• 수산해양기술연구
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• 제55권4호
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• pp.285-293
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• 2019

The mesh selectivity of hairtail (Trichiurus lepturus) caught by coastal drift gill net was examined in field experiments with three different mesh sizes (45, 50 and 55 mm) from October to November, 2013 in the coastal areas of south-west of Jeju province. The mesh selectivity tests were conducted with the experimental net to be set middle part of conventional driftnets. The mesh selectivity tests were carried out the total of four times. The selectivity curve was estimated by the Kitahara's and Fujimori's method. In the results, the catch number of hairtail was 653 (125.8 kg) and occupied 34.8% in total catches weight. The optimal mesh size for 50% selection on the minimum landing size (180 mm, AL) and the first maturity size (260 mm, AL) of hairtail were estimated as 47.2 mm and 64.5 mm by master selectivity curves, respectively.

• 한국멀티미디어학회논문지
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• 제24권7호
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• pp.849-859
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• 2021

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

• 소성∙가공
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• 제31권4호
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• 인터넷정보학회논문지
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• 제20권4호
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• pp.91-102
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• 2019

자율주행 시스템에서, 카메라에 포착된 영상을 통하여 보행자를 분류하는 기능은 보행자 안전을 위하여 매우 중요하다. 기존에는 HOG(Histogram of Oriented Gradients)나 SIFT(Scale-Invariant Feature Transform) 등으로 보행자의 특징을 추출한 후 SVM(Support Vector Machine)으로 분류하는 기술을 사용했었으나, 보행자 특징을 위와 같이 수동(handcrafted)으로 추출하는 것은 많은 한계점을 가지고 있다. 따라서 본 논문에서는 CNN(Convolutional Neural Network)의 깊은 특징(deep features)과 전이학습(transfer learning)을 사용하여 보행자를 안정적이고 효과적으로 분류하는 방법을 제시한다. 본 논문은 2가지 대표적인 전이학습 기법인 고정특징추출(fixed feature extractor) 기법과 미세조정(fine-tuning) 기법을 모두 사용하여 실험하였고, 특히 미세조정 기법에서는 3가지 다른 크기로 레이어를 전이구간과 비전이구간으로 구분한 후, 비전이구간에 속한 레이어들에 대해서만 가중치를 조정하는 설정(M-Fine: Modified Fine-tuning)을 새롭게 추가하였다. 5가지 CNN모델(VGGNet, DenseNet, Inception V3, Xception, MobileNet)과 INRIA Person데이터 세트로 실험한 결과, HOG나 SIFT 같은 수동적인 특징보다 CNN의 깊은 특징이 더 좋은 성능을 보여주었고, Xception의 정확도(임계치 = 0.5)가 99.61%로 가장 높았다. Xception과 유사한 성능을 내면서도 80% 적은 파라메터를 학습한 MobileNet이 효율성 측면에서는 가장 뛰어났다. 그리고 3가지 전이학습 기법중 미세조정 기법의 성능이 가장 우수하였고, M-Fine 기법의 성능은 미세조정 기법과 대등하거나 조금 낮았지만 고정특징추출 기법보다는 높았다.

• 수산해양기술연구
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• 제34권4호
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• pp.386-392
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• 1998

• 한국어류학회지
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• 제29권1호
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• pp.41-51
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• 2017

어류양식용 가두리로서 동합금가두리망의 현장활용 가능성을 파악하기 위해 양식어류인 2년산 참돔을 동합금망과 합성섬유망에서 9개월 동안 각각 사육관리하면서 참돔에 미치는 양식생물학적 및 병리학적 영향을 조사하여 생물학적 안전성을 평가하였다. 동합금가두리망은 지름 25 m, 깊이 10 m의 규모로 제작하여 경남 통영시 욕지면 주변해역 연구교습어장에 설치하였다. 동합금가두리망과 합성섬유망에서 사육한 참돔의 생존율은 각각 99.75%와 99.70%로 유의한 차이는 나타나지 않았다. 일일체중성장률은 동합금가두리망과 합성섬유망에서 각각 2.13 g/day와 1.65 g/day로 동합금가두리망에서 사육한 참돔의 성장률이 빠른 것으로 나타났다. 혈액성분 분석에 따른 건강도 평가는 합성섬유망에 비해 동합금가두리망에서 양호한 것으로 나타났다. 가두리망 종류별 사육중인 참돔의 구리와 아연의 축적은 다른 기관에 비해 생식소에서 축적이 높게 나타났으며, 가용부분인 근육내 중금속 축적은 허용기준치에 비해 매우 낮았다. 또한 병원체 감염조사결과 동합금가두리망과 합성섬유망에서 기생충은 Microcotyle tai, 세균은 Vibrio alginolyticus 외 5종 등이 공통적으로 관찰되었으나, 동합금가두리망에서 Vibrio속의 세균 감염률에는 다소 차이를 보여 항균작용이 있을 것으로 예상된다. 따라서 동합금가두리망의 시스템안정성, 재활용가능성, 항균성 및 식품안전성 등을 고려할 때 어류양식용 가두리로 현장에서 활용이 가능할 것으로 여겨진다.

