的核心推动者。故此,文章以多语言用户,即掌握两种及以上语言的用户作为研究对象探究其如何借助信息分享行为传播中华文化相关内容。通过众包方式收集来自全球355个多语言用户跨文化信息分享数据,采用主题分析法构建了多语言用户信息分享行为过程模型,旨在揭示具体的形成机制和行为规律。[结果/结论]构建了包含动机驱动阶段、信息交互阶段和体验感知阶段的多语言用户信息分享行为过程模型。立足中国文化语境,有助于凸显跨文化研究情境,对于促进新时代跨文化传播具有重要意义。

基于频域阈值处理广义互相关和空间筛选的多源局部放电定位方法

该文针对电力设备的多源局部放电定位问题,提出了基于频域阈值处理广义互相关(frequency domain threshold processing generalized cross-correlation,FDTP-GCC)和空间筛选的多源局部放电定位方法。为实现多源局部放电超声信号的同源时间差提取问题,该文提出了FDTP-GCC算法,该算法直接应用于超声传感设备采集的原始信号,在频域内通过设置幅度谱阈值排除干扰频段,幅度谱归一化突出多源信号时间差的冲激函数特征,直接得到对应同源信号的时间差。通过仿真证明了FDTP-GCC算法进行时间差估计的高准确性、高稳定性、高抗噪能力。为解决FDTP-GCC算法带来的同源时间差分离的问题,该文提出了基于空间筛选的多源局部放电定位方法,并分析了采用基于解析解的算法和智能算法进行空间筛选的定位性能。最后采用双局部放电源进行了定位试验,试验结果表明该文方法提取的时间差误差均在1μs以内,两个局部放电源的定位误差为20.53cm和4.93cm。