• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.134-143
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• 2010

This paper proposes a framework for classifying metric-based software quality prediction models, especially case of software criticality, into four types. Models are classified along two vectors: input metric forms and the necessity of past project data. Each type has its own characteristics and its strength and weakness are compared with those of other types using newly defined criteria. Through this qualitative evaluation each organization can choose a proper model to suit its environment. My earlier studies of criticality prediction model implemented specific models in each type and evaluated their prediction performances. In this paper I analyze the experimental results and show that the characteristics of a model type is the another key of successful model selection.

• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.143-150
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• 1998

We propose an adaptive monitoring a, pp.oach for serially correlated data. This algorithm uses the adaptive linear prediction lattice filter (ALPLF) which makes it compute process parameters in real time and recursively update their estimates. It involves computation of the forward and backward prediction errors. CUSUM control charts are a, pp.ied to prediction errors simulaneously in both directions as an omnibus method for detecting changes in process parameters. Results of computer simulations demonstrate that the proposed adaptive monitoring a, pp.oach has great potentials for real-time industrial a, pp.ications, which vary frequently in their control environment.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.655-673
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• 2004

This paper is concerned with analysing the bankruptcy prediction power of three models: Multivariate Discriminant Analysis(MDA ), Logit Analysis, Neural Network. The after-crisis bankrupted companies were limited to the research data and the listed companies belonging to manufacturing industry was limited to the research data so as to improve prediction accuracy and validity of the model. In order to assure meaningful bankruptcy prediction, training data and testing data were not extracted within the corresponding period. The result is that prediction accuracy of neural network model is more excellent than that of logit analysis and MDA model when considering that execution of testing data was followed by execution of training data.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.84-90
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• 2019

Tremendous computation of full search or lossless motion estimation algorithms for video coding has led development of many fast motion estimation algorithms. We still need proper control of computation and prediction quality. In the paper, we suggest an algorithm that reduces computation effectively and controls computational amount and prