• 전력전자학회논문지
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• 제20권5호
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• pp.471-478
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• 2015

The new islanding detection method using wavelet energy is proposed in this paper. The proposed method is based on the autocorrelation of the wavelet energies, which is obtained from the high-frequency components of the grid voltage. It has the enhanced detection capabilities in the UV/OV/UF/OF region, which the conventional passive methods cannot obtain. The mathematical theories on the wavelet are presented, and the performance effectiveness is proved by the experimental results.

• Journal of Radiation Protection and Research
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• 제38권4호
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• pp.172-178
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• 2013

본 연구에서는 미래 파이로 시설에서의 핵물질 계량 연구를 위하여 개발하고 있는 통합비파괴측정장치(Unified Non-Destructive Assay, UNDA)의 중성자 검출 효율을 MCNPX 코드를 이용하여 평가하였다. 검출 효율 평가는 두 개의 다른 설계안의 UNDA에 대하여 수행되었으며, $^{252}Cf$ 중성자 발생 선원 위치에 따른 검출 효율 평가와 감손우라늄의 용기 두께 및 위치에 따른 검출 효율 평가를 수행하였다. $^{252}Cf$ 중성자 선원의 위치에 따른 UNDA의 검출 효율 결과는 6.83%부터 13.35%까지 분포로 나타났으며, $^{252}Cf$ 선원이 장치 내부의 상단에 위치할수록 검출 효율은 증가 후 감소하는 경향을 나타냈고, 선원이 외각에 위치될수록 효율이 증가하는 경향을 보였다. 감손우라늄 용기의 두께 및 위치에 따른 검출 효율 평가에서는 용기 두께가 증가할수록 검출 효율은 낮아지는 경향을 보이며, 용기 위치가 장치 상부에 위치될수록 효율은 감소하고, 외각에 위치할수록 효율은 증가하였다. 검출 효율은 $^{252}Cf$ 선원의 경우보다 약간 높게 나타났다(10.31~13.61%). 또한, 장치 상단에 고밀도 폴리에틸렌 덮개가 있는 설계안이 덮개가 없는 설계안 보다 평균적으로 약 2% 정도 중성자 검출 효율이 높은 것으로 평가되었다.

• 분석과학
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• 제35권2호
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• pp.69-81
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• 2022

The method of measuring and classifying the energy category of neutrons directly using raw data acquired through a CZT detector is not satisfactory, in terms of accuracy and efficiency, because of its poor energy resolution and low measurement efficiency. Moreover, this method of measuring and analyzing the characteristics of low-energy or low-activity gamma-ray sources might be not accurate and efficient in the case of neutrons because of various factors, such as the noise of the CZT detector itself and the influence of environmental radiation. We have therefore developed an efficient method of analyzing radiation characteristics using a neutron and gamma-ray analysis algorithm for the rapid and clear identification of the type, energy, and radioactivity of gamma-ray sources as well as the detection and classification of the energy category (fast or thermal neutrons) of neutron sources, employing raw data acquired through a CZT detector. The neutron analysis algorithm is based on the fact that in the energy-spectrum channel of 558.6 keV emitted in the nuclear reaction ¹¹³Cd + ¹n → ¹¹⁴Cd + in the CZT detector, there is a notable difference in detection information between a CZT detector without a PE modulator and a CZT detector with a PE modulator, but there is no significant difference between the two detectors in other energy-spectrum channels. In addition, the gamma-ray analysis algorithm uses the difference in the detection information of the CZT detector between the unique characteristic energy-spectrum channel of a gamma-ray source and other channels. This efficient method of analyzing radiation characteristics is expected to be useful for the rapid radiation detection and accurate information collection on radiation sources, which are required to minimize radiation damage and manage accidents in national disaster situations, such as large-scale radioactivity leak accidents at nuclear power plants or nuclear material handling facilities.

