-Variations of monsoon wind field in the sea area along the southeastern coast of China during the ENSO events and its influence on the sea level and sea surface temperature (SST) are explored mainly on the basis of t...-Variations of monsoon wind field in the sea area along the southeastern coast of China during the ENSO events and its influence on the sea level and sea surface temperature (SST) are explored mainly on the basis of the data of monthly mean wind at 850 hPa and five coastal stations during 1973-1987. The results from the analyses of the data and theoretical estimation show that the southwest wind anomalies appeared in the study area during the events, and northeast wind anomalies occurred in general before the events. With the coastline of the area being parallel basically to the direction of the wind, an Ekman transport will result in an accumulation of the water near the coast or a departure of the water from the coast. As a result , the sea level and SST there will be affected markedly. During the events, southwest wind will intensify in the summer, and northeast wind will weaken in the winter. Their total effect is that a large negative anomaly of the sea level and SST will occur. The estimations indicate that the monsoon wind is stronger in the summer and weaker in the winter than the normal by 1-1. 5 m/s during the events, and this anomaly will cause a decrease of the sea level by 7-11 cm . Changes of the wind field, therefore, is mainly responsible for a large negative anomalies of the sea level and SST there during the ENSO events.展开更多
It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component anal...It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component analysis network(PCAnet)and kernel principal component analysis(kPCA),is proposed to address this problem.Using surveillance video sequences of different scenes as raw data,the PCAnet is trained to extract high-level semantics of the crowd’s situation.Next,kPCA,a one-class classifier,is trained to identify anomalies within the scene.In contrast to some prevailing deep learning methods,this framework is completely self-supervised because it utilizes only video sequences of a normal situation.Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota(UMN dataset)and Anomaly Detection dataset from University of California,San Diego(UCSD dataset),and competitive results that yield a better equal error rate(EER)and area under curve(AUC)than other state-of-the-art methods are observed.Furthermore,by adding a local response normalization(LRN)layer,we propose an improvement to the original AED-Net.The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity.展开更多
To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved...To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.展开更多
Shanghai-Hong Kong Stock Connect Program,which is a new starting point for the opening up of the mainland capital market,still has many uncertainties.Research on the benefits and market volatility of such policies can...Shanghai-Hong Kong Stock Connect Program,which is a new starting point for the opening up of the mainland capital market,still has many uncertainties.Research on the benefits and market volatility of such policies can provide investors with time to invest in such policies,fluctuations in the underlying stocks of the Chinese stock market,and decision support for the formulation and revision of relevant policies.This paper studies whether there is significant abnormal rate of return in the selected stocks which are in the Shanghai Stock Connect Program within the specified period,the excess return gap between the stocks which are in the program and which are not in the program,and the impact of the Shanghai Stock Connect Program on the volatility of the relevant stocks.Based on the CAPM model and the Fama-French 3-factor model,this paper uses t test to study the significance of the abnormal rate of return.By establishing a difference-in-difference(DID)model,the regression of the abnormal rate of return is tested,and the sample volatility is analyzed according to the influence of the fund transaction.The study found that the stocks in the program have significant abnormal rate of returns during the window period.The Shanghai Stock Connect has brought about a huge change in transaction amount,and policy makers need to improve related and similar policies.展开更多
Abnormal events in earth science have great influence on both the natural envi-ronment and the human society. Finding association patterns among these events has great significance. Because data in earth science has c...Abnormal events in earth science have great influence on both the natural envi-ronment and the human society. Finding association patterns among these events has great significance. Because data in earth science has characteristics of mass,high dimension,spatial autocorrelation and time delay,existing mining technolo-gies cannot be directly used on it. We propose a RSNN (range-based searching nearest neighbors) spatial clustering algorithm to reduce the data size and auto-correlation. Based on the clustered data,we propose a GEAM (geographic episode association pattern mining) algorithm which can deal with events time lags and find interesting patterns with specific constraints,to mine the association patterns. We carried out experiments on global climate datasets and found many interesting association patterns. Some of the patterns are coincident with known knowledge in climate science,which indicates the correctness and feasibilities of our methods,and the others are unknown to us before,which will give new information to this research field.展开更多
为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU...为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。展开更多
Security surveillance of public scene is closely relevant to routine safety of individual.Under the stimulus of this concern,abnormal event detection is becoming one of the most important tasks in computer vision and ...Security surveillance of public scene is closely relevant to routine safety of individual.Under the stimulus of this concern,abnormal event detection is becoming one of the most important tasks in computer vision and video processing.In this paper,we propose a new algorithm to address the visual abnormal detection problem.Our algorithm decouples the problem into a feature descriptor extraction process,followed by an AutoEncoder based network called cascade deep AutoEncoder(CDA).The movement information is represented by a novel descriptor capturing the multi-frame optical flow information.And then,the feature descriptor of the normal samples is fed into the CDA network for training.Finally,the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure.We validate the proposed method on several video surveillance datasets.展开更多
文摘-Variations of monsoon wind field in the sea area along the southeastern coast of China during the ENSO events and its influence on the sea level and sea surface temperature (SST) are explored mainly on the basis of the data of monthly mean wind at 850 hPa and five coastal stations during 1973-1987. The results from the analyses of the data and theoretical estimation show that the southwest wind anomalies appeared in the study area during the events, and northeast wind anomalies occurred in general before the events. With the coastline of the area being parallel basically to the direction of the wind, an Ekman transport will result in an accumulation of the water near the coast or a departure of the water from the coast. As a result , the sea level and SST there will be affected markedly. During the events, southwest wind will intensify in the summer, and northeast wind will weaken in the winter. Their total effect is that a large negative anomaly of the sea level and SST will occur. The estimations indicate that the monsoon wind is stronger in the summer and weaker in the winter than the normal by 1-1. 5 m/s during the events, and this anomaly will cause a decrease of the sea level by 7-11 cm . Changes of the wind field, therefore, is mainly responsible for a large negative anomalies of the sea level and SST there during the ENSO events.
