基于离散萤火虫算法的自由曲面测量序列规划

为了解决接触式测量序列规划问题,建立了该问题的等效旅行商模型,并利用萤火虫算法对该模型进行求解。对萤火虫算法进行了离散化操作,提出一种新的萤火虫距离表征方法适用于测量序列规划问题,同时对离散萤火虫算法迭代规则和随机搜索方式进行改进,得到一种改进型离散萤火虫算法;建立了综合路径长度、路径光滑度和触头旋转距离三个评价指标的适应度函数,并以叶片型零件为例,进行了离散萤火虫算法和改进型离散萤火虫算法对比实验,验证了改进型离散萤火虫算法的有效性以及适应度函数的合理性;最后以另一自由曲面零件为例,将改进型离散萤火虫算法和遗传算法进行对比,结果表明了改进型离散萤火虫算法的优越性。

月球探测器差分VLBI测量的模型及可估计参数研究

基于月球探测器离地面较近的特点和差分VLBI技术可以消除部分非几何延迟的优势,采用差分VLBI技术对月球探测器进行观测,推导具体可行的定位模型,并分析模型中可估计的地月大地测量参数序列。

基于公交IC卡数据的车辆运行方向相似性测量研究

在公交IC卡数据挖掘中,为了获取乘客流量及流向等信息,需要获知每个班次的运行方向.本文通过对公交IC卡数据的聚类分析,将IC卡数据解析成单班次站点客流数据,利用基于时间序列的相似性测量算法(相关性测量及动态时间扭曲法),测量单班次数据与经验数据的相似性,从而获取班次运行方向.研究结果表明,在线路客流方向性差别明显时,相似性测量方法精度较高.且经过数据聚类后,相关性测量法与动态时间扭曲法在计算精度与运算速度方面表现相近,适用于客流方向性差别较明显的公交线路.

GNSS位置时间序列分析理论与进展

GNSS时间序列分析对大地测量和地球物理领域研究具有重要意义。梳理和总结了大地测量时间序列特别是GNSS位置时间序列的研究现状和进展。重点介绍了GNSS时间序列分析的理论和方法,并同时指出了GNSS时间序列分析中存在的不足和下一步改进的方向。

Feature-based sequential partial vision measurement method for large scale machine parts

To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.

APPLICATION OF CHAOS IN MEASUREMENT

Using the high sensitivity to initial values of chaotic systems, this paper describes an application of chaos in the field of measurement. A general method for signal coding based on symbolic sequences and the relationship between the variable (to be measured) and its symbolic sequence are presented. Some performances of the chaos based measurement system are also discussed. Theoretical analysis and experimental results show that chaotic systems are potentially attractive in the field of measurement.

一种阵列式传感器数据融合方法的研究

提出了一种基于支持度和自适应加权的阵列式传感器数据融合方法。其特点是通过关联融合多组测量信号序列以降低静态数据的随机测量误差。对单传感器测量信号序列,采用支持度方法计算每个测量数据的综合支持度和加权因子,然后对测量信号序列进行加权融合。对阵列式传感器多组测量信号序列,基于单传感器数据融合,利用自适应加权方法,在总均方误差最小意义下进行多组测量信号序列数据融合。仿真结果表明,该阵列式传感器数据融合方法是有效的。

基于高分辨率SAR影像的上海临港新城沉降格局分析

上海临港新城是通过围垦造地工程建设而成,特殊的地质条件和地理位置,使其地质环境变化备受关注。而建立和完善该新城的水准测量网需要一定时间,且基于点的水准监测难以获得区域沉降情况。时间序列InSAR方法可通过空间信息实施大范围监测,对于此类海塘新区的地面沉降监测具有独特优势。本文处理和分析了11景TerraSAR-X影像,并与之前的研究成果进行分析。实验结果表明,临港新城的沉降情况与围垦成陆的建设施工时序密切相关,九四塘以西的老冲填土区已趋于稳定,并有回弹趋势;九四塘以东的新冲填土区形变量较大,沉降速率近35mm/a。利用14个水准点的监测数据进行验证,其平均误差为1.510mm、中误差为2.016,说明该方法可以满足城市形变监测的需求。

Application of Geodetic Receivers to Timing and Time Transfer

Two methods for smoothing pseudorange observable by Carrier and Doppler are discussed. Then the procedure based on the RINEX observation files is tested using the Ashtech Z-XII3T geodetic receivers driven by a stable external frequency at UNSO. This paper proposes to adapt this procedure for the links between geodetic receivers, in order to take advantage of the P codes available on L 1 and L 2. This new procedure uses the 30-second RINEX observations files, the standard of the International GPS Service (IGS), and processes the ionosphere-free combination of the codes P 1 and P 2; the satellite positions are deduced from the IGS rapid orbits, available after two days.

Oil–water two-phase flow pattern analysis with ERT based measurement and multivariate maximum Lyapunov exponent

Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus the information extracted from each electrode represents the local phase distribution and fraction change at that location. The multivariate maximum Lyapunov exponent(MMLE) is extracted from the 16-dimension time-series to demonstrate the change of flow pattern versus the superficial velocity ratio of oil to water. The correlation dimension of the multivariate time-series is further introduced to jointly characterize and finally separate the flow patterns with MMLE. The change of flow patterns with superficial oil velocity at different water superficial velocities is studied with MMLE and correlation dimension, respectively, and the flow pattern transition can also be characterized with these two features. The proposed MMLE and correlation dimension map could effectively separate the flow patterns, thus is an effective tool for flow pattern identification and transition analysis.