• 한국수소및신에너지학회논문집
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• 제30권6호
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• pp.556-566
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• 2019

Recently, as the supply of residential polymer electrolyte membrane fuel cells (PEMFCs) increases, the durability and lifetime of the PEMFC system are becoming important. The related studies have been mainly focused on the durability and lifetime of materials while the research on the durability and maintenance of the system level is insufficient. In this paper, a model-based fault detection method is developed considering an air supply system that is dominant to the system performance and efficiency. A commercial 1 kW residential fuel cell system is built, and experiments are conducted under various operation loads and states (normal, 6 faults). From the experimental data, nominal models and residuals are generated. With the residual pattern obtained from real-time data, the detection and classification of various faults can be possible. The technical importance of this paper is to minimize extra sensor installation by using the empirical model rather than a complex mathematical model, and to decrease the number of models by using the applicable model at three loads. Finally, the model-based fault detection method for the air supply system of a PEMFC is established and is expected to be applicable to other subsystems.

• 한국정보통신학회논문지
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• 제23권12호
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• pp.1535-1541
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• 2019

방사선 사고 지역 및 제염이 필요한 지역에서의 안전하고 신속한 제염작업을 진행하기 위해서는 방사선 오염원에 대한 다양한 정보 획득이 필요하다. 특히 방사선원의 정확한 위치와 분포 정보의 파악은 신속한 후속 조치 및 오염원 제거를 위해 반드시 필요하며, 작업자의 방사선 피폭을 최소화할 수 있다. 방사선원의 위치와 분포 정보를 획득하기 위해서는 방사선 분포 탐지 장치를 사용한다. 방사선 분포 탐지 장치의 경우 일반적으로 탐지 센서 부가 단일 센서로 구성되며, 단일 센서의 물리적 한계로 인해 탐지 범위가 제한되는 문제점이 있다. 본 논문에서는 방사선 오염 분포 영상화 장치에 사용되는 단일 센서의 탐지 감도 제어를 위하여 보정 검출기를 적용하였으며, 이를 통해 제한적이었던 선량률 탐지 범위를 향상하였다. 또한 감마선 조사 시험을 통해 방사선 분포 탐지 범위의 개선을 확인하였다.

• 대한원격탐사학회지
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• 제39권4호
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• 전기학회논문지P
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• 제66권3호
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• pp.144-149
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• 2017

There is a little research on DC arc detection when compared to a large number of literature and patents on AC arc detection. However, as DC energy sources such as photovoltaic power generation facilities and fuel cells are introduced, research on DC arc has become as important as AC arc detection in terms of circuit protection and system reliability enhancement. In this paper, we have developed an arc detection method for photovoltaic system using frequency analysis. Through various experiments, it was confirmed that the proposed method effectively detects the arc.

• Journal of information and communication convergence engineering
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• 제1권4호
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• pp.205-208
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• 2003

Voice activity detection is a important problem in the speech recognition and speech communication. This paper introduces new feature parameter which are reconstructed by spectral entropy of information theory for robust voice activity detection in the noise environment, then analyzes and compares it with energy method of voice activity detection and performance. In experiments, we confirmed that spectral entropy and its reconstructed parameter are superior than the energy method for robust voice activity detection in the various noise environment.

• International Journal of Air-Conditioning and Refrigeration
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• 제11권3호
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• pp.125-131
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.

• 한국태양에너지학회 논문집
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• 제26권1호
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• pp.91-98
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• 2006

Islanding phenomenon of utility-connected photovoltaic power conditioning systems(PV PCS) can cause a variety of problems and must be prevented. If the real and reactive power supplied by PV PCS are closely matched to those of load, islanding detection by passive methods becomes difficult. The active frequency drift(AFD) method, called the frequency bias method, enables islanding detection by forcing the frequency of the voltage in the islanding to drift up or down. In this paper, non-detection zone(NDZ) of AFD is analyzed for the islanding detection method of utility-connected PV PCS by the simulation software tool PSIM.