In clinical work,many soft medical pipelines are located deep within the body,resulting in a lack of feedback regarding bending or folding conditions,which presents significant challenges for medical staff.To solve th...In clinical work,many soft medical pipelines are located deep within the body,resulting in a lack of feedback regarding bending or folding conditions,which presents significant challenges for medical staff.To solve the problem,this study innovatively designs a flexible bending sensor,which can be attached to the medical pipelines and monitor the bending conditions.Based on a flexible substrate with secondary microstructures copied from champagne rose petals,the interdigital electrodes are designed to enhance the sensitivity of the sensor due to the amplifying effect.A high sensitivity of 2.209%?1in a bending strain range of 8.9%,and a stable repeatability for over 6000 cycles under 1.8%bending strain are achieved by the sensor.By integrating the bending sensor,here,the nasogastric tube,femoral vein catheter,and tracheal intubation are used to demonstrate the sensing performance.Additionally,during the measurement,the sensing signals are processed and transformed to the bending angles simultaneously,enabling the direct visualization of the bending conditions of the pipelines.This work proposes innovative applications for bending sensors in medical technology and establishes a foundation for further research on flexible bending sensors.展开更多
In this study,we proposed a recognition method based on deep artificial neural networks to identify various elements in pipelines and instrumentation diagrams(P&ID)in image formats,such as symbols,texts,and pipeli...In this study,we proposed a recognition method based on deep artificial neural networks to identify various elements in pipelines and instrumentation diagrams(P&ID)in image formats,such as symbols,texts,and pipelines.Presently,the P&ID image format is recognized manually,and there is a problem with a high recognition error rate;therefore,automation of the above process is an important issue in the processing plant industry.The China National Offshore Petrochemical Engineering Co.provided the image set used in this study,which contains 51 P&ID drawings in the PDF.We converted the PDF P&ID drawings to PNG P&IDs with an image size of 8410×5940.In addition,we used labeling software to annotate the images,divided the dataset into training and test sets in a 3:1 ratio,and deployed a deep neural network for recognition.The method proposed in this study is divided into three steps.The first step segments the images and recognizes symbols using YOLOv5+SE.The second step determines text regions using character region awareness for text detection,and performs character recognition within the text region using the optical character recognition technique.The third step is pipeline recognition using YOLOv5+SE.The symbol recognition accuracy was 94.52%,and the recall rate was 93.27%.The recognition accuracy in the text positioning stage was 97.26%and the recall rate was 90.27%.The recognition accuracy in the character recognition stage was 90.03%and the recall rate was 91.87%.The pipeline identification accuracy was 92.9%,and the recall rate was 90.36%.展开更多
基金supported by the National Natural Science Foundation of China(52105299,52175271,52375287)Science and Technology Development Plan Project of Jilin Province(20240101036JJ)+1 种基金Scientific Research Project of the Education Department of Jilin Province(JJKH20241269KJ)China Postdoctoral Science Foundation(2024M751086).
文摘In clinical work,many soft medical pipelines are located deep within the body,resulting in a lack of feedback regarding bending or folding conditions,which presents significant challenges for medical staff.To solve the problem,this study innovatively designs a flexible bending sensor,which can be attached to the medical pipelines and monitor the bending conditions.Based on a flexible substrate with secondary microstructures copied from champagne rose petals,the interdigital electrodes are designed to enhance the sensitivity of the sensor due to the amplifying effect.A high sensitivity of 2.209%?1in a bending strain range of 8.9%,and a stable repeatability for over 6000 cycles under 1.8%bending strain are achieved by the sensor.By integrating the bending sensor,here,the nasogastric tube,femoral vein catheter,and tracheal intubation are used to demonstrate the sensing performance.Additionally,during the measurement,the sensing signals are processed and transformed to the bending angles simultaneously,enabling the direct visualization of the bending conditions of the pipelines.This work proposes innovative applications for bending sensors in medical technology and establishes a foundation for further research on flexible bending sensors.
文摘In this study,we proposed a recognition method based on deep artificial neural networks to identify various elements in pipelines and instrumentation diagrams(P&ID)in image formats,such as symbols,texts,and pipelines.Presently,the P&ID image format is recognized manually,and there is a problem with a high recognition error rate;therefore,automation of the above process is an important issue in the processing plant industry.The China National Offshore Petrochemical Engineering Co.provided the image set used in this study,which contains 51 P&ID drawings in the PDF.We converted the PDF P&ID drawings to PNG P&IDs with an image size of 8410×5940.In addition,we used labeling software to annotate the images,divided the dataset into training and test sets in a 3:1 ratio,and deployed a deep neural network for recognition.The method proposed in this study is divided into three steps.The first step segments the images and recognizes symbols using YOLOv5+SE.The second step determines text regions using character region awareness for text detection,and performs character recognition within the text region using the optical character recognition technique.The third step is pipeline recognition using YOLOv5+SE.The symbol recognition accuracy was 94.52%,and the recall rate was 93.27%.The recognition accuracy in the text positioning stage was 97.26%and the recall rate was 90.27%.The recognition accuracy in the character recognition stage was 90.03%and the recall rate was 91.87%.The pipeline identification accuracy was 92.9%,and the recall rate was 90.36%.