基金This work is partially supported by the National Key Research and Development Program of China(2016YFE0204200)the National Natural Science Foundation of China(61503017)+3 种基金the Fundamental Research Funds for the Central Universities(YWF-18-BJ-J-221)the Aeronautical Science Foundation of China(2016ZC51022)the Platform CAPSEC(capteurs pour la sécurité)funded by Région Champagne-ArdenneFEDER(fonds européen de développement régional).
文摘It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component analysis network(PCAnet)and kernel principal component analysis(kPCA),is proposed to address this problem.Using surveillance video sequences of different scenes as raw data,the PCAnet is trained to extract high-level semantics of the crowd’s situation.Next,kPCA,a one-class classifier,is trained to identify anomalies within the scene.In contrast to some prevailing deep learning methods,this framework is completely self-supervised because it utilizes only video sequences of a normal situation.Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota(UMN dataset)and Anomaly Detection dataset from University of California,San Diego(UCSD dataset),and competitive results that yield a better equal error rate(EER)and area under curve(AUC)than other state-of-the-art methods are observed.Furthermore,by adding a local response normalization(LRN)layer,we propose an improvement to the original AED-Net.The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity.
基金National Natural Science Foundation of China(61701029)。
文摘To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.
文摘Shanghai-Hong Kong Stock Connect Program,which is a new starting point for the opening up of the mainland capital market,still has many uncertainties.Research on the benefits and market volatility of such policies can provide investors with time to invest in such policies,fluctuations in the underlying stocks of the Chinese stock market,and decision support for the formulation and revision of relevant policies.This paper studies whether there is significant abnormal rate of return in the selected stocks which are in the Shanghai Stock Connect Program within the specified period,the excess return gap between the stocks which are in the program and which are not in the program,and the impact of the Shanghai Stock Connect Program on the volatility of the relevant stocks.Based on the CAPM model and the Fama-French 3-factor model,this paper uses t test to study the significance of the abnormal rate of return.By establishing a difference-in-difference(DID)model,the regression of the abnormal rate of return is tested,and the sample volatility is analyzed according to the influence of the fund transaction.The study found that the stocks in the program have significant abnormal rate of returns during the window period.The Shanghai Stock Connect has brought about a huge change in transaction amount,and policy makers need to improve related and similar policies.
基金the National Hi-Tech Research and Development Program of China (Grand No. 2006AA12Z217)the National Natural Science Foundation of China (Grant No. 60703066)
文摘Abnormal events in earth science have great influence on both the natural envi-ronment and the human society. Finding association patterns among these events has great significance. Because data in earth science has characteristics of mass,high dimension,spatial autocorrelation and time delay,existing mining technolo-gies cannot be directly used on it. We propose a RSNN (range-based searching nearest neighbors) spatial clustering algorithm to reduce the data size and auto-correlation. Based on the clustered data,we propose a GEAM (geographic episode association pattern mining) algorithm which can deal with events time lags and find interesting patterns with specific constraints,to mine the association patterns. We carried out experiments on global climate datasets and found many interesting association patterns. Some of the patterns are coincident with known knowledge in climate science,which indicates the correctness and feasibilities of our methods,and the others are unknown to us before,which will give new information to this research field.
文摘为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。
基金the National Key R&D Program of China(2016YFE0204200)the National Natural Science Foundation of China(Grant Nos.61503017,U1435220)+2 种基金the Fundamental Research Funds for the Central Universities(YWF-14-RSC-102)the Aeronautical Science Foundation of China(2016ZC51022)the ANR AutoFerm project,the Platform CAPSEC funded by Region Champagne-Ardenne and FEDER.
文摘Security surveillance of public scene is closely relevant to routine safety of individual.Under the stimulus of this concern,abnormal event detection is becoming one of the most important tasks in computer vision and video processing.In this paper,we propose a new algorithm to address the visual abnormal detection problem.Our algorithm decouples the problem into a feature descriptor extraction process,followed by an AutoEncoder based network called cascade deep AutoEncoder(CDA).The movement information is represented by a novel descriptor capturing the multi-frame optical flow information.And then,the feature descriptor of the normal samples is fed into the CDA network for training.Finally,the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure.We validate the proposed method on several video surveillance datasets.
