Cross-Correlation between Global Temperature and Atmospheric CO2 with a Temperature-Leading Time Lag

The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Distribution Characteristics of Global Seismic Clusters and Associated Spatial Factors

Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.

Performance of global look-up table strategy in digital image correlation with cubic B-spline interpolation and bicubic interpolation

Global look-up table strategy proposed recently has been proven to be an efficient method to accelerate the interpolation, which is the most time-consuming part in the iterative sub-pixel digital image correlation （DIC） algorithms. In this paper, a global look-up table strategy with cubic B-spline interpolation is developed for the DIC method based on the inverse compositional Gauss-Newton （IC-GN） algorithm. The performance of this strategy, including accuracy, precision, and computation efficiency, is evaluated through a theoretical and experimental study, using the one with widely employed bicubic interpolation as a benchmark. The global look-up table strategy with cubic B-spline interpolation improves significantly the accuracy of the IC-GN algorithm-based DIC method compared with the one using the bicubic interpolation, at a trivial price of computation efficiency.

A positive trend in the stability of global offshore wind energy

The recognition on the trend of wind energy stability is still extremely rare,although it is closely related to acquisition efficiency,grid connection,equipment lifetime,and costs of wind energy utilization.Using the 40-year(1979–2018)ERA-Interim data from the European Center for Medium-Range Weather Forecasts,this study presented the spatial-temporal distribution and climatic trend of the stability of global offshore wind energy as well as the abrupt phenomenon of wind energy stability in key regions over the past 40 years with the climatic analysis method and Mann-Kendall(M-K)test.The results show the following 5 points.(1)According to the coefficient of variation(C_(v))of the wind power density,there are six permanent stable zones of global offshore wind energy:the southeast and northeast trade wind zones in the Indian,Pacific and Atlantic oceans,the Southern Hemisphere westerly,and a semi-permanent stable zone(North Indian Ocean).(2)There are six lowvalue zones for both seasonal variability index(S_(v))and monthly variability index(M_(v))globally,with a similar spatial distribution as that of the six permanent stable zones.M_(v) and S_(v) in the Arabian Sea are the highest in the world.(3)After C_(v),M_(v) and S_(v) are comprehensively considered,the six permanent stable zones have an obvious advantage in the stability of wind energy over other sea areas,with C_(v) below 0.8,M_(v) within 1.0,and S_(v) within 0.7 all the year round.(4)The global stability of offshore wind energy shows a positive climatic trend for the past four decades.C_(v),M_(v) and S_(v) have not changed significantly or decreased in most of the global ocean during 1979 to2018.That is,wind energy is flat or more stable,while the monthly and seasonal variabilities tend to shrink/smooth,which is beneficial for wind energy utilization.(5)C_(v) in the low-latitude Pacific and M_(v) and S_(v) in both the North Indian Ocean and the low-latitude Pacific have an obvious abrupt phenomenon at the end of the20th century.

Changshanian Maximum Flooding Event in China and Its Global Correlation

Various orders of condensed sections are recognized in the Cambrian of North China Carbonate Platform. Study of comparative sedimentology proves that CS4-CS5 in the Changshanian age is the maximum sea flooding sediments in the Cambrian, regardless of the distribution, thickness, internal structure of the condensed section series and K2O abundance. It is a two-layered composite condensed section series, characterized by the enrichment of such elements as K, P, Mn, Ti, Co, V, Cr, Cu, Zr, Ni, Li, Th, La, Ce, Nd, Dy, Y, Sc and Be. The Changshanian maximum flooding event can be correlated on a global scale, and the corresponding sedimentary records are discovered in 26 intercontinental sections in N. Europe, N. America, and Tarim and the Yangtze Platform of China. Through detailed sedimentological research, meter-scale cycle analysis and Fischer plots, it is concluded that the Changshanian maximum flooding was a composite effect of the second-order eustacy superimposed by the third-and fourth-order eustacy, which lasted for 1.2-3 Ma. An intercontinental model of the Changshanian maximum flooding is proposed at the end of this paper.

Research on the correlation between North China earthquake and global regional strong earthquakes

Active belts of global strong earthquakes were divided into 17 seismic zones based on the principles of global plate tectonics.The seismicity index number A(b) was used to denote the yearly seismicity level.Using the stepwise regression method,an empirical formula was derived to predict the North China seismic risk in the next year by the aid of strong earthquakes in the world.

Spatial-temporal dynamics of desert vegetation and its responses to climatic variations over the last three decades:a case study of Hexi region in Northwest China

Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies （GIMMS） NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer （MODIS） NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax （the maximum NDVI during the growing season） for desert vegetation in Hexi region increased at the rate of 0.65x10-3/a （P〈0.05） from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin （an extremely arid area）, indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.

Multi-Label Learning Based on Transfer Learning and Label Correlation

In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.

