基于Lyapunov指数改进算法的边坡位移预测

给出了一种最大 Ｌｙａｐｕｎｏｖ指数的改进算法，这种改进算法不仅对小数据序列可靠，而且计算量小，相对容易操作。通过对边坡位移历史数据序列进行特征分析，计算出最大ｂ叩ｕｎｏｖ指数，并利用最大Ｌｙａｐｕｎｏｖ指数的一维模式进行边坡位移预测。这种改进的方法比己有的研究方法更可靠，而且操作起来比较方便。通过对三峡升船机高边坡和新滩滑坡实际位移数据进行预测，结果令人满意。

蚂蚁算法在日用水量预测中的应用研究

城市日用水量预测是城市供水管网系统动态模拟的基础和前提,用神经网络对城市日用水量预测的非线性回归组合模型求解的过程中,提出了采用新型的仿生算法——蚂蚁算法来训练神经网络的权值.此方法简化了训练过程,避免了BP算法易陷于局部极值等问题.将经过蚂蚁算法训练的神经网络应用到S.X市日用水量预测模型中,显示了此网络模型具有良好的预测能力,验证了基于蚂蚁算法的神经网络在城市日用水量的预测中具有有效性和可行性.

堆浸浸出率灰色系统GM(1,1)模型的建立及预测

应用灰色系统理论的数学原理及建模过程,建立了堆浸浸出率的GM(1,1)模型。应用该模型对某铀矿石室内模拟试验的浸出率进行灰色预测,采用“残差检验法”和“灰关联度检验法”进行检验。结果表明,GM(1,1)模型的预测精度高,适合于具有灰色特征的地质、采矿、选矿、经济等原始数据序列的模拟控制和预测分析。

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

A Preliminary Analysis of Phylogenetic Relationships in Cymbidium (Orchidaceae) Based on nrITS Sequence Data

The infrageneric classification currently in use for Cymbidium is based on gross morphology, with emphasis on the number of pollinia and state of fusion between lip and column. The sequences of nrDNA regions of 27 species and 3 cultivars of Cymbidium and 3 outgroup species ( Eulophia graminea, Geodorum densiflorum, Amitostigma pinguiculum) were analyzed using PCR amplification and direct DNA sequencing. The phylogenetic trees generated from maximum parsimony analysis, however, show that the existing division among three subgenera (subgen. Cymbidium , subgen. Cyperorchis and subgen. Jensoa ) should be evaluated with more data. Subgenus Cyperorchis was not a monophyletic group, with the unexpected nesting of C. dayanum (subgen. Cymbidium ) within it; subgenus Jensoa also appeared paraphyletic, with C. lancifolium being the sister group to the remainder of the genus; species of subgen. Cymbidium appeared polyphyletic, being split into several clades and intermixed with the main subgen. Cyperorchis and subgen. Jensoa clades, respectively. However, because of the insufficiency of informative characters of ITS sequences, some of the clades identified, especially the major lineages of Cymbidium , received relatively low support; sectional delimitations were also not clear within each subgenus. Further study is needed for achieving a robust phylogeny of Cymbidium .

Effects of Freezing Disaster on Green-up Date of Vegetation Using MODIS/EVI Time Series Data

In the field of global changes, the relationship between plant phenology and climate, which reflects the response of terrestrial ecosystem to global climate change, has become a key subject that is highly concerned. Using the moderate-resolution imaging spectroradiometer (MODIS)/enhanced vegetation index(EVI) collected every eight days during January- July from 2005 to 2008 and the corresponding remote sensing data as experimental materials, we constructed cloud-free images via the Harmonic analysis of time series (HANTS). The cloud-free images were then treated by dynamic threshold method for obtaining the vegetation phenology in green up period and its distribution pattern. And the distribution pattern between freezing disaster year and normal year were comparatively analyzed for revealing the effect of freezing disaster on vegetation phenology in experimental plot. The result showed that the treated EVI data performed well in monitoring the effect of freezing disaster on vegetation phenology, accurately reflecting the regions suffered from freezing disaster. This result suggests that processing of remote sensing data using HANTS method could well monitor the ecological characteristics of vegetation.

