Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

Spatio-temporal changes of underground coal fires during 2008-2016 in Khanh Hoa coal field(North-east of Viet Nam) using Landsat time-series data

Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.

Classification of Vegetation in North Tibet Plateau Based on MODIS Time-Series Data

Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.

An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data

The increasing emergence of the time-series single-cell RNA sequencing(scRNA-seq)data,inferring developmental trajectory by connecting transcriptome similar cell states(i.e.,cell types or clusters)has become a major challenge.Most existing computational methods are designed for individual cells and do not take into account the available time series information.We present IDTI based on the Increment of Diversity for Trajectory Inference,which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq data.We apply IDTI to simulated and three real diverse tissue development datasets,and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching accuracy.The results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting cells.In the performance test,we further demonstrate that IDTI has the advantages of high accuracy and strong robustness.

HXPY: A High-Performance Data Processing Package for Financial Time-Series Data

A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential value.In recent years,we have witnessed the successful adoption of machine learning models on financial data,where the importance of accuracy and timeliness demands highly effective computing frameworks.However,traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues,such as the outlier handling with stock suspension in Pandas and TA-Lib.In this paper,we propose HXPY,a high-performance data processing package with a C++/Python interface for financial time-series data.HXPY supports miscellaneous acceleration techniques such as the streaming algorithm,the vectorization instruction set,and memory optimization,together with various functions such as time window functions,group operations,down-sampling operations,cross-section operations,row-wise or column-wise operations,shape transformations,and alignment functions.The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts.From MiBs to GiBs data,HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.

Fusing multi-source data to map spatio-temporal dynamics of winter rape on the Jianghan Plain and Dongting Lake Plain, China

Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.

Construction of lake bathymetry from MODIS satellite data and GIS from 2003 to 2011

In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was-0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.

Inter-hour direct normal irradiance forecast with multiple data types and time-series

Boosted by a strong solar power market,the electricity grid is exposed to risk under an increasing share of fluctuant solar power.To increase the stability of the electricity grid,an accurate solar power forecast is needed to evaluate such fluctuations.In terms of forecast,solar irradiance is the key factor of solar power generation,which is affected by atmospheric conditions,including surface meteorological variables and column integrated variables.These variables involve multiple numerical timeseries and images.However,few studies have focused on the processing method of multiple data types in an interhour direct normal irradiance(DNI)forecast.In this study,a framework for predicting the DNI for a 10-min time horizon was developed,which included the nondimensionalization of multiple data types and time-series,development of a forecast model,and transformation of the outputs.Several atmospheric variables were considered in the forecast framework,including the historical DNI,wind speed and direction,relative humidity time-series,and ground-based cloud images.Experiments were conducted to evaluate the performance of the forecast framework.The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41%and a normalized root mean square error(n RMSE)of20.53%,and outperforms the persistent model with an improvement of 34%in the nRMSE.

Multi-threaded compression of Earth observation time-series data

Earth observation data are typically compressed using general-purpose single-threaded compression algorithms that operate at a fraction of the bandwidth of modern storage and processing systems.We present evidence that recently developed multi-threaded compression codecs offer substantial benefits over widely used single-threaded codecs in terms of compression efficiency when applied to a selection of moderate resolution imaging spectroradiometer(MODIS)datasets stored in the HDF5 format.Compression codecs from the LZ77 and Rice families are shown to vary in efficacy when applied to different MODIS data products,highlighting the need for compression strategies to be tailored to different classes of data.We also introduce LPC-Rice,a new multi-threaded codec,that performs particularly well when applied to time-series data.

A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter

A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.

基于Storm的电网时间序列数据实时预测框架

对电网运行产生的时间序列数据展开实时预测研究,提出基于Storm平台和ARIMA模型的预测框架。分析不同类型电网时序数据的特点,预设拟合模型以降低模型构建的盲目性,缩短预测时间,同时设计基于HBase的新型时序数据存储模式加快数据检索速度。通过对海量的时序数据源进行并发预测,比较不同数据样本对预测值的影响并实时分析预测误差。经实例从预测精度、运算速度、占用资源3个角度验证了该框架的有效性与实用性。

基于XML的温盐深数据Schema设计

XML是W3C组织于1998年2月发布的一种标记语言标准,其具有易于扩展、结构性强、交互性好、语义丰富、基于内容的数据标识、可格式化、易于处理、与平台无关的特点,使得数据层在XML技术的支持下得到统一。通过对海洋温盐深数据进行结构分析,本文设计了温盐深数据XML Schema,定义了温盐深数据的XML数据结构。

Time-series surface water reconstruction method(TSWR)based on spatial distance relationship of multi-stage water boundaries

Spatiotemporal continuity of surface water datasets widely known for its significance in the surface water dynamic monitoring and assessments,are faced with drawbacks like cloud influence,which hinders the direct extraction of data from time-series remote sensing images.This study proposes a Time-series Surface Water Reconstruction method(TSWR).The initial stage of this method involves the effective use of remote sensing images to automatically construct multi-stage surface water boundaries based on Google Earth Engine(GEE).Then,we reconstructed regions the reconstruction of regions with missing water pixels using the distance relationship between the multi-stage water boundaries in previous and later periods.When applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 km2.Furthermore,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image availability.These findings demonstrated that this method could not only be used to accurately reconstruct the surface water distribution missing water images,but also to depict a more pronounced time variation characteristic.The successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.

