• 융합보안논문지
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• 제22권1호
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• pp.81-92
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• 2022

빠르게 성장하고 있는 IoT 시장은 일반 가정에서뿐만 아니라 스마트홈이나 스마트시티까지 확대되고 있다. IoT에서 사용하는 주요 프로토콜 중 ZigBee는 스마트홈의 도어락 시장에서 90% 이상 차지하고 있고 소형화된 센서 디바이스에서 주로 사용하고 있어 프로토콜의 안전성이 매우 중요하다. 하지만, ZigBee를 사용하는 디바이스가 네트워크에 연결되는 인증과정에서 고정된 키를 사용하고 있어 전방향 안전성을 만족하지 못하고 있고, 최근에 개발한 ZigBee 3.0에서도 해결되지 못하였다. 본 논문에서는 ZigBee 인증 프로토콜에 전방향 안전성을 제공함과 동시에 기존 프로토콜에서도 빠르게 적용할 수 있는 설계방법을 제안한다. 제안하는 개선된 ZigBee 인증 프로토콜은 IoT에서 연산량이 적고 전방향 안전성을 제공하는 ECDH를 적용하기 위해 최근 개발된 OWE 프로토콜을 분석 및 적용하였다. 이를 바탕으로 ZigBee 인증 프로토콜의 안전성을 제공하며, 별도의 인증서나 패스워드 입력이 필요하지 않아 사용자의 편의성 또한 제공할 수 있을 것으로 본다.

• Korean Journal of Radiology
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• 제22권4호
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• 한국컴퓨터정보학회논문지
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• 제29권1호
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• pp.31-40
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• 2024

국내 온라인 패션 플랫폼은 개인사업자가 제품정보를 직접 등록하기 때문에 개인사업자의 불편함을 초래한다. 많은 제품군을 한꺼번에 수동 등록하므로 수기 입력된 제품정보로 인한 신뢰성 문제가 발생한다. 등록된 상품 이미지의 저품질 및 데이터 수의 불균형으로 인한 편향도 심각하게 제기된다. 본 연구는 오버샘플링 기법을 통해 데이터 편향을 최소화하고 13개 패션 카테고리의 다중 분류를 수행하는 ResNet50 모델을 제안한다. 컴퓨팅 자원과 오랜 학습시간을 최소화하기 위해 전이학습을 활용했다. 결과적으로, 데이터 수가 매우 부족했던 클래스의 데이터 증강을 통해 기본 CNN 모델에 비해 최대 33.4%의 향상된 식별력을 보여주었다. 모든 결과의 신뢰성은 정밀도-재현율 곡선으로 보장한다. 본 연구는 국내 온라인 패션 플랫폼 산업의 발전을 한 단계 끌어올릴 수 있을 것으로 기대한다.

• Computers and Concrete
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• 제33권1호
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• 대한치매학회지
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• 제22권2호
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• pp.61-68
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• 2023

Background and Purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

• 한국간담췌외과학회지
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• 제28권1호
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• pp.14-24
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• 2024

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

• 폴리머
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• 제30권1호
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• pp.85-89
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• 2006

이축압출기를 이용한 용융혼련으로 제조한 기상성장 탄소섬유(Vapor Grown Carbon Fiber, VGCF) 충전 폴리페닐렌설파이드(polyphenylene sulfide, PPS) 복합체외 VGCF 함량에 따른 전기적, 유변학적 특성을 살펴보았다. 복합체의 파단면 모폴로지 관찰결과, 본 방법은 PPS 매트릭스 내에 VGCF를 균일하게 분산시키는데 있어서 효과적임을 확인할 수 있었다. $5\;wt\%$, VGCF 혼입까지는 미충전 PPS와 거의 유사한 전기적 성질과 유변학적 거동을 보였으며 $10\;wt\%$로 VGCF의 혼입양을 증가시켰을 때 현저한 도전성 발현 및 점도 상승, 탄성률의 주파수 무의존성 등 유변학절 성질의 변동이 관찰되었다. 고충전 PPS계에서의 탄성률의 주파수 무의존성은 복합체 내에서의 VGCF의 네트워크 형성으로 인한 건으로 추정되며, 이는 전기적 성질뿐만 아니라 유변학적 성질의 측정결과로부터 복합체 내의 도전성 네트워크의 형성을 확인할 수 있음을 보여준다.

