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.

Individualization of Data-Segment-Related Parameters for Improvement of EEG Signal Classification in Brain-Computer Interface

In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.

A Partition Checkpoint Strategy Based on Data Segment Priority

A partition checkpoint strategy based on data segment priority is presented to meet the timing constraints of the data and the transaction in embedded real-time main memory database systems（ERTMMDBS） as well as to reduce the number of the transactions missing their deadlines and the recovery time.The partition checkpoint strategy takes into account the characteristics of the data and the transactions associated with it;moreover,it partitions the database according to the data segment priority and sets the corresponding checkpoint frequency to each partition for independent checkpoint operation.The simulation results show that the partition checkpoint strategy decreases the ratio of trans-actions missing their deadlines.

Semantic Segmentation Based Remote Sensing Data Fusion on Crops Detection

Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.

Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data

Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.

Distributed C-Means Algorithm for Big Data Image Segmentation on a Massively Parallel and Distributed Virtual Machine Based on Cooperative Mobile Agents

The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.

Analysis of an Active Fault Geometry Using Satellite Sensor and DEM Data: Gaziköy-Saros Segment (NAFZ), Turkey

In this study, Landsat 5 Thematic Mapper (TM) and SPOT HRV Panchromatic data were analysed to determine the geometry of an active fault segment (the Ganos segment) in Gazikoy-Saros region, west of Marmara Sea, Turkey. Gazikoy-Saros/Ganos segment is a part of North Anatolian Fault Zone (NAFZ). North-Anatolian fault is considered to be one of the most important active strike-slip faults in the world. Thus far in relevant researches based on Gazikoy-Saros segment a single straight fault line representation is used on the fault descriptive geological maps. This study, with the aid of enhanced remotely sensed data aims to reveal the linear details of the NAFZ fault segment, which subsequently were superposed with a Digital Elevation Model (DEM) data. Respectively, using these data the surface geometry expression of Gazikoy-Saros fault segment was detailed and remapped. According to the results of the analysis two small releasing steps were identified on this segment. The first one is situated between Mürseli and Güzelkoy villages, and the second one is between Mürseli and Yorguc villages. In addition to this, it is found that the fault strike bends approximately 7° further to in south-eastern (SE) direction between Yenikoy and Sofular villages. This angular change was defined with the advantage of multi-angular viewing capability of the multi-satellite sensors and DEM data. The newly generated surface geometry expression of Ganos segment was compared with Global Positioning System (GPS) velocity vectors.

Interpretation of the west segment of the coastal fault zone in the coastal region of South China based on the gravity data

By systemic processing, comprehensive analysis, and interpretation of gravity data, we confirmed the existence of the west segment of the coastal fault zone(west of Yangjiang to Beibu Bay) in the coastal region of South China. This showed an apparent high gravity gradient in the NEE direction, and worse linearity and less compactness than that in the Pearl River month. This also revealed a relatively large curvature and a complicated gravity structure. In the finding images processed by the gravity data system, each fault was well reflected and primarily characterized by isolines or thick black stripes with a cutting depth greater than 30 km. Though mutually cut by NW-trending and NE-trending faults, the apparent NEE stripe-shaped structure of the west segment of the coastal fault zone remained unchanged,with good continuity and an activity strength higher than that of NW and NE-trending faults. Moreover,we determined that the west segment of the coastal fault zone is the major seismogenic structure responsible for strong earthquakes in the coastal region in the border area of Guangdong, Guangxi, and Hainan.

基于注意力机制的高光谱图像降维在纸质文物霉斑识别的研究

纸质文物作为文物传承的重要工具,用于记录不同时期人类历史及人文风貌,其在保存过程中极易受到霉菌等微生物的侵害。霉菌会加速纤维素的降解,在纸张表面生成霉斑,并且散落的孢子会随空气流动大范围传播,增加其他纸质文物发生霉变的风险。因此,定期对纸质文物进行霉斑检测对了解纸质文物现状和纸质文物修复至关重要。高光谱成像技术是一种非接触性、非破坏性的检测技术,能同时获得空间数据和光谱数据,与计算机技术结合可以实现纸质文物的大批次实时无损检测。针对黑曲霉这一广泛出现的霉菌,提出一种基于注意力机制的高光谱数据降维方法,通过采集其高光谱数据,实现了高光谱冗余数据的自适应预处理。采集了来自重庆中国三峡博物馆提供的20份纸质文物黑曲霉霉斑样本,使用ENVI软件分析得出在413~855 nm波段范围内,黑曲霉霉斑感染区域和健康区域的平均光谱曲线,平均反射率差异明显;在855~1021 nm波段范围内,黑曲霉霉斑感染区域和墨迹区域的平均光谱曲线,平均反射率差异明显。文中将所提出方法与传统主成分分析和独立成分分析预处理方法分别处理原始高光谱数据,并将结果在经典U-Net、SegNet、DeepLabV3+和PSPNet四个语义分割网络上进行了对比。结果表明,该算法预处理的数据在U-Net和SegNet经典网络中有明显优势,相较于主成分分析法和独立成分分析法,霉斑识别精度取得了较大提升达到89.49%和88.46%,验证了本文所提出算法的有效性,为文物保护领域提供有效的支撑和新的思路。