Time Series Forecasting with Multiple Deep Learners: Selection from a Bayesian Network

Considering the recent developments in deep learning, it has become increasingly important to verify what methods are valid for the prediction of multivariate time-series data. In this study, we propose a novel method of time-series prediction employing multiple deep learners combined with a Bayesian network where training data is divided into clusters using K-means clustering. We decided how many clusters are the best for K-means with the Bayesian information criteria. Depending on each cluster, the multiple deep learners are trained. We used three types of deep learners: deep neural network (DNN), recurrent neural network (RNN), and long short-term memory (LSTM). A naive Bayes classifier is used to determine which deep learner is in charge of predicting a particular time-series. Our proposed method will be applied to a set of financial time-series data, the Nikkei Average Stock price, to assess the accuracy of the predictions made. Compared with the conventional method of employing a single deep learner to acquire all the data, it is demonstrated by our proposed method that F-value and accuracy are improved.

Feature Extraction of Time Series Data Based on CNN-CBAM

Methods for extracting features from time series data using deep learning have been widely studied,but they still suffer from problems of severe loss of feature information across different network layers and parameter redun-dancy.Therefore,a new time-series data feature extraction model(CNN-CBAM)that integrates convolutional neural networks(CNN)and convolutional attention mechanisms(CBAM)is proposed.First,the parameters of the CNN and BiGRU prediction models are optimized through uniform design methods.Next,the CNN is used to extract features from the time series data,outputting multiple feature maps.These feature maps are then subjected to feature re-extraction by the CBAM attention mechanism at both the spatial and channel levels.Finally,the feature maps are input into the BiGRU model for prediction.Experimental results show that after CNN-CBAM processing,the stability and accuracy of the BiGRU pre-diction model improved by 77.6%and 76.3%,respectively,outperforming other feature extraction methods.Meanwhile,the training time of the model has only increased by 7.1%,demonstrating excellent time efficiency.

Basic Unit: As a Common Module of Neural Networks

In this paper,the logic is developed assuming that all parts of the brain are composed of a combination of modules that basically have the same structure.The feeding behavior of searching for food while avoiding the dangers of animals in the early stages of evolution is regarded as the basis of time series data processing.The module that performs the processing is presented by a neural network equipped with a learning function based on Hebb's rule,and is called a basic unit.The basic units are arranged in lay­ers,and the information between the layers is bidirectional.This new neural network is an extension of the traditional neural network that has evolved from pattern recognition.The biggest feature is that in the processing of time series data,the activated part changes according to the context struc­ture inherent in the data,and can be mathematically expressed the method of predicting events from the context of learned behavior and utilizing it in best action.

高速弯沉仪检测技术研究进展

高速弯沉仪检测技术是一种新型的路面工程智能无损检测技术。为了推动高速弯沉仪检测技术的发展与应用,基于国内外最新研究成果及文献,阐述了现有高速弯沉仪检测技术的理论模型及分析方法,将弯沉计算方法归纳总结为基于力学理论计算法和曲线面积积分法两大类。阐明了高速弯沉仪结果精度主要受到4个因素的影响,包括外部变量、平均值单元路段长度的选择、短期的重复性、与其他弯沉设备测量值的转换。对高速弯沉仪的数据分析及应用方面的研究成果与进展进行了详细介绍,并指出了国内高速弯沉仪检测技术存在理论及数据分析方法简单,测试结果利用率低、弯沉指标过于单一等问题与不足,对今后研究方向给出了一些建议,虽然目前高速弯沉仪检测技术在柔性路面网级路面评估中的应用研究成果已基本成型,但技术理论体系与实体工程应用方面还有待进一步的深入与推广,应结合大数据分析、地理信息技术、影像分析及信息技术等,将高速弯沉仪检测技术有效地融入到路面管理系统中,利用功能性指标与路面结构性能指标对路面进行综合评估,实现路面病害的精准定位与动态监测,对路面病害进行预警,从而为公路养护与管理提供更高效、更经济的决策依据,并开展在项目级路面评估以及刚性路面的应用研究。

A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit

Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.

A spatial frequency/spectral indicator-driven model for estimating cultivated land quality using the gradient boosting decision tree and genetic algorithm-back propagation neural network

Cultivated land quality(CLQ)is related to national food security.Rapid and high-precision monitoring of CLQ is crucial for the sustainable development of agriculture.However,current satellite image-based evaluation methods that only consider the crop's spatial spectrum characteristics in the key growth stages cannot accurately estimate CLQ.This study proposes a new method based on time-series spectral data of crop growth to improve the accuracy of CLQ estimation.This study was conducted in the Conghua District of Guangzhou,Guangdong Province,China.The results showed that seven spectral indicators were determined as the optimal indicators based on the gradient boosting decision tree(GBDT)and variance inflation factor(VIF).And the genetic algorithm-back propagation neural network(GA-BPNN)model provided more accurate CLQ estimates than the partial least squares regression(PLSR)model,indicating a nonlinear relationship between CLQ and the indicators.In addition,the GA-BPNN model with a normalized root mean square error(NRMSE)of 9.91%demonstrates the excellent potential for mapping CLQ over large areas.The model based on the seven optimal indicators of crop phenology provided higher accuracy than the GA-BPNN model based on the normalized difference vegetation index(NDVI)indicators in the spatial domain,significantly decreasing the NRMSE of the CLQ estimates by 3.17%.This further implied that the spectral indicators in the spatial frequency domain can improve the accuracy of estimating CLQ.