Public cloud computing for seismological research:Calculating large-scale noise cross-correlations using ALIYUN

The amount of seismological data is rapidly increasing with accumulating observational time and increasing number of stations, requiring modern technique to provide adequate computing power. In present study, we proposed a framework to calculate large-scale noise crosscorrelation functions(NCFs) using public cloud service from ALIYUN. The entire computation is factorized into small pieces which are performed parallelly on specified number of virtual servers provided by the cloud. Using data from most seismic stations in China, five NCF databases are built. The results show that, comparing to the time cost using a single server, the entire time can be reduced over two orders of magnitude depending number of evoked virtual servers. This could reduce computation time from months to less than 12 hours. Based on obtained massive NCFs, the global body waves are retrieved through array interferometry and agree well with those from earthquakes. This leads to a solution to process massive seismic dataset within an affordable time and is applicable to other large-scale computing in seismological researches.

Improvement of a new rotation function for molecular replacement by designing new scoring functions and dynamic correlation coefficient

A previously published new rotation function has been improved by using a dynamic correlation coefficient as well as two new scoring functions of relative entropy and mean-square-residues to make the rotation function more robust and independent of a specific set of weights for scoring and ranking. The previously described new rotation function calculates the rotation function of molecular replacement by matching the search model directly with the Patterson vector map. The signal-to-noise ratio for the correct match was increased by averaging all the matching peaks. Several matching scores were employed to evaluate the goodness of matching. These matching scores were then combined into a single total score by optimizing a set of weights using the linear regression method. It was found that there exists an optimal set of weights that can be applied to the global rotation search and the correct solution can be ranked in the top 100 or less. However, this set of optimal weights in general is dependent on the search models and the crystal structures with different space groups and cell parameters. In this work, we try to solve this problem by designing a dynamic correlation coefficient. It is shown that the dynamic correlation coefficient works for a variety of space groups and cell parameters in the global search of rotation function. We also introduce two new matching scores： relative entropy and mean-square-residues. Last but not least, we discussed a valid method for the optimization of the adjustable parameters for matching vectors.

Correlation evaluation of tested and calculated modes of a launch vehicle equipment cabin

First, discusses some conventional modal correlation evaluation methods. And then, introduces the concepts of global modes and local modes to solve difficulties in analyzing large and complex structures with dense modes like the equipment cabin, establishes a criterion with the ratio of modal strain energy to conveniently distinguish these modes. Finally, investigates the methods of modal vector reduction, error localization and model updating used to achieve a high correlation between the tested and calculated modes of the cabin, and verifies the finite element model of the equipment cabin as a foundation for further design and analysis.

Two new approaches for image registration based onspatial-temporal relationship

How to improve the probability of registration and precision of localization is a hard problem, which is desiderated to solve. The two basic approaches (normalized cross-correlation and phase correlation) for image registration are analysed, two improved approaches based on spatial-temporal relationship are presented. This method adds the correlation matrix according to the displacements in x- cirection and y- directions, and the registration pose is searched in the added matrix. The method overcomes the shortcoming that the probability of registration decreasing with area increasing owing to geometric distortion, improves the probability and the robustness of registration.

Spatial-temporal characters of Antarctic sea ice variation

Using sea ice concentration dataset covering the period of 1968-2002 obtained from the Hadley Center of UK, this paper investigates characters of Antarctic sea ice variations .The finding demonstrates that the change of mean sea-ice extent is almost consistent with that of sea-ice area, so sea-ice extent can be chosen to go on this research. The maximum and the minimum of Antarctic sea ice appear in September and February respectively. The maximum and the maximal variation of sea ice appear in Weddell Sea and Ross Sea, while the minimum and the minimal variation of sea-ice appear in Antarctic Peninsula. In recent 35 years, as a whole, Antarctic sea ice decreased distinctly. Moreover, there are 5 subdivision characteristic regions considering their different variations. Hereinto, the sea-ice extent of Weddell Sea and Ross Sea regions extends and area increases, while the sea-ice extent of the other three regions contracts and area decreases. They are all of obvious 2-4 years and 5-7 years significant oscillation periods. It is of significance for further understanding the sea-ice-air interaction in Antarctica region and discussing the relationship between sea-ice variation and atmospheric circulation.

Spatial-temporal Variation of Air Temperature for the Recent 40 Years in Lhasa

It was all known that Lhasa went through a high temperature of 30.8℃in late June 2019,which hit record highs.To better understand the reasons,based on observations recorded at automatic weather stations in Lhasa,the characteristics of temperature variation at multiple time scales were studied using the linear trend method,Mann-Kendall mutation test,Morlet wavelet analysis,R/S analysis and so on.The results showed that:(a)the annual mean temperature(AMT)is rising at a rate of 0.5℃/10 a,and the average temperature for different seasons also increased significantly,especially in winter.(b)Although there was an intersection in 1995,it was found that AMT,did not pass the reliability test of significance levelα=0.05,this means there are no abrupt changes for AMT,the values are 7.97 and 9.15℃respectively before and after the intersection point.(c)AMT has a periodic oscillation for 18-25 and 25-32 a based on a mass of data and the wavelet variance diagrams in Lhasa.AMT has a main cycle of 28 a,cyclic patterns of temperature changes in spring,summer and autumn is similar to AMT,but it is relatively complex in winter.(d)The Hurst index of AMT and different seasons demonstrates that the temperature is likely to continue to rise in the future in Lhasa.