Pattern recognition and prediction study of rock burst based on neural network

Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod.

High-throughput sequencing exclusively identified a novel Torque teno virus genotype in serum of a patient with fatal fever

Torque teno virus(TTV) has been found to be prevalent world-wide in healthy populations and in patients with various diseases, but its etiological role has not yet been determined. Using high-throughput unbiased sequencing to screen for viruses in the serum of a patient with persistent high fever who died of suspected viral infection and prolonged weakness, we identified the complete genome sequence of a TTV(isolate Hebei-1). The genome of TTV-Hebei-1 is 3649 bp in length, encoding four putative open reading frames, and it has a G+C content of 49%. Genomic comparison and a BLASTN search revealed that the assembled genome of TTV-Hebei-1 represented a novel isolate, with a genome sequence that was highly heterologous to the sequences of other reported TTV strains. A phylogenetic tree constructed using the complete genome sequence showed that TTV-Hebei-1 and an uncharacterized Taiwan Residents strain, TW53A37, constitute a new TTV genotype. The patient was strongly suspected of carrying a viral infection and died eventually without any other possible causes being apparent. No virus other than the novel TTV was identified in his serum sample. Although a direct causal link between the novel TTV genotype infection and the patient's disease could not be confirmed, the findings suggest that surveillance of this novel TTV genotype is necessary and that its role in disease deserves to be explored.

Ambient noise during rough weather and cyclones in the shallow Bay of Bengal

This paper presents ambient noise analysis during rough weather, using time series measurements from an automated noise measurement system in the shallow southwest Bay of Bengal during October–November 2010. The period witnessed low-pressure events including depressions and cyclones, with JAL cyclone passing close to the measurement site. The time series noise level shows a shift in mid-October, after which deep depressions and cyclones formed, with an average increase of 5–10 dB in the lower band and 2–3 dB in the higher band of frequencies. Furthermore, correlation between noise level and wave height(data from wave rider buoy deployed at the site) for sea state scale 3 and above shows good correlation with an increase in noise level with increase in wave height, the effect being most pronounced at 0.5 kHz. The noise captured during JAL was analysed to identify the spectrum components due to convective precipitation and heavy wind/wave activity and shows anomalously high levels during the crossing of the cyclone. Rain noise spectra from the rain bands associated with the wall of the cyclone are reported. This has been correlated with radar refl ectivity measurements to ascertain the presence of rain, and discriminate between convective and stratiform types. Also, vertical directionality pattern of ambient noise during JAL showed clearly distinct surface contributions. On the whole, knowledge of ambient noise fields during high sea states and precipitation is useful in optimizing SONAR performance. The findings at the study site have been compared with measurements from other shallow water locations during rough weather.

Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

义能矿区开采沉陷演化的TS-DInSAR监测与影响分析

为研究济宁市义能矿区开采沉陷演化特点及其对周边建筑物的影响,本文采用69景长时间序列中等分辨率Sentinel-1影像,以TS-DInSAR技术体系中的小基线集DInSAR分析为研究方法,获取了2016年初至2018年底该矿区开采面周边的沉降信息。结果表明,义能矿区在监测时段内沉陷范围较为集中,最大累计沉降量约为627 mm,通过居民点位置与沉陷结果的叠加分析,发现周边3处村庄受到了不同程度的影响,该结果能为煤矿开采沉陷控制方案的效果评估及设计优化提供重要参考作用。

Calculation of Significant Wave Height Using the Linear Mean Square Estimation Method

Significant wave height is an important criterion in designing coastal and offshore structures.Based on the orthogonality principle, the linear mean square estimation method is applied to calculate significant wave height in this paper.Twenty-eight-year time series of wave data collected from three ocean buoys near San Francisco along the California coast are analyzed.It is proved theoretically that the computation error will be reduced by using as many measured data as possible for the calculation of significant wave height.Measured significant wave height at one buoy location is compared with the calculated value based on the data from two other adjacent buoys.The results indicate that the linear mean square estimation method can be well applied to the calculation and prediction of significant wave height in coastal regions.

Mining Data Correlation from Multi-Faceted Sensor Data in Internet of Things

Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.

Monitoring the Coasts around Taipei Port with a Marine Radar

Wave field around Taipei Port is studied. Using marine radar as a monitoring device, sequences of the wave field images were obtained on an hourly basis. A 3D-FFT was applied to the image sequences leading to the so-called intensity wavenumber-frequency spectrum. Wave field information can then be extracted from these spectra and compared with on-site measurements. It is shown that, when the prevailing winds are weak, estimated wave heights agree miserably with those measured. On the other hand, when the winds are relatively strong, our estimates follow closely with the trends, but are, in general, lower than measured. Possible reasons leading to these discrepancies are discussed.

SHAPE-BASED TIME SERIES SIMILARITY MEASURE AND PATTERN DISCOVERY ALGORITHM

Pattern discovery from time series is of fundamental importance. Most of the algorithms of pattern discovery in time series capture the values of time series based on some kinds of similarity measures. Affected by the scale and baseline, value-based methods bring about problem when the objective is to capture the shape. Thus, a similarity measure based on shape, Sh measure, is originally proposed, andthe properties of this similarity and corresponding proofs are given. Then a time series shape pattern discovery algorithm based on Sh measure is put forward. The proposed algorithm is terminated in finite iteration with given computational and storage complexity. Finally the experiments on synthetic datasets and sunspot datasets demonstrate that the time series shape pattern algorithm is valid.

Parameter selection in time series prediction based on nu-support vector regression

The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.