• 한국컴퓨터정보학회논문지
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• 제25권12호
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• pp.63-71
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• 2020

본 논문에서는 최근 가장 신뢰도 높은 인공지능 탐지 알고리즘인 YOLOv3와 EfficientDet을 이용한 화재 탐지 기술과 문자, 웹, 앱, 이메일 등 4종류의 알림을 동시에 전송하는 알림서비스 그리고 화재 탐지와 알림서비스를 연동하는 AWS 시스템을 제안한다. 우리의 정확도 높은 화재 탐지 알고리즘은 두 종류인데, 로컬에서 작동하는 YOLOv3 기반의 화재탐지 모델은 2000개 이상의 화재 데이터를 이용해 데이터 증강을 통해 학습하였고, 클라우드에서 작동하는 EfficientDet은 사전학습모델(Pretrained Model)에서 추가로 학습(Transfer Learning)을 진행하였다. 4종류의 알림서비스는 AWS 서비스와 FCM 서비스를 이용해 구축하였는데, 웹, 앱, 메일의 경우 알림 전송 직후 알림이 수신되며, 기지국을 거치는 문자시스템의 경우 지연시간이 1초 이내로 충분히 빨랐다. 화재 영상의 화재 탐지 실험을 통해 우리의 화재 탐지 기술의 정확성을 입증하였으며, 화재 탐지 시간과 알림서비스 시간을 측정해 화재 발생 후 알림 전송까지의 시간도 확인해보았다. 본 논문의 AI 화재 탐지 및 알림서비스 시스템은 과거의 화재탐지 시스템들보다 더 정확하고 빨라서 화재사고 시 골든타임 확보에 큰 도움을 줄 것이라고 기대된다.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.769-782
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• 2022

A study on agricultural automation became more important. In Korea, sweet persimmon farmers spend a lot of time and effort on classifying profitable persimmons. In this paper, we propose and implement an efficient grading algorithm for persimmons before shipment. We gathered more than 1,750 images of persimmons, and the images were graded and labeled for classifications purpose. Our main algorithm is based on EfficientDet object detection model but we implemented more exquisite method for better classification performance. In order to improve the precision of classification, we adopted a machine learning algorithm, which was proposed by PyCaret machine learning workflow generation library. Finally we acquired an improved classification model with the accuracy score of 81%.

• Journal of Electrochemical Science and Technology
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• 제8권2호
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• pp.87-95
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• 2017

Enzymatic fuel cells are promising low cost, compact and flexible energy resources. The basis of enzymatic fuel cells is transfer of electron from enzyme to the electrode surface and vice versa. Electron transfer is done either by direct or mediated electron transfer (DET/MET), each one having its own advantages and disadvantages. In this study, the DET and MET of laccase-based biocathodes are compared with each other. The DET of laccase enzyme has been studied using two methods; assemble of needle-like carbon nanotubes (CNTs) on the electrode, and CNTs/Nafion polymer. MET of laccase enzyme also is done by use of ceramic electrode containing, ABTS (2,2'-azino-bis [3-ethylbenzthiazoline-6-sulphonic acid]) /sol-gel. Cyclic voltammetric results of DET showed a pair of well-defined redox peaks at $200{\mu}A$ and $170{\mu}A$ in a solution containing 5and $10{\mu}M$ o-dianisidine as a substrate for needle-like assembled CNTs and CNTs-Nafion composite respectively. In MET method using sol-gel/ABTS, the maximum redox peak was $14{\mu}A$ in the presence of 15 M solution o-dianisidine as substrate. The cyclic voltammetric results showed that laccase immobilization on needle-like assembled CNTs or CNTs-Nafion is more efficient than the sol-gel/ABTS electrode. Therefore, the expressed methods can be used to fabricate biocathode of biofuel cells or laccase based biosensors.

