• Korea-Australia Rheology Journal
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• 제16권4호
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• pp.163-168
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• 2004

In this study, an ultrasonic technique was employed to obtain pressure-volume-temperature (PVT) rela­tionship of polymer melt by measuring ultrasonic velocities under various temperatures and pressures. The proposed technique was applied to on-line monitoring of injection molding process as an attempt to predict quality of molded parts. From the comparison based on Tait equation, it was confirmed that the PVT behav­ior of a polymer is well described by the variation of ultrasonic velocities measured within the polymer medium. In addition, the changes in part weight and moduli were successfully predicted by combining the data collected from ultrasonic technique and artificial neural network algorithm. The results found from this study suggest that the proposed technique can be effectively utilized to monitor the evolution of solid­ification within the mold by measuring ultrasonic responses of various polymers during injection molding process. Such data are expected to provide a critical basis for the accurate prediction of final performance of molded parts.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.836-839
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• 2016

본 논문에서는 비디오 압축에서 사용되는 중요한 요소 중 하나인 움직임 예측 방법을 제안한다. 기존의 방법들은 연산량 감축으로 인한 화질 저화와 같은 문제점과 연산량 증가로 인한 문제점을 가지고 있다. 본 논문에서는 전 영역 탐색기반 예측 방법과 유사한 화질을 유지하면서 최적후보점을 이용하여 계산량을 줄이는 움직임 예측 방법을 제안한다. 실험결과에서 제안한 방법은 화질 저하가 전 영역 탐색 기반 예측과 비교하여 0.01dB 이하로 나타났으며, 계산량은 평균 3%~5% 정도이다.

• Journal of Biosystems Engineering
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• 제35권6호
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• pp.450-457
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• 2010

Although cherry tomato is one of major vegetables consumed in fresh vegetable market, the quality grading method is mostly dependant on size measurement using drum shape sorting machines. Using Visible/Near-infrared spectroscopy, apparatus to be able to acquire transmittance spectrum data was made and used to estimate firmness, sugar content, and acidity of cherry tomatoes grown at hydroponic and soil culture. Partial least square (PLS) models were performed to predict firmness, sugar content, and acidity for the acquired transmittance spectra. To enhance accuracy of the PLS models, several preprocessing methods were carried out, such as normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), and derivatives, etc. The coefficient of determination ($R^2_p$) and standard error of prediction (SEP) for the prediction of firmness, sugar, and acidity of cherry tomatoes from green to red ripening stages were 0.859 and 1.899 kgf, with a preprocessing of normalization, 0.790 and $0.434^{\circ}Brix$ with a preprocessing of the 1st derivative of Savitzky Golay, and 0.518 and 0.229% with a preprocessing normalization, respectively.

• 한국해양공학회지
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• 제22권2호
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• pp.34-41
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• 2008

As a basic study for developing a forecasting/estimating system that predicts water quality changes when Deep Sea Water (DSW) drains to the ocean after using it, this study was carried out as follows: 1) numerical simulation of the present state at DSW developing region in the East sea using SWEM, 2) numerical prediction of water quality changes by effluent DSW, 3) analysis of influence degree 'With defined DEI (DSW effect index) at F station. On the whole, when DSW drained to the ocean, Chl-a, COD and water-temperature were decreased and DIN, DIP and DO were increased by effluent DSW, and Salinity was steady. According to analysis of influence degree, the influence degree of DIN was the highest and it was high in order of Chl-a, COD, Water-temperature, DO, DIP and Salinity. The influence degree classified by DSW effluent position was predicted that suiface outflow was lower than bottom outflow. Ad When DSW discharge increased 10 times, the influence degree increased about $5{\sim}14$ times.

• 전기전자학회논문지
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• 제24권4호
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• pp.1136-1140
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• 2020

미세먼지로 인한 대기오염이 심각해지면서 미세먼지의 확산과 대기질의 예측에 대한 관심이 높아지고 있다. 미세먼지의 원인은 매우 다양한데, 일부 미세먼지는 산불, 황사 등을 통해 자연적으로 발생하기도 하지만 대부분은 석유, 석탄과 같은 화석연료를 태우거나 자동차 매연가스에서 나오는 대기오염물질에서 유발되는 것으로 알려져 있다. 본 논문에서는 미국 EPA에서 추천하는 CALPUFF 모델을 사용하고, CALPUFF에서 필요한 기상 요소인 3차원 바람장을 생성하는 기상 전처리 프로그램으로 CALMET 모델을 통해 바람장을 생성하여 CALPUFF 확산 모델링을 수행한다. 이를 통해 복잡한 지형을 반영한 미세먼지 확산모델링과 대기질 예측 시스템의 구조를 제안한다.

• 상하수도학회지
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• 제35권6호
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• pp.507-516
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• 2021

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

• Nuclear Engineering and Technology
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• 제56권1호
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• pp.48-55
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• 2024

The safety-related raw water system's strong operational condition supports the radiation defense and biological shield of nuclear plant containment structures. Gaps and failures in maintaining proper working condition of main equipment like pump were among the most common causes of unavailability of safety related raw water systems. We integrated the advanced data analytics tools to evaluate the maintenance records of water systems and gave special consideration to deficiencies related to pump. We utilized maintenance data over a three-and-a-half-year period to produce metrics like MTBF, MTTF, MTTR, and failure rate. The visual analytic platform using tableau identified the efficacy of maintenance & deficiency in the safety raw water systems. When the number of water quality violation was compared to the other O&M deficiencies, it was discovered that water quality violations account for roughly 15% of the system's deficiencies. The pumps were substantial contributors to the deficit. Pump availability was predicted and optimized with real time data using response surface method. The prediction model was significant with r-squared value of 0.98. This prediction model can be used to predict forth coming pump failures in nuclear plant.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1463-1466
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• 2002

This paper suggests the effective channel assignment scheme for mobility prediction handover. For maintaining required quality of service (QoS) during handover, there are handover algorithms these reserve the channel where the movement is predicted. But channel assignment schemes these have been studied are not considered mobility prediction handover. This paper suggests the channel assignment scheme that considers mobility predicted handover. The suggested algorithm maintains dropping probability of handover calls, decreases blocking probability of new calls and increases channel utilization.

• 한국빅데이터학회지
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• 제4권2호
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• pp.61-71
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• 2019

반도체 웨이퍼의 표면을 연마하여 평탄화하는 Chemical Mechanical Planarization(CMP) 공정은 다양한 화학물질과 물리적인 기계장치에 의한 작용을 받기 때문에 공정을 안정적으로 관리하기 힘들다. CMP 공정에서 품질 지표로는 Material Removal Rate(MRR)를 많이 사용하고, CMP 공정의 안정적 관리를 위해서는 MRR을 예측하는 것이 중요하다. 본 연구에서는 머신러닝 기법들을 이용하여 CMP 공정에서 수집된 시계열 센서 데이터를 분석하여 MRR을 예측하는 모형과 공정 품질을 해석하기 위한 분류 모형을 개발한다. 나아가 분류 결과를 분석하여, CMP 공정 품질에 영향을 미치는 유의미한 변수를 파악하고 고품질을 유지하기 위한 공정 조건을 설명한다.

• International Journal of Internet, Broadcasting and Communication
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• 제10권2호
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• pp.25-30
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• 2018

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.