The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,w...The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,which is important for object retrieval and treatment of the patient.This case report describes a male child,2 years and 2 mo of age,who accidentally swallowed a lithium battery while playing at home.A plain X-ray showed that the battery was in the abdomen,but it was unclear whether the object was still inside the stomach.A second X-ray examination performed after oral administration of a bloating agent to produce expansion of the stomach and provide negative contrast confirmed that the ingested battery was still in the stomach.The battery was then carefully removed using magnetic and balloon catheters under fluoroscopic guidance.This case report describes the successful use of an orally administered bloating agent without pain to the child in orderto determine the precise location of a foreign object in the abdomen.展开更多
Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult...Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.展开更多
With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WP...With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WPT)because of its high reliability,safety,and convenience,especially high suitability for the future self-driving scenario.Foreign object detection(FOD),mainly including metal object detection and living object detection,is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety.In the last decade,especially in the past three years,many pieces of research on FOD have been reported.This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity,reliability,adaptability,complexity,and cost.Future challenges for research and development are also discussed to encourage commercialisation of EV-WPT technique.展开更多
文摘The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,which is important for object retrieval and treatment of the patient.This case report describes a male child,2 years and 2 mo of age,who accidentally swallowed a lithium battery while playing at home.A plain X-ray showed that the battery was in the abdomen,but it was unclear whether the object was still inside the stomach.A second X-ray examination performed after oral administration of a bloating agent to produce expansion of the stomach and provide negative contrast confirmed that the ingested battery was still in the stomach.The battery was then carefully removed using magnetic and balloon catheters under fluoroscopic guidance.This case report describes the successful use of an orally administered bloating agent without pain to the child in orderto determine the precise location of a foreign object in the abdomen.
基金supported by the Laboratory of Lingnan Modern Agriculture Project(Grant No.NT2021009)Guangdong University Key Field(Artificial Intelligence)Special Project(No.2019KZDZX1012)and the 111 Project(D18019)+3 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515110554)China Postdoctoral Science Foundation(Grant No.2022M721201)the National Natural Science Foundation of China(Grant No.31901411)The Open Competition Program of the Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province(No.2022SDZG03).
文摘Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.
基金Key R&D Program of Guangdong Province,China(No.2020B0404030004)partly by the open research fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)(No.GML-KF-22-19)partly by the National Natural Science Foundation of China(No.62001301).
文摘With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WPT)because of its high reliability,safety,and convenience,especially high suitability for the future self-driving scenario.Foreign object detection(FOD),mainly including metal object detection and living object detection,is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety.In the last decade,especially in the past three years,many pieces of research on FOD have been reported.This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity,reliability,adaptability,complexity,and cost.Future challenges for research and development are also discussed to encourage commercialisation of EV-WPT technique.
基金the financial support from the National Natural Science Foundation of China(Nos.52172067,92160202)Natural Science Foundation of Guangdong Province,China(Nos.2021B1515020038,2020B1515020036)+1 种基金Guangdong Special Support Program,China(No.2019BT02C629)Guangdong Academy of Sciences Program,China(No.2020GDASYL20200104030)。
