As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.H...As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.展开更多
The purpose of this paper was to develop a reliable body shape analysis approach based on cluster analysis, k. nearestneighbor( KNN), and multi-class support vector machine( MSVM). Firstly,a total of 357 Chinese men w...The purpose of this paper was to develop a reliable body shape analysis approach based on cluster analysis, k. nearestneighbor( KNN), and multi-class support vector machine( MSVM). Firstly,a total of 357 Chinese men were selected to make a dataset. Secondly, the experiences of these data were not accumulated to build general models. Five body angles were extracted as independent variables. Four clusters were the most efficient cluster number for our study. Finally,the accuracy of body classifications is compared between KNN and MSVM. In this study,the body classification framework was studied to transfer the body feature data to intuitive types. Moreover,the adaptive made-tomeasure( MTM) framework based on body classification was studied. The case demonstration and analysis show the effectiveness of the study.展开更多
To improve the classification method of body type, 103 young female college students in Jiaodong area(Shandong, China) were measured by a 3 D body scanning system, and variables of upper body parts were selected and a...To improve the classification method of body type, 103 young female college students in Jiaodong area(Shandong, China) were measured by a 3 D body scanning system, and variables of upper body parts were selected and analyzed by SPSS software. According to the indices such as the chest ratio, the chest sagittal diameter ratio, and the shoulder angle, the tested population was quickly clustered into six categories by the classification method of “size feature+shape index+front and back indices”, which were divided into flat chest body, graceful body, breast augmentation body, normal body, convex back body, and flat body. The proportion of various body types and classification rules were obtained. According to the classification rules, 103 samples and 15 new females’ body data were analyzed and verified. Finally, according to the descriptive statistical analysis of upper body-related indicators of young female in this area, the height and the chest circumference were selected as independent variables, regression analysis was carried out on 11 related indicators, and the mapping relationship between height and chest circumference was studied, which provided a mathematical model for the design of fit clothing structure of young females in Jiaodong area.展开更多
Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients.It affect their quality of life drastically and millions of seniors are diagnosed...Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients.It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide.Several models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,etc.Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD.The recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify PD.With this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of PD.The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not.CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of features.In addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of PD.CBO algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic performance.The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.展开更多
利用裂纹扩展分析方法和质量分类法研究铝合金车体含缺欠焊接结构的疲劳寿命问题。首先,研究IIW:2015的基于Paris定律的裂纹扩展分析的理论基础和BS 7910:2019的基于考虑焊接缺欠质量的S-N曲线的质量分类法的算法原理;其次,归纳总结了这...利用裂纹扩展分析方法和质量分类法研究铝合金车体含缺欠焊接结构的疲劳寿命问题。首先,研究IIW:2015的基于Paris定律的裂纹扩展分析的理论基础和BS 7910:2019的基于考虑焊接缺欠质量的S-N曲线的质量分类法的算法原理;其次,归纳总结了这2种方法进行考虑焊接质量的焊接结构疲劳寿命评估流程;再次,在疲劳载荷作用下,基于BS EN 1999-1-3:2007的名义应力法对某动车组铝合金车体进行疲劳分析,确定车体疲劳关切焊缝部位;最后,分别利用裂纹扩展分析方法和质量分类法对车体关切焊缝侧门下角进行考虑初始裂纹的疲劳寿命评估。结果表明:基于质量分类法的剩余寿命为1.4×106次,基于裂纹扩展分析法的剩余寿命为2.64×106次。展开更多
针对传统人体部位体型分类方法费时费力、成本较高的问题,设计一种融合注意力机制的体型分类网络(Attention Body Classification Net,A_BCN)。该网络由弱监督的注意力学习和数据增强两个模块组成,其中:弱监督的注意力学习模块通过注意...针对传统人体部位体型分类方法费时费力、成本较高的问题,设计一种融合注意力机制的体型分类网络(Attention Body Classification Net,A_BCN)。该网络由弱监督的注意力学习和数据增强两个模块组成,其中:弱监督的注意力学习模块通过注意力机制获得注意力图;数据增强模块通过注意力图指导图像的数据增强,包括注意力裁剪、注意力丢弃和注意力平均。将增强后的图像重新输入到网络中得到特征图,将得到的特征图和注意力图融合进行分类。在后续自制的人体图像数据集中,该算法准确率为90.52%,提高了分类准确率并节省了成本。展开更多
基金Under the auspices of Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28020503,XDA23100102)National Key Research and Development Program of China(No.2019YFA0607101)+1 种基金Project of China Geological Survey(No.DD20230505)Excellent Scientific Research and Innovation Team of Universities in Anhui Province(No.2023AH010071)。
文摘As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.
