• Defense and Technology
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• no.1 s.155
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• pp.44-49
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• 1992

세계 여러나라의 유도무기 체계에서 사용되고 있는 착화기술에 대한 현황을 기술과 성능, 신뢰성, 안전성, 가격 측면에서 분석한 것이다. 이로써 유도무기체계 적용을 위한 각 착화기술의 상대적인 장단점을 비교할수있고, 가격대비 성능개선에 대한 예측과 무기체계 시스템에 진보된 기술들이 대체되는 경향을 평가할 수있을 것이다. 동시에 각 무기체계에 효과적인 비용과 기술로 최적의 착화시스템을 선택할수 있도록 하는 안내역으로서 보다 현실적이고 다양한 결정을 하는 기초를 제공할수 있으리라 생각된다

• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.572-574
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• 2003

웹 서버 클러스터의 성능 개선을 위한 연구가 다양한 분야에서 이루어졌지만 로그 파일 분석과 같은 방식으로 접속 빈도를 통한 실시간 동적 자원 재분배에 관한 연구에만 대부분 초점을 맞추었다. 본 논문에서는 시구간별 접속 패턴 분석 결과를 기반으로 패턴을 예측하여 자원을 동적으로 재분배하는 메커니즘을 제안한다. 제안한 메커니즘은 불필요한 자원 낭비를 감소시켜 효율적인 자원 재분배를 통해 클러스터의 성능을 향상시킨다. 또한 시구간별 접속 패턴의 유사성을 증명한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.199-200
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• 2017

In order to meet the international performance requirements for eLoran testbed operation, it is necessary to measure ASF (Additional Secondary Factor) of vessel's route as well as differential correction and the provision using differential Loran (dLoran) station operation. According to HEA (Harbor Entrance and Approach) performance of the IMO, the position accuracy should be within 10meters. Therefore this paper presents the possibility to meet the position accuracy of the IMO HEA through simulation results.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.109-111
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• 2003

클러스터 시스템의 성능 향상을 위해서는 컴퓨팅 자원을 효과적으로 사용하여야 한다. 과거에는 전체 시스템 자원을 효과적으로 사용하기 위해 각 노드들의 부하를 균등하게 하는 방향으로 연구가 진행되어 왔으나, 부하 분산 시스템이 작업의 자원 요구 형태를 고려하여 작업을 배치하는 경우 성능을 더욱 향상시킬 수 있다. 현재까지는 이런 자원 요구 형태에 대한 선행지식을 과거 작업 실행 기록에 기반하여 유추해내는 방법을 많이 사용하였으나 이 방법은 잘못된 예측을 가져와 실행시간을 증가시킬 수 있다. 본 논문에서는 이를 해결하기 위해 유효 작업수라 불리는 새로운 노드의 부하 측정 척도를 제시한다. 유효 작업수를 이용한 부하 분산 시스템은 작업의 자원 요구 사항을 알지 못하더라도 부하 분산 과정에서 작업이 잘못 배치되어 실행시간이 증가하는 경우를 방지한다. 성능분석 결과는 과거 자료에 의한 예측을 사용하는 기존 방법에 비해 전체 실행시간의 감소로 성능이 향상되었음을 보여준다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.1-12
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• 2023

This paper assesses the suitability of existing standards for plastic material models and performance level evaluation methods in seismic performance evaluations of concrete dam piers during Maximum Credible Earthquakes (MCE). Dynamic plastic analysis was conducted to examine the applicability of the plastic material model under various conditions. As a result reveal that when the minimum reinforcement ratio is not met, the average stress-average strain method recommended in current dam seismic performance evaluation guidelines tends to underestimate pier responses compared to the predicted outcomes of dynamic elastic analysis. Consequently, the paper proposes an improvement plan that treats dam piers with an insufficient minimum reinforcement ratio as unreinforced and integrates fracture energy into concrete tensile behavior characteristics for performance level evaluation. Implementing these improvements can lead to more conservative evaluation outcomes compared to current seismic performance evaluation methods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.247-256
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• 2021

In this paper, we propose a packet loss concealment (PLC) algorithm using pitch harmonic motion prediction and adaptive signal amplitude prediction and. The spectral motion prediction method divides the spectral motion of the previous usable frame into predetermined sub-bands to predict and restore the motion of the lost signal. In the proposed algorithm, the speech signal is classified into voiced and unvoiced sounds. In the case of voiced sounds, it is further divided into pitch harmonics using the pitch frequency to predict and restore the pitch harmonic motion of the lost frame, and for the unvoiced sound, the lost frame is restored using the spectral motion prediction method. When the continuous loss of speech frames occurs, a method of adjusting the gain using the least mean square (LMS) predictor is proposed. The performance of the proposed algorithm was evaluated through the objective evaluation method, PESQ (Perceptual Evaluation of Speech Quality) and was showed MOS 0.1 improvement over the conventional method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.593-601
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• 2014

