• The Journal of the Acoustical Society of Korea
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• v.25 no.3
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• pp.144-150
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• 2006

Clinicians usually use stethoscopic auscultation for the diagnosis of heart diseases. However, the heart sound signal has varying characteristics due to the noise and/or the conditions of the patients. Also, it is not easy for junior clinicians to find the acoustical differences between different kinds or heart sound signals. which may result in errors in the diagnosis. Thus it will be quite useful for the clinicians to make use of an automatic classification system using signal processing techniques. In this paper, we propose to use hidden Markov models in stead of artificial neural networks which have been conventionally used for the automatic classification of heart sounds. In the experiments classifying heart sound signals. we could see that the proposed methods were quite successful in the classification accuracy.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.2
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• pp.91-101
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• 2006

Researches on services of vehicles have been mainly focused on how to provide useful information and entertainment for an in-vehicle driver. However, the needs are appreciably increased for more advanced services that help drivers to check and manage their vehicles conveniently, without requiring drivers to attach to their vehicles. It is a sort of ubiquitous computing, providing an intelligent interactive services for human at any time and any where. In this paper, we present an intelligent vehicle information system to enable a driver to remotely diagnose and control a vehicle via CDMA communication network connected to the Internet. The system improves mobility for diagnosis and control of vehicle by implementing a cut and call back mechanism, which allows the vehicle terminal to have access to the information server on the Internet via CDMA call. No matter where the driver is, he can obtain the remote diagnosis and control services on the web browser without any additional application installation. Design methodology is introduced and evaluation results are analyzed for the CDMA-based intelligent vehicle information system. The experimental results show that the response time of the vehicle terminal to a web client request is 10.302 seconds at the beginning and 646.44ms thereafter. The average response time of CAN sensor node to a vehicle terminal request is 6.669ms.

• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.21-28
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• 2007

The goal of this paper is to represent a technique of fault detection for the control surface as a base research of the fault tolerant control system for safety improvement of UAV. The real-time system identification based on the recursive Fourier Transform was implemented for the fault detection of the control surface and verified through the HILS and flight test. The failures of the control surface are detected by comparing the control derivatives in fault condition with the normal condition. As a result from the flight test, we have confirmed that the control derivatives of fault condition less than normal condition.

• Explosives and Blasting
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• v.22 no.3
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• pp.43-55
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• 2004

Use term of tower style construction exceeds recently. Accordingly, according to construction safety diagnosis result, achieve removal or Improvement construction. But when work removal, must shorten shut down time. Therefore, removal method of construction to use blasting demolition of construction is very profitable. Influence construction and equipment by blasting vibration and occurrence vibration caused by felling impact. Is using disadvantageous machine removal method of construction by and economic performance by effect of such vibartion. Therefore, this research studied techniques to forecast vibartion level happened at blasting demolition and vibration reduction techniques by use a scaled model test.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.315-318
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• 2011

본 논문에서는 구조물 연결부의 실시간 손상 검색을 통해 이상이 감지되었을 경우 자가치유까지 가능한 지능형 볼트접합부 시스템에 관한 실험적 연구결과가 제시되었다. 지능형 센서인 PZT센서의 전기-역학적 커플링 특성을 이용한 전기역학적 임피던스 기반의 구조물 건전성 평가 방법이 사용되었다. 전기역학적 임피던스의 측정을 통한 계측값을 베이스라인 값과 비교하는 손상 평가를 통해 구조물 볼트접합부의 볼트풀림 손상을 진단하고, 손상은 손상지수 RMSD를 통하여 정량화되었다. 볼트접합부의 손상이 감지되었을 경우 형상기억합금(SMA) 와셔에 부착되어있는 히팅 필름에 전원을 가함으로써 형상기억합금에 열을 가하고, 가열된 형상기억합금 와셔는 축방향으로 팽창을 함으로써 잃었던 볼트의 토크력을 회복시켜주었다. 실험 결과, 제안된 전기역학적 임피던스 기반의 구조물 건전성 평가기법과 형상기억합금 와셔 기반의 볼트접합부 자가치유 시스템의 성능 평가와 검증이 이루어졌다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.553-558
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• 1999

