• Nuclear Engineering and Technology
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• 제53권2호
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• pp.532-548
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• 2021

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.261-273
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• 2022

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.737-739
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• 1996

Abnormal conditions and disturbances in distribution system cause an immediate influence to the customers. Conventional detection schemes for the distribution abnormalities have been applied in limited extents mainly because of their low reliability. In this paper, we developed a disturbance identification system which monitors the load level after a transient, checks the harmonic behavior of the load, and finally makes decision on the cause of the disturbance. This system identifies and discriminates overcurrent faults, arcing ground faults, recloser activities, and foreign object or tree contacts. In the implementation of the identification system, we applied fuzzy logic to better represent some variables whose Quantities are expressed only in non-numerical terms.

• 자원환경지질
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• 제45권3호
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• pp.295-306
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• 2012

이 연구에서는 경기육괴내에 존재하는 것으로 알려진 대규모 단층대인 신갈단층을 기흥지역에서 확인하였다. 기흥저수지 지역의 지질공학적 조사를 실시하여 단층대 위치 및 특징을 파악하였다. 이 단층대는 신갈단층으로 알려져 있으며, 남북방향으로 발달된 이 단층대는 추가령 열곡 내에서 리델 타입(Riedel-type)의 주향이동 단층으로 해석된다. 단층대를 따라서 62공의 시추조사와 전기비저항탐사 약 11 km, 바이브로사이즈 탄성파탐사 약 500 m를 실시하였다. 시추조사 및 물리탐사 결과, 최대 폭 300미터의 단층대는 주로 가우지 및 단층각력으로 구성되며, 망상의 2차 균열이 단층대 안에 서로 평행하게 발달함이 확인되었다. 시추조사 및 물리탐사 결과, 신갈단층의 휘어지는 부분인 기흥저수지 지역에서 꽃 구조(flower structure)와 유사한 지질구조의 발달 가능성이 있을 것으로 예측되지만, 연구지역의 특성상 노두 확인이 불가능하기 때문에 이와 같은 해석에는 불확실성이 있다. 따라서 단층대의 기하학적 특징, 단층간의 연결 형태 등에 대한 자세한 조사가 이루어질 필요가 있다.

• 한국정보통신학회논문지
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• 제20권3호
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• pp.654-662
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• 2016

사고수목을 이루는 게이트나 기본사상이 많아질수록 정상사상 확률의 정확한 계산이 어려워진다. 이를 극복하기 위해 BDD 방법을 적용하면 중소형 사고수목의 경우 짧은 시간에 근사계산 없이 정확한 값을 구할 수 있다. CUDD 함수를 이용하여 사고수목을 BDD로 변환하고 그로부터 정상사상의 발생확률을 구하는 고장경로 탐색 알고리즘을 고안하였다. 후방탐색 알고리즘은 전방탐색 알고리즘보다 고장경로의 탐색과 확률계산 시간에서 효과적이다. 이 탐색 알고리즘은 BDD에서 고장경로를 찾는데 있어서 탐색시간을 줄일 수 있고, 해당 사고수목의 단절집합과 최소단절집합을 찾는 유용한 방법이다.

• 지구물리
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• 제2권1호
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• pp.65-74
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• 1999

양산단층이 통과하는 언양 부근에서 기록한 굴절파 4개 측선자료를 대상으로 구배변화 지시자를 구하고 단층 분포상태를 해석하였다. XY=3 m인 속도분석 함수를 이용하여 구한 대상 굴절면의 평균속도는 2,250-2,870 m/s 정도이며, 가장 서쪽에 위치한 측선 1은 다른 측선보다 굴절파 속도가 약 600 m/s 정도 작게 나타난다. XY값이 6 m와 0 m인 속도분석 함수의 차이를 이용하여 구한 구배변화 지시자의 진폭이 0.5 ms/m 이상인 곳은 고해상도 반사파 단면상에 해석된 단층위치와 대체로 일치하며, 전반적으로 한 개의 측점간격(3 m) 정도 벗어난 양상을 보인다. 진폭이 큰 구배변화 지시자는 35번 국도를 중심으로 밀집되어 나타나며, 가장 큰 단층은 지질도상의 예상단층선에서 약 930 m 서쪽에 위치하는 것으로 해석된다.

• Journal of Power Electronics
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• 제17권4호
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• pp.1014-1026
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• 2017

The traditional fault diagnosis method for photovoltaic (PV) inverters has a difficult time meeting the requirements of the current complex systems. Its main weakness lies in the study of nonlinear systems. In addition, its diagnosis time is long and its accuracy is low. To solve these problems, a hidden Markov model (HMM) is used that has unique advantages in terms of its training model and its recognition for diagnosing faults. However, the initial value of the HMM has a great influence on the model, and it is possible to achieve a local minimum in the training process. Therefore, a genetic algorithm is used to optimize the initial value and to achieve global optimization. In this paper, the HMM is combined with a genetic algorithm (GHMM) for PV inverter fault diagnosis. First Matlab is used to implement the genetic algorithm and to determine the optimal HMM initial value. Then a Baum-Welch algorithm is used for iterative training. Finally, a Viterbi algorithm is used for fault identification. Experimental results show that the correct PV inverter fault recognition rate by the HMM is about 10% higher than that of traditional methods. Using the GHMM, the correct recognition rate is further increased by approximately 13%, and the diagnosis time is greatly reduced. Therefore, the GHMM is faster and more accurate in diagnosing PV inverter faults.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.147-152
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• 2023

Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• 대한전자공학회논문지SD
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• 제43권9호
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• pp.59-63
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• 2006

VLSI의 복잡도가 증가함에 따라, 보다 복잡한 고장이 나타나게 되었다. 단일 고장 진단을 위한 많은 방법들이 연구되어 왔다. 때로는 오류가 존재하는 칩에 대한 다중 결함이 실제 현상을 보다 더 정확하게 반영한다. 따라서 다중 고착 고장을 위한 효율적인 고장 진단 알고리듬을 제한하겠다. 제안하는 매칭 알고리듬은 완전일치공통부분을 고장 진단의 중요한 기준으로 사용함으로써 단일 고착 고장 시뮬레이터 환경에서도 다중 고착 고장을 진단할 수 있다. 또한 각 고장간의 식별성을 높여 다중 고착 고장을 진단함에도 불구하고, 고장 후보의 수를 획기적으로 줄일 수 있었다. 이를 위하여 출력단의 수에 따른 가중치 개념과 가산, 감산 연산을 사용하였다. 제안한 매칭 알고리듬은 ISCAS85회로와 완전 주사 스캔이 삽입된 ISCAS89회로에서 실험하여 성능을 입증하였다.