Motion time study is employed by manufacturing industries to determine operation time.An accurate estimate of operation time is crucial for effective process improvement and production planning.Traditional motion time...Motion time study is employed by manufacturing industries to determine operation time.An accurate estimate of operation time is crucial for effective process improvement and production planning.Traditional motion time study is conducted by human analysts with stopwatches,which may be exposed to human errors.In this paper,an automated time study model based on computer vision is proposed.The model integrates a convolutional neural network,which analyzes a video of a manual operation to classify work elements in each video frame,with a time study model that automatically estimates the work element times.An experiment is conducted using a grayscale video and a color video of a manual assembly operation.The work element times from the model are statistically compared to the reference work element time values.The result shows no statistical difference among the time data,which clearly demonstrates the effectiveness of the proposed model.展开更多
This study aimed to model and identify the most productive cutting methods of tree plantations by comparing a forward felling technique(C)with sideways techniques outside(A and D)or inside cutting edge(B and E).Drone ...This study aimed to model and identify the most productive cutting methods of tree plantations by comparing a forward felling technique(C)with sideways techniques outside(A and D)or inside cutting edge(B and E).Drone video material of each tree was analyzed by comparing time distribution of work phases.The relation between this input data and harvester production data was analyzed by regression models.A quadratic model predicted productiv-ity precisely(R^(2)=0.95)and explained the effective-hour productivity in cutting cycle with dummy variables of har-vesting conditions.The productivity was explained by tree size and cutting cycle time,while effects of operator and harvester were eliminated by statistical analysis.In loblolly pine(Pinus taeda L.)plantations on flat terrain,cutting method B was 4.8 m3/E0h(effective working hour)more productive than method A,and 6.7 m^(3)/E0h than method C.In Sydney blue gum(Eucalyptus saligna Sm.)plantations,cutting method E was 1.8 m^(3)/E0h more productive than cut-ting method D on sloping terrain.Of the time-cycle vari-ables,time consumption of the“moving of tree”changed significantly between the cutting methods,of which the ones that used the sideways felling technique inside cutting edge were most efficient.This quadratic modeling structure can be recommended for precise studies in similar harvesting conditions.展开更多
By combining laboratorial experiments,theoretical analysis and mathematical model,theeffect of sediment motion on transport-transformation of heavy-metal pollutants is studied. (1)Previous studies on adsorption-desorp...By combining laboratorial experiments,theoretical analysis and mathematical model,theeffect of sediment motion on transport-transformation of heavy-metal pollutants is studied. (1)Previous studies on adsorption-desorption of heavy-metal pollutants by sedimentparticles are systematically summarized.Based on this summary,subjects that need to be furtherstudied are put forward. In rivers most heavy-metal pollutants concentrate on sediment particles.In order tocontrolling water pollution aused by heavy-metal pollutants following topics should beemphasized:studies on the effect of suspended matter and deposit on transport-transformation of展开更多
基金This work is jointly supported by the SIIT Young Researcher Grant,under a Contract No.SIIT 2019-YRG-WP01the Excellent Research Graduate Scholarship,under a Contract No.MOU-CO-2562-8675.
文摘Motion time study is employed by manufacturing industries to determine operation time.An accurate estimate of operation time is crucial for effective process improvement and production planning.Traditional motion time study is conducted by human analysts with stopwatches,which may be exposed to human errors.In this paper,an automated time study model based on computer vision is proposed.The model integrates a convolutional neural network,which analyzes a video of a manual operation to classify work elements in each video frame,with a time study model that automatically estimates the work element times.An experiment is conducted using a grayscale video and a color video of a manual assembly operation.The work element times from the model are statistically compared to the reference work element time values.The result shows no statistical difference among the time data,which clearly demonstrates the effectiveness of the proposed model.
文摘This study aimed to model and identify the most productive cutting methods of tree plantations by comparing a forward felling technique(C)with sideways techniques outside(A and D)or inside cutting edge(B and E).Drone video material of each tree was analyzed by comparing time distribution of work phases.The relation between this input data and harvester production data was analyzed by regression models.A quadratic model predicted productiv-ity precisely(R^(2)=0.95)and explained the effective-hour productivity in cutting cycle with dummy variables of har-vesting conditions.The productivity was explained by tree size and cutting cycle time,while effects of operator and harvester were eliminated by statistical analysis.In loblolly pine(Pinus taeda L.)plantations on flat terrain,cutting method B was 4.8 m3/E0h(effective working hour)more productive than method A,and 6.7 m^(3)/E0h than method C.In Sydney blue gum(Eucalyptus saligna Sm.)plantations,cutting method E was 1.8 m^(3)/E0h more productive than cut-ting method D on sloping terrain.Of the time-cycle vari-ables,time consumption of the“moving of tree”changed significantly between the cutting methods,of which the ones that used the sideways felling technique inside cutting edge were most efficient.This quadratic modeling structure can be recommended for precise studies in similar harvesting conditions.
文摘By combining laboratorial experiments,theoretical analysis and mathematical model,theeffect of sediment motion on transport-transformation of heavy-metal pollutants is studied. (1)Previous studies on adsorption-desorption of heavy-metal pollutants by sedimentparticles are systematically summarized.Based on this summary,subjects that need to be furtherstudied are put forward. In rivers most heavy-metal pollutants concentrate on sediment particles.In order tocontrolling water pollution aused by heavy-metal pollutants following topics should beemphasized:studies on the effect of suspended matter and deposit on transport-transformation of