• 수산해양기술연구
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• 제35권1호
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• pp.19-24
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• 1999

This paper is second part on the auditory thresholds and fish behaviors to the underwater sounds for luring of target species at the set-net in the coast of Cheju. In order to obtain the critical ratio of yellow tails(Seriola quinqueradiata) and the emission level of underwater sound for luring of them, we make experiments to measure the auditory threshold of them using conditioning with electric shock. In state that the white noise with 10dB higher sound pressure level than ambient noise is emitted, the auditory thresholds of yellow tails are measured with 100~116.5dB and they are higher than those in state of no emission of white noise by the masking effects of it. Although sound pressure level of background noise go down, the auditory thresholds go up with frequency above than 300Hz.The critical ratio of yellow-tails in frequency of 80Hz, 100Hz, 200Hz, 500Hz, 800Hz are 46dB, 40dB, 50dB, 52dB, 60dB, 70dB respectively. The sound pressure level of which the signal sound is recognized by yellow tails under the ambient noise is above 100dB and the critical ratio of them is above 40dB.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2007년 가을학술발표회
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• pp.558-569
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• 2007

It is important to obtain real-time data from long-term monitoring of landfills and develop leachate leakage detection system for the integrated management of landfills. A novel real time monitoring system and early leakage detection system was suggested in this study. The suggested monitoring system is composed of two parts; (1) a set of moisture sensors which monitor the areas surrounding the landfill, and (2) a set of moisture and temperature sensors which monitor the landfill inside. For the assessment for landfills stabilization, real-time monitoring system was evaluated in dry and wet cell of pilot-site. In addition, the grid-net electrical conductivity measurement system was also suggested as early leakage detection system. In this study, the field applicability of suggested systems was evaluated through pilot-scale field tests. The results of pilot-scale field model tests indicate that the grid-net electrical conductivity measurement method can be applicable to the detection of landfill leachate at the initial stage of intrusion, and thus has a potential for monitoring leachate leakage at waste landfills.

• 한국컴퓨터정보학회논문지
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• 제8권4호
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• pp.176-185
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• 2003

SCM은 제품생산을 위한 프로세스를 부품조달에서 생산계획, 납품, 재고관리 등을 효율적으로 처리할 수 있는 관리 솔루션이다. SCM은 그 기본이 공급망 전체를 보고 최대의 효율을 목표로 프로세스를 꾸준히 혁신하는 활동이다. 여기에는 조직, 예산, 책임과 권한이 재조정되고 설정돼야 하기 때문에 어떤 접근방법으로 구축하는 냐에 따라 성패를 좌우한다. 이에 SCM통합전략과 추진방법으로 통합모형과 시스템의 구성요소, 정보기반기술, 응용기술, 추진모델에 의한 구축방안을 제안하였다. 이러한 방안에 접근하기 위해서는 무엇보다 기업의 공급망상의 보완점과 어떤 부분이 비효율성인가를 판별해야 하고 둘째, 공급사슬의 미래비젼과 목표를 설정하여 무엇이 성공적인 공급망을 좌우하는가를 고려한다. 셋째, 현재의 공급망과 미래공급망사이의 갭을 없애기 위한 조치를 도출한다. 넷째, 위의 결과로서 기업의 일치된 공급망전략의 통합모형 및 구축모델에 대한 대안이 도출되어야 할 것이다.