基金Talent Project of Xiamen University of Technology,China(No.90030617)
文摘The purpose of this paper was to develop a reliable body shape analysis approach based on cluster analysis, k. nearestneighbor( KNN), and multi-class support vector machine( MSVM). Firstly,a total of 357 Chinese men were selected to make a dataset. Secondly, the experiences of these data were not accumulated to build general models. Five body angles were extracted as independent variables. Four clusters were the most efficient cluster number for our study. Finally,the accuracy of body classifications is compared between KNN and MSVM. In this study,the body classification framework was studied to transfer the body feature data to intuitive types. Moreover,the adaptive made-tomeasure( MTM) framework based on body classification was studied. The case demonstration and analysis show the effectiveness of the study.
文摘To improve the classification method of body type, 103 young female college students in Jiaodong area(Shandong, China) were measured by a 3 D body scanning system, and variables of upper body parts were selected and analyzed by SPSS software. According to the indices such as the chest ratio, the chest sagittal diameter ratio, and the shoulder angle, the tested population was quickly clustered into six categories by the classification method of “size feature+shape index+front and back indices”, which were divided into flat chest body, graceful body, breast augmentation body, normal body, convex back body, and flat body. The proportion of various body types and classification rules were obtained. According to the classification rules, 103 samples and 15 new females’ body data were analyzed and verified. Finally, according to the descriptive statistical analysis of upper body-related indicators of young female in this area, the height and the chest circumference were selected as independent variables, regression analysis was carried out on 11 related indicators, and the mapping relationship between height and chest circumference was studied, which provided a mathematical model for the design of fit clothing structure of young females in Jiaodong area.
基金Taif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia.
文摘Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients.It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide.Several models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,etc.Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD.The recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify PD.With this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of PD.The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not.CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of features.In addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of PD.CBO algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic performance.The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.
文摘利用裂纹扩展分析方法和质量分类法研究铝合金车体含缺欠焊接结构的疲劳寿命问题。首先,研究IIW:2015的基于Paris定律的裂纹扩展分析的理论基础和BS 7910:2019的基于考虑焊接缺欠质量的S-N曲线的质量分类法的算法原理;其次,归纳总结了这2种方法进行考虑焊接质量的焊接结构疲劳寿命评估流程;再次,在疲劳载荷作用下,基于BS EN 1999-1-3:2007的名义应力法对某动车组铝合金车体进行疲劳分析,确定车体疲劳关切焊缝部位;最后,分别利用裂纹扩展分析方法和质量分类法对车体关切焊缝侧门下角进行考虑初始裂纹的疲劳寿命评估。结果表明:基于质量分类法的剩余寿命为1.4×106次,基于裂纹扩展分析法的剩余寿命为2.64×106次。
文摘针对传统人体部位体型分类方法费时费力、成本较高的问题,设计一种融合注意力机制的体型分类网络(Attention Body Classification Net,A_BCN)。该网络由弱监督的注意力学习和数据增强两个模块组成,其中:弱监督的注意力学习模块通过注意力机制获得注意力图;数据增强模块通过注意力图指导图像的数据增强,包括注意力裁剪、注意力丢弃和注意力平均。将增强后的图像重新输入到网络中得到特征图,将得到的特征图和注意力图融合进行分类。在后续自制的人体图像数据集中,该算法准确率为90.52%,提高了分类准确率并节省了成本。
