• 수면정신생리
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• 제2권1호
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• pp.31-43
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• 1995

Recently, the methods that measure and analyze brain electrical activity quantitatively have been available with the rapid development of computer technology. The quantitative electroencephalography(QEEG) is a method of computer-assisted analyzing brain electrical activity. The QEEG allows for a more sensitive, precise and reproducible examination of EEG data than that can be accomplished by conventional EEG. It is possible to compare various EEG parameters each other by using QEEG. Neurometrics, a kind of the quantitative EEG. is to compare EEG characteristics of the patient with normative data to determine in what way the patient's EEG deviates from normality and to discriminate among psychiatric disorders. Nowadays, QEEG is far superior to conventional EEG in its detection of abnormality and in its usefulness in psychiatric differential diagnosis. The abnormal findings of QEEG in various psychiatric disorders are also discussed.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.579-582
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• 1997

The two dimnesional echocardiography is widely used to evaluate regional wall motion abnormaility, because of its abilities to depict left ventricluar wall motion. A new method, color kinesis is a technology or echocardiographic assessment of left ventricular wall motion. In this paper, we proposed a algorithm or color kinesis which is based on acoustic quantification and automatically detects endocardial motion during systole on a frame-by-frame basis. The echocardiograms were obtained in the short-axis views in normal subjects. Automated edge detection and endocardial contour tracing algorithm was applied to each frames, quantitative analysis based on segmentation was performed, and pre-defined color overlays superimposed on the gray scale images. Segmental analysis of color kinesis provided automated, quantitative diagnosis of regional wall motion abnormality.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.183-184
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• 1998

Respiration monitoring is important in many clinical situations due to its relationship to vitality. But present commercial monitoring systems are bulky and expensive, so they are inadequate to be used for long term recording or out-patients application. We have developed a low cost, low power, handhold respiration monitoring system based on airflow measurement. Respiration flow is indirectly detected using a thermister or a themocouple sensor. Real time recording of respiration rate, abnormality detection and apnea alarming are available.

• Progress in Superconductivity
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• 제8권1호
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• pp.1-7
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• 2006

We have developed magnetocardiography(MCG) and impedance magnetocardiography(I-MCG) for detecting heart disease by using dc-SQUID technology. The MCG system, using low-Tc SQUID, is being applied commercially for diagnosing heart disease. Using the low-Tc MCG system, many clinical studies on detection of abnormality have been performed. Furthermore, we have developed a portable MCG system using high-Tc SQUID. For detecting changes in kinetic impedance in the heart, an I-MCG system has been demonstrated. The I-MCG system could detect the mechanical movement of the heart. In this report, we review current clinical applications of magnetocardiography and impedance magnetocardiography.

• 대한두경부종양학회지
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• 제34권1호
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• pp.29-32
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• 2018

The parathyroid glands are usually located in the posterolateral area of the thyroid gland. Due to their embryologic origin, they are sometimes found in an ectopic position from the angle of the jaw to the mediastinum. However, their incidental detection in the hypopharyngeal wall is rare. Herein, we report a case of an ectopic parathyroid gland found in the hypopharyngeal wall of a 39-year old woman with no known endocrine abnormality.

• Journal of Genetic Medicine
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• 제14권1호
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• pp.34-37
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• 2017

5p deletion syndrome, also known as Cri-du-Chat syndrome, is a chromosomal abnormality caused by a deletion in the short arm of chromosome 5. Clinical features of 5p deletion syndrome are difficult to identify prenatally by ultrasound examination, thus most cases of 5p deletion syndrome have been diagnosed postnatally. Here, we report eight cases of 5p deletion syndrome diagnosed prenatally, but were unable to find common prenatal ultrasound findings among these cases. However, we found that several cases of 5p deletion syndrome were confirmed prenatally when karyotyping was performed on the basis of abnormal findings in a prenatal ultrasound scan. Hence, it is necessary to carefully perform prenatal ultrasonography for detection of rarer chromosomal abnormalities as well as common aneuploidy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5035-5048
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• 2016