In this study, we investigated the methods of improving the capturing ability of acid fume by assessing the performance of slot-type external hood installed on both sides of an open surface tank for acid washing process. A field survey and the results of computational fluid dynamics revealed that capturing performance of existing hoods is very poor. To solve such problem, 'push-pull hood' that pushes from one side of an open surface tank and pulls on the other side was suggested. The initial prediction was that if a push-pull hood is used, the acid fume of an acid-washing tank surface could be moved towards the hood through the push flow. However, this study has confirmed that if the push flow velocity becomes too high, it could spread to other areas due to flooding from the hood. Therefore, if the push air supply is maintained at around 25 $m^3/min$(push 10 m/s), proper control flow is formed on the surface of a tank and acid fume that stayed at the upper part of the tank is smoothly captured toward the hood, significantly enhancing the capturing performance.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.59-64
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• 2022

한국표준질병사인분류(KCD)는 사람의 질병과 사망 원인을 유사성에 따라 체계적으로 유형화한 분류체계이다. KCD는 계층적 분류체계로 구성되어 있어 분류마다 연관성이 존재하지만, 일반적인 텍스트 분류 모델은 각각의 분류를 독립적으로 예측하기 때문에 계층적 정보를 반영하는 데 한계가 있다. 본 논문은 계층적 분류체계를 적용한 KCD 예측 모델을 제안한다. 제안 방법의 효과를 입증하기 위해 비교 실험을 진행한 결과 F1-score 기준 최대 0.5%p의 성능 향상을 확인할 수 있었다. 특히 비교 모델이 잘 예측하지 못했던 저빈도의 KCD에 대해서 제안 모델은 F1-score 기준 최대 1.1%p의 성능이 향상되었다.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.386-395
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• 2006

Multi-view video coding technology demands the very high efficient coding technologies, because it has to encode a number of video sequences which are achieved from a number of video cameras. For this purpose, multi-view video coding introduces the inter-view prediction scheme between different views, but it shows a limitation of coding performance enhancement by adopting only new prediction method. Accordingly, we are going to achieve the more coding performance by enhancing dequantizer perfermance. Multi-view video coding is implemented basically based on H.264/AVC and uses the same quantization/de-quantization method as H.264/AVC does. The conventional quantizer and dequantizer is designed with the assumption that input residual signal follows the Laplacian PDF. However, it doesn't follow the fixed PDF type always. This mismatch between assumption and real data causes degradation of coding performance. To solve this problem, we propose the efficient de-quantization method based on quantized coefficients distribution at decoder without extra information. The extensive simulation results show that the proposed algorithm produces maximum $1.5\;dB{\sim}0.6\;dB$ at high bitrate compared with that of conventional method.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.107-115
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• 2023

High turbidity in source water can have adverse effects on water treatment plant operations and aquatic ecosystems, necessitating turbidity management. Consequently, research aimed at predicting river turbidity continues. This study developed a multi-class classification model for prediction of turbidity using LightGBM (Light Gradient Boosting Machine), a representative ensemble machine learning algorithm. The model utilized data that was classified into four classes ranging from 1 to 4 based on turbidity, from low to high. The number of input data points used for analysis varied among classes, with 945, 763, 95, and 25 data points for classes 1 to 4, respectively. The developed model exhibited precisions of 0.85, 0.71, 0.26, and 0.30, as well as recalls of 0.82, 0.76, 0.19, and 0.60 for classes 1 to 4, respectively. The model tended to perform less effectively in the minority classes due to the limited data available for these classes. To address data imbalance, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm was applied, resulting in improved model performance. For classes 1 to 4, the Precision and Recall of the improved model were 0.88, 0.71, 0.26, 0.25 and 0.79, 0.76, 0.38, 0.60, respectively. This demonstrated that alleviating data imbalance led to a significant enhancement in Recall of the model. Furthermore, to analyze the impact of differences in input data composition addressing the input data imbalance, input data was constructed with various ratios for each class, and the model performances were compared. The results indicate that an appropriate composition ratio for model input data improves the performance of the machine learning model.