基于直觉模糊的ISODATA算法

ISODATA算法能自动地进行类的分裂和合并,但这种硬分类算法没有充分考虑图像本身的特点和人类的视觉特性,其分类效果一般差于模糊聚类算法。而大多数模糊识别方法都需要设置类别数目,有其自身的缺点,而直觉模糊则弥补了传统模糊理论不足。结合直觉模糊和ISODATA优点,将与隶属度和非隶属度相关的判定函数作为分类度量,提出了一种基于直觉模糊的ISODATA算法,结合实际改进了隶属度函数,以区域为待分类样本以提高算法速度,将其应用到图像分割,经实验证明了算法的有效性。

一种融合分水岭与ISODATA的岩心图像分割方法

分水岭算法用于图像分割,能获得封闭的和位置准确的轮廓,但容易造成过分割;ISODATA算法能将岩心砾石颗粒聚集成一类,但会使砾石目标边缘位置漂移。为克服以上两种方法的缺点,提出融合分水岭和ISODATA的图像分割方法。该方法首先利用分水岭算法得到过分割图像,ISODATA算法得到聚类图像,然后以ISODATA算法聚类的结果为依据,校正分水岭法的过分割问题。该方法用于砾岩图像的分割,取得了较好的实验效果。

考虑不确定性的红外视频气羽分割及其在VOCs泄漏检测中的应用

针对红外气体成像实现泄漏检测自动化存在的安全问题,提出一种可同时进行视频气羽分割与不确定性估计的挥发性有机物(VOCs)泄漏检测框架。通过分析视频帧判断泄漏位置与范围,并将深度模型的不确定性结果作为检测结果的可靠性指标,为后续决策提供参考,以增强算法使用中的安全性。通过记忆模块来关联视频时序信息,实现快速高效的视频气羽分割与不确定性评估,提升了检测结果的准确性和连续性。为缓解VOCs视频数据稀缺问题,使用真实与合成数据混合训练。结果表明,该方法在给出泄漏检测结果的同时,可以输出模型置信度评价,避免对深度模型的盲目依赖,为人工决策和修复方案的制定提供更多依据。

移动设备日志结构文件系统综述

NAND闪存(NAND flash)因为其大容量、轻便、抗震等优异特性,被广泛使用于移动设备.面向闪存特性设计的闪存友好型文件系统(flash friendly file system,F2FS)是典型的日志结构文件系统(log-structured file system,LFS),它采用日志结构写机制提升了随机写性能,使用前滚恢复技术实现快速的一致性保护,经常被用作移动设备的文件系统.文件系统因碎片化和段清理问题导致性能下降,而日志结构文件系统的异地更新机制和移动应用的高并发随机同步小写模式进一步加剧了碎片化,导致I/O请求响应变慢、设备运行卡顿.首先介绍了移动设备日志结构文件系统的相关概念和内容,随后总结了日志结构文件系统碎片化和段清理问题的研究现状.一方面分析了碎片产生的原因与影响,从预防碎片产生和重整碎片2个角度总结了减少碎片的研究工作.另一方面分析了冷热数据混合对段清理的影响,从静态分类和动态分类2方面总结了冷热数据区分技术的研究现状,从管理数据分布和调整段清理时机、频率、对象2个角度总结了段清理的研究现状.最后展望了移动设备日志结构文件系统研究的主要挑战和未来研究工作.

