• 전자공학회논문지C
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• 제35C권6호
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• pp.67-75
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• 1998

다층퍼셉트론의 학습에서 나타나는 출력노드의 부적절한 포화를 해결하기 위해서 n차 크로스-엔트로피 오차함수가 제안되었으나, 이 오차함수를 이용한 학습성능은 오차함수의 차수에 민감하여 적절한 차수를 결정해야 하는 문제점이 있다. 이 논문에서는, 학습의 진행에 따라 학습률을 가변시키는 새로운 방법을 제시하여 다층퍼셉트론의 학습성능이 n차 크로스-엔트로피 오차함수의 차수에 덜 민감하도록 한다. 또한, 가변학습률이 매우 커지는 경우에 학습이 불안정해지는 것을 방지하기 위해서 오차신호의 크기를 제한하는 방법을 제시한다. 마지막으로, 필기체 숫자 인식 문제와 갑상선 진단 문제의 시뮬레이션으로 제안한 방법의 효용성을 검증한다.

• 한국통신학회논문지
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• 제34권11B호
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• pp.1289-1295
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• 2009

조기심실수축(premature ventricular contractions, PVC)은 가장 보편적인 부정맥으로 심실세동, 심실빈맥 등과 같은 위험한 상황을 유발할 수 있는 가능성을 가지고 있기 때문에 이의 조기 검출은 매우 중요하다. 특히 일반인들의 건강상태를 지속적으로 모니터링 해야하는 헬스케어 시스템에서는 이를 위한 ECG 신호의 실시간 처리가 필요하다. 즉, 최소한의 연산량으로 정확한 R파를 검출하고, 이를 이용하여 PVC를 분류할 수 있는 적합한 알고리즘의 설계가 필요하다. 따라서 본 연구에서는 PVC 실시간 분류를 위한 적응형 문턱치와 윈도우 기반의 R파 검출 알고리즘을 제안한다. 이를 위해 전처리 과정과 적응가변형 문턱치를 통해 R파를 검출하였으며, 검출의 효율성을 위하여 R-R 간격을 이용한 적응가변형 윈도우를 적용하였다. 제안한 알고리즘의 R파 검출 및 PVC 분류 성능을 평가하기 위해서 MIT-BIH 부정맥 데이터베이스를 사용하였다. 성능평가 결과, R파는 평균 99.33%, PVC는 평균 88.86%의 검출결과가 나타났다.

• 센서학회지
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• 제22권5호
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• pp.338-345
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• 2013

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and the prevention of possible life threatening cardiac diseases. Most methods for detecting arrhythmia require pp interval, or the diversity of P wave morphology, but they are difficult to detect the p wave signal because of various noise types. Thus, it is necessary to use noise-free R wave. So, the new approach for the detection of PVC is presented based on the rhythm analysis and the beat matching in this paper. For this purpose, we removed baseline wandering of low frequency band and made summed signals that are composed of two high frequency bands including the frequency component of QRS complex using the wavelet filter. And then we designed R wave detection algorithm using the adaptive threshold and window through RR interval. Also, we developed algorithm to classify PVC using RR interval. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate average detection rate of 99.76%, sensitivity of 99.30% and specificity of 98.66%; accuracy respectively for R wave and PVC detection.

• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.36-44
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• 2010

This paper presents the design of an algorithm to detect, identify, and locate faults in radial distribution feeders of Provincial Electricity Authority (PEA). The algorithm consists of three major steps. First, the adaptive algorithm is applied to track/estimate the system electrical parameter, i.e. current phasor, voltage phasor, and impedance. Next process, the impedance rule base is used to detect and identify the type of fault. Finally, the current compensation technique and a geographic information system (GIS) are applied to evaluate a possible fault location. The paper also shows the results from field tests of the automate fault location and illustrates the effectiveness of the proposed fault location scheme.

• 전력전자학회논문지
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• 제26권6호
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• pp.381-389
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• 2021

Most diagnosis approaches rely on historical failure data that might not be feasible in real operating conditions because the battery voltage and internal parameters are nonlinear according to various operating conditions, such as cell-to-cell configuration and initial condition. To overcome this issue, the estimator and the predictor require integrated approaches that consider comprehensive data, with the degradation process and measured data taken into account. In this paper, vector autoregressive models (VAR) with various parameters that affect overdischarge to the cell in the battery pack were constructed, and the cell-to-cell parameters were identified using an adaptive model to analyze the influence of failure prognosis. The theoretical analysis is validated using experimental results in terms of the feasibility and advantages of fault prognosis.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권10호
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• pp.468-476
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• 2002

Digital images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in medical radio-fluoroscopy images is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. We proposed adaptive weighed median(AWM) filters based on local statistics. We showed two ways of realizing the AWM filters. One is a simple type of AWM filter, which is constructed by Homogeneous factor(HF). Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by Visual C++ language on a IBM-PC Pentium 550 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of NMSE(normalized mean square error) with the value of the other existing filtering methods.

• 한국지능시스템학회논문지
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• 제15권6호
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• pp.673-681
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• 2005

원자력 발전소는 안정성 및 신뢰성 확보가 가장 중요하므로 고장의 감지 및 진단 시스템의 개발은 원전 자체가 구축하고 있: 다중의 하드웨어 중첩도(hardware redundancy)에도 불구하고 가장 중요한 문제로 취급되고 있다. 본 논문에서는 원저 PWR 증기발생기에서 발생한 고장을 진단하기 위한 알고리듬의 개발을 위해 시스템에서 발생한 고장을 감지하고 분류할 수 있는 ART2 시경회로망 기반 고장진단방법을 제안한다. 고장진단시스템은 발생한 고장을 감지하기 위한 고장감지부, 변화된 시스템파라미터를 추정하기 위한 파라미터 추정부 및 발생한 고장의 종류를 알아내기 위한 고장분류부로 구성된다. 고장분류부는 여러 경계인수를 갖는 ART2(adaptive resonance theory 2) 신경회로망을 이용한 고장분류기로 구성된다. 제안한 고장진단 알고리듬을 증기발생기의 고장진단문제에 적용하여 성능을 확인하였다.

• 전자공학회논문지S
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• 제34S권7호
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• pp.89-101
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• 1997

A technical method of noise modeling and adaptive filtering reducing of speckle noise in ultrasonic medical images is presented. By adjusting the characteristics of the filer according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performance of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2775-2778
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• 2003

Neural network-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.