• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.408-413
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• 2015

The quality prediction of the semiconductor industry has been widely recognized as important and critical for quality improvement and productivity enhancement. The main objective of this paper is to predict the final quality of semiconductor chips based on fail bit count information obtained from probe tests. Our proposed method consists of solving the data imbalance problem, non-parametric variable selection, and adjusting the parameters of the model. We demonstrate the usefulness and applicability of the proposed procedure using a real data from a semiconductor manufacturing.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.38-43
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• 1999

The EVRC vocoder is a toll quality coder, but it shows significant degradation or the quality in weak RF environment. In this paper, the speech quality degradation phenomenon of the EVRC is analyzed, and two methods are proposed as the solution - the pitch prediction and the gradual increase. The preference tests for various Rf environment are performed for speech quality assessments and both the methods show better performance.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.313-322
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• 1998

The distributions of chemical oxygen demand (COD) and suspended solid (SS) in Ulsan Bay were simulated and reproduced by a numerical ecosystem model for the practical application to the management of marine water quality and the prediction of water quality change due to coastal developments or the constructions of breakwater and marine facilities. Comparing the computed with the observed data of COD and SS in Ulsan bay the results of simulation were found to be good enough to satisfy the practical applications.

• Smart Media Journal
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• v.11 no.2
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• Journal of Environmental Science International
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• v.3 no.2
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• pp.77-88
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• 1994

When we conduct environmental impact assessment, main contents consist of summary, project outline, environmental conditions, environmental impacts due to the project, mitigation devices, and alternative measures of harmful impact on environment. In this Paper, to understand how they really conduct air quality impact assessment and prediction and examine their effectiveness, we considered the provisions and actual case of environmental impact assessment in Korea with that in Japan. As a result, we propose a method of improving air quality impact assessment and Prediction, such as reflection of the result in environmental impact assessment, detailed assessment focused on relatively important environmental impact elements, field measurement investigation over four season and seven sucessive days, the uniformity of units, the proper model development to predict environmental concentration and a biennial environmental impact assessment for ex post management.

• Design & Manufacturing
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• v.16 no.3
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.1-6
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• 2021

For competitive hotel business, the hotel must have a sound prediction capability to balance the demand and supply of hospitality products. To have a sound prediction capability in the hotel, it should be prepared to be equipped with a new technology such as quantum computing. The quantum computing is a brand new cutting-edge technology. It will change hotel business and even the whole world too. Therefore, we study the impact of quantum computing on supply chain management (SCM) and hotel performance. Toward the goal we have developed the research model including six constructs: quantum (computing) prediction, communication, supplier relationship, service quality, non-financial performance, and financial performance. The result of the study shows a significant influence of quantum (computing) prediction on hotel performance through the mediating role of SCM in the hotel. Quantum prediction is highly significant in enhancing the SCM in the hotel. However, the direct effect between the quantum prediction and hotel performance is not significant. The finding indicates that hotels which would install the quantum computing technology and utilize the quantum prediction could hugely benefit from the performance improvement.

• Journal of Environmental Impact Assessment
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• v.21 no.4
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• pp.543-552
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• 2012

Namgang mid-watershed is located in downstream of Nakdong river basin. There are many pollution sources arround this area and it's control is important to manage a water quality of Nakdong river. A target year of Namgang mid-watershed water environment management plan is 2013. To predict a water quality at downstream of Namgang, we have investigated and forecasted the pollutant source and it's loading. There are some plan to construction the sewage treatment plants to improve the water quality of Nam river. Those are considered on predicting water quality. As results, it is shown that the population is 343,326 and sewerage supply rate is 79.2% and the livestock is 1,662,000 in Namgang mid-watershed. It is estimated that the population is 333,980, the sewerage supply rate is 86.9% in 2013. The milk cow and cattle were estimated upward and the pigs were downward by 2013. The generated loading of BOD and TP is 75,957 kg/day and 4,311 kg/day, discharged loading is 18,481 kg/day and 988 kg/day respectively in 2006. It were predicted upward the discharged loading of BOD and TP by 4.08% and 6.3% respectively. The results of water quality prediction of Namgang4 site were 2.5 mg/L of BOD and 0.120 mg/L of TP in 2013. It is over the target water quality at that site in 2015 about 25.0% and 9.1% respectively. Consequently, there need another counterplan to reduce the pollutants in that mid-watershed.