Local Data Analysis for Eliminating End Restraint of Triaxial Specimen

A data processing method was proposed for eliminating the end restraint in triaxial tests of soil. A digital image processing method was used to calculate the local deformations and local stresses for any region on the surface of triaxial soil specimens. The principle and implementation of this digital image processing method were introduced as well as the calculation method for local mechanical properties of soil specimens. Comparisons were made between the test results calculated by the data from both the entire specimen and local regions, and it was found that the deformations were more uniform in the middle region compared with the entire specimen. In order to quantify the nonuniform characteristic of deformation, the non-uniformity coefficients of strain were defined and calculated. Traditional and end-lubricated triaxial tests were conducted under the same condition to investigate the effects of using local region data for deformation calculation on eliminating the end restraint of specimens. After the statistical analysis of all test results, it was concluded that for the tested soil specimen with the size of 39.1 mm × 80 ram, the utilization of the middle 35 mm region of traditional specimens in data processing had a better effect on eliminating end restraint compared with end lubrication. Furthermore, the local data analysis in this paper was validated through the comparisons with the test results from other researchers.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Numerical differentiation of noisy data with local optimum by data segmentation

A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process is used to achieve acceptable segmentation. Numerical results are presented by using the data segmentation method, compared with the regularization method. For further investigation, the proposed algorithm is applied to the resistance capacitance （RC） networks identification problem, and improvements of the result are obtained by using this algorithm.

Multi-Sensor Data Fusion Technologies for Blanket Jamming Localization

The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed. Radars only can acquire angle information without range information when encountering the blanket jamming. Netted radars could get position information of the blanket jamming by make use of radars' relative position and the angle information, when there is one blanket jamming. In the presence of error, the localization method and the accuracy analysis of one blanket jamming are given. However, if there are more than one blanket jamming, and the two blanket jamming and two radars are coplanar, the localization of jamming could be error due to localization ambiguity. To solve this confusion, the Kalman filter model is established for all intersections, and through the initiation and association algorithm of multi-target, the false intersection can be eliminated. Simulations show that the presented method is valid.

A Localized-Statistic-Based Approach for Biomarker Identification of Omics Data

Omics data provides an essential means for molecular biology and systems biology to capture the systematic properties of inner activities of cells. And one of the strongest challenge problems biological researchers have faced is to find the methods for discovering biomarkers for tracking the process of disease such as cancer. So some feature selection methods have been widely used to cope with discovering biomarkers problem. However omics data usually contains a large number of features, but a small number of samples and some omics data have a large range distribution, which make feature selection methods remains difficult to deal with omics data. In order to overcome the problems, wepresent a computing method called localized statistic of abundance distribution based on Gaussian window(LSADBGW) to test the significance of the feature. The experiments on three datasets including gene and protein datasets showed the accuracy and efficiency of LSADBGW for feature selection.

Study on noise in simultaneous geomagnetic difference data caused by the effect of S_q local-time variation

A new concept is suggested on tectonomagnetic research about the noise in simultaneous geomagnetic difference data caused by the effect of Sq local-time variation, together with the method of theoretical calculation. The level of the noise and its contribution to the total noises of the differences data are analyzed. The result indicates that the noise increases linearly with the increase of the distance between the two stations in the range of 40° longitude-difference, and its increasing rate is about 0.4 nT/（°）at latitude 40°N. The example calculated at a pair of sites with longitude-difference 0.357°, shows that the noise is about one fifth of the total noises of the difference data on geomagnetic quiet-day.

浅析震源位置准确度及其影响因素

地震定位是地震学研究的基础,然而地震定位和地震学研究之间存在“供给”矛盾.不同研究对地震位置准确度级别的要求不尽相同,震源机制和壳幔结构研究要求震源位置的准确度为千米级别,工业生产活动和诱发地震研究要求震源位置的准确度为百米级别.然而,地震监测台网给出的地震位置准确度仅为数千米.诸多地震定位方法从不同方面对地震定位过程进行优化和改进,但它们的侧重点不尽相同.总体而言,已有的定位方法对地震位置的准确度关注程度尚显不足.在大量的地震定位实践中,前人获得了用于优化地震位置准确度的若干经验法则,这些经验法则不但存在地区差异,而且还有一定的适用条件,经验法则仍需要被进一步地优化和修正.本文简要分析地震定位准确度的多方面影响因素,有针对性地开展研究,在地震定位算法和控制观测数据质量方面获得一定的研究进展;在地震定位耦合关系方面补充了定位速度模型、发震位置和发震时刻三者之间的制约关系;在地震定位流程方面提出了使用逐步消元定位的建议.

