• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1994년도 하계학술대회 논문집 C
• /
• pp.1298-1300
• /
• 1994

An ability of $NO_2$ gas detection has been investigated using dilithium phthalocyanine($Li_2Pc$) Langmuir-Blodgett (LB) films. It is a well-known gas sensitive material and has been manufactured under a surface pressure of 30mN/m. A status of deposited films was confirmed by UV-visible absorption spectrum, ellipsometry measurements and current-voltage characteristics. Gas-detection characteristics of the films were studied through an electrical conductivity, response time, recovery time, and reproducibility under 200 ppm of $NO_2$ gases.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 B
• /
• pp.797-799
• /
• 1999

In this paper, the edge intensification and detection algorithm which is one of image processing operations is considered. Edge detection algorithm is the most useful and important method for image processing or image analysis. The vision system based on these processing and concerned in specific project is proposed and is applied to the inverted pendulum in order to automatically acquire the angles between the bar and the perpendicular reference line. In this paper, the angles that are obtained from some images of computer vision system can offer useful informations for control of real inverted pendulum system. Next, the inverted pendulum will be controlled by the proposed method.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
• /
• pp.746-749
• /
• 1988

This paper describes an arrhythmia detection algorithm using PP and PR Interval. In order to detect P-wave accurately, an improved 5-point derivative method is used. In this paper, the RR, PP and PR interval. and the number of P-waves per RR Interval are detected for arrhythmia detection. These parameters can be utilized to diagnose in the varied types of AV block, atrial fibrillation, and PVC.

• 한국전기전자재료학회논문지
• /
• 제36권5호
• /
• pp.520-524
• /
• 2023

An algorithm was developed to detect and block serial arc currents using HPF. The AC series arc problem is that the load current is greater than the fault current and no leakage current occurs. As a solution, an arc detection method utilizing differences in high- frequency amplitudes was developed. HPT was applied to the load current and FFT was applied to eliminate low frequencies. An algorithm has been developed to detect arc waveforms when they exceed a certain value compared to the average of normal waveforms. Using one cycle of data, arc detection is faster and arc accidents are prevented.

• 한국정보통신학회논문지
• /
• 제24권2호
• /
• pp.192-197
• /
• 2020

대규모 발전소나 화학공정과 같은 다변량 공정은 매우 위험한 환경에서 운전되기 때문에 고장이 발생하면 심각한 인적·물적 손실이 발생할 수 있다. 따라서 시스템의 고장을 사전에 탐지할 수 있는 온라인 모니터링 기술이 필수적이다. 본 논문에서는 세 가지의 다른 다변량 공정 데이터에 ICA를 적용하여 고장탐지를 수행하였고, PCA와 성능을 비교하였다. ICA 기반의 고장탐지 절차는 크게 오프라인 과정과 온라인 과정으로 나뉜다. 오프라인 과정에서는 시스템이 정상일 때 계측된 데이터를 이용하여 고장판별을 위한 문턱 값을 설정한다. 그리고 온라인 과정에서는 실시간으로 계측되는 질의벡터에 대한 통계량을 계산한 후, 계산된 통계량과 사전에 정의된 문턱 값과 비교하여 고장을 판별한다. 본 논문에서 이용한 세 가지의 다변량 공정 데이터에 실험한 결과, ICA 기반 고장탐지 방법이 시스템의 고장을 사전에 탐지하였고, PCA 보다 우수한 고장탐지 성능을 보여주었다.

• 전기학회논문지
• /
• 제66권10호
• /
• pp.1445-1453
• /
• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1996년도 춘계학술대회
• /
• pp.288-292
• /
• 1996

This paper describes far the detection of heart event that is, QRS complex and P wave which are result from heart activity. The proposed QRS detection method by using the spatial velocity was identified as having the 99.6% detection accuracy as well as fast processing time. Atrial flutter, coupled P wave, and noncoupled P wave as well as atrial fibrillation could be detected correctly by three different algorithms according to their origination farm. About 99.6% correction accuracy coupled P wave could be obtained and we could be found that most detection errors are caused by establishing wrong search interval.

• 방송공학회논문지
• /
• 제24권5호
• /
• pp.791-801
• /
• 2019

전기화재의 원인중의 하나는 직렬 아크이다. 최근까지 아크 신호를 검출하기 위해 다양한 기법들이 진행되고 있다. 시간 신호에 푸리에 변환, 웨이블릿, 또는 통계적 특징 등을 활용하여 아크 검출을 하는 방법들이 소개되었지만, 다양한 불규칙 아크 파형 때문에, 실제 환경에서는 아크 성능이 저하되는 문제가 있다. 따라서, 기존의 부족한 특징 데이터를 증가시켜, 성능을 개선하는 것이 요구된다. 본 논문에서는 입력신호를 변분 모드 분할을 통해 원신호를 분할한 후 통계적 특징을 추출한다. 변분 모드 분할으로부터 추출한 통계적 특징의 성능이 원신호로부터 얻은 특징보다 개선된 성능을 얻는다. 아크 분류기로 인공 신경망을 이용하고, 14,000개의 학습 데이터에 적용한 결과 VMD의 사용이 약 4%의 아크 검출 성능을 높혔다.

• Journal of Communications and Networks
• /
• 제17권6호
• /
• pp.609-621
• /
• 2015

Recently, a variety of reduced complexity soft-in soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-in soft-output detectors for MIMO systems due to heavy burden in computational complexity. In this paper, we propose a soft detection algorithm for MIMO systems which performs close to the full dimensional joint detection, yet offers significant complexity reduction over the existing detectors. The proposed algorithm, referred to as soft-input soft-output successive group (SSG) detector, detects a subset of symbols (called a symbol group) successively using a deliberately designed preprocessing to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of the interfering symbol groups successively using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results on realistic MIMO systems demonstrate that the proposed SSG detector achieves considerable complexity reduction over the conventional approaches with negligible performance loss.

• 한국군사과학기술학회지
• /
• 제25권5호
• /
• pp.443-461
• /
• 2022

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.