• Journal of Chest Surgery
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• 제45권4호
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• pp.236-241
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• 2012

Background: Prolonged usage of extracorporeal membrane oxygenation (ECMO) may induce multi-organ failure. This study is aimed to evaluate prognostic factors in the patients with ECMO. Also, the prognosis of ECMO with Kidney Injury Network Scoring system is studied. Materials and Methods: From May 2005 to July 2011, 172 cases of ECMO were performed. The cases of perioperative use of ECMO were excluded. Renal failure patient and younger than 15 years old one were also excluded. As a result, 26 cases were enrolled in this study. Male patients were 15 (57.7%), and mean age was $56.57{\pm}17.03$ years old. Demographic data, ECMO parameters, weaning from ECMO, and application of continuous renal replacement therapy are collected and Acute Kidney Injury Network (AKIN) scores were evaluated just before ECMO and day 1, day 2 during application of ECMO. Results: Venoarterial ECMO was applied in 22 cases (84.6%). The reasons for applications of ECMO were cardiac origin in 21 (80.8%), acute respiratory distress syndrome in 4, and septic shock in 1 case. Successful weaning from ECMO was achieved in 15 cases (57.7%), and survival discharge rate was 9 cases (34.6%). Mean duration of application of ECMO was $111.39{\pm}54.06$ hours. In univariate analysis, myocarditis was independent risk factors on weaning failure. Using the receiver operating characteristic curve, level of hemoglobin on 24 hours after ECMO, and base excess on 48 hours after ECMO were showed more than 0.7. AKIN score was not matched the prognosis of the patients with ECMO. Conclusion: In our study, the prognosis of the patients with myocarditis was poor. Hemoglobin level at first 24 hours, and degree of acidosis at 48 hours were useful methods in relating with prognosis of ECMO. AKIN scoring system was not related with the prognosis of the patients. Further study for prognosis and organ injury during application ECMO may be needed.

• 한국구조물진단유지관리공학회 논문집
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• 제15권3호
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• pp.116-124
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• 2011

대부분의 구조물 안전성 평가에 있어서 전체적인 거동을 나타내는 인자, 즉 기하학적인 형상 변화를 추정하는 것은 매우 중요하다. 종래에는 현장에서 교량의 처짐을 손쉽게 측정할 수 있는 적절한 수단과 방법의 부재로 말미암아, 처짐의 측정이 제한된 측정점에 국한되었고, 또한 변위계를 설치한 개소에 한정되었다. 따라서, 본 연구에서는 USN(Ubiquitous Sensor Network) 기반의 무선 경사센서모듈(Wireless Tiltmeter)을 통해 건설구조물의 처짐을 추정하는 방법을 개발하고, 기존의 변위 측정 자기 센서(Linear Variable Differential Transformer: LVDT)를 이용해 측정하는 기술 대신, 유비쿼터스 개념의 무선 경사 센서 모듈의 경사 변화에 따른 저항의 변화를 전압의 형식으로 출력하고, 교정계수를 이용하여 실제 처짐각 및 처짐으로 환산하여 최대 처짐을 구하도록 개발된 유비쿼터스 기반의 처짐 추정방법을 검증하기 위하여 실내 실험을 수행하였고, 그 결과, 측정점에 상관없이 균일한 측정이 가능하고, 기존의 방법과 거의 일치하는 값을 나타내는 것으로 확인되었다.

• 대한치과교정학회지
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• 제52권1호
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• pp.3-19
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• 2022

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.