• Geomechanics and Engineering
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• 제37권1호
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• ETRI Journal
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• 제46권2호
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• Korean Journal of Radiology
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• 제20권4호
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• pp.649-661
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• 2019

Objective: To systematically review the evaluation of the diagnostic accuracy of pre-treatment apparent diffusion coefficient (ADC) and change in ADC during the intra- or post-treatment period, for the prediction of locoregional failure in patients with head and neck squamous cell carcinoma (HNSCC). Materials and Methods: Ovid-MEDLINE and Embase databases were searched up to September 8, 2018, for studies on the use of diffusion-weighted magnetic resonance imaging for the prediction of locoregional treatment response in patients with HNSCC treated with chemoradiation or radiation therapy. Risk of bias was assessed by using the Quality Assessment Tool for Diagnostic Accuracy Studies-2. Results: Twelve studies were included in the systematic review, and diagnostic accuracy assessment was performed using seven studies. High pre-treatment ADC showed inconsistent results with the tendency for locoregional failure, whereas all studies evaluating changes in ADC showed consistent results of a lower rise in ADC in patients with locoregional failure compared to those with locoregional control. The sensitivities and specificities of pre-treatment ADC and change in ADC for predicting locoregional failure were relatively high (range: 50-100% and 79-96%, 75-100% and 69-95%, respectively). Meta-analytic pooling was not performed due to the apparent heterogeneity in these values. Conclusion: High pre-treatment ADC and low rise in early intra-treatment or post-treatment ADC with chemoradiation, could be indicators of locoregional failure in patients with HNSCC. However, as the studies are few, heterogeneous, and at high risk for bias, the sensitivity and specificity of these parameters for predicting the treatment response are yet to be determined.

• Korean Journal of Radiology
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• 제20권6호
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• pp.880-893
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• 2019

Objective: To assess the technical performance of two-dimensional shear wave elastography (2D-SWE) for measuring liver stiffness. Materials and Methods: The Ovid-MEDLINE and EMBASE databases were searched for studies reporting the technical performance of 2D-SWE, including concerns with technical failures, unreliable measurements, interobserver reliability, and/or intraobserver reliability, published until June 30, 2018. The pooled proportion of technical failure and unreliable measurements was calculated using meta-analytic pooling via the random-effects model and inverse variance method for calculating weights. Subgroup analyses were performed to explore potential causes of heterogeneity. The pooled intraclass correlation coefficients (ICCs) for interobserver and intraobserver reliability were calculated using the Hedges-Olkin method with Fisher's Z transformation of the correlation coefficient. Results: The search yielded 34 articles. From 20 2D-SWE studies including 6196 patients, the pooled proportion of technical failure was 2.3% (95% confidence interval [CI], 1.3-3.9%). The pooled proportion of unreliable measurements from 20 studies including 6961 patients was 7.5% (95% CI, 4.7-11.7%). In the subgroup analyses, studies conducting more than three measurements showed fewer unreliable measurements than did those with three measurements or less, but no intergroup difference was found in technical failure. The pooled ICCs for interobserver reliability (from 10 studies including 517 patients) and intraobserver reliability (from 7 studies including 679 patients) were 0.87 (95% CI, 0.82-0.90) and 0.93 (95% CI, 0.89-0.95), respectively, suggesting good to excellent reliability. Conclusion: 2D-SWE shows good technical performance for assessing liver stiffness, with high technical success and reliability. Future studies should establish the quality criteria and optimal number of measurements.

• 대한수의학회지
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• 제23권2호
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• pp.137-148
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• 1983

