• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.133-136
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• 2003

In these days, the reliability analysis and prediction are applied for many industrial products and many products require guaranteeing the quality and efficiency of their products. In this study reliability prediction for core units of machine tools has been performed in order to improve and analyze its reliability. ATC(Automatic Tool Changer) and interface Card of PC-NC that are core component of the machine tools were chosen as the target of the reliability prediction. A reliability analysis tool was used to obtain the reliability data(failure rate database) for reliability prediction. It is expected that the results of reliability prediction be applied to improve and evaluate its reliability. Failure rate, MTBF (Mean Time Between Failure) and reliability for core units of machine tools were evaluated and analyzed in this study.

• 환경영향평가
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• 제4권3호
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• pp.31-39
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• 1995

Environmental Impact Assessment (EIA) is composed of various procedures, such as screening, scoping, inventory survey, prediction, assessment, alternative assessment, mitigation measures, and post management. Environmental monitoring data for air quality or water quality, etc. is applied in the EIA process, especially in prediction and post management. As an effective tool of environmental monitoring, the remote sensing method, introduced recently, was used in collecting nationwide data concerning ecosystem and land use. This article explains the current monitoring status in Korea. Monitoring factors include air quality, water quality, soil, ocean, odor, noise, and ecosystems. This report explains the organization of the environmental monitoring system managed by the Ministry of Environment in Korea. Furthermore, it shows the environmental criteria and environmental policies applied to EIA in Korea.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.137-140
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• 2007

A prediction model for the sound quality of household refrigerator noise was proposed by investigating subjective and objective attributes of the noise [Jeon et al. (2007) Appl. Acoust.]. In the present study, the just noticeable difference (JND) of each sound quality metric - Zwicker's loudness, sharpness, roughness and fluctuation strength - which constitute the prediction model was investigated. Loudness of recorded sound samples from five refrigerators were varied according to constant intervals in sound pressure levels. Sharpness was also changed at 14-16 barks. Auditory experiments were conducted to discriminate the JNDs of loudness and sharpness by method of limit. The results indicated that JNDs of loudness and sharpness were 0.50 sone and 0.08 acum, respectively.

• 한국농공학회지
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• 제45권7호
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• pp.83-93
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• 2003

This research compared the observed and model predicted results that include; runoff, sediment yield, and nutrient losses from a 2.71 ha cattle grazing pasture field in North Alabama. Application of water quality computer simulation models can inexpensively and quickly assess the impact of pasture management practices on water quality. AGNPS single storm based model was applied to the three pasture species; Bermudagrass, fescue, and Ryegrass. While comparing model predicted results with observed data, it showed that model can reasonably predict the runoff, sediment yield and nutrient losses from the watershed. Over-prediction and under-prediction by the model occurred during very high and low rainfall events, respectively. The study concluded that AGNPS model can be reasonably applied to assess the impacts of pasture management practices and chicken litter application on water quality.

• IEIE Transactions on Smart Processing and Computing
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• 제4권6호
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• pp.422-433
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• 2015

In this paper, we demonstrate inter-layer prediction tools for scalable video coders. The proposed scalable coder is designed to support not only spatial, quality and temporal scalabilities, but also view scalability. In addition, we propose quad-tree inter-layer prediction tools to improve coding efficiency at enhancement layers. The proposed inter-layer prediction tools generate texture prediction signal with exploiting texture, syntaxes, and residual information from a reference layer. Furthermore, the tools can be used with inter and intra prediction blocks within a large coding unit. The proposed framework guarantees the rate distortion performance for a base layer because it does not have any compulsion such as constraint intra prediction. According to experiments, the framework supports the spatial scalable functionality with about 18.6%, 18.5% and 25.2% overhead bits against to the single layer coding. The proposed inter-layer prediction tool in multi-loop decoding design framework enables to achieve coding gains of 14.0%, 5.1%, and 12.1% in BD-Bitrate at the enhancement layer, compared to a single layer HEVC for all-intra, low-delay, and random access cases, respectively. For the single-loop decoding design, the proposed quad-tree inter-layer prediction can achieve 14.0%, 3.7%, and 9.8% bit saving.