Molecular Phylogeny of the Lardizabalaceae Based on TrnL-F Sequences and Combined Chloroplast Data

The molecular phylogeny of the Lardizabalaceae is reconstructed based on chloroplast trn L_F sequences alone and combined trn L_F and rbc L sequences. The phylogenetic topologies agree well with Qin's and Takhtajan's tribal classification in both analyses. Decaisneae and Sinofranchetieae are basal clades in the phylogenetic trees and external to all other taxa in the family. Lardizabaleae consisting of Boquila and Lardizabala are well supported in both trn L_F (100%) analysis and trn L_F and rbc L combined analysis (99%). Tribe Akebieae are strongly supported by a bootstrap value of 100% in both trn L_F analysis and trn L_F and rbc L combined analysis. However, the new genus Archakebia is nested within the genus Akebia in the trn L_F trees. In the combined trees, Archakebia is sister to Akebia with high bootstrap support. The inter_relationships among three closely related genera Parvatia , Holboellia and Stauntonia are still problematic. P. brunoniana ssp. elliptica is sister to H. latifolia in both analyses with low bootstrap support. H. parviflora is nested within the Stauntonia and sister to S. cavalerieana . Therefore, these three genera of tribe Akebieae may not be monophylytic and their generic boundary and delimitation need to be further studied, by exploring more molecular data, together with more morphological characters.

Real-time road traffic states estimation based on kernel-KNN matching of road traffic spatial characteristics

The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.

Middle matching mining algorithm

A new algorithm for fast discovery of sequential patterns to solve the problems of too many candidate sets made by SPADE is presented, which is referred to as middle matching algorithm. Experiments on a large customer transaction database consisting of customer_id, transaction time, and transaction items demonstrate that the proposed algorithm performs better than SPADE attributed to its philosophy to generate a candidate set by matching two sequences in the middle place so as to reduce the number of the candidate sets.

Vegetation Change of Ecotone in West of Northeast China Plain Using Time-series Remote Sensing Data

Multi-temporal series of satellite SPOT-VEGETATION normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data from 1998 to 2007 were used for analyzing vegetation change of the ecotone in the west of the Northeast China Plain. The yearly and monthly maximal values,anomalies and change rates of NDVI and NDWI were calculated to reveal the interannual and seasonal changes in vegetation cover and vegetation water content. Linear regression method was adopted to characterize the trends in vegetation change. The yearly maximal NDVI decreased from 0.41 in 1998 to 0.37 in 2007,implying the decreasing trend of vegetation activity. There was a significant decrease of maximal NDVI in spring and summer over the study period,while an increase trend was observed in autumn. The vegetation-improved regions and vegetation-degraded regions occupied 17.03% and 20.30% of the study area,respectively. The maximal NDWI over growing season dropped by 0.027 in 1998–2007,and about 15.15% of the study area showed a decreasing trend of water content. Vegetation water stress in autumn was better than that in spring. Vegetation cover and water content variations were sensitive to annual precipitation,autumn precipitation and summer temperature. The vegetation degradation trend in this ecotone might be induced by the warm-drying climate especially continuous spring and summer drought in the recent ten years.

A Novel Parallel Scheme for Fast Similarity Search in Large Time Series

The similarity search is one of the fundamental components in time series data mining,e.g.clustering,classification,association rules mining.Many methods have been proposed to measure the similarity between time series,including Euclidean distance,Manhattan distance,and dynamic time warping(DTW).In contrast,DTW has been suggested to allow more robust similarity measure and be able to find the optimal alignment in time series.However,due to its quadratic time and space complexity,DTW is not suitable for large time series datasets.Many improving algorithms have been proposed for DTW search in large databases,such as approximate search or exact indexed search.Unlike the previous modified algorithm,this paper presents a novel parallel scheme for fast similarity search based on DTW,which is called MRDTW(MapRedcuebased DTW).The experimental results show that our approach not only retained the original accuracy as DTW,but also greatly improved the efficiency of similarity measure in large time series.

Progress in Research on Homogenization of Climate Data

The observation data from ground surface meteorological stations is an important basis on which climate change research is carried out, while the homogenization of the data is necessary for improving the quality and homogeneity of the time series. This paper reviews recent advances in the techniques of identifying and adjusting inhomogeneity in climate series. We briefly introduce the results of applying two commonly accepted and well-developed methods (RHtest and MASH) to surface climate observations such as temperature and wind speed in China. We then summarize current progress and problems in this field, and propose ideas for future studies in China. Along with collecting more detailed metadata, more research on homogenization technology should be done in the future. On the basis of comparing and evaluating advantages and disadvantages of different homogenization methods, the homogenized climate data series of the last hundred years should be rebuilt.

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.

