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and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution.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.

A spatial decomposition approach for accelerating buffer analysis of vector data

Parallel vector buffer analysis approaches can be classified into 2 types:algorithm-oriented parallel strategy and the data-oriented parallel strategy.These methods do not take its applicability on the existing geographic information systems(GIS)platforms into consideration.In order to address the problem,a spatial decomposition approach for accelerating buffer analysis of vector data is proposed.The relationship between the number of vertices of each feature and the buffer analysis computing time is analyzed to generate computational intensity transformation functions(CITFs).Then,computational intensity grids(CIGs)of polyline and polygon are constructed based on the relative CITFs.Using the corresponding CIGs,a spatial decomposition method for parallel buffer analysis is developed.Based on the computational intensity of the features and the sub-domains generated in the decomposition,the features are averagely assigned within the sub-domains into parallel buffer analysis tasks for load balance.Compared with typical regular domain decomposition methods,the new approach accomplishes greater balanced decomposition of computational intensity for parallel buffer analysis and achieves near-linear speedups.

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

Big spatial vector data management: a review

Spatial vector data with high-precision and wide-coverage has exploded globally,such as land cover,social media,and other data-sets,which provides a good opportunity to enhance the national macroscopic decision-making,social supervision,public services,and emergency capabilities.Simultaneously,it also brings great challenges in management technology for big spatial vector data(BSVD).In recent years,a large number of new concepts,parallel algorithms,processing tools,platforms,and applications have been proposed and developed to improve the value of BSVD from both academia and industry.To better understand BSVD and take advantage of its value effectively,this paper presents a review that surveys recent studies and research work in the data management field for BSVD.In this paper,we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management.It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures.To support them more fully,firstly,we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era.Then,we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD,including data storage and organization,spatial index,processing methods,and spatial analysis.Finally,based on the above commentary and related work,several opportunities and challenges are listed as the future research interests and directions for reference.

基于Vector 3D Tiles格式的三维矢量数据可视化研究

为了探究Vector 3D Tiles格式在三维矢量地物表达方面的效果和性能,研究了一套Vector 3D Tiles格式生产和表达工具,用于在Cesium平台上对矢量数据进行可视化展示。主要工作包括两部分:一是将数据从传统的矢量格式转换为Vector 3D Tiles格式,二是在Cesium平台上展示转换后的Vector 3D Tiles数据。为了验证方法可行性,采用广州市地下管线数据开展了实验,对Shapefile、GeoJSON二维矢量格式进行处理,生成Vector 3D Tiles格式后,在Cesium平台上进行三维可视化展示。通过不同格式数据的加载效率和呈现效果比较,证明了矢量切片数据比原始矢量格式加载更快、渲染更平滑。在此基础上,对矢量切片数据基于自定义三维样式的渲染能力进行了验证。

A virtual globe-based vector data model:quaternary quadrangle vector tile model

This study proposes a virtual globe-based vector data model named the quaternary quadrangle vector tile model(QQVTM)in order to better manage,visualize,and analyze massive amounts of global multi-scale vector data.The model integrates the quaternary quadrangle mesh(a discrete global grid system)and global image,terrain,and vector data.A QQVTM-based organization method is presented to organize global multi-scale vector data,including linear and polygonal vector data.In addition,tilebased reconstruction algorithms are designed to search and stitch the vector fragments scattered in tiles to reconstruct and store the entire vector geometries to support vector query and 3D analysis of global datasets.These organized vector data are in turn visualized and queried using a geometry-based approach.Our experimental results demonstrate that the QQVTM can satisfy the requirements for global vector data organization,visualization,and querying.Moreover,the QQVTM performs better than unorganized 2D vectors regarding rendering efficiency and better than the latitude–longitude-based approach regarding data redundancy.

A New Vector Data Compression Approach for WebGIS

High compression ratio,high decoding performance,and progressive data transmission are the most important require-ments of vector data compression algorithms for WebGIS.To meet these requirements,we present a new compression approach.This paper begins with the generation of multiscale data by converting float coordinates to integer coordinates.It is proved that the distance between the converted point and the original point on screen is within 2 pixels,and therefore,our approach is suitable for the visualization of vector data on the client side.Integer coordinates are passed to an Integer Wavelet Transformer,and the high-frequency coefficients produced by the transformer are encoded by Canonical Huffman codes.The experimental results on river data and road data demonstrate the effectiveness of the proposed approach:compression ratio can reach 10% for river data and 20% for road data,respectively.We conclude that more attention needs be paid to correlation between curves that contain a few points.

Assessment of distortion in watermarked geospatial vector data using different wavelets

With the rapid growth of the Internet,the copyright protection problem occurs frequently,and unauthorized copying and distributing of geospatial data threaten the investments of data producers.Digital watermarking is a possible solution to solve this issue.However,watermarking causes modifications in the original data resulting in distortion and affects accuracy,which is very important to geospatial vector data.This article provides distortion assessment of watermarked geospatial data using wavelet-based invisible watermarking.Eight wavelets at different wavelet decomposition levels are used for accuracy evaluation with the help of error measures such as maximum error and mean square error.Normalized correlation is used as a similarity index between original and extracted watermark.It is observed that the increase in the strength of embedding increases visual degradation.Haar wavelet outperforms the other wavelets,and the third wavelet decomposition level is proved to be optimal level for watermarking.

Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

A method for rapid transmission of multi-scale vector river data via the Internet

Due to the conflict between huge amount of map data and limited network bandwidth, rapid trans- mission of vector map data over the Internet has become a bottleneck of spatial data delivery in web-based environment. This paper proposed an approach to organizing and transmitting multi-scale vector river network data via the Internet progressively. This approach takes account of two levels of importance, i.e. the importance of river branches and the importance of the points belonging to each river branch, and forms data packages ac- cording to these. Our experiments have shown that the proposed approach can reduce 90% of original data while preserving the river structure well.

ENHANCED MULTISTAGE VECTOR QUANTIZATION FOR SAR RAW DATA COMPRESSION

Multistage Vector Quantization(MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional MSVQ is suboptimal with respect to the overall performance measure. This paper proposes a new technology to design the decoder codebook, which is different from the encoder codebook to optimise the overall performance. The performance improvement is achieved with no effect on encoding complexity, both storage and time consuming, but a modest increase in storage complexity of decoder.

Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction

Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter （EnKF） technology was used for the prediction of soil moisture in different soil layers： 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches： the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.

ArcGIS Data Reviewer在林业矢量数据质量管理中的应用

介绍ArcGIS Data Reviewer基本功能和特性,对其应用于林业地理信息矢量数据质量检查,如图斑重复、重叠,图斑间有间隙、多部件、狭长面、急锐角化、漏绘等空间关系,以及属性字段之间的逻辑性检查等的方法和步骤,举例进行了详细叙述,可为该软件模块的使用提供参考。

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

基于状态可观测性和多模态数据PF的移动目标跟踪

为了实现对移动目标的跟踪,提出了一种新颖的基于目标状态可观测性和多模态数据粒子滤波(Particle Filter,PF)的跟踪方案。通过部署在目标移动区域中的传感器获得跟踪目标的距离和到达方向测量值,对接收到的数据进行预处理来计算PF的观测值,以形成一个临时距离图像。通过利用状态更新函数和形成的候选图像模板确定目标状态向量;在PF器中加入额外的加权阶段,使得PF器可自适应地同步多模态数据流,以实现鲁棒的目标跟踪。仿真实验结果验证了所提方案能够有效地跟踪移动目标。

考虑环境参数的农业生产数据深度挖掘仿真

农业生产数据受到多种因素的影响,如气候、土壤、病虫害等,这些因素会导致数据存在较大的误差和不确定性,从而影响数据挖掘的准确性。为了能够获取精准的农业生产数据深度挖掘结果,提出一种环境参数影响下农业生产数据深度挖掘算法。分析环境参数对农业生产数据和农业生产数据深度挖掘的影响。采用支持向量机聚类和数据域描述方法,对全部农业生产数据展开样本数据归类处理。根据原始空间农业生产数据属性值的极值,确定样本的属性范围。利用获取的支持向量提取规则,完成农业生产数据深度挖掘。实验结果表明,所提算法的农业生产数据拟合度大于0.75,且挖掘准确性保持在0.9以上,表明上述算法可以获取高精度和高效率的农业生产数据深度挖掘结果。

Data Selection Using Support Vector Regression

Geophysical data sets are growing at an ever-increasing rate,requiring computationally efficient data selection （thinning） methods to preserve essential information.Satellites,such as WindSat,provide large data sets for assessing the accuracy and computational efficiency of data selection techniques.A new data thinning technique,based on support vector regression （SVR）,is developed and tested.To manage large on-line satellite data streams,observations from WindSat are formed into subsets by Voronoi tessellation and then each is thinned by SVR （TSVR）.Three experiments are performed.The first confirms the viability of TSVR for a relatively small sample,comparing it to several commonly used data thinning methods （random selection,averaging and Barnes filtering）,producing a 10％ thinning rate （90％ data reduction）,low mean absolute errors （MAE） and large correlations with the original data.A second experiment,using a larger dataset,shows TSVR retrievals with MAE ＜ 1 m s-1 and correlations ≥ 0.98.TSVR was an order of magnitude faster than the commonly used thinning methods.A third experiment applies a two-stage pipeline to TSVR,to accommodate online data.The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment,is an order of magnitude faster than the nonpipeline TSVR.Therefore,pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set.This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.

基于深度自回归模型的电网异常流量检测算法

针对电网中行为种类复杂多样且数量众多的问题,提出了一种基于自回归模型的电网异常流量检测算法。该算法利用深度自编码网络自动提取网络流量数据的特征,降低异常流量检测的分析周期,并自动挖掘数据的层次关系。通过支持向量机对提取的特征进行分类,实现对异常流量的检测。仿真实验结果表明,所提算法可以分析不同攻击向量,避免噪声数据的干扰,进而提高电网异常流量检测的精度,对于流量数据处理具有重要意义。

一种曲线数据压缩的自编码器神经网络方法

矢量空间数据的压缩是减少存储空间及节省网络传输带宽最直接有效的途径。本文利用自编码器神经网络构建了一种面向矢量曲线数据的压缩和解压模型。该模型针对不同规模和复杂度的曲线数据,首先进行分段和重采样处理进而规范化输入向量,然后通过优化自编码器结构和调整损失函数实现了高精度的压缩解码模块,最后通过弧段数据的坐标闭合差分配确保了数据的稳定性。对山西、湖南、江西3省的1∶100万县级弧段进行了压缩和还原试验,分析发现:数据还原精度随压缩率变小而变小,当压缩率小于35%后,数据还原精度呈波动趋势,25%为满足精度要求的适宜压缩率。比较傅里叶级数及贝塞尔曲线拟合方法发现本文模型压缩精度和处理速度在一定的压缩率范围内存在优势,同时该模型说明了深度学习在提取空间要素几何特征方面有一定的潜力。