• 전자공학회논문지
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• 제53권7호
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• pp.85-91
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• 2016

레이더 시스템은 방사 신호의 탐지를 회피하기 위해 펄스 반복주기(PRI, Pulse Repetition Interval)와 PRI 패턴을 변조하고 있으며, 반대로 레이더 신호 탐지 시스템은 다양한 노력을 기울여 PRI와 PRI 패턴을 감지하려고 한다. 일반적으로 레이더 신호의 PRI 패턴을 검출하기 위해 펄스열의 도착시각에 대한 히스토그램 또는 자기 상관관계 기법으로 펄스 변조를 검출하고 있다. 본 논문에서는 유한 상태 머신 개념을 도입하여 펄스 반복주기를 검출하는 알고리즘을 제안한다. 이 알고리즘은 PRI 순서와 PRI 패턴을 찾을 수 있는 특징이 있다.

• 한국산학기술학회논문지
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• 제10권12호
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• pp.3688-3693
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• 2009

본 논문에서는 두 가지 혼돈맵들 -톱니맵 $S_2(x)$ 와 텐트맵 $T_2(x)-$ 을 연결시킨 하나의 합성맵을 기초로 사용하는 독립된 하나의 합성상태머신을 설계하는 방법 및 그 결과을 제시하였다. 두 가지 다른 혼돈맵들 -톱니맵과 텐트맵- 의 합성 논리를 이용하여 설계된 독립된 하나의 합성상태머신에서 발생하는 혼돈적인 상태들을 그래프적으로 보였으며, 발생하는 의사 난수적인 상태들의 주기는 이산화된 진리표의 정밀도에 따른 길이를 갖는다는 것도 보였다.

• 한국소음진동공학회논문집
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• 제22권5호
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• pp.413-421
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• 2012

In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.

• 한국분말야금학회:학술대회논문집
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• 한국분말야금학회 2006년도 Extended Abstracts of 2006 POWDER METALLURGY World Congress Part2
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• pp.1084-1085
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• 2006

We studied formation of nanostructured $TiB_2$-Cu composites under shock wave conditions. We investigated the influence of preliminary mechanical activation (MA) of Ti-B-Cu powder mixtures on the peculiarities of the reaction between Ti and B under shock wave. In the MA-ed mixture the reaction proceeded completely while in the non-activated mixture the reagents remained along with the product . titanium diboride. The size of titanium diboride particles in the central part of the compact was 100-300 nm.

• 한국기계가공학회지
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• 제16권5호
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• pp.31-39
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• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• 한국게임학회 논문지
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• 제10권3호
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• pp.93-102
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• 2010

본 논문은 비둘기들의 다양한 상태에 대하여 집단행동을 자연스럽게 시뮬레이션하는 시스템을 설계하고 구현하는 것이다. 이것을 하기 위하여 비둘기들의 집단행동은 '날아가기', '내려앉기', '먹이먹기' 및 '날아오르기'와 같이 4개의 액션모델로 나뉘었다. 각 액션모델을 구성하는 조종힘들이 찾아졌으며, 유한상태기계 기법을 사용하여 설계되었다. 설계된 시스템은 오우거 엔진과 집적하여 구현되었다. 구현된 시스템의 시뮬레이션으로부터 비둘기들의 자연스러운 집단행동을 표현하는 조종힘에 대한 다양한 파라미터 값들을 찾을 수 있었다.

• Journal of Multimedia Information System
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• 제5권2호
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• pp.131-138
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• 2018

This work describes a binary image based fast Difference of Gaussian (DoG) filter using zero-dimensional (0-d) convolution and state machine look up tables (LUTs) for image and video stitching hardware platforms. The proposed approach for using binary images to obtain DoG filtering can significantly reduce the data size compared to conventional gray scale based DoG filters, yet binary images still preserve the key features of the image such as contours, edges, and corners. Furthermore, the binary image based DoG filtering can be realized with zero-dimensional convolution and state machine LUTs which eliminates the major portion of the adder and multiplier blocks that are generally used in conventional DoG filter hardware engines. This enables fast computation time along with the data size reduction which can lead to compact and low power image and video stitching hardware blocks. The proposed DoG filter using binary images has been implemented with a FPGA (Altera DE2-115), and the results have been verified.

• 정보처리학회논문지D
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• 제16D권1호
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• pp.65-72
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• 2009

현재 컴포넌트 기반 개발 기법은 재사용성과 생산성 측면에서 효과적인 소프트웨어 개발 방법으로 많은 각광을 받고 있다. 하지만 기존의 UML을 이용한 컴포넌트 기반 개발에서는 컴포넌트의 행위를 배제하고 컴포넌트 인터페이스만 참조하여 컴포넌트를 설계함으로써, 컴포넌트의 구체적인 행위에 대한 파악과 컴포넌트 간 인터페이스 호환성 보장이 불가능하다. 이에 따라 컴포넌트 설계 단계에서 컴포넌트의 행위를 상태기계 다이어그램으로 표현하고, 표현된 상태기계 다이어그램을 통해 컴포넌트의 행위 호환성을 보장할 필요가 있다. 이 연구에서는 상태기계 다이어그램으로 표현된 컴포넌트의 행위를 관찰 일치(observation equivalence)와 호출 일관성(invocation consistency)의 개념을 이용하여 행위 호환성을 검증하는 방법을 제공하고, 동적으로 이를 수행하는 도구를 개발한다.

• Nuclear Engineering and Technology
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• 제52권5호
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• pp.1008-1012
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• 2020

In order to make reasonable design for the improvement of comprehensive mechanical properties of RAFM steels, the design system with both machine learning and high-throughput optimization algorithm was established. As the basis of the design system, a dataset of RAFM steels was compiled from previous literatures. Then, feature engineering guided random forests regressors were trained by the dataset and NSGA II algorithm were used for the selection of the optimal solutions from the large-scale solution set with nine composition features and two treatment processing features. The selected optimal solutions by this design system showed prospective mechanical properties, which was also consistent with the physical metallurgy theory. This efficiency design mode could give the enlightenment for the design of other metal structural materials with the requirement of multi-properties.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.