The ECG signal is very important information for diagnosis of patient and a cardiac disorder That signal is hard to filter the noise because that is mixed with a lot of noise, and the error of the filtering will distort the ECG signal. The existing method for the filtering of the ECG signal has structure that has many steps for filtering, so that structure is complex and the processing speed is slow. For the improvement of that problem, we propose the method of filtering that has simple structure using the RBF neural networks and have good results.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.139-142
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• 2006

본 논문에서는 햅틱 피드백과 스테레오 비쥬얼 큐를 혼합한 다중 감각 기반의 지능형 3차원 형상 분석 방법을 소개한다. 지능형 형상 분석 방법은 3차원 모델의 구조에 대한 보다 상세한 정보를 제공한다. 특히 의료 분야에 사용될 경우 전문가의 개입을 최소화하여 질병 진단 및 치료 등에 사용될 수 있다. 본 연구에서는, MRI나 CT 영상으로부터 생성된 3차원 매개변수형 모델을 이용하여 유사 모델 집단을 대표하는 통계 형상을 구축한 후, SVM (Support Vector Machine) 학습 알고리즘을 이용하여 두 집단간 형상 차이를 분석한다. 3차원 형상에 대한 신속한 시각적 이해와 직관적 조작감은 물체 표면의 형상 변화를 분석하는데 효과적으로 사용될 수 있다. 본 논문에서는 물체 조작 및 관찰 등의 작업을 수행할 때, 햅틱 피드백과 스테레오 비쥬얼 큐를 혼합한 인터랙션 기법을 사용하여 공간감과 깊이감을 향상시켜 형상 분석 결과를 효과적으로 분석한다. 본 연구에서는 해마, 관상 동맥, 뇌와 같은 인체 장기를 실험 데이터로 사용하여 제안한 SVM 기반의 분석 방법과 인터랙션 환경의 성능을 평가한다. 본 연구에서 구현한 SVM 기반 이진 분류기는 두 집단간 형상 차이를 효과적으로 분석하며, 또한 다중 감각 인터랙션은 사용자가 분석 결과를 관찰하고 카메라 및 형상을 효율적으로 조작하는 데 도움을 준다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.29-38
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• 2021

Riverfront facilities are river space facilities used by citizens for the rest and convenience. Recently, although the importance of efficient maintenance of riverfront facilities is increasing, damaging facilities cases are increasing due to frequent floods. Currently, the inspections and diagnosis of river space facilities are limited to the main flood control facilities. And the standards for the maintenance and management of the riverfront facilities are insufficient. Utilization survey, which is the standard for managing river space facilities, is also inefficient in terms of manpower consumption and economic feasibility. This study uses mobile big data to classify river usage and conducts a survey for usability of river facilities to derive economic evaluation for usage performance. In the future, if economical method system that considers safety, usability, and durability is conducted and demanding analysis for each convenience facility is evaluated, it is expected that the efficient maintenance of riverfront facilities is perfomed better and the use of rivers by citizens will further increase.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.473-482
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• 2007

Network-based IDS(Intrusion Detection System) gathers network packet data and analyzes them into attack or normal. They raise alarm when possible intrusion happens. But they often output a large amount of low-level of incomplete alert information. Consequently, a large amount of incomplete alert information that can be unmanageable and also be mixed with false alerts can prevent intrusion response systems and security administrator from adequately understanding and analyzing the state of network security, and initiating appropriate response in a timely fashion. So it is important for the security administrator to reduce the redundancy of alerts, integrate and correlate security alerts, construct attack scenarios and present high-level aggregated information. False alarm rate is the ratio between the number of normal connections that are incorrectly misclassified as attacks and the total number of normal connections. In this paper we propose a false alarm classification model to reduce the false alarm rate using classification analysis of data mining techniques. The proposed model can classify the alarms from the intrusion detection systems into false alert or true attack. Our approach is useful to reduce false alerts and to improve the detection rate of network-based intrusion detection systems.