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병렬 구조의 다중 필터 CNN을 이용한 골절합용 판의 불량 탐지 모델에 관한 연구
이송연,허용정 한국정밀공학회 2023 한국정밀공학회지 Vol.40 No.9
Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous. In this study, we proposed a defect detection model based on a parallel type convolution neural network for detecting bone plate crack and pore deformation. All size filters were different according to the defect shape. A convolution neural network detected pore defects. Another convolution neural network detected the crack. Two convolution neural networks simultaneously detected different defect types. The performance of the defect detection model was measured and used for the F1-score. We confirmed that performance of the defect detection model was 98.4%. We confirmed that the defect detection time was 0.21 seconds.
5 Step 실용트리즈 기법을 이용한 PLGA 인공지지체의 변형 문제 해결에 관한 연구
이송연,허용정,박종순 한국반도체디스플레이기술학회 2017 반도체디스플레이기술학회지 Vol.16 No.4
In this paper, we have studied the deformation problem of the scaffold caused by the FDM type 3D printer. The Practical TRIZ technique was used to solve the deformation problem of the scaffold generated from the adhesion surface between the scaffold and the bed. The Practical TRIZ methodology was used to derive the solution and the experiment was conducted on the derived solution. As a result of evaluating the experimental results obtained for the solution, it was found that the deformation of the scaffold was much improved to the satisfactory level.
머신 러닝 회귀 방안을 이용한 인공지지체 기공 크기 예측모델 성능에 관한 연구
이송연,허용정,Lee, Song-Yeon,Huh, Yong Jeong 한국반도체디스플레이기술학회 2020 반도체디스플레이기술학회지 Vol.19 No.1
In this paper, We need to change all print factors when which print scaffold with 400 ㎛ pore using FDM 3d printer. Therefore the print quantity is 10 billion times, So we are difficult to print on workplace. To solve the problem, we used the prediction model based machine learning regression. We preprocessed and learned the securing print condition data, and we produced different kinds of prediction models. We predicted the pore size of scaffolds not securing with new print condition data using prediction models. We have derived the print conditions that satisfy the pore size of 400 ㎛ among the predicted print conditions of pore size. We printed the scaffolds 5 times on the condition. We measured the pore size of the printed scaffold and compared the average pore size with the predicted pore size. We confirmed that error was less than 1%, and we were identify the model with the highest pore size prediction performance of scaffold.
인공지지체 불량 검출을 위한 딥러닝 모델 성능 비교에 관한 연구
이송연,허용정,Lee, Song-Yeon,Huh, Yong Jeong 한국반도체디스플레이기술학회 2021 반도체디스플레이기술학회지 Vol.20 No.2
When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.
3D프린팅 공정 중 공기 습도에 따른 출력물의 인장 강도에 관한 연구
이송연,허용정 한국반도체디스플레이기술학회 2021 반도체디스플레이기술학회지 Vol.20 No.4
Scaffolds protect the sensor in the body. Scaffolds are made of a bioabsorbable polymer. The polymer process is sensitive to humidity. Inside of the 3D printer has been improved to control the humidity. Specimens were produced by injection molding and 3D printer. 3D printed specimens were printed under various humidity conditions. We measured tensile strength of the injection-molded specimen and tensile strength of the 3d printing specimen. We compared tensile strength of the injection-molded specimen and tensile strength of the 3d printing specimen. Tensile strength of the injection-molded specimen is 557 kgf/㎠. We confirmed tensile strength of the specimen was highest at 741 kgf/㎠ when the humidity was 10 %. We confirmed lower the humidity, higher tensile strength of the polymer product.