Nano scale networks are futuristic networks deemed as enablers for the Internet of Nano Things, Body area nano networks, target tracking, anomaly/ abnormality detection at molecular level and neuronal therapy / drug delivery applications. Molecular communication is considered the most compatible communication technology for nano devices. However, connectivity in such networks is very low due to inter-symbol interference (ISI). Few research papers have addressed the issue of ISI mitigation in molecular communication. However, many of these methods are not adaptive to dynamic environmental conditions. This paper presents an enhancement over original Memory-1 ISI cancellation scheme using maximum likelihood estimation of a channel parameter (λ) to make it adaptable to variable channel conditions. Results of the Monte Carlo simulation show that, the connectivity (P_conn) improves by 28% for given simulation parameters and environmental conditions by using enhanced Memory-1 cancellation method. Moreover, this ISI mitigation method allows reduction in symbol time (Ts) up to 50 seconds i.e. an improvement of 75% is achieved.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.

• Journal of Information Technology Applications and Management
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• 제29권6호
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• pp.63-80
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• 2022

In this paper, we study structural changes and performance gains in condition-based maintenance process redesign when mobile IoT technology is embedded into urban railway rolling stock. We first develop condition-based maintenance To-Be process model in accordance with the IoT deployment scheme. Secondly, we draw upon theoretical framework of redesign process analysis to develop performance evaluation method suitable to predictive maintenance shift from As-Is ordinary maintenance practice. Subsequently, To-Be process performance evaluations are conducted adopting both the quantitative and qualitative method for time, cost, and dependability dimensions. The results ascertain the considerable benefits captured through detection abnormality prior to actual rolling stock failure occurrence, and details of performance improvements and enhancement of standardization level is revealed. The procedures and results presented in this paper offers useful insights in the fields of IoT economic analysis, condition based maintenance, and business process redesign.

• 지능정보연구
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• 제25권3호
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• pp.43-62
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• 2019

한때, 이상 탐지 분야는 특정 데이터로부터 도출한 기초 통계량을 기반으로 이상 유무를 판단하는 방법이 지배적이었다. 이와 같은 방법론이 가능했던 이유는 과거엔 데이터의 차원이 단순하여 고전적 통계 방법이 효과적으로 작용할 수 있었기 때문이다. 하지만 빅데이터 시대에 접어들며 데이터의 속성이 복잡하게 변화함에 따라 더는 기존의 방식으로 산업 전반에 발생하는 데이터를 정확하게 분석, 예측하기 어렵게 되었다. 따라서 기계 학습 방법을 접목한 SVM, Decision Tree와 같은 모형을 활용하게 되었다. 하지만 지도 학습 기반의 모형은 훈련 데이터의 이상과 정상의 클래스 수가 비슷할 때만 테스트 과정에서 정확한 예측을 할 수 있다는 특수성이 있고 산업에서 생성되는 데이터는 대부분 정답 클래스가 불균형하기에 지도 학습 모형을 적용할 경우, 항상 예측되는 결과의 타당성이 부족하다는 문제점이 있다. 이러한 단점을 극복하고자 현재는 클래스 분포에 영향을 받지 않는 비지도 학습 기반의 모델을 바탕으로 이상 탐지 모형을 구성하여 실제 산업에 적용하기 위해 시행착오를 거치고 있다. 본 연구는 이러한 추세에 발맞춰 적대적 생성 신경망을 활용하여 이상 탐지하는 방법을 제안하고자 한다. 시퀀스 데이터를 학습시키기 위해 적대적 생성 신경망의 구조를 LSTM으로 구성하고 생성자의 LSTM은 2개의 층으로 각각 32차원과 64차원의 은닉유닛으로 구성, 판별자의 LSTM은 64차원의 은닉유닛으로 구성된 1개의 층을 사용하였다. 기존 시퀀스 데이터의 이상 탐지 논문에서는 이상 점수를 도출하는 과정에서 판별자가 실제데이터일 확률의 엔트로피 값을 사용하지만 본 논문에서는 자질 매칭 기법을 활용한 함수로 변경하여 이상 점수를 도출하였다. 또한, 잠재 변수를 최적화하는 과정을 LSTM으로 구성하여 모델 성능을 향상시킬 수 있었다. 변형된 형태의 적대적 생성 모델은 오토인코더의 비해 모든 실험의 경우에서 정밀도가 우세하였고 정확도 측면에서는 대략 7% 정도 높음을 확인할 수 있었다.