This study was made for the better information of the male reproductive system on the meat-type drake, Cherry Belly X White Golden. The epithelium of ductules of epididymal region and deferent duct were observed histologically and histochemically with the progress of their development. India-ink absorbability on the luminal epithelium was also investigated after the administration of India-ink. The results are as follows; 1. Rete testis and various round ductules in immature form appeared in epididymis within 6 weeks after hatching, and simple cuboidal and simple columnar epithelium were found in the epithelia of the ductules within 8 weeks after hatching. Larger ductules were found on epididymal surface which was in the developing stage near to the immature efferent ductule. From 10th to 20th week, various ductules appeared in epididymis, and developing form of efferent ductules were much more increased on epididymal surface. The luminal epithelium of the ductules were composed of ciliated simple columnar and pseudostratified ciliated columnar cells. At the same time, deferent duct appeared. From the 21th week, various ductules in epididymis became abruptly matured. Lumen of rete testis was lined by simple squamous or simple cuboidal epithelium, and that of efferent ductules, having many folds and being larger than any others were lined by pseudostratified ciliated columnar epithelium in which ciliated columnar cells, non-ciliated cells(clear cells) and basal cells were noted. Connecting tubules of star shaped lumen were composed of pseudostratified ciliated columnar epithelium in which ciliated columnar cells, nonciliated cells, and basal cells were observed. The luminal surface of epididymal ducts was smooth and has thick pseudostratified columnar epithelium which was composed of high columnar cells and basal cells. From 26th week after hatching, sperm pooling was started in various ductules. 2. From 4th to 10th week, simple cuboidal epithelium of deferent duct transformed to simple columnar epithelium with the progress of aging. At the basement of epithelium, clear round cells were noted. From 12th to 20th week, high columnar cells with enlongated nucleus were noted on the luminal border of deferent ducts, forming folds of pseuclostratified columnar epithelium. From 20th week, the deferent duct started to have septa in it's lumen and composed mainly of pseudostratified columnar epithelium, and round cells disappeared. From 20th week, the lumen diameter of deferent duct became wider with the progress of aging, but there was no difference among the values of lumen diameter in upper, middle, and lower part of deferent ducts. At 26th week, the pooling period of sperms in deferent ducts, the lumen diameter became rapidly widen, especially in the lower part of deferent ducts. Thickness of muscular layer of ductus deferens showed gradual growth within 24 weeks but did abrupt thickening from 26th week. 3. Saliva resistant PAS granules were dotted on the top of nucleus in efferent ductules epithelium but the amount of the granules were little in the connecting ductules's epithelium. The granules reactive to acid phosphatase were abundant in the some epithelial cells of efferent ductules and connecting ductules, especially above the nucleus of cells. The granules reactive to alkaline phosphatase were noted on the luminal border of efferent ductules. Parts of free border of efferent ductules and middle portion of deferent ducts were stained slightly by alcian blue technique. India ink granules were found mainly in the epithelium of efferent ductules but were few in that of connecting ductules.

• 전기전자학회논문지
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• 제25권3호
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• pp.493-500
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• 2021

본 논문에서는 다각형 용기의 품질 향상을 위한 딥러닝 구조 개발을 제안한다. 딥러닝 구조는 convolution 층, bottleneck 층, fully connect 층, softmax 층 등으로 구성된다. Convolution 층은 입력 이미지 또는 이전 층의 특징 이미지를 여러 특징 필터와 convolution 3x3 연산하여 특징 이미지를 얻어 내는 층이다. Bottleneck 층은 convolution 층을 통해 추출된 특징 이미지상의 특징들 중에서 최적의 특징들만 선별하여 convolution 1x1 ReLU로 채널을 감소시키고convolution 3x3 ReLU를 실시한다. Bottleneck 층을 거친 후에 수행되는 global average pooling 연산과정은 convolution 층을 통해 추출된 특징 이미지의 특징들 중에서 최적의 특징들만 선별하여 특징 이미지의 크기를 감소시킨다. Fully connect 층은 6개의 fully connect layer를 거쳐 출력 데이터가 산출된다. Softmax 층은 입력층 노드의 값과 연산을 진행하려는 목표 노드 사이의 가중치와 곱을 하여 합하고 활성화 함수를 통해 0~1 사이의 값으로 변환한다. 학습이 완료된 후에 인식 과정에서는 학습 과정과 마찬가지로 카메라를 이용한 이미지 획득, 측정 위치 검출, 딥러닝을 활용한 비원형 유리병 분류 등을 수행하여 비원형 유리병을 분류한다. 제안된 다각형 용기의 품질 향상을 위한 딥러닝 구조의 성능을 평가하기 위하여 공인시험기관에서 실험한 결과, 양품/불량 판별 정확도 99%로 세계최고 수준과 동일한 수준으로 산출되었다. 검사 소요 시간은 평균 1.7초로 비원형 머신비전 시스템을 사용하는 생산 공정의 가동 시간 기준 내로 산출되었다. 따라서 본 본문에서 제안한 다각형 용기의 품질 향상을 위한 딥러닝 구조의 성능의 그 효용성이 입증되었다.