Data Matrix二维条形码解码器图像预处理研究

DM码是一种常见的二维条形码,图像预处理是DM码解码器自动识别过程中的重要步骤。提出一种实用的DM码识别图像预处理方法。它没有使用传统的边缘检测和直线检测手段,因此受背景噪声、几何失真的影响较小。此外,使用了校正铁路线坐标,并按区域取样生成码流,显著提高了DM码的识别速度和识别率。实验结果表明,该算法可以克服DM码识别过程中易受噪声干扰、光照不均和几何失真等影响的问题。

OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization

This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.

Local Storage存储机制性能的研究与改进

随着互联网产业和技术的不断发展,各型各色的基于互联网页面的Web应用也成为人们获取信息、娱乐、服务等的重要途径之一。这就对各个Web应用系统的性能提出了高要求。文中对Web性能优化技术中的Local Storage的存储性能进行了研究与分析。针对技术本身存在的性能问题,设计了一系列实验来进行验证,探究解决性能问题的方案。实验发现,对Local Storage存储空间频繁访问,会对系统性能造成较大影响。针对实验所暴露的Local Storage所存在的性能问题,文中设计了对Local Storage存储性能改进的方案,并根据该方案,对Local Storage的存储结构进行了改进。以解决因对Local Storage存储空间频繁访问所造成的系统效率降低。

Makran Coastal Zoning for the Construction of New Marine Areas based on Hydrological and Hydraulic Characteristics Using Satellite Data and Spatial Analysis

On one hand,the diversity of activities and on the other hand,the conflicts between beneficiaries necessitate the efficient management and supervision of coastal areas.Accordingly,monitoring and evaluation of such areas can be considered as a critical factor in the national development and directorship of the sources.With regard to this fact,remote sourcing technologies with use of analytical operations of geographic information systems(GIS),will be remarkably advantageous.Iran’s south-eastern Makran coasts are geopolitically and economically,of importance due to their strategic characteristics but have been neglected and their development and transit infrastructure are significantly beyond the international standards.Therefore,in this paper,with regard to the importance of developing Makran coasts,a Multi-Criterion Decision Analysis(MCDA)method was applied to identify and prioritize the intended criteria and parameters of zoning,in order to establish new maritime zones.The major scope of this study is to employ the satellite data,remote sensing methods,and regional statistics obtained from Jask synoptic station and investigate the region’s status in terms of topography,rainfall rate and temperature changes to reach to a comprehensive monitoring and zoning of the coastal line and to provide a pervasive local data base via use of GIS and MCDA,which will be implemented to construct the coastal regions.In this article,while explaining the steps of coastal monitoring,its main objectives are also explained and the necessary procedures for doing so are presented.Then,the general steps of marine climate identification and study of marine parameters are stated and the final achievements of the coastal monitoring process are determined.In the following,considering that this article focuses on the monitoring of Makran beaches,the method of work in the mentioned region will be described and its specific differences and complexities will be discussed in detail.Also,the impact of such projects on future research results will be discussed.

CDMA2000基站模块中PCI和Local总线冲突问题的研究

CDMA2000 BTS(基站收发信机)中的信道处理模块与主控模块之间的通信存在异常导致系统无法启动。为了找出故障的根源,对基站系统进行逐层深入的分析,并且进行了大量的时序测试工作。故障最终定位为数据在PCI总线与Local总线之间转移时存在冲突,产生了一个异常的重读操作,导致基于FIFO架构的存储数据存在丢失。问题的定位对于研究CDMA2000基站的架构、对于正确应用PCI和Local总线,具有参考价值。

金属表面Data Matrix条码高光区域的信息重构

为实现工业产品的可追溯性,直接将条码加工在零件表面的直接零件标识(Direct Part Marking,DPM)技术,在国内外受到了越来越多的关注。对于金属零件,由于其具有较高的反光性,由相机捕获的金属表面的条码图像常常产生局部高光现象,影响条码的正确读取。为此,针对金属表面激光标刻二维条码出现的局部高光现象,提出了基于五步重构模型的条码重构法,以重构高光区域的条码信息。对获得的条码图像进行倾斜校正,使"L"型实线边界位于图像左下角,对条码进行网格划分实现各个模块的定位。基于Modified Specular-Free(MSF)图像对高光区域进行检测。采用五步重构模型对条码的各个模块进行数值填充,对条码进行读取。实验表明,该算法能达到去除金属表面上条码局部高光的目的,并取得了较高的识读正确率。

