• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.1
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• pp.57-62
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• 2017

The purpose of this study is to develop a method for diagnosing the degree of gradual degradation of a reciprocating pump caused by continuous use as a water supply pump in a nuclear power plant. Normally, the progress of such degradation is too slow to be noticed. Hence, it is difficult to determine the degree of degradation using the existing diagnostic methods. In this paper, we propose a new method by which the normal state and the degraded state of the pump can be differentiated, so that the degree of degradation can be identified. First, an emulator was developed using FPGA by providing the parameters of the pump under normal state, so that the emulator generates the information of the pump in the healthy state. Then, by comparing this information with the parameters received from various output sensors of the emulator during the current state, it is possible to identify and measure the degree of gradual degradation. This paper presents some of the results obtained during the development process, and results that show how the emulator operates, by comparing the data collected from an actual pump.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.853-862
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• 2019

In this paper, we designed a 4.5kW X-band SSPA transmitter to replace the TWTA search radar transmitter with low MTBF and high maintenance cost. The transmitter is designed for the performance of over 520W average transmission output and 4.0kW maximum transmission output. In particular, by implementing a graceful degradation, it is designed to maintain better performance than conventional TWTA transmitter up to 40% (13 assembly modules) failure level of 200W power amplifier assembly. Through an experiment on the effective range of X-band, the performance of proposed transmitter verified the values of the maximum transmission output 6.1kW, spurious output 69.16dBc, RF pulse rising time 15.2ns and RF pulse falling time 16.3ns. The experiment confirmed the change of output power according to the graceful degradation due to fault injection.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.317-319
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• 1999

공간 데이터베이스에서 공간 데이터간의 근접성을 알아보아야 할 필요가 많이 발생한다. 이를 위해, 본 연구에서는 방향 관계와 거리 관계가 혼합된 형태의 질의에서 공간적으로 가까운 객체를 순서적으로 추출해내는 방법을 제안한다. 점진적 거리 조인 알고리즘을 근간으로 섹터 기반 모델을 적용하여 객체들을 순서적으로 추출할 수 있도록 한다. 섹터의 분류와 섹터들간 정렬 순서에 따라 추출된 값을 필요에 따라 제한조건의 조절이 가능하다는 장점이 있다. 또한, 점진적 거리조인 알고리즘에 있어서의 데이터 크기에 따른 성능 저하 문제도 어느 정도 해결할 수 있음을 기술하고 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.1-7
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• 2002

Since multimedia data such as video and audio data are displayed within a certain time constraint, their computation and manipulation should be handled under limited condition. Traditional real-time scheduling algorithms could not be directly applicable, because they are not suitable for multimedia scheduling applications which support many clients at the same time. Rate Regulating Proportional Share Scheduling Algorithm is a scheduling algorithm considered the time constraint of the multimedia data. This scheduling algorithm uses a rate regulator which prevents tasks from receiving more resource than its share in a given period. But this algorithm loses fairness, and does not show graceful degradation of performance under overloaded situation. This paper proposes a new modified algorithm, namely Modified Proportional Share Scheduling Algorithm considering the characteristics of multimedia data such as its continuity and time dependency. Proposed scheduling algorithm shows graceful degradation of performance in overloaded situation and the reduction in the number of context switching. Furthermore, a new evaluation method is proposed which can evaluate the flexibility of scheduling algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.221-222
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• 2014

본 논문은 레일리 채널에서 차량이 이동할 때 같은 차선에서 같은 방향으로 이동하는 인접 차량들과의 통신성능과 다른 차선에서 반대 방향으로 이동하는 차량들과의 통신성능을 일반도로에서 상대속도에 따라 분석한 것으로서 그결과 상대속도가 점진적으로 증가할수록 통신성능이 저하되는 현장이 발생하였다.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.540-545
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• 2023

AESA radar is able to instantaneously and adaptively position and control the beam, and this enables to have interleaved mode in modern airborne AESA radar which can maximize situational awareness capability. Interleaved mode provides two or more modes simultaneously, such as Air to Air mode and Sea Surface mode by time sharing technique. In this interleaved mode, performance degradation is inevitable, compared with single mode operation, and effective resource allocation is the key component for the success of interleaved mode. In this paper, we identified performance evaluation items for each mode to analyze interleaved mode performance and proposed effective resource allocation methodology to achieve graceful performance degradation of each mode, focusing on detection range. We also proposed beam scheduling techniques for interleaved mode.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.497-501
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• 2022

대화 상태 추적(Dialogue State Tracking)은 특정 목적을 달성하기 위한 대화 시스템인 목적 지향 대화 시스템의 핵심 부분으로, 대화에서 표현된 사용자의 목적을 추출한다. 조기 위험 검출 시스템은 연속적으로 들어오는 정보를 바탕으로 분류 대상인지 아닌지를 판별하며, 정확도 저하를 피하면서 최대한 빠르게 분류하는 것을 목표로 한다. 본 연구에서는 대화 상태 추적 시스템에서 나온 은닉층을 입력으로 하여 실시간으로 공황 장애 여부를 점진적으로 조기 분류하는 시스템과 조기 분류를 위한 새로운 손실 함수를 제안한다. 조기 위험 검출 시스템에 대화 상태인 belief state의 정보를 함께 사용했을 때, 큰 성능 향상을 보였으며 대화 상태가 조기 위험 검출에 필요한 정보를 담고 있음을 확인할 수 있다.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.568-576
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• 2001

Most of clustering algorithms data to degenerate rapidly on high dimensional spaces. Moreover, high dimensional data often contain a significant a significant of noise. which causes additional ineffectiveness of algorithms. Therefore it is necessary to develop algorithms adapted to the structure and characteristics of the high dimensional data. In this paper, we propose a clustering algorithms CLIP using the projection The CLIP is designed to overcome efficiency and/or effectiveness problems on high dimensional clustering and it is the is based on clustering on each one dimensional subspace but we use the incremental projection to recover high dimensional cluster and to reduce the computational cost significantly at time To evaluate the performance of CLIP we demonstrate is efficiency and effectiveness through a series of experiments on synthetic data sets.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.236-239
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• 2011

품사 태깅에서 오류는 같은 가중치를 가지는 것으로 간주되어 왔다. 하지만 품사 태깅의 결과를 활용하는 다른 자연어 처리 기술에 태깅 오류가 얼마나 영향을 미칠 수 있는가에 따라 품사 태깅 시 발생하는 오류가 가지는 가중치를 다르게 보아야 한다. 심각한 오류는 이를 활용하는 자연어 처리 기술의 성능 저하를 크게 야기하지만, 사소한 오류는 성능의 저하를 야기하지 않거나 그 영향이 미미하다. 본 논문에서는 품사 태깅 시, 전체적인 성능을 유지하면서 심각한 오류를 줄이는 것을 목표로 한다. 이를 위해 두 가지 점진적 손실 함수(gradient loss function)를 제안한다. 제안한 손실 함수는 심각한 오류에 사소한 오류보다 더 큰 가중치를 줌으로써 품사 태깅 모델이 심각한 오류에 더 집중하여 성능을 최적화하도록 한다. 실험에서 제안한 손실 함수를 활용한 태깅 모델은 기존의 방법에 비해 심각한 오류를 효과적으로 줄일 뿐만 아니라 전체적으로 더 높은 정확도를 보였다.