• 한방비만학회지
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• 제7권1호
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• pp.55-69
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• 2007

Objectives : The aim of the study was to investigate the principal symptoms and a syndrome differentiation in the obesity using surveys from Oriental medical doctors and obese patients. Methods : Seventy three Oriental medical doctors who participated in the 2006 autumn annual conference of Korean Oriental Association for Study of Obesity and 243 obese patients responded to the survey. Results : Twenty nine percent of Oriental medical doctors replied that the syndrome differentiation is the most important diagnosis index, and 21 percent of them replied they use Sasang Constitution classification during diagnostic process. The syndrome differentiations used were mainly phlegm-fluid, blood stasis, spleen vacuity, food accumulation, damp phlegm, and Gi deficiency order. In the response of doctors and patients about principle symptoms of 6 syndrom differentiation belong inside 5 place except phlegm fluid and liver stasis Conclusions : We should develop syndrome differentiation questionnaire about obese symptoms.

• Journal of Chest Surgery
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• 제33권8호
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• pp.688-692
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• 2000

Descending Necrotizing Mediastinitis(DNM) is a complication of oropharyngeal infections that can spread to the mediastinum. It is difficult to diagnose early because clinical and radiologic findings appear in the late stage of the infection. late diagnosis is the principal reason for the high mortality in DNM. An 18-year-old female admitted with Ludwig's angina from dental caries. Despite of combined antibiotics, dental extraction and drainge of submental abscess, infection spread to the cervical area. Chest computed tomogram revealed extension of the abscess to the pretracheal and periaortic space and development of bilateral pleural empyema. We performed bilateral cervical mediastinotomy and thoracotomy for drainage and debridement. Tracheostomy to secure the airway and postoperative pleural irrigation were performed. Postoperative course was uneventful and patient was discharged on the 40th postoperative day. It is important to perform chest CT scanning for early diagnosis of DNM when oropharyngeal infection spreads to the cervical area. Improved survival of patients with DNM implies early and radical surgical drainage and debridement via a cervical mediastinomy and thoracotomy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2723-2725
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• 2001

Nuclear power generations have been developed gradually since 1950. Nowadays, 440 nuclear power generations are taking charge of 16% of electric power production in the world. The most important factor to operate the nuclear power generations is safety. It is not easy way to control nuclear power generations with safety because nuclear power generations are very complicated systems. In the main control room of the nuclear power generations, about 4000 numbers of alarms and monitoring devices are equipped to handle the signals corresponding to operating equipments. Thus, operators have to deal with massive information and to grasp the situation immediately. If they could not achieve these task, then they should make big problem in the power generations Owing to too many variables, operators could be also in the uncontrolled situation. So in this paper, automatic systems to diagnose the fault are constructed using 2 steps neural networks. This diagnosis method is based on the pattern of the principal variables which could represent the type and severity of faults.

• 한국소음진동공학회논문집
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• 제26권6_spc호
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• pp.714-720
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• 2016

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.

• 한국기계가공학회지
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• 제20권9호
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• 한국멀티미디어학회논문지
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• 제24권5호
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• 스마트미디어저널
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• 제12권3호
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• 한국컴퓨터정보학회논문지
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• 제18권4호
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• pp.45-55
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• 2013

본 논문에서는 설진을 위하여 얼굴 영상으로부터 혀 영역을 추출하고, 혀 영역을 6개 세부 영역으로 분할한 다음 영역별 설태 비율을 검출하는 방법을 제안한다. 얼굴 영상으로부터 혀 영역을 추출하기 위해 능동적 형태 모델방법의 하나인 ASM을 이용하였다. 검출된 혀 영역을 한의학에서 사용하는 일반적인 6개 영역으로 분할하였고, 분할된 영역 내에서의 설태 분포 정도를 SVM을 이용하여 검출하였다. SVM 분류 시 특징 벡터로는 RGB, HSV, Lab, Luv로 구성된 12차원의 벡터로부터 주성분 분석을 통하여 구해진 3차원의 벡터를 사용하였다. 실험 결과 ASM을 사용하여 혀 영역을 안정적으로 검출할 수 있었고 주성분 분석과 SVM을 활용함으로써 설태 검출율이 높아짐을 알 수 있었다.

• 대한세포병리학회지
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• 제7권2호
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• pp.192-196
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• 1996

The principal significance of the urothelial changes caused by polyomavirus activation is in an erroneous diagnosis of urothelial cancer; however, the clue to their benign nature is the smooth structureless nuclear configuration and the relative paucity of affected cells. Though virologic studies and electron microscopy are usually needed to firmly establish the diagnosis, cytology is the most readily available and rapid means of establishing a presumptive diagnosis of human polyomavirus infection. A urine specimen of a 24-year-old man with hemorrhagic cystitis beginning two months after bone marrow transplantation for acute myeloblastic leukemia(M2) was submitted for cytologic evaluation. Cytologic findings revealed a few inclusion-bearing epithelial cells intermingled with erythrocytes, neutrophils, lymphocytes, and macrophages. Most of the inclusion-bearing fells had large, round to ovoid nuclei almost completely filled with homogeneous dark, basophilic inclusion. The chromatin was clumped along the periphery and the cytoplasm was mostly degenerated. The other cells exhibited irregular inclusions attached to the nuclear membrane surrounded by an indistinct halo. These findings were consistent with polyomavirus infection.

• 한국융합학회논문지
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• 제8권5호
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• pp.87-96
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• 2017

이 연구는 퇴원손상심층조사 자료를 이용해 손상 및 외상 사망환자의 재원일수 특성을 분석하여 손상 및 외상으로 인한 사망 예방을 위한 기초 자료 제공을 목적으로 시행되었다. 조사대상은 퇴원손상심층조사 자료에서 2014년 1월 1일부터 12월 31일까지 퇴원한 환자 중, 치료결과가 '사망'이며, 주진단이 손상 및 사고의 외인(S00-T98)인 환자 233명이었다. 연구결과, 여자의 재원일수가 남자보다 길었다. 주진단은 기타 내부 인공삽입장치, 삽입물 및 이식편의 합병증(T85)에서 재원일수가 가장 길었다. 이상의 연구결과로 볼 때 손상으로 인한 사망 재원일수 영향 요인을 파악하여 이들을 집중적으로 관리하기 위한 정책개발이 필요한 것으로 판단된다.