Phylogenetic analyses of four species of Ulva and Monostroma grevillei using ITS, rbcL and 18S rDNA sequence data

Chlorophyta species are common in the southern and northern coastal areas of China. In recent years, frequent green tide incidents in Chinese coastal waters have raised concerns and attracted the attention of scientists. In this paper, we sequenced the 18S rDNA genes, the internal transcribed spacer （ITS） regions and the rbcL genes in seven organisms and obtained 536-566 bp long ITS sequences, 1 377-I 407 bp long rbcL sequences and 1 718-1 761 bp long partial 18S rDNA sequences. The GC base pair content was highest in the ITS regions and lowest in the rbcL genes. The sequencing results showed that the three Ulvaprolifera （or U. pertusa） gene sequences from Qingdao and Nan＇ao Island were identical. The ITS, 18S rDNA and rbcL genes in U. prolifera and U. pertusa from different sea areas in China were unchanged by geographic distance. U.flexuosa had the least evolutionary distance from U. californica in both the ITS regions （0.009） and the 18S rDNA （0.002）. These data verified that Ulva and Enteromorpha are not separate genera.

A MODIS Time Series Data Based Algorithm for Mapping Forest Fire Burned Area

Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter （MODIS） time series data in Heilongjiang Province, China. The algorithm is divided into two steps： Firstly, the ＇core＇ pixels were extracted to represent the most possible burned pixels based on the comparison of the tem- poral change of Global Environmental Monitoring Index （GEMI）, Burned Area Index （BAI） and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ＇core＇ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the hiehest accuracy of 96.61%.

Comparison of different inversion methods of D-T_(2)two-dimensional nuclear magnetic resonance logging and applicability analysis

D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.

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.

Molecular phylogeny and species separation of five morphologically similar Holosticha-complex ciliates(Protozoa, Ciliophora) using ARDRA riboprinting and multigene sequence data

To separate and redefine the ambiguous Holosticha-complex, a confusing group of hypotrichous ciliates, six strains belonging to five morphospecies of three genera, Holosticha heterofoissneri, Anteholosticha sp. popl, Anteholosticha sp. pop2, A. manca, A. gracilis and Nothoholostichafasciola, were analyzed using 12 restriction enzymes on the basis of amplified ribosomal DNA restriction analysis. Nine of the 12 enzymes could digest the DNA products, four （HinfⅠ, Hind Ⅲ, Msp Ⅰ, Taq Ⅰ） yielded species-specific restriction patterns, and Hind Ⅲ and Taq Ⅰ produced different pattems for two Anteholosticha sp. populations. Distinctly different restriction digestion haplotypes and similarity indices can be used to separate the species. The secondary structures of the five species were predicted based on the ITS2 transcripts and there were several minor differences among species, while two Anteholosticha sp. populations were identical. In addition, phylogenies based on the SSrRNA gene sequences were reconstructed using multiple algorithms, which grouped them generally into four clades, and exhibited that the genus Anteholosticha should be a convergent assemblage. The fact that Holosticha species clustered with the oligotrichs and choreotrichs, though with very low support values, indicated that the topology may be very divergent and unreliable when the number of sequence data used in the analyses is too low.

Highly nonlinear internal solitary waves over the continental shelf of the northwestern South China Sea

Large amplitude internal solitary waves(ISWs) often exhibit highly nonlinear effects and may contribute significantly to mixing and energy transporting in the ocean.We observed highly nonlinear ISWs over the continental shelf of the northwestern South China Sea(19°35'N,112°E) in May 2005 during the Wenchang Internal Wave Experiment using in-situ time series data from an array of temperature and salinity sensors,and an acoustic Doppler current profiler(ADCP).We summarized the characteristics of the ISWs and compared them with those of existing internal wave theories.Particular attention has been paid to characterizing solitons in terms of the relationship between shape and amplitude-width.Comparison between theoretical prediction and observation results shows that the high nonlinearity of these waves is better represented by the second-order extended Korteweg-de Vries(KdV) theory than the first-order KdV model.These results indicate that the northwestern South China Sea(SCS) is rich in highly nonlinear ISWs that are an indispensable part of the energy budget of the internal waves in the northern South China Sea.

Relationship Between Social Economic Agglomeration and Labor Productivity of Core Cities in Northeast China

Dalian, Shenyang, Changchun and Harbin are the four core cities which play an essential role in terms of promoting the economic development in Northeast China. In this paper, the impact of urban agglomeration on labor productivity is explored by making comparisons among these four cities. The model used for analysis is a classical model derived from previous studies. Some indicators, such as population density and economic density, were selected to examine the impact of urban agglomeration on the labor productivity based on the time-series data for the four cities from 1990 to 2007. The four main conclusions are： l） The promotion from the growth rate of population density on the growth rate of labor productivity is limited. 2） The negative relationship exists between the growth rate of employment density and the growth rate of labor productivity. 3） Agglomeration effect exists in the four cities, the highest one is Dalian, Shenyang takes the second place, followed by Changchun and Harbin, and the predominant promotion exerted on the labor productivity is the output density.