• 해양환경안전학회지
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• 제14권1호
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• pp.1-7
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• 2008

1999년 6월 한국 남동 연안역의 울산과 감포 앞바다에 출현한 용승에 의한 냉수출현현상을 3차원 진단 수치모델을 이용하여 조사하였다. 수치실험에 의한 냉수출현은 바람 영향으로 인하여 울산-감포 앞바다의 수심 50-100m 층에서 발생한 상승류의 효과로 나타났다. 이러한 용승현상은 인접한 부산, 울산 및 감포에서 관측한 바람의 2배인 5.0/m/sec 크기의 바람을 모델에 적용했을 때 발생했다. 따라서 용승현상과 같은 특이한 해양현상을 규명하기 위해서는 육지에서 관측한 자료가 아닌 그 해역에 적절한 바람자료를 이용해야 한다.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제52권1호
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• pp.42-46
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• 2003

Arresters are deteriorated by overvoltages or impulse currents, and the resistive leakage current of arresters increases as the deterioration of the arrester progresses, showing an increase in the 3$^{rd}$ harmonic component of the leakage current. In this reason, arrester diagnostic techniques based on the 3$^{rd}$ harmonic leakage current as a reference parameter of deterioration are widely used. The technique, however, includes an error due to the harmonics of power system voltage. Therefore, the influence of the harmonics on arrester diagnostics should be considered. In this paper, we designed a PSpice ZnO arrester model to simulate the influence of the voltage harmonics described above. A pure sinusoidal voltage and its the 3r harmonic voltage were applied to the model, and the leakage current components were analyzed. From the simulation results, it is confirmed that the peak value of resistive leakage current depends not only on the phase of the 3$^{rd}$ harmonic voltage but also on the magnitude of it. Consequently, the errors caused 1)y the harmonic voltage could be minimized by correcting the magnitude of leakage current upon analyzing the harmonics.cs.

• Journal of Radiation Protection and Research
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• 제49권1호
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• pp.1-18
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• 2024

Exponential growth has been observed in nuclear medicine procedures worldwide in the past decades. The considerable increase is attributed to the advance of positron emission tomography and single photon emission computed tomography, as well as the introduction of new radiopharmaceuticals. Although nuclear medicine procedures provide undisputable diagnostic and therapeutic benefits to patients, the substantial increase in radiation exposure to nuclear medicine patients raises concerns about potential adverse health effects and calls for the urgent need to monitor exposure levels. In the current article, model-based internal dosimetry methods were reviewed, focusing on Medical Internal Radiation Dose (MIRD) formalism, biokinetic data, human anatomy models (stylized, voxel, and hybrid computational human phantoms), and energy spectrum data of radionuclides. Key results from many articles on nuclear medicine dosimetry and comparisons of dosimetry quantities based on different types of human anatomy models were summarized. Key characteristics of seven model-based dose calculation tools were tabulated and discussed, including dose quantities, computational human phantoms used for dose calculations, decay data for radionuclides, biokinetic data, and user interface. Lastly, future research needs in nuclear medicine dosimetry were discussed. Model-based internal dosimetry methods were reviewed focusing on MIRD formalism, biokinetic data, human anatomy models, and energy spectrum data of radionuclides. Future research should focus on updating biokinetic data, revising energy transfer quantities for alimentary and gastrointestinal tracts, accounting for body size in nuclear medicine dosimetry, and recalculating dose coefficients based on the latest biokinetic and energy transfer data.

• Asian Pacific Journal of Cancer Prevention
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• 제16권2호
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• pp.707-712
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• 2015

Background: To evaluate use of magnetic resonance imaging (MRI) and a logistic model including risk factors for lymph node metastasis for improved diagnosis. Materials and Methods: The subjects were 176 patients with rectal cancer who underwent preoperative MRI. The longest lymph node diameter was measured and a cut-off value for positive lymph node metastasis was established based on a receiver operating characteristic (ROC) curve. A logistic model was constructed based on MRI findings and risk factors for lymph node metastasis extracted from logistic-regression analysis. The diagnostic capabilities of MRI alone and those of the logistic model were compared using the area under the curve (AUC) of the ROC curve. Results: The cut-off value was a diameter of 5.47 mm. Diagnosis using MRI had an accuracy of 65.9%, sensitivity 73.5%, specificity 61.3%, positive predictive value (PPV) 62.9%, and negative predictive value (NPV) 72.2% [AUC: 0.6739 (95%CI: 0.6016-0.7388)]. Age (<59) (p=0.0163), pT (T3+T4) (p=0.0001), and BMI (<23.5) (p=0.0003) were extracted as independent risk factors for lymph node metastasis. Diagnosis using MRI with the logistic model had an accuracy of 75.0%, sensitivity 72.3%, specificity 77.4%, PPV 74.1%, and NPV 75.8% [AUC: 0.7853 (95%CI: 0.7098-0.8454)], showing a significantly improved diagnostic capacity using the logistic model (p=0.0002). Conclusions: A logistic model including risk factors for lymph node metastasis can improve the accuracy of MRI diagnosis of rectal cancer.

