Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the...Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.展开更多
Background:Oncomelania hupensis is only intermediate snail host of Schistosomajaponicum,and distribution of 0.hupensis is an important indicator for the surveillance of schistosomiasis.This study explored the feasibil...Background:Oncomelania hupensis is only intermediate snail host of Schistosomajaponicum,and distribution of 0.hupensis is an important indicator for the surveillance of schistosomiasis.This study explored the feasibility of a random forest algorithm weighted by spatial distance for risk prediction of schistosomiasis distribution in the Yangtze River Basin in China,with the aim to produce an improved precision reference for the national schistosomiasis control programme by reducing the number of snail survey sites without losing predictive accuracy.Methods:The snail presence and absence records were collected from Anhui,Hunan,Hubei,Jiangxi and Jiangsu provinces in 2018.A machine learning of random forest algorithm based on a set of environmental and climatic variables was developed to predict the breeding sites of the 0.hupensis intermediated snail host of S.japonicum.Different spatial sizes of a hexagonal grid system were compared to estimate the need for required snail sampling sites.The predictive accuracy related to geographic distances between snail sampling sites was estimated by calculating Kappa and the area under the curve(AUC).Results:The highest accuracy(AUC=0.889 and Kappa=0.618)was achieved at the 5 km distance weight.The five factors with the strongest correlation to 0.hupensis infestation probability were:(1)distance to lake(48.9%),(2)distance to river(36.6%),(3)isothermality(29.5%),(4)mean daily difference in temperature(28.1%),and(5)altitude(26.0%).The risk map showed that areas characterized by snail infestation were mainly located along the Yangtze River,with the highest probability in the dividing,slow-flowing river arms in the middle and lower reaches of the Yangtze River in Anhui,followed by areas near the shores of China's two main lakes,the Dongting Lake in Hunan and Hubei and the Poyang Lake in Jiangxi.Conelusions:Applying the machine learning of random forest algorithm made it feasible to precisely predict snail infestation probability,an approach that could improve the sensitivity of the展开更多
The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenz...The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.展开更多
Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester.It is affected by operating parameters of a combine such as feeding rate,the peripheral speed of the ...Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester.It is affected by operating parameters of a combine such as feeding rate,the peripheral speed of the threshing cylinder and concave clearance,and shows complex non-linear law.Real-time acquisition of the breakage rate is an effective way to find the correlation of them.In addition,real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard.In this study,a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed.The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process.According to the working characteristics of the combine,the illumination and installation of the light source were optimized,and the lateral lighting system was constructed.A two-step method of“color training-verification”was applied to identify the whole and broken kernels.In the first step,the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation,extract the color spectrum of each particle in color-space HSL and output the recognition model file.The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory.The experiments of about 2300 particles showed that the recognition accuracy of 96%was attained,and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency.The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.展开更多
基金the National Natural Science Foundation of China (51638007, 51478149, 51678203,and 51678204).
文摘Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.
基金the National Key Research and Development Program of China(2016YFD0702001)the Key Research and Development Program of Jiangsu Province(BE2017358)+1 种基金the Graduate Innovative Projects of Jiangsu Province 2016(KYLX16_0879)Zhenjiang Key Research and Development Program(NY2016016)
基金funded by grants from The International Development Research Centre(IDRC),Canada(No.108100-001)also partially supported by the Strengthen Action Plan for Shanghai Public Health System Construction 2011-2013(GW-11)by the National S&TKey Project(No.2016YFC1202000).
文摘Background:Oncomelania hupensis is only intermediate snail host of Schistosomajaponicum,and distribution of 0.hupensis is an important indicator for the surveillance of schistosomiasis.This study explored the feasibility of a random forest algorithm weighted by spatial distance for risk prediction of schistosomiasis distribution in the Yangtze River Basin in China,with the aim to produce an improved precision reference for the national schistosomiasis control programme by reducing the number of snail survey sites without losing predictive accuracy.Methods:The snail presence and absence records were collected from Anhui,Hunan,Hubei,Jiangxi and Jiangsu provinces in 2018.A machine learning of random forest algorithm based on a set of environmental and climatic variables was developed to predict the breeding sites of the 0.hupensis intermediated snail host of S.japonicum.Different spatial sizes of a hexagonal grid system were compared to estimate the need for required snail sampling sites.The predictive accuracy related to geographic distances between snail sampling sites was estimated by calculating Kappa and the area under the curve(AUC).Results:The highest accuracy(AUC=0.889 and Kappa=0.618)was achieved at the 5 km distance weight.The five factors with the strongest correlation to 0.hupensis infestation probability were:(1)distance to lake(48.9%),(2)distance to river(36.6%),(3)isothermality(29.5%),(4)mean daily difference in temperature(28.1%),and(5)altitude(26.0%).The risk map showed that areas characterized by snail infestation were mainly located along the Yangtze River,with the highest probability in the dividing,slow-flowing river arms in the middle and lower reaches of the Yangtze River in Anhui,followed by areas near the shores of China's two main lakes,the Dongting Lake in Hunan and Hubei and the Poyang Lake in Jiangxi.Conelusions:Applying the machine learning of random forest algorithm made it feasible to precisely predict snail infestation probability,an approach that could improve the sensitivity of the
文摘The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.
基金This research was supported by the National Key Research and Development Program of China(2016YFD0702001)the Key Research and Development Program of Jiangsu Province(BE2017358)+2 种基金the Graduate Innovative Projects of Jiangsu Province 2016(KYLX16_0879)the Anhui Natural Science Foundation(1608085ME112)and the Jiangsu Province Graduate Research and Practice Innovation Program(SJCX19_0550).
文摘Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester.It is affected by operating parameters of a combine such as feeding rate,the peripheral speed of the threshing cylinder and concave clearance,and shows complex non-linear law.Real-time acquisition of the breakage rate is an effective way to find the correlation of them.In addition,real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard.In this study,a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed.The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process.According to the working characteristics of the combine,the illumination and installation of the light source were optimized,and the lateral lighting system was constructed.A two-step method of“color training-verification”was applied to identify the whole and broken kernels.In the first step,the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation,extract the color spectrum of each particle in color-space HSL and output the recognition model file.The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory.The experiments of about 2300 particles showed that the recognition accuracy of 96%was attained,and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency.The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.