• 환경생물
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• 제40권2호
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• pp.164-171
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• 2022

꽃노랑총채벌레(Frankliniella occidentalis)는 500종 이상의 기주를 가지고 토마토반점위조바이러스(Tomato spotted wilt virus; TSWV)를 매개하는 해충이다. 전 세계적으로 방제를 위해 노력하고 있지만 살충제를 이용한 방제는 저항성 그리고 환경 및 경제적 부담으로 인한 한계를 보였기 때문에 고정 정확도를 설정한 표본조사법(Fixed-precision level sampling plan)을 개발하였다. 고추(Capsicum annuum)의 꽃노랑총채벌레 성충 방제를 위한 표본 조사법은 공간분포분석, 표본추출 정지선 그리고 의사결정법으로 구성되었다. 표본추출은 식물체를 상단(지상에서 180 cm 이상), 중단(지상에서 120~160 cm 이상), 하단(지상에서 70~110 cm 이상)으로 나누어 각 높이별로 꽃 3개에서 나오는 꽃노랑총채벌레의 성충의 마리 수를 조사하였다. 표본 추출을 통해 꽃노랑총채벌레 성충의 밀도는 다른 식물체 위치(중단, 하단)보다 상단에서 높은 것으로 나왔다. 공간분포분석에서는 Taylor's power law (TPL)를 통해 도출한 각 위치별 계수를 공분산분석(ANCOVA)하여 차이를 비교하였다. ANCOVA 결과에서 도출된 절편과 기울기의 P 값이 각각 0.94, 0.87인 것을 통해 식물체 내 위치별로 차이가 없음을 확인한 후, 자료를 통합(pooling)하여 계산된 TPL 계수를 이용하여 표본추출 정지선을 구하였다. 꽃노랑총채벌레의 방제의사결정을 위한 방제밀도 수준(m₀)은 문헌을 참조하여 3과 18로 설정하였으며 설정값(m₀)을 이용해 최대표본수(N_max)도 조사하였다. 조사 결과, m₀=3, 18일 때 N_max값은 각각 약 97개, 1149개로 계산되었다. 개발된 모델의 적합성 검정을 위해 분석에 사용하지 않은 독립자료를 이용해 Resampling Validation for Sampling Program (RVSP) 프로그램으로 개발된 표본추출법의 적합성 평가를 실시하였고 적합한 정확도를 보이는 것으로 조사되었다.

• 지능정보연구
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• 제24권1호
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• pp.167-181
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• 2018

지난 10여 년간 딥러닝(Deep Learning)은 다양한 기계학습 알고리즘 중에서 많은 주목을 받아 왔다. 특히 이미지를 인식하고 분류하는데 효과적인 알고리즘으로 알려져 있는 합성곱 신경망(Convolutional Neural Network, CNN)은 여러 분야의 분류 및 예측 문제에 널리 응용되고 있다. 본 연구에서는 기계학습 연구에서 가장 어려운 예측 문제 중 하나인 주식시장 예측에 합성곱 신경망을 적용하고자 한다. 구체적으로 본 연구에서는 그래프를 입력값으로 사용하여 주식시장의 방향(상승 또는 하락)을 예측하는 이진분류기로써 합성곱 신경망을 적용하였다. 이는 그래프를 보고 주가지수가 오를 것인지 내릴 것인지에 대해 경향을 예측하는 이른바 기술적 분석가를 모방하는 기계학습 알고리즘을 개발하는 과제라 할 수 있다. 본 연구는 크게 다음의 네 단계로 수행된다. 첫 번째 단계에서는 데이터 세트를 5일 단위로 나눈다. 두 번째 단계에서는 5일 단위로 나눈 데이터에 대하여 그래프를 만든다. 세 번째 단계에서는 이전 단계에서 생성된 그래프를 사용하여 학습용과 검증용 데이터 세트를 나누고 합성곱 신경망 분류기를 학습시킨다. 네 번째 단계에서는 검증용 데이터 세트를 사용하여 다른 분류 모형들과 성과를 비교한다. 제안한 모델의 유효성을 검증하기 위해 2009년 1월부터 2017년 2월까지의 약 8년간의 KOSPI200 데이터 2,026건의 실험 데이터를 사용하였다. 실험 데이터 세트는 CCI, 모멘텀, ROC 등 한국 주식시장에서 사용하는 대표적인 기술지표 12개로 구성되었다. 결과적으로 실험 데이터 세트에 합성곱 신경망 알고리즘을 적용하였을 때 로지스틱회귀모형, 단일계층신경망, SVM과 비교하여 제안모형인 CNN이 통계적으로 유의한 수준의 예측 정확도를 나타냈다.