• 조명전기설비학회논문지
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• 제23권6호
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• pp.40-51
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• 2009

인버터 스위치의 개당 혹은 인버터와 모터 터미널의 분리로 인해 발생하는 고장을 해석하고 진단 알고리즘의 시험 평가를 위해 효과적으로 사용할 수 있는 영구자석 동기전동기 구동 인버터의 개방 고장에 의한 제어 특성해석 및 고장모델이 제시된다. 기존의 전동기의 해석과 제어에 많이 사용되는 dq 모델은 상전압 모델을 변환한 것으로 고장 상황에서는 3상평형 조건이 성립하지 않기 때문에 개방 회로의 입력 전압을 구하기가 쉽지 않아 고장 모델의 해석을 위해 직접 사용하기 어렵다. 이를 해결하기 위해 스위치 개방에 따른 전통기의 선전압 관계를 이용한 인버터의 고장 모델이 제안되고 제안된 고장모델의 타당성을 입증하기 위해 시뮬레이션이 수행된다. 전체 시스템이 DSP TMS320F28335에 의해 구현되어 동일 고장 조건에서의 비교 실험과 특성 해석이 수행된다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• The Korean Journal of Pain
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• 제32권2호
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• pp.120-128
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• 2019

Background: We aimed to explore the American College of Rheumatology (ACR) 1990 and 2011 fibromyalgia (FM) classification criteria's items and the components of Fibromyalgia Impact Questionnaire (FIQ) to identify features best discriminating FM features. Finally, we developed a combined FM diagnostic (C-FM) model using the FM's key features. Methods: The means and frequency on tender points (TPs), ACR 2011 components and FIQ items were calculated in the FM and non-FM (osteoarthritis [OA] and non-OA) patients. Then, two-step multiple logistic regression analysis was performed to order these variables according to their maximal statistical contribution in predicting group membership. Partial correlations assessed their unique contribution, and two-group discriminant analysis provided a classification table. Using receiver operator characteristic analyses, we determined the sensitivity and specificity of the final model. Results: A total of 172 patients with FM, 75 with OA and 21 with periarthritis or regional pain syndromes were enrolled. Two steps multiple logistic regression analysis identified 8 key features of FM which accounted for 64.8% of variance associated with FM group membership: lateral epicondyle TP with variance percentages (36.9%), neck pain (14.5%), fatigue (4.7%), insomnia (3%), upper back pain (2.2%), shoulder pain (1.5%), gluteal TP (1.2%), and FIQ fatigue (0.9%). The C-FM model demonstrated a 91.4% correct classification rate, 91.9% for sensitivity and 91.7% for specificity. Conclusions: The C-FM model can accurately detect FM patients among other pain disorders. Re-inclusion of TPs along with saving of FM main symptoms in the C-FM model is a unique feature of this model.

• 대한한의학회지
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• 제40권4호
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• pp.49-60
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• 2019

Objectives: To develop a standardized diagnostic pattern identification equation for stroke patients, our group conducted a study to derive the predictive logistic equations. However, the sample size was relatively small. In the current study, we aimed to derive new predictive logistic equations for each diagnostic pattern using an expanded number of subjects. Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients' general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied. Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined. Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients' pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.

• 한국방사선학회논문지
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• 제12권3호
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• pp.289-295
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• 2018

대부분의 진단용 방사선 장치는 엑스선을 사용하며, 엑스선은 다양한 에너지를 갖는 스펙트럼을 갖는다. 진단 영상에서 엑스선의 정량적 및 정성적 분석은 선량을 줄이면서 영상 화질을 유지하는데 필수적이다. 본 연구의 목적은 진단 영상에 사용되는 엑스선 스펙트럼을 몬테칼로 시뮬레이션으로 측정하는 것이다. 다양한 엑스선 에너지 스펙트럼이 몬테칼로 시뮬레이션으로 측정되었다. 이 스펙트럼들은 다항식을 보간 한 양극 텅스텐 모델에 의해 계산된 결과와 비교하였다. 엑스선 관전압은 50, 60, 80, 100, 110 kV 였다. 검출기로는 카드뮴 텔루라이드와 비정질 셀레늄 물질을 사용하였다. 엑스선 에너지 스펙트럼의 시뮬레이션 결과는 참조 결과와 일치하였고, NRMSD 값은 최소 1.1%에서 최대 5.7%를 보였다. 시뮬레이션 결과에 의하면 진단 영상을 획득할 때 적절한 관전압의 선택을 가능하게 할 것이다. 또한, 영상 획득 전 환자에 전달되는 선량을 예측하는데 기여할 것이다.

• 사상체질의학회지
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• 제20권3호
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• pp.82-93
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• 2008

1. Objective The purpose of this study was to develop and upgrade the On-line SSCQ (Sasang Constitution Questionnaire) by making an analysis of diagnostic accuracy rate of Sasang Constitution Questionnaire for doctors. 2. Methods We have collected SSCQ-D(Sasang Constitution Questionnaire for Doctors) from the dept. of Sasang constitutional medicine in the four other university. We classified data according to Sasang constitution, sex, age and BMI and made an analysis using the chiefly discriminant analysis model, additionally frequency analysis, and Cronbach's alpha coefficient. 3. Results and Conclusion 1) Diagnostic accuracy rate of the SSCQ-D was between 71.33 and 95.14%. (1) About the whole subject the accuracy rate was 71.33%. (2) About the whole female the accuracy rate was 73.26%. (3) About the whole male the accuracy rate was 81.41%. 2) The more classification variables we used in this analysis study, the higher the diagnostic accuracy rate increased.