• 인터넷정보학회논문지
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• 제11권1호
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• pp.153-165
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• 2010

본 논문에서는 분산응용의 관점에서 네트워크 상에 응용을 구성하는 분산된 객체들을 효율적으로 관리하는 분산객체그룹 프레임워크를 기반으로 사용자 맞춤형 분산응용 서비스를 제공하는 프로액티브응용서비스엔진을 제안한다. 본 엔진은 물리계층, 미들웨어 계층, 응용 계층으로 구성되며, 사용자의 요청에 의해 하드웨어 기기로부터 수집된 데이터 및 응용을 구성하는 객체의 속성정보를 그룹으로 관리하는 그룹서비스와 수집된 데이터 및 객체에 대한 사용자의 권한별 접근을 관리하는 보안서비스, 수집된 데이터를 추출 및 가공하여 응용에 제공하는 필터링서비스, 과거의 데이터를 이용한 통계서비스, 수집된 데이터를 토대로 현재의 운영 상태를 진단하는 진단서비스, 통계서비스와 진단서비스를 통해 미래의 발생 가능한 상황을 예측하기 위한 예측서비스를 제공한다. 최종적으로 엔진이 제공하는 서비스의 수행성을 검증하기 위하여 유비쿼터스 농업 분야의 온실 자동제어 응용에 적용하여 결과를 확인했다.

• Investigative Magnetic Resonance Imaging
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• 제26권2호
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• pp.135-149
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• 2022

Purpose: To assess clinically significant imaging findings of malignant intramammary lymph nodes (IMLNs) in breast cancer patients and to evaluate their diagnostic performance in predicting malignant IMLN. Materials and Methods: A total of 110 cases with IMLN of BI-RADS category 3 or more, not typical benign IMLN, in MR of breast cancer patients between January 2016 and January 2021 were retrospectively reviewed. After excluding 33 cases, 77 cases were finally included. Among them, 58 and 19 were confirmed as benign and malignant, respectively. Qualitative and quantitative MR imaging features of the IMLN were retrospectively analyzed. Sizes and final assessment categories of IMLN on MRI, mammography, and ultrasound were reviewed. Diagnostic performances of imaging features on MRI, mammography, and ultrasound were then evaluated. Results: For qualitative MR features, shape, margin, and preserved central hilum were significantly different between benign and malignant groups (P < 0.05). For quantitative MR features, long diameter over 6 mm, short diameter over 4 mm, and cortical thickening over 3 mm showed high sensitivities in predicting malignant IMLNs (89.5%, 94.7%, and 100%, respectively). Size exceeding 1 cm showed high specificity and accuracy in predicting malignant IMLN on MR, mammography, and ultrasound (91.4% and 80.5%; 96.6% and 79.25; 98.3% and 80.5%, respectively). Conclusion: Various MR imaging features and size can be helpful for predicting malignant IMLN in breast cancer patients.

• 대기
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• 제31권5호
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• pp.473-488
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• 2021

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Design & Manufacturing
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• 제16권4호
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Journal of Korean Neurosurgical Society
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• 제67권1호
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• pp.94-102
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• 2024

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

• 한국광학회지
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• 제11권6호
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• pp.452-456
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• 2000

동맥혈의 맥동성분에 의한 파장별 광흡수도를 측정하여 비침습적으로 산소포화도 값을 알수 있는 펄스옥시미터 장치와 신호처리방법을 개발하고 예측 알고리즘을 적용하였다. 본 장치는 광원 및 검출기로 구성된 프로브와 광신호 처리부, LED 구동회로 PC 인터페이스부로 구성되었고 데이터의 수집을 위한 구동소프트웨어 및 데이터 처리 소프트웨어를 개발하였다. 개발된 산소포화도 측정장치의 성능을 평가하는데에는 Bio-Tek 사의 펄스 옥시미터 시뮬레이터를 사용하여 다양한 알고리즘 및 데이터처리 방법들을 비교분석한 결과 맥동파형의 $In(I_p/I_v) 값을 I_{avr}$값으로 보정하는 계산 알고리즘의 방법과 진폭비보다 면적비를 이용한 계산방법이 산소포화도와의 상관관계가 우수한 것으로 나타났다. 정확한 신호 획득을 위해 개발된 맥동의 기저선 보상처리 프로그램을 inv-vivo 테스트의 데이터 처리방법에 적용하여 결과가 향상되는 것을 확인하였고 광원으로 660nm(Red)와 805nm(IR)파장을 이용한 경우보다 660nm(Red)와 940nm(IR) 파장을 이용했을 때 산소포화도와의 상관관계 및 정밀도에서 더 우수한 결과를 얻을 수 있었다.

• 대한치과교정학회지
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• 제27권3호
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• pp.473-479
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• 1997

성장기 환자의 치료 계획 수립시 치아 이동만으로 치료를 할 것인지, 악 정형 치료를 동반할 것인지, 아니면 성장후 수술을 할 것인지를 결정하는 일은 쉬운 일이 아니며 특히, 악골의 성장을 예측하는 과정을 치료계획 수립에 매우 중요한 부분이다. 전두동은 조기에 성장이 완료되고 하악골은 20세까지 성장을 지속한다는 점에 착안하여, 측모 두부방사선 사진상에 나타나는 전두동의 크기와 하악골 크기간에 상관성을 알아보기 위해 228명을 골격선 제I급, 제II급, 제III급 부정교합의 3군으로 분류하고 하악골의 장경과 악골의 전후방 관계를 나타내는 3가지 지수(ANB, APDI, Wits)를 측정하여, 서로간의 상관성을 검토한 결과 다음의 결론을 얻었다. 1. 전두동의 크기와 ANB(-0.3633), APDI(0.296), Wits(-0.2380), 하악골 장경(0.2704)은 높은 상관성을 (p<0.001) 보였다. 2. 골격성 제III급 부정교합군에서, 측모두부 방사선 사진상에 나타나는 전두동의 크기가 골격성 제 I 급 부정교합군이나 II 급 부정교합군보다 크게 나타났다.

• Clinical and Experimental Pediatrics
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• 제59권3호
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• pp.139-144
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• 2016

Purpose: Acute pyelonephritis (APN) is a serious bacterial infection that can cause renal scarring in children. Early identification of APN is critical to improve treatment outcomes. The neutrophil-lymphocyte ratio (NLR) is a prognostic marker of many diseases, but it has not yet been established in urinary tract infection (UTI). The aim of this study was to determine whether NLR is a useful marker to predict APN or vesicoureteral reflux (VUR). Methods: We retrospectively evaluated 298 pediatric patients ($age{\leq}36months$) with febrile UTI from January 2010 to December 2014. Conventional infection markers (white blood cell [WBC] count, erythrocyte sedimentation rate [ESR], C-reactive protein [CRP]), and NLR were measured. Results: WBC, CRP, ESR, and NLR were higher in APN than in lower UTI (P<0.001). Multiple logistic regression analyses showed that NLR was a predictive factor for positive dimercaptosuccinic acid (DMSA) defects (P<0.001). The area under the receiver operating characteristic (ROC) curve was high for NLR (P<0.001) as well as CRP (P<0.001) for prediction of DMSA defects. NLR showed the highest area under the ROC curve for diagnosis of VUR (P<0.001). Conclusion: NLR can be used as a diagnostic marker of APN with DMSA defect, showing better results than those of conventional markers for VUR prediction.