Regional international cooperation in seismic data exchange and joint seismological analysis in the Asian-Pacific regions plays an important role in the reduction of earthquake disasters in these regions as well as th...Regional international cooperation in seismic data exchange and joint seismological analysis in the Asian-Pacific regions plays an important role in the reduction of earthquake disasters in these regions as well as the development of global seismology. Based on the up-to-date development of new technology and digital broadband seismology, we discussed the Asian Seismological Commission (ASC) Proposal “Asian-Pacific Seismological Data Center”. We applied the concept of virtual seismological network (Ottemoeller and Havskov, 1999) to the proposed ASC data exchange program. Based on the development of digital seismology, we introduced a concept of “modern earthquake catalogues” which include not only location parameters and magnitudes as in the conventional earthquake catalogues but also new parameters of earthquakes such as CMT, radiated energy, STF, and earthquake rupture process. We recommended a web-based regional international data exchange program between the ASC members with the purpose of forming a virtual Asian-Pacific seismological network, and the interpretation and analysis of broadband digital seismic waveforms conducted at distributed “data centers”.展开更多
为准确识别乘客搭乘自动扶梯时的异常行为,避免安全事故的发生,提出了一种基于人体骨架的扶梯乘客异常行为识别方法。首先使用YOLOX-Tiny对视频中乘客位置进行检测,通过Alphapose算法提取骨骼关键点坐标,降低复杂背景的干扰;再使用多流...为准确识别乘客搭乘自动扶梯时的异常行为,避免安全事故的发生,提出了一种基于人体骨架的扶梯乘客异常行为识别方法。首先使用YOLOX-Tiny对视频中乘客位置进行检测,通过Alphapose算法提取骨骼关键点坐标,降低复杂背景的干扰;再使用多流膨胀3D卷积模块增强时空特征提取能力,聚合乘客骨架的全局特征;然后将其输入改进后的时空图卷积网络中提取乘客骨架信息,通过MS-TCN模块扩大接受域以增强时间特征的提取,联合人体关键点注意力模块(Key Point Attention Module,KPAM)提升网络对相似动作的关键骨架的关注度;最后通过Softmax对异常动作进行分类。采集扶梯运行现场视频制作数据集,试验结果表明,本文算法对乘客异常行为的识别精度达到96.1%,可应用于扶梯现场的视频监控系统,提高安全管理信息化水平。展开更多
Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a...Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm(KVA)is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net(GR-ARNet)based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98%and a precision of 89.31%applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.展开更多
文摘Regional international cooperation in seismic data exchange and joint seismological analysis in the Asian-Pacific regions plays an important role in the reduction of earthquake disasters in these regions as well as the development of global seismology. Based on the up-to-date development of new technology and digital broadband seismology, we discussed the Asian Seismological Commission (ASC) Proposal “Asian-Pacific Seismological Data Center”. We applied the concept of virtual seismological network (Ottemoeller and Havskov, 1999) to the proposed ASC data exchange program. Based on the development of digital seismology, we introduced a concept of “modern earthquake catalogues” which include not only location parameters and magnitudes as in the conventional earthquake catalogues but also new parameters of earthquakes such as CMT, radiated energy, STF, and earthquake rupture process. We recommended a web-based regional international data exchange program between the ASC members with the purpose of forming a virtual Asian-Pacific seismological network, and the interpretation and analysis of broadband digital seismic waveforms conducted at distributed “data centers”.
文摘为准确识别乘客搭乘自动扶梯时的异常行为,避免安全事故的发生,提出了一种基于人体骨架的扶梯乘客异常行为识别方法。首先使用YOLOX-Tiny对视频中乘客位置进行检测,通过Alphapose算法提取骨骼关键点坐标,降低复杂背景的干扰;再使用多流膨胀3D卷积模块增强时空特征提取能力,聚合乘客骨架的全局特征;然后将其输入改进后的时空图卷积网络中提取乘客骨架信息,通过MS-TCN模块扩大接受域以增强时间特征的提取,联合人体关键点注意力模块(Key Point Attention Module,KPAM)提升网络对相似动作的关键骨架的关注度;最后通过Softmax对异常动作进行分类。采集扶梯运行现场视频制作数据集,试验结果表明,本文算法对乘客异常行为的识别精度达到96.1%,可应用于扶梯现场的视频监控系统,提高安全管理信息化水平。
基金supported by the Changsha Municipal Natural Science Foundation,China(kq2014160)in part by the Key Projects of Department of Education of Hunan Province,China(21A0179)+1 种基金the Hunan Key Laboratory of Intelligent Logistics Technology,China(2019TP1015)the National Natural Science Foundation of China(61902436)。
文摘Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm(KVA)is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net(GR-ARNet)based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98%and a precision of 89.31%applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.
