可量测序列影像的加权整体最小二乘导航

提出顾及观测方程系数矩阵误差的可量测影像定位定姿算法及其权值确定方法,实现了利用可量测序列视觉影像与DGPS/IMU融合导航计算.计算结果表明:采用加权最小二乘算法可较好地克服可量测序列影像定位定姿算法中系数矩阵误差的影响,精度高于最小二乘法,当GPS失锁2.5 km时可达到优于13 m的定位精度和0.1°的定姿精度.

杂波环境下机动输入序列和量测序列的联合最优估计

为了提高在杂波环境下跟踪强机动目标的精度,提出了一种新的基于期望极大化(EM)算法的机动目标状态估计方法.首先建立了基于EM算法的最大后验概率意义下的状态估计数学模型,然后采用离散优化技术解决EM算法中的极大化问题,最终确定出作用于系统的实际机动输入序列,同时分离出源于目标的量测序列,进而获得对目标状态更精确的估计.它有效地解决了最大后验概率状态估计中的不完全数据问题.Monte Carlo仿真结果表明,新算法比传统的交互式多模型概率数据关联算法具有更优越的跟踪性能.

基于灰色关联分析法的电力系统隐蔽性数据攻击检测新方法

传统状态估计难以对带有隐蔽性粗差的注入量测进行辨识,本文提出一种基于灰色关联分析法的电网状态估计隐蔽性数据攻击检测新方法。该方法将历史相邻断面量测向量的差分构成量测变化序列,通过灰色关联分析法计算各序列之间的加权关联度,构成未受攻击时的加权关联度阈值域,若当前断面量测变化序列的加权关联度超出阈值域,则判定该断面量测数据受到信息恐怖攻击。为了使低出线数节点的强相关量测受到攻击后,增大其在加权关联度中的影响,进一步提出了基于节点出线度的量测加权权重计算方法。通过IEEE14节点系统分析了多个状态变量被不同模式的数据攻击时的检测结果,验证了本文方法的有效性。

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.

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.

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.

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.

Traffic An o ma ly De te ctio n in Backbone Networks Using C la s s ifica tio n o f M u Itid ime n s io n a I Time Series of Entropy

Detecting traffic anomalies is essential for diagnosing attacks. HighSp eed Backbone Net works （HSBN） require Traffic Anomaly Detection Systems （TADS） which are accurate （high detec tion and low false positive rates） and efficient. The proposed approach utilizes entropy as traffic distributions metric over some traffic dimensions. An efficient algorithm, having low computational and space complexity, is used to estimate entro py. Entropy values over all dimensions are

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.

High-throughput sequencing-based genome-wide identification of micro RNAs expressed in developing cotton seeds

Micro RNAs(mi RNAs) have been shown to play critical regulatory roles in gene expression in cotton. Although a large number of mi RNAs have been identified in cotton fibers, the functions of mi RNAs in seed development remain unexplored. In this study, a small RNA library was constructed from cotton seeds sampled at 15 days post-anthesis(DPA) and was subjected to high-throughput sequencing. A total of 95 known mi RNAs were detected to be expressed in cotton seeds. The expression pattern of these identified mi RNAs was profiled and 48 known mi RNAs were differentially expressed between cotton seeds and fibers at 15 DPA. In addition, 23 novel mi RNA candidates were identified in 15-DPA seeds. Putative targets for 21 novel and 87 known mi RNAs were successfully predicted and 900 expressed sequence tag(EST) sequences were proposed to be candidate target genes, which are involved in various metabolic and biological processes, suggesting a complex regulatory network in developing cotton seeds. Furthermore, mi RNA-mediated cleavage of three important transcripts in vivo was validated by RLM-5′ RACE. This study is the first to show the regulatory network of mi RNAs that are involved in developing cotton seeds and provides a foundation for future studies on the specific functions of these mi RNAs in seed development.

Robust QKD-based private database queries based on alternative sequences of single-qubit measurements

Quantum channel noise may cause the user to obtain a wrong answer and thus misunderstand the database holder for existing QKD-based quantum private query(QPQ) protocols. In addition, an outside attacker may conceal his attack by exploiting the channel noise. We propose a new, robust QPQ protocol based on four-qubit decoherence-free(DF) states. In contrast to existing QPQ protocols against channel noise, only an alternative fixed sequence of single-qubit measurements is needed by the user(Alice) to measure the received DF states. This property makes it easy to implement the proposed protocol by exploiting current technologies. Moreover, to retain the advantage of flexible database queries, we reconstruct Alice's measurement operators so that Alice needs only conditioned sequences of single-qubit measurements.

SEQUENCE-BASED PROTEIN-PROTEIN INTERACTION PREDICTION VIA SUPPORT VECTOR MACHINE

This paper develops sequence-based methods for identifying novel protein-protein interactions （PPIs） by means of support vector machines （SVMs）. The authors encode proteins ont only in the gene level but also in the amino acid level, and design a procedure to select negative training set for dealing with the training dataset imbalance problem, i.e., the number of interacting protein pairs is scarce relative to large scale non-interacting protein pairs. The proposed methods are validated on PPIs data of Plasmodium falciparum and Escherichia coli, and yields the predictive accuracy of 93.8% and 95.3%, respectively. The functional annotation analysis and database search indicate that our novel predictions are worthy of future experimental validation. The new methods will be useful supplementary tools for the future proteomics studies.

LARGE DEVIATIONS FOR STATIONARY Φ-MIXING SEQUENCES IN τ-TOPOLOGY

The results of Bryc on large deviations for empirical measures of stationary Φ-mixing sequences are extended. Bryc states his results in the usual weak topology on the space ofprobability measures. In this paper, under somewhat weaker assumptions than those of Bryc,the author extends Bryc's results by taking the finer topology which is generated by the integralsover bounded measurable functions.