• 한국병원경영학회지
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• 제23권3호
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• pp.87-103
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• 2018

Purpose: This study investigates the best appointment pattern which can enhance customer's satisfaction and hospital's efficient management reflecting plastic surgery clinic's service characteristics. Methodology: The data of this study is obtained from Plastic surgery Clinic which is located in the civic center. By collecting and analyzing the data, we build the simulation model using ARENA. Based on 5 appointment patterns that was suggested in formal appointment scheduling studies, we experiment 3 simulation models; 'Basic Appointment Pattern' that has no restriction, 'Restriction on Second Customer' that restricts the number of second customer's in each slot, 'Restriction on Process Time' that restricts the number of second customer who has long process time in each slot. We can check robustness of the appointment patterns by experimenting on off-peak day and peak day, during peak season. Findings: This study confirms that these 2 restrictions can give a better result than 'Basic Appointment Pattern' that just simply distributes customers by number. Especially, the performance of Triangle-like pattern which is the best appointment pattern in the formal study has been improved by adding restrictions. Based on 'DET', 'Restriction on Second Customer' shows a better result. Meanwhile, based on 'E(WT)', 'Restriction on Process Time' shows a better result. Overall, based on 'DET+E(WT)', 'Restriction on Second Customer' shows a better result. Practical Implications: The purpose of each hospital may alter as demand for plastic surgery grows increasingly. Thus, each hospital should be always prepared to introduce appointment pattern for changed purpose. In order to respond flexibly to these changes, it is necessary for medical personnel to improve the awareness or for hospital to create an environment by constructing appointment program so that medical personnel does not need to put more labor on work.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2024년도 제69차 동계학술대회논문집 32권1호
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• pp.73-75
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• 2024

본 논문에서는 밀 수확량을 증가시키기 위한 일반화된 검출 모델을 제안한다. 일반화 성능을 높이기 위해 CutMix 알고리즘으로 데이터를 증식시켰고, 라벨링 되지 않은 데이터를 최대한 활용하기 위해 Fast R-CNN 기반 Pseudo labeling을 사용하였다. 학습의 정확성과 효율성을 높이기 위해 사전에 훈련된 EfficientDet 모델로 학습하였으며, OOF를 이용하여 검증하였다. 최신 객체 검출 모델과 IoU(Intersection over Union)를 이용한 성능 평가 결과, 제안된 모델이 가장 높은 성능을 보이는 것을 확인하였다.

• 한국에너지공학회:학술대회논문집
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• 한국에너지공학회 2004년도 추계 학술발표회 논문집
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• pp.133-138
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• 2004

Proton exchange membrane fuel cell (PEMFC) is considered as a clean and efficient energy conversion det ice for mobile and stationary applications. Anions all the components of the PEMFC, the interface between the electrolyte ,and electrode catalyst plays an important role in determining tile cell performance since the electrochemical reactions take place at the interface in contact with tile reactant gases. Therefore, to increase the interface area and obtain a high-performance PEMFC, surface of the electrolyte membrane was roughened by Ar$^{+}$ beam bombardment. The results imply that by modifying surface of the electrolyte membrane, platinum loading can be reduced significantly without performance loss. To optimize the surface treatment condition, effects of ion dose density on characteristics of the membrane/electrode interface were examined by measuring the cell performance, impedance spectroscopy, and cyclic voltammograms. Surface of the modified membranes were characterized using scanning electron microscopy and FT-IR.R.

• Geomechanics and Engineering
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• 제38권1호
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• 방송공학회논문지
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• 제27권2호
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• pp.185-197
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• 2022

최근 객체 인식에 높은 성능을 가진 딥러닝 네트워크가 나오고 있다. 딥러닝을 이용한 객체 인식의 경우 성능 향상을 위해 학습 데이터 셋 구축이 중요하다. 데이터 셋을 구축하기 위해서는 이미지를 수집하고 라벨링 해야 한다. 이 과정은 많은 시간과 인력이 필요하다. 때문에 오픈 데이터 셋을 사용한다. 그러나 방대한 오픈 데이터 셋을 가지고 있지 않는 객체도 존재한다. 그 중 하나가 번호판 검출과 인식에 필요한 데이터이다. 이에 본 논문에서는 이미지를 최소화 하여 대용량 데이터 셋을 만들 수 있는 인조 번호판 생성기 시스템을 제안한다. 또한 인조 번호판 배치구조에 따른 검출률을 분석했다. 분석결과 가장 좋은 배치구조는 FVC_III, B이며 가장 적합한 네트워크는 D2Det이었다. 인조 데이터셋 성능은 실제 데이터셋의 성능보다 2~3%가 낮았지만, 인조 데이터를 구축하는 시간이 실제 데이터셋을 구축하는 시간보다 약 11배 빨라 시간적으로 효율적인 데이터 셋 구축 시스템임을 증명하였다.