묽은 용액의 성질에 대한 화학전공 예비교사들의 이해 및 화학교사 양성교육에 대한 인식 사례 연구
이송연,김성혜,백성혜,Lee, Song-Yeon,Kim, Soeng-Hye,Paik, Seoung-Hey 대한화학회 2010 대한화학회지 Vol.54 No.6
이 연구에서는 화학교육을 전공하는 4명의 예비교사들과 화학을 전공한 2명의 예비교사들을 대상으로 고등학교 화학II 교과서에 제시된 "묽은 용액의 성질" 단원에 관련된 개념의 이해를 비교하였다. 연구결과, 예비교사들 중에 고등학교 화학II 교과서에 제시된 내용을 깊이 있게 이해하는 예비교사들이 많지 않았으며, 묽은 용액의 성질에 관련된 오개념을 가지고 있는 경우도 나타났다. 그리고 사범대학에서 화학교육을 전공한 예비교사들의 이해수준과 비사범대학에서 화학을 전공한 예비교사들의 이해수준은 차이가 없는 것으로 나타났다. 연구에 참여한 대부분의 예비교사들은 대학과 상관없이 그들의 예비교사 교육과정에서 실천적 지식이 부족하다고 느끼고 있었다. We compared the understanding of 4 pre-service teachers of chemistry education major and 2 pre-service teachers of chemistry major related to conceptions of "properties of dilute solutions" chapter in high school Chemistry II textbooks. As results, few pre-service teachers understood fully the concepts of high school Chemistry II textbooks. Some pre-service teachers had misconceptions related to properties of dilute solutions. We found that few differences existed between the pre-service teachers' understanding regardless of whether they took a major in chemistry education of a education college or a major in chemistry of noneducation college. Most of the pre-service teachers who attended this research recognized the lack of practical knowledge in their pre-service teacher curriculum.
3SC 실용트리즈와 실험계획법을 이용한 PLGA 인공지지체 제작조건에 관한 연구
이송연,허용정 한국반도체디스플레이기술학회 2018 반도체디스플레이기술학회지 Vol.17 No.4
In this paper, we have studied the deformation problem of the scaffold caused by the FDM type 3D printer. The DOE (Design of experiment) and 3SC was used to solve the deformation problem of the scaffold generated from the adhesion surface between the scaffold and the bed. The methodology was used to derive the solution and the experiment was conducted on the derived solution. As a result of evaluating the experimental results obtained for the solution, it was found that the deformation of the scaffold was much improved. By using the DOE, We were possible to derive the output condition of scaffold
CNN 기반 딥러닝을 이용한 인공지지체의 외형 변형 불량 검출 모델에 관한 연구
이송연,허용정,Lee, Song-Yeon,Huh, Yong Jeong 한국반도체디스플레이기술학회 2021 반도체디스플레이기술학회지 Vol.20 No.1
Warpage defect detecting of scaffold is very important in biosensor production. Because warpaged scaffold cause problem in cell culture. Currently, there is no detection equipment to warpaged scaffold. In this paper, we produced detection model for shape warpage detection using deep learning based CNN. We confirmed the shape of the scaffold that is widely used in cell culture. We produced scaffold specimens, which are widely used in biosensor fabrications. Then, the scaffold specimens were photographed to collect image data necessary for model manufacturing. We produced the detecting model of scaffold warpage defect using Densenet among CNN models. We evaluated the accuracy of the defect detection model with mAP, which evaluates the detection accuracy of deep learning. As a result of model evaluating, it was confirmed that the defect detection accuracy of the scaffold was more than 95%.
CNN 알고리즘을 이용한 인공지지체의 3D프린터 출력 시 실시간 출력 불량 탐지 시스템에 관한 연구
이송연,허용정,Lee, Song Yeon,Huh, Yong Jeong 한국반도체디스플레이기술학회 2021 반도체디스플레이기술학회지 Vol.20 No.3
Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.
기계 학습을 이용한 골절합용 판의 3D 프린터 출력 조건 예측 모델에 관한 연구
이송연,허용정 한국정밀공학회 2022 한국정밀공학회지 Vol.39 No.4
Bone plates made of biodegradable polymers have been used to fix broken bones. 3D printers are used to produce the bone plates for fracture fixing in the industry. The dimensional accuracy of the product printed by a 3D printer is less than 80%. Fracture fixing plates with less than 80% dimensional accuracy cause problems during surgery. There is an urgent need to improve the dimensional accuracy of the product in the industry. In this paper, a methodology using machine learning was proposed to improve the dimensional accuracy. The proposed methodology was evaluated through case studies. The results predicted by the machine learning methodology proposed in this paper and the experimental results were compared through the experiment. After verification, results of the proposed prediction model and the experimental results were in good agreement with each other.