Improved Circular Correlation Methods for Acquisition in Software GPS Receiver

In this paper the authors explore the Global Positioning System （GPS） signal acquisition and tracking algorithms used in software GPS receiver. Acquisition time is the most important parameter in evaluating the performance of a software GPS receiver in terms of its speed. A trade-off study is done to seek a good balance between the acquisition accuracy and the processing time. The frequency-domain acquisition method by circular correlation, used in a software GPS receiver, has been improved by studying the power spectrum of the Coarse Acquisition （C/A） code alone. The analysis of C/A code reveals that its power spectrum is symmetrical; hence only half the points are required to perform circular correlation. Besides, either half of the spectrum is asymmetrical where a larger amount of power is concentrated in almost one-quarter of the spectrum on its either sides. This further reduces the number of points used to perform correlation. Comparative results of MATLAB simulation of full-size, half-size and quarter-size circular correlation done on actual data stored on hard disk are provided, and they agree with those obtained using GPS receiver. Further reduction in acquisition time has been achieved by investigating the effect of length of the noncoherent pre-integration period. The improved acquisition methods pave way for further development of new algorithms to enhance software GPS receiver performance.

OPERATIONAL MODAL ANALYSIS SCHEMES USING CORRELATION TECHNIQUE

For some large-scale engineering structures in operating conditions, modal param- eters estimation must base itself on response-only data. This problem has received a considerable amount of attention in the past few years. It is well known that the cross-correlation function between the measured responses is a sum of complex exponential functions of the same form as the impulse response function of the original system. So this paper presents a time-domain operating modal identifcation global scheme and a frequency-domain scheme from output-only by cou- pling the cross-correlation function with conventional modal parameter estimation. The outlined techniques are applied to an airplane model to estimate modal parameters from response-only data.

Spatial-Temporal Correlation 3D Vehicle Detection and Tracking System with Multiple Surveillance Cameras

Compared to 3D object detection using a single camera,multiple cameras can overcome some limitations on field-of-view,occlusion,and low detection confidence.This study employs multiple surveillance cameras and develops a cooperative 3D object detection and tracking framework by incorporating temporal and spatial information.The framework consists of a 3D vehicle detection model,cooperatively spatial-temporal relation scheme,and heuristic camera constellation method.Specifically,the proposed cross-camera association scheme combines the geometric relationship between multiple cameras and objects in corresponding detections.The spatial-temporal method is designed to associate vehicles between different points of view at a single timestamp and fulfill vehicle tracking in the time aspect.The proposed framework is evaluated based on a synthetic cooperative dataset and shows high reliability,where the cooperative perception can recall more than 66%of the trajectory instead of 11%for single-point sensing.This could contribute to full-range surveillance for intelligent transportation systems.

用于篡改图像检测和定位的双通道渐进式特征过滤网络

针对现有基于深度学习的篡改图像检测网络通常存在检测精度不高、算法可迁移性弱等问题,提出一种双通道渐进式特征过滤网络。利用两个通道并行提取图像的双域特征,一个通道提取图像空间域的浅层和深层特征,另一个通道提取图像噪声域的特征分布;同时,使用渐进式细微特征筛选机制过滤冗余特征,逐步定位篡改区域;为了更准确地提取篡改掩码,提出一个双输入细微特征提取模块,结合空间域和噪声域的细微特征,生成更准确的篡改掩码;在解码过程中,通过融合不同尺度的过滤特征和网络的上下文信息,提高网络对篡改区域的定位能力。实验结果表明,在检测和定位方面,与现有先进的篡改检测网络ObjectFormer、MVSS-Net(Multi-View multi-Scale Supervision Network)和PSCC-Net(Progressive Spatio-Channel Correlation Network)相比,所提网络的F1分数在CASIA V2.0数据集上分别提高10.4、5.9和12.9个百分点;面对高斯低通滤波、高斯噪声和JPEG压缩攻击时,相较于ManTra-Net(Manipulation Tracing Network)、SPAN(Spatial Pyramid Attention Network),所提网络的曲线下面积(AUC)分别至少提高了10.0、5.4个百分点。验证了所提网络可以有效解决篡改检测算法存在的检测精度不高、迁移性差等问题。

基于CNFET电路段内关键门的全局布局算法

针对传统硅基电路布局算法在碳纳米管(CNT)密度变化的碳纳米管场效应晶体管(CNFET)电路上表现出时序良率不高的问题,提出一种基于段内关键门的全局布局算法.首先自底向上逐级分析电路各个层级,依次建立门延迟、门树延迟模型,在此基础上结合CNFET电路相关矩阵建立包含延迟均值和方差的段统计延迟模型;然后通过理论分析确定时序良率与段的统计延迟之间的相关关系;最后利用CNFET电路不对称空间相关性,使用网格搜索策略不断迭代调整段内关键门位置,以降低段延迟.在OpenCores中4个测试电路上的实验结果表明,所提算法平均提高了20%的电路时序良率,在执行时间上比CNT密度变化感知的基准方法降低25%,揭示了其在高时序良率要求的大规模电路中应用的潜力.