• 대한의용생체공학회:의공학회지
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• 제23권3호
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• pp.235-241
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• 2002

결핵을 진단하는 방법 중에서 신속하고 비교적 비용이 적게드는 방법은 객담을 통한 결핵균 도말 검사이다. 결핵균 도말 검사는 슬라이드에 도말한 환자의 객담을 가온 과정을 통해 고착시키고. acid-fast 염색방법을 통해 염색시킨 후 현미경으로 결핵균을 관찰하는 것이다. Acid-fast 염색방법은 크게 hot staining과 cold staining 방법 두 가지가 있으며, 우리나라에서는 염색 결과가 선명한 hot staining 방법인 Ziehl-Neelsen 방법을 주로 이용한다. 그러나, 기존의 결핵균 자동염색기는 가온 기능이 없어 환자의 객담을 슬라이드에 검사자가 고착을 시켜야 하고. 선명도도 낮은 문제점을 가지고 있다. 본 연구에서는 검사자의 인력 절감과 검사자 개인의 염색 능력에 따른 염색 정도의 변화를 줄이기 위해 가온이 가능한 결핵균 자동염색기를 개발하였다 개발된 염색기는 객담의 고착에서 염색 그리고 건조가지 전 과정이 자동으로 이루어진다. 염색 시간은 5개의 슬라이드를 고품질로 염색할 경우 21분이 소요되었다. 성능 평가를 위해 총 91개 객담을 대상으로 자동과 수동염색을 시행하여 일치율을 비교해 본 결과 75%로 통계적으로 유의한 차이를 보이지 않았다 (P>0.05).

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1299-1311
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• 2017

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the sounds emitted by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We present here an automatic fault diagnosis system of hand drills using discrete wavelet transform (DWT) and pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The diagnosis system consists of three steps. Because of the presence of many noisy patterns in our signals, we first conduct a filtering analysis based on DWT. Second, the wavelet coefficients of the filtered signals are extracted as our features for the pattern recognition part. Third, PCA is performed over the wavelet coefficients in order to reduce the dimensionality of the feature vectors. Finally, the very first principal components are used as the inputs of an ANN based classifier to detect the wear on the drills. The results show that the proposed DWT-PCA-ANN method can be used for the sounds based automated diagnosis system.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1986년도 하계학술대회논문집
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• pp.209-211
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• 1986

This paper describes the design of portable arrhythmia monitor and associated algorithm for automated diagnosis based-on micro-computer in the ambulatory ECG recording, analysis, and transmitting to a hospital host computer immediately through the telephone system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2278-2280
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• 1998

This paper discusses a technique that can detect cascading disturbances for automated monitoring and diagnosis systems in power plants. A multi-layered directed AND/OR graph called a disturbance interrelation analysis graph (DIAG) is utilized to represent the relationships among cascading disturbances and trace them. Disturbances that cannot be observed directly from sensors can be traced using techniques similar to interpolations and extrapolations on the DIAG.

• Parasites, Hosts and Diseases
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• 제56권1호
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• pp.71-74
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• 2018

Soluble antigens from an axenic culture of Entamoeba histolytica were used to develop a commercial ELISA kit to quantify anti-E. histolytica antibodies in sera of patients with extraintestinal amebiasis in non-endemic settings. The diagnostic specificity and sensitivity of the test were assessed retrospectively using 131 human serum samples with amoebic serologic status available. They were selected according to their results in immunofluorescence (IFAT) and were separated in 2 sample categories: 64 sera with positive results by IFAT and 67 with negative results by IFAT. The sensitivity and specificity of the ELISA kit were assessed at 95.0% and 94.0% compared to the IFAT. The test can be useful to exclude a potential diagnosis of amebiasis and could be used as a screening method since ELISA is an automated technique.

• 대한의용생체공학회:의공학회지
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• 제12권2호
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• pp.89-98
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• 1991

This study is concerned on the automation for cell diagnosis which has better objectivity and speed of test than human beings. Diagnosis is on the basis of shape change of abnormal Cells. Used parameters are nucleus area, nucleus perimeter, nucleus shape, cytoplasm area, nucleus/cytoplsm ratio, which was obtained using image processing technics. A new mode method is proposed on the automatic threshold selection for superior process time compared with Otsu's. Contour of the cytoplasm of abnormal cell is obtained using me- dian filter and sorel operator. The mask to get only original shape of abnormal cells is formed uslng the contour filling algorithm. In the result the normal cells are separated from the abnormal cells and the abnormal cells can be distinguished through screwing of abnormal cell's image with reference data to judge abnormal cells. Owing to this study the number of inspections which the pathologists should examine will be decreased and the time for inspection will be shortened.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.383-386
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• 1997

In the EMG evaluation, the neuropathy may be diagnosed by a detection of denervation potentials in the group of muscles. These abnomal potentials might be confused with normal endplate spikes. In this paper we present the software algorithm in C, which automatically detects spontaneous activity such as denervation potentials and endplate potentials and distingushes between those potentials. Parameters with statistically significant differences were used for this automated algorithm. It was concluded that the algorithm established in this study will improve accuracy in EMG diagnosis.

• 한국안전학회지
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• 제15권2호
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• pp.36-45
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• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.1051-1053
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• 2011

본 논문은 병리진단지식을 활용한 조직세포 영상의 암진단을 위한 온톨로지 기반의 진단 프레임워크를 제안한다. 병리진단 분야는 환자로부터 획득한 조직셈플을 전자현미경을 이용하여 조직의 구조적 특징과 형태학적특징을 기반으로 진단을 한다. 이러한 형태의 진단은 의사의 주관적인 경험에 많이 의존하기 때문에 같은 병증에 대해서도 의사들마다 다른 진단을 하게 된다. 최근 이러한 주관적인 경험에 의한 오진을 줄이고자 주어진 조직세포 영상의 형태학적 특징들의 정량적인 수치들을 이용하는 컴퓨터 보조진단(CAD)시스템들이 많이 이용되고 있다. 그러나 이러한 진단 시스템의 요소기법들은 하나의 병증만을 진단하는데 활용되기 때문에 구성기술의 재사용성은 매우 떨어진다. 따라서 본 논문은 요소기술들의 재활용성을 높이고, 객관화된 병리진단을 위한 온톨로지 기반의 진단 프레임워크를 제시한다.