• 소음진동
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• 제6권6호
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• pp.827-834
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• 1996

본연구에서는 핵연료집합체의 검증계획의 일환으로 2차측 배관파단의 영향을 조사하였다. 원자로노심의 상세모델을 이용한 동적해석으로 배관파단에 의한 응답을 구하였다. 파단적 누설개념의 적용으로 10인치 이상의 고에너지 배관에 대하여 양단 파단이 설계에서 배제됨에 따라 본 연구에서는 주증기관과 급수관의 파단을 가정 하였다. 핵연료 집합체의 전단력, 굽힘모우멘트, 변위 및 지지격자체의 충격하중에 대하여 자세히 고찰하였고 이들 동적해석 결과를 이용하여 핵연료집합체의 구조적 건전성을 평가하였으며 사고조건에서 2차측 배관파단이 핵연료집합체의 구조적 건전성 에 미치는 영향을 검토하였다.

• 한국자동차공학회논문집
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• 제7권8호
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• pp.150-159
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• 1999

Exhaust system is composed of several parts. Among, them , design of muffler system strongly influences on engine efficiency and noise reduction. So , through comprehension of flow characteristics inside muffler is necessary . In this study , three-dimensional steady and unsteady compressible flow analysis was performed to understand the flow characteristics, pressure loss and amplitude variation of pulsating pressure. The computational grid generation was carried out using commercial preprocessor ICEM CFD/CAE. And the three-dimensional fluid motion inside the muffler was analyzed by STAR-CD, the computational fluid dynamics code. RNG k-$\varepsilon$ tubulence model was applied to consider the complexity of the geometry and fluid motion. The steady and unsteady flow field inside muffler such as velocity distribution, pulsating pressure and pressure loss was examined. In case of unsteady state analysis, velocity of inlet region was converted from measured pulsating pressure. Experimental measurement of pressure and temperature was carried out to provide the boundary and initial condition for computational study under three engine operating conditions. As a result of this study, we could identify the flow characteristics inside the muffler and obtain the pressure loss, amplitude variation of pulsating exhaust gas.

• 한국인터넷방송통신학회논문지
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• 제11권1호
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• pp.29-34
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• 2011

본 논문에서는 자동 원격검침을 위한 전력선 통신에서의 등화기 기법인 zero-forcing (ZF)와 minimum mean square error (MMSE) 기법을 제안하고 분석하였다. 전력선 통신을 이용한 효율적인 AMR 시스템 구현을 위해서 임펄스 노이즈와 다중경로 채널의 영향들은 완화되어야 한다. 이러한 영향들을 극복하기 위해, 앞서 말한 등화기 기법들을 사용하였다. 시스템 성능은 비트오류율 (BER)에 의해서 평가되어진다. 모의실험 결과로부터 등화기가 전력선 통신 시스템의 BER 성능을 현저하게 향상시키고 MMSE가 ZF보다 더 나은 성능을 제공함을 확인하였다. 본 논문의 실험 결과는 AMR 시스템뿐만 아니라, 다양한 스마트 그리드 서비스에도 적용될 수 있다.

• ETRI Journal
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• 제41권3호
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• pp.316-325
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• 2019

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

• 반도체디스플레이기술학회지
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• 제20권2호
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• pp.126-130
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• 2021

The ball grid array is one of the packaging methods that used in high density printed circuit board. Solder void defects caused by voids in the solder ball during the BGA process do not directly affect the reliability of the product, but it may accelerate the aging of the device on the PCB layer or interface surface depending on its size or location. Void inspection is important because it is related in yields with products. The most important process in the optical inspection of solder void is the segmentation process of solder and void. However, there are several segmentation algorithms for the vision inspection, it is impossible to inspect all of images ideally. When X-Ray images with poor contrast and high level of noise become difficult to perform image processing for vision inspection in terms of software programming. This paper suggests the solution to deal with the suggested problem by means of using Mask R-CNN instead of digital image processing algorithm. Mask R-CNN model can be trained with images pre-processed to increase contrast or alleviate noises. With this process, it provides more efficient system about complex object segmentation than conventional system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3889-3903
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• 2022

To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• 전력전자학회논문지
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• 제27권1호
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• pp.33-39
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• 2022

A multi-phase brushless direct current (BLDC) motor is widely used in large-capacity electric propulsion systems such as submarines and electric ships. In particular, in the field of military submarines, the polyphaser motor must suppress torque ripple in various failure situations to reduce noise and ensure stable operation for a long time. In this paper, we propose a polyphaser current control method that can improve efficiency and reduce torque ripple by minimizing the increase in stator winding loss at maximum output torque by controlling the phase angle and amplitude of the steady-state current during open circuit failure of the stator winding. The proposed control method controls the magnitude and phase angle of the healthy phase current, excluding the faulty phase, to compensate for the torque ripple that occurs in the case of a phase open failure of the motor. The magnitude and phase angle of the controlled steady-state current are calculated for each phase so that copper loss increase is minimized. The proposed control method was verified using hardware-in-the-loop simulation (HILS) of a 12-phase BLDC motor. HILS verification confirmed that the increase in the loss of the stator winding and the magnitude of the torque ripple decreased compared with the open phase fault of the motor.

• 한국통신학회논문지
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• 제35권12B호
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• pp.1183-1191
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• 2010

본 논문은 스마트 그리드를 위한 전력선 통신 시스템에서 데이터 전송률을 향상시키는 적응형 OFDM CP 길이 알고리즘에 대해 연구하였다. 본 논문에서 제안한 기법은 수신단의 CP controller에서 수신된 데이터 프레임과 지연된 동일 데이터 프레임을 상관 처리를 취함으로써 채널 지연 정보를 계산한 후, 즉시 그 정보를 송신단에 피드백 한다. 그 다음, 송신단에서는 다음 데이터 프레임에 대한 CP 길이를 조절하게 된다. Impulsive noise 모델로서, Middleton Class A 간섭 모델을 사용하였고, 성능은 패킷 전송률과 누적 패킷 전송률, 비트 오류율 측면에서 평가되었다. 모의실험 결과로부터 패킷 수가 증가할수록 데이터 이득(감소된 비트 양)이 커지지만, branch 수($N_{br}$)가 증가할수록 데이터 이득 폭은 감소한다는 것을 알 수 있었다. $N_{br}$ 이 3, 5, 10인 경우, 적응형 CP 길이 알고리즘과 고정된 CP 길이 기법의 BER 성능은 비슷하였다. 따라서 제안한 기법은 기존의 고정 CP 길이 기법과 비교하여 BER 성능 감소 없이 데이터 전송률 증가를 달성하였음을 확인할 수 있었다.

• 대한원격탐사학회지
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• 제24권2호
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• pp.179-188
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• 2008

본 연구는 3차원 소음지도 제작에 요구되는 도시공간 모델을 생성하고자한다. 이를 위해 센서데이터를 사용하지 않고 기 구축된 수치지도와 도화원도만을 이용하여 지면 및 건물의 3차원 모델을 생성하는 효율적인 방법을 제시하였다. 지면모델은 수치지도의 표고점과 등고선의 고도값을 격자로 내삽하여 생성한다. 건물모델은 수치지도에서 추출한 건물의 2차원 경계와 도화원도에 취득한 건물의 고도를 융합하여 생성한다. 제안된 방법은 영등포구 전역을 포함하는 수치지도와 서로 다른 시기에 생성된 3 set의 도화원도에 적용하였다. 생성된 도시공간모델은 소음분석 및 분석결과의 3차원 가시화에 성공적으로 활용되었다.