结合Voronoi划分HMRF模型的模糊ISODATA图像分割

为了解决传统模糊聚类图像分割方法对噪声敏感及无法自动准确确定聚类数的问题,提出结合Voronoi划分HMRF模型的模糊ISODATA图像分割方法。利用Voronoi划分将图像域划分为若干子区域,以划分子区域为基本单元定义基于隐马尔科夫随机场(HMRF)模型的模糊聚类目标函数,以解决噪声敏感问题;通过迭代自组织数据分析技术算法(ISODATA)中聚类分裂、合并技术改变聚类数,以实现聚类数的自动确定。对模拟、合成图像和真实图像分割结果的定性、定量分析表明:提出算法不仅可以有效克服噪声和像素异常值对分割结果的影响,而且还能自动准确确定聚类数,实现自动变类图像分割。

基于用户行驶工况的整车耐久试验目标研究

作为整车开发过程中重要的环节之一,整车结构耐久试验具有对整车结构和车辆系统进行重要验证和考核的功能。制定一个与用户关联,符合用户实际使用的整车耐久试验目标尤为关键。文章提出了一种基于用户行驶工况的整车耐久试验目标制定方法。首先,基于用户实际行驶数据利用机器学习的聚类算法获取用户典型行驶工况以及各工况里程占比;其次,进行了公共道路实车路谱载荷采集,获取对应行驶工况下的载荷损伤;最后将用户行驶工况与实车路谱载荷损伤相互关联,获得了与用户关联的整车耐久试验损伤目标。

改进的自适应模糊ISODATA灰度图像分割算法

传统模糊ISODATA(Fuzzy ISODATA,FISODATA)算法中,分裂-合并操作需人工选取阈值参数。而不适当的阈值往往使算法陷入局部极值,因而得到错误的类属数并最终影响图像分割结果。为此,在模糊集理论基础上提出一种改进的自适应FISODATA算法。该算法设计了自适应分裂-合并操作,即在每次分裂-合并后,根据该次计算结果改变参数阈值,解决了人为选取参数带来的诸多问题。利用该算法对模拟图像和真实IKONOS图像进行分割实验,均能得到良好的分割结果。

基于遥感和社交媒体数据的城市洪涝灾害监测

城市洪涝监测对灾害管理与应急响应至关重要,单一数据源往往存在各自的缺陷,不利于多维度、全方位地分析城市复杂洪涝灾害灾情。结合不受云雨影响且覆盖范围大的GF-3 SAR影像与实时性较强的社交媒体数据,建立“7·20”特大暴雨郑州市中心城区洪涝监测方法,采用阈值分割、随机森林对GF-3 SAR影像进行多方案的灾前灾中水体提取,获取精度最高的水体提取与洪涝监测结果,分析典型区域的水体提取效果;利用Python工具获取城市内涝的社交媒体数据,对其进行处理、可视化与空间分析;结合GF-3 SAR影像与社交媒体数据洪涝监测结果探讨二者优势互补性。结果表明:SAR水体总体提取精度从高到低依次为随机森林、Otsu阈值法、水体指数法,但是在一些典型区域分析中,随机森林的提取效果低于其他方法;基于SAR影像提取的洪涝淹没范围主要集中在三环外的城市边缘地区与大型水体周围,基于社交媒体数据提取的洪涝信息主要集中在城市人口和建筑密集的三环内。

Scaling up Kernel Grower Clustering Method for Large Data Sets via Core-sets

核栽培者是聚类最近 Camastra 和 Verri 建议的方法的一个新奇的核。它证明为各种各样的数据的好性能关于流行聚类的算法有利地设定并且比较。然而，方法的主要缺点是在处理大数据集合的弱可伸缩能力，它极大地限制它的应用程序。在这份报纸，我们用核心集合建议一个可伸缩起来的核栽培者方法，它是比为聚类的大数据的原来的方法显著地快的。同时，它能处理很大的数据集合。象合成数据集合一样的基准数据集合的数字实验显示出建议方法的效率。方法也被用于真实图象分割说明它的性能。

Using ALS raster data in forest planning

Raster type of forest inventory data with site and growing stock variables interpreted for small squareshaped grid cells are increasingly available for forest planning.In Finland,there are two sources of this type of lattice data:the multisource national forest inventory and the inventory that is based on airborne laser scanning(ALS).In both cases,stand variables are interpreted for 16 m×16 m cells.Both data sources cover all private forests of Finland and are freely available for forest planning.This study analyzed different ways to use the ALS raster data in forest planning.The analyses were conducted for a grid of 375×375 cells(140,625 cells,of which 97,893 were productive forest).The basic alternatives were to use the cells as calculation units throughout the planning process,or aggregate the cells into segments before planning calculations.The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan.In addition,allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas.The use of segments led to 5–9%higher growth predictions than calculations based on cells.In addition,the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used.The shape of the treatment blocks was more irregular in cell-based planning.Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000.