• 한국육종학회지
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• 제41권4호
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• pp.574-578
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• 2009

"고풍적축면"은 "안동적축면"과 "뚝섬적축면"을 교배하여 육성한 적축면 잎상추 품종이다. 기존의 같은 type의 품종보다 추대가 늦은 중만생 품종이며, 적색발현이 좋아, 안토시안닌 함량이 29.4 mg/100 g으로 "뚝섬적축면"보다 10배이상 많았으며 수확후 $4^{\circ}C$에서 3주간 이상 저장이 가능하였다. 재배 적기는 연중재배가 가능하나 시험재배결과 평난지에서는 봄, 가을에 고랭지에서는 봄, 여름, 가을에 노지 및 비가림하우스 재배가 가능한 품종이다. 수확 가능한 엽수는 52매이며, 주중은 330 g으로 상품수량성은 19.5 ton/ha로 뚝섬적축면 보다 17% 증수되었다.

• 한국분말야금학회:학술대회논문집
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• 한국분말야금학회 2006년도 Extended Abstracts of 2006 POWDER METALLURGY World Congress Part2
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• pp.1119-1120
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• 2006

High Energy Milling (HEM) is applied for the grinding of cement and this can lead to substantial refinement $(<2{\mu}m)$ and mechanically activation of the powder particles. The present paper reviews the preliminary studies, explains the novel technique and suggests the route into commercial application. Particular attention is paid to wear results with an applied $Si_3N_4-grinding$ unit where no substantial wear was found after 4000 h of operation.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• 한국해안해양공학회:학술대회논문집
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• 한국해안해양공학회 1993년도 정기학술강연회 발표논문 초록집
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• pp.20-25
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• 1993

In coastal waters, more often than not, waves propagate on currents driven by tidal forces, earth’s gravity, or wind. There have been a number of studies for dealing with the change of wave spectrum due to tile presence of current. Based on the conservation of wave action, Hedges et al. (1985) have proposed an equation which describes the influence of current on the change of wave spectrum in water of finite depth. (omitted)

• Biotechnology and Bioprocess Engineering:BBE
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• 제10권4호
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• pp.367-371
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• 2005

About 3,000 bacterial colonies with esterase activities were isolated from soil samples by enrichment culture and halo-size on Luria broth-tributyrin (LT) plates. The colonies were assayed for esterase activity in microtiter plates using enantiomerically pure (R)- and (S)-2-phenylbutyric acid resorufin ester (2PB-O-res) as substrates. Two enantioselective strains (JH2 and JH13) were selected by the ratio of initial rate of hydrolysis of enantiomerically pure (R)- and (S)-2-PB-O-res. When cell pellets were used, both strains showed high apparent enantioselectivity ($E_{app}>100$) for (R)-2PB-O-res and were identified as Exiguobacterium acetylicum. The JH13 strain showed high esterase activity on p-nitrophenyl acetate (pNPA), but showed low lipase activity on p-nitrophenyl palmitate (pNPP). The esterase was located in the soluble fraction of the cell extract. The crude intracellular enzyme preparation was stable at a pH range from 6.0 to 11.0.

• 한국공간구조학회논문집
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• 제22권2호
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• pp.19-27
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• 2022

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• 한국측량학회지
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• 제39권3호
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• pp.141-148
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• 2021

본 논문은 SegNet과 ResNet을 조합한 딥러닝을 이용하여 횡단보도를 검출하는 방법을 제안한다. 시각 장애인의 경우 횡단보도가 어디에 있는지 정확히 아는 게 안전한 교통 시스템에서 중요하다. 딥러닝에 의한 횡단보도 검출은 이 문제에 대한 좋은 해결책이 될 수 있다. 로봇 시각 기반 보조 기술은 지난 몇년 동안 카메라를 사용하는 특정 장면에 초점을 두고 제안되어 왔다. 이러한 전통적인 방법은 비교적 긴 처리 시간으로 의미있는 결과를 얻었으며 횡단보도 인식을 크게 향상시켰다. 그러나 전통적인 방법은 지연 시간이 길고 웨어러블 장비에서 실시간을 만족시킬 수 없다. 본 연구에서 제안하는 방법은 취득한 영상에서 횡단보도를 빠르고 안정적으로 검출하기 위한 모델을 제안한다. 모델은 SegNet과 ResNet을 기반으로 개선되었으며 3단계로 구성된다. 첫째, 입력 영상을 서브샘플링하여 이미지 특징을 추출하고 ResNet의 컨벌루션 신경망을 수정하여 새로운 인코더로 만든다. 둘째, 디코딩 과정에서 업샘플링 네트워크를 통해 특징맵을 원영상 크기로 복원한다. 셋째, 모든 픽셀을 분류하고 각 픽셀의 정확도를 계산한다. 이 실험의 결과를 통하여 수정된 시맨틱 분할 알고리즘의 적격한 정확성을 검증하는 동시에 결과 출력 속도가 비교적 빠른 것으로 파악되었다.

• 한국세라믹학회:학술대회논문집
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• 한국세라믹학회 1986년도 Priceedings Of The Third Korea-Japan Seminar On New Ceramics
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• pp.185-201
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• 1986

• 한국육종학회지
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• 제41권4호
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• pp.579-582
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• 2009

"미홍"은 "단홍적축면"과 "뚝섬적축면"을 교배하여 육성한 추대가 늦고, 엽색이 매우진한 적축면 잎상추 품종이다. 기존 같은 type의 품종보다 추대가 늦은 중만생 품종이며, 적색발현이 좋아, 안토시안닌 함량이 28.9 mg/100 g으로 "뚝섬적축면"보다 10배이상 많았으며 수확 후 $4^{\circ}C$에서 3주간 이상 저장이 가능하였다. 재배적기는 연중재배가 가능하나 시험재배 결과 평난지에서는 봄, 가을에 고랭지에서는 여름재배가 가능하며, 노지 및 비가림하우스 등 어느 곳에서나 재배가 가능한 품종이다. 수확 가능한 엽수는 주당 54매이며, 주중은 291 g으로 상품수량성은 17.0 ton/ha로 "뚝섬적축면"보다 2% 증수되었다.