• 한국임상수의학회지
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• 제24권4호
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• pp.603-607
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• 2007

계군 수준에서 닭 전염성 기관지염(IBV)을 검출하는데 필요한 표본크기를 추정하기 위하여 강원도 충북 및 충남 지역의 총 9,980수의 산란계로부터 회수된 총 48회의 혈청시료를 사용하였다. 의뢰된 모든 혈청에 대해서는 개별 시료와 크기가 10인 합병혈청(pool)으로 구분하여 HI 역가를 측정하였다. 적어도 1개의 감염된 pool을 검출하는 것을 95%신뢰하기 위해서는 총 48회의 의뢰건 중 5개 이하의 pool이 요구되는 비율이 72.9%를 차지하였고 90% 신뢰수준에서는 77.1%로 나타났다. 전체적으로 볼 때 필요한 pool의 개수는 양성 pool의 개수가 증가할수록 감소하였다. 개별시료에서 양성판정을 위한 HI 역가의 기준을 9 이상과 10 이상으로 설정할 때 혈청 유병율은 각각 50.1%와 33.4%로 나타났으며, 합병혈청에 대한 양성 판정기준을 8 이상으로 설정할 경우 59.9%로 분석되었다. 매 의뢰된 시료에서 개별시료와 합병혈청 간의 상관계수는 판정기준 9 이상에서 0.592(p<0.001), 10 이상에서 0.561(p<0.001)로 두 상관계수간 차이가 없었고 공통상관계수는 0.576으로 나타났다. 이러한 결과에 근거할 때 IBV 감염증을 검출하기 위하여 합병혈청을 사용하는 전략은 조사의 목적이 계군의 감염여부에만 관심을 두는 경우 한가지 대안이 될 것으로 사료된다.

• Archives of Plastic Surgery
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• 제45권5호
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• pp.449-457
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• 2018

Background Although previous studies have focused on determining prognostic and causative variables associated with fistula-related complications after head and neck reconstructive surgery, only a few studies have addressed preventive measures. Noting that pooled saliva complicates wound healing and precipitates fistula-related complications, we devised a continuous suction system to remove saliva during early postoperative recovery. Methods A continuous suction system was implemented in 20 patients after head and neck reconstructive surgery between January 2012 and October 2017. This group was compared to a control group of 16 patients at the same institution. The system was placed orally when the lesion was on the anterior side of the retromolar trigone area, and when glossectomy or resection of the mouth floor was performed. When the orohypopharynx and/or larynx were eradicated, the irrigation system was placed in the pharyngeal area. Results The mean follow-up period was $9.2{\pm}2.4$ months. The Hemovac system was applied for an average of 7.5 days. On average, 6.5 days were needed for the net drain output to fall below 10 mL. Complications were analyzed according to their causes and rates. A fistula occurred in two cases in the suction group. Compared to the control group, a significant difference was noted in the surgical site infection rate (P<0.031). Conclusions Clinical observations showed reduced saliva pooling and a reduction in the infection rate. This resulted in improved wound healing through the application of a continuous suction system.