The paper investigates a few of the major areas of the next generation technological advancement,“smart city planning concept”.The areas that the paper focuses are vehicle to grid(V2G),sun to vehicle(S2V),and vehicl...The paper investigates a few of the major areas of the next generation technological advancement,“smart city planning concept”.The areas that the paper focuses are vehicle to grid(V2G),sun to vehicle(S2V),and vehicle to infrastructure(V2I).For the bi-directional crowd energy single entity concept,V2G and building to grid(B2G)are the primary parts of distributed renewable generation(DRG)under smart living.This research includes an in-depth overview of this three major areas.Next,the research conducts a case analysis of V2G,S2V,and V2I along with their possible limitations in order to find out the novel solutions for future development both for academia and industry levels.Lastly,few possible solutions have been proposed to minimize the limitations and to develop the existing system for future expansion.展开更多
The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designe...The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.展开更多
文摘The paper investigates a few of the major areas of the next generation technological advancement,“smart city planning concept”.The areas that the paper focuses are vehicle to grid(V2G),sun to vehicle(S2V),and vehicle to infrastructure(V2I).For the bi-directional crowd energy single entity concept,V2G and building to grid(B2G)are the primary parts of distributed renewable generation(DRG)under smart living.This research includes an in-depth overview of this three major areas.Next,the research conducts a case analysis of V2G,S2V,and V2I along with their possible limitations in order to find out the novel solutions for future development both for academia and industry levels.Lastly,few possible solutions have been proposed to minimize the limitations and to develop the existing system for future expansion.
基金National Natural Science Foundation of China(No.U1831123)。
文摘The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.
