[Objective] The study aimed at analysing the change characteristics of temperature in Jiamusi region of Sanjiang Plain.[Method] Based on temperature data of Jiamusi region in Sanjiang Plain from 1961 to 2010,including...[Objective] The study aimed at analysing the change characteristics of temperature in Jiamusi region of Sanjiang Plain.[Method] Based on temperature data of Jiamusi region in Sanjiang Plain from 1961 to 2010,including Jiamusi,Huanan,Fujin and Fuyuan station,we studied the change trends,abrupt climate change and abnormal years of temperature in Jiamusi region.[Result] Annual average temperature of Jiamusi region in Sanjiang Plain showed increasing trend,with the increase of 0.249-0.412 ℃/10 a.The order of annual average temperature in Jiamusi region was east> south> north> west.In addition,abrupt climate change of annual average temperature occurred in the early 1980s.Abrupt climate change of annual average temperature appeared in 1981 in Jiamusi,Huanan and Fujin,but in 1984 in Fuyuan.Annual average temperature in the mid-1960s and late 1960s was abnormally low in Jiamusi,Fujin and Huanan,while it was abnormally high in Huanan,Fuyuan and Jiamusi from 2007 to 2008,but Fujin in the early 1990s.Meanwhile,anomalies of seasonal average temperature in distinct regions appeared in various years.[Conclusion] The research could provide references for the prediction of temperature in Jiamusi region of Sanjiang Plain in furture.展开更多
In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring s...In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network.The traditional traffic monitoring system not only costs a lot in construction and maintenance,but also may not cover the road network comprehensively,which could not meet the basic needs of traffic management.Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly,so that it can provide more effective support for traffic management decisions.The extensive use of positioning equipment made us able to obtain accurate trajectory data.This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data.This model uses deep learning to detect abnormal trajectory on the traffic road network.The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region,which provides an important reference for the traffic department to take effective traffic control measures.Finally,the paper uses Internet vehicle trajectory data from Chengdu(China)to test and obtains an accurate result.展开更多
With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classification also increases.Although it has mul...With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classification also increases.Although it has multiple applications,the problem is very challenging.In this paper,a novel approach for detecting nor-mal/abnormal activity has been proposed.We used the Gaussian Mixture Model(GMM)and Kalmanfilter to detect and track the objects,respectively.After that,we performed shadow removal to segment an object and its shadow.After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded humans.Fuzzy c-mean is utilized to verify human silhouettes and motion based features including velocity and opticalflow are extracted for each identified silhouettes.Gray Wolf Optimizer(GWO)is used to optimize feature set followed by abnormal event classification that is performed using the XG-Boost classifier.This system is applicable in any surveillance appli-cation used for event detection or anomaly detection.Performance of proposed system is evaluated using University of Minnesota(UMN)dataset and UBI(Uni-versity of Beira Interior)-Fight dataset,each having different type of anomaly.The mean accuracy for the UMN and UBI-Fight datasets is 90.14%and 76.9%respec-tively.These results are more accurate as compared to other existing methods.展开更多
基金the financial support from National Key Research and Development Program of China(2021YFC2900500)Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(52161135301).
文摘[Objective] The study aimed at analysing the change characteristics of temperature in Jiamusi region of Sanjiang Plain.[Method] Based on temperature data of Jiamusi region in Sanjiang Plain from 1961 to 2010,including Jiamusi,Huanan,Fujin and Fuyuan station,we studied the change trends,abrupt climate change and abnormal years of temperature in Jiamusi region.[Result] Annual average temperature of Jiamusi region in Sanjiang Plain showed increasing trend,with the increase of 0.249-0.412 ℃/10 a.The order of annual average temperature in Jiamusi region was east> south> north> west.In addition,abrupt climate change of annual average temperature occurred in the early 1980s.Abrupt climate change of annual average temperature appeared in 1981 in Jiamusi,Huanan and Fujin,but in 1984 in Fuyuan.Annual average temperature in the mid-1960s and late 1960s was abnormally low in Jiamusi,Fujin and Huanan,while it was abnormally high in Huanan,Fuyuan and Jiamusi from 2007 to 2008,but Fujin in the early 1990s.Meanwhile,anomalies of seasonal average temperature in distinct regions appeared in various years.[Conclusion] The research could provide references for the prediction of temperature in Jiamusi region of Sanjiang Plain in furture.
基金supported by the National Natural Science Foundation of China (Grant No.52172310).
文摘In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network.The traditional traffic monitoring system not only costs a lot in construction and maintenance,but also may not cover the road network comprehensively,which could not meet the basic needs of traffic management.Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly,so that it can provide more effective support for traffic management decisions.The extensive use of positioning equipment made us able to obtain accurate trajectory data.This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data.This model uses deep learning to detect abnormal trajectory on the traffic road network.The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region,which provides an important reference for the traffic department to take effective traffic control measures.Finally,the paper uses Internet vehicle trajectory data from Chengdu(China)to test and obtains an accurate result.
基金The authors acknowledge the Deanship of Scientific Research at King Faisal University for the financial support under Nasher Track(Grant No.NA000239)this research was supported by a Grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classification also increases.Although it has multiple applications,the problem is very challenging.In this paper,a novel approach for detecting nor-mal/abnormal activity has been proposed.We used the Gaussian Mixture Model(GMM)and Kalmanfilter to detect and track the objects,respectively.After that,we performed shadow removal to segment an object and its shadow.After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded humans.Fuzzy c-mean is utilized to verify human silhouettes and motion based features including velocity and opticalflow are extracted for each identified silhouettes.Gray Wolf Optimizer(GWO)is used to optimize feature set followed by abnormal event classification that is performed using the XG-Boost classifier.This system is applicable in any surveillance appli-cation used for event detection or anomaly detection.Performance of proposed system is evaluated using University of Minnesota(UMN)dataset and UBI(Uni-versity of Beira Interior)-Fight dataset,each having different type of anomaly.The mean accuracy for the UMN and UBI-Fight datasets is 90.14%and 76.9%respec-tively.These results are more accurate as compared to other existing methods.