• 한국전자파학회논문지
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• 제19권5호
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• pp.587-596
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• 2008

도심지 무선 인터넷 서비스에서 전파 특성(wave propagation characteristics)을 정화하게 예측하여 서비스 영역을 결정하는데 있어서 최적의 기지국 선정, 셀 설계 등은 매우 중요한 과정이다. 서비스 지역의 지형 지물 및 인위적 구조물의 건물 재질 및 높이와 폭 등 각기 다른 특징으로 인하여 무선망 서비스의 송수신 거리에 큰 영향을 미치고 있으며, 이는 기본적으로 요구되어지는 무선 인터넷 품질을 정확하게 예측 및 분석하여 이용자에게 서비스를 제공하는데 큰 어려움을 갖게 한다. 본 논문에서는 이러한 문제점을 개선하기 위한 전파 예측 모델에 의한 기본 기지국 위치 선정 후 가장 서비스 영향을 미치는 기지국 위치 이동 및 안테나의 각도 등 무선망 최적화를 결정짓는 파라미터 값의 변화에 따라 서비스 영역이 최적화 되어 서비스 지역 덴 품질이 개선되는 과정을 시뮬레이션 함으로써 무선방 기지국 최적화 과정을 통하여 동일 지역 내 서비스 커버리지가 넓어지고 개선된 품질로서 이용자들이 질 높은 무선 인터넷 서비스를 제공받을 수 있게 된다는 것을 보여 주었다.

• 상하수도학회지
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• 제36권4호
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• 한국음향학회지
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• 제32권3호
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• pp.227-236
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• 2013

본 논문에서는 SAOC의 Karaoke 모드의 출력 신호 내에 존재하는 잔여 보컬 성분을 추정하여 억제시킴으로써 음질을 향상시킬 수 있는 알고리듬을 제안하였다. 잔여 보컬 성분은 Karaoke 모드 환경으로 합성된 신호와 Solo 모드로 새로 합성된 신호를 서로 교차 예측하여 추정될 수 있다. 그러나, 두 신호는 모두 같은 다운 믹스 신호로부터 합성되는 신호이므로, 두 신호간의 높은 상관성으로 인하여 가라오케 신호내의 잔여 보컬 성분뿐만 아니라 음악 성분도 함께 제거된다. 이러한 열화를 해결하기 위해, 본 논문에서는 교차 예측 과정에서 심리 음향적 특성을 고려한 예측 방해 신호를 적용하였으며, 이 신호의 크기는 심리음향모델의 마스킹 특성에 따라 음악적 음질의 열화가 최소화되도록 적응적으로 설정되었다. 실험은 보컬 객체가 포함된 음악 신호에 대해서 객관적 및 주관적 음질평가를 수행하였으며, 전체적으로 성능 향상이 있음을 확인하였다.

• 방송공학회논문지
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• 제24권6호
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• pp.1002-1012
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• 2019

360VR 콘텐츠는 시청자의 시점변화에 따른 즉각적인 반응이 필요하고 고화질의 영상이 제공되어야 한다. 따라서 한정된 대역폭에서 360VR 시청자의 만족도를 보장하는 효율적인 전송기술이 필요하다. 그 일환으로 사용자의 시점을 예측하고 시점에 해당하는 영역과 해당하지 않는 영역에 다른 비트율을 할당하여 전체 대역폭 소모를 감소시키는 연구들이 소개되고 있다. 본 논문에서는 시점 예측의 정확도 향상을 목표로 기존 시각인지 정보만 활용했던 방식에 추가적으로 청각인지 정보인 360VR 콘텐츠의 음원위치정보를 활용한 시점 예측을 제안한다. 또한, 향상된 시점예측 방식을 이용하여 비트율을 효율적으로 할당함으로써 개선된 성능을 제공하는 전송 방식을 제안한다. 성능 분석 결과 제안한 시점 예측방식은 기존 방식 대비 시점 예측의 정확도가 향상되었으며, 이를 바탕으로 제안한 전송 방식은 제한된 대역폭 내에서 사용자의 시점에 해당하는 타일에 고품질의 영상을 제공할 수 있음을 확인하였다.

• 한국물환경학회지
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• 제38권6호
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• pp.292-305
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• 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data.The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.