基于改进的ISODATA的超球覆盖仿生模式分类算法

现有仿生模式识别分类器难以解决含有多个聚集点、非线性和稀疏性样本的分类问题。因此,引入特征分类贡献度,提出了基于改进的迭代自组织数据分析(M-ISODATA)的超球覆盖仿生模式识别算法。首先引入马氏距离对自组织数据分析方法(ISODATA)的欧氏距离替换,并引入熵权法对马氏距离进行加权以赋予各特征不同的贡献度;同时为了去除干扰样本点,引入改进的局部离群因子检测方法(M-LOF)对样本进行训练,减少了不同类别流形之间的重叠区域。再利用改进的自组织数据分析方法(M-ISODATA)对每类训练样本点动态聚类,寻找到同一类的多个小类覆盖区中心后,用超球进行该类的有效覆盖,并对落入重叠区域的测试样本点进行二次划分,实现测试样本的正确分类。最后在iris数据集上验证该算法的有效性,并将该算法应用于雷达辐射源信号的分类识别。实验结果表明,该算法具有很好的拒识、免重训能力,对于雷达信号的识别率能达到97.29%,相比于传统典型模式识别算法具有更好的识别能力。

An Improved PLS (IPLS) Method Utilizing Local Standardization Strategy for Multimode Process Monitoring

Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.

ESTIMATORS AND SOME BEHAVIORS FORA PARTIALLY LINEAR MODEL WITH CENSORED DATA

This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.

Influence of the modified global ocean tide model with local tides of East and South China Seas on load gravity in China and its neighbor area

By using 11 global ocean tide models and tidal gauge data obtained in the East China Sea and South China Sea, the influence of the ocean loading on gravity field in China and its neighbor area is calculated in this paper. Furthermore, the differences between the results from original global models and modified models with local tides are discussed based on above calculation. The comparison shows that the differences at the position near the sea are so large that the local tides must be taken into account in the calculation. When the global ocean tide models of CSR4.0, FES02, GOT00, NAO99 and ORI96 are chosen, the local effect for M2 is less than 0.10 × 10-8 m·s-2 over the area far away from sea. And the local effect for O1 is less than 0.05 × 10-8 m·s-2 over that area when choosing AG95 or CSR3.0 models. This numerical result demonstrates that the choice of model is a complex problem because of the inconsistent accuracy of the models over the areas of East and South China Seas.

Job schedulers for Big data processing in Hadoop environment： testing real-life schedulers using benchmark programs

At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, manage, store, distribute, and analyze petabyte or larger-sized datasets having different structures with high speed. Big data can be structured, unstructured, or semi structured. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper gives an overview of big data and highlights the problems and challenges in big data. It then highlights Hadoop Distributed File System （HDFS）, Hadoop MapReduce, and various parameters that affect the performance of job scheduling algorithms in big data such as Job Tracker, Task Tracker, Name Node, Data Node, etc. The primary purpose of this paper is to present a comparative study of job scheduling algorithms along with their experimental results in Hadoop environment. In addition, this paper describes the advantages, disadvantages, features, and drawbacks of various Hadoop job schedulers such as FIFO, Fair, capacity, Deadline Constraints, Delay, LATE, Resource Aware, etc, and provides a comparative study among these schedulers.

我国地方数据政策体系的构建进展与展望

[目的/意义]研究我国地方数据政策体系的构建情况,旨在明确各地数据政策制定的进展与提点,进而推动政策体系的优化建设。[方法/过程]对31个省级政府的数据政策进行统计,并采用文本分析法梳理政策的总体内容框架,基于此,对比与解析政策内容的地区差异。[结果/结论]我国地方数据政策的发布可划分为国家信息化发展时期、大数据发展时期以及数据要素市场化时期三个阶段;政策总体内容框架包含数据基本内涵、数据资源建设、数据共享、数据开放、数据资产化、数据安全6大要点;不同地区涵盖上述政策要点的程度不同,也显示了我国数据政策在地方层面的特点。由此,我国地方数据政策需进一步面向多元数据对象、多层数据行动、多元责任主体,实现内容的体系化建设,并为国家数据政策体系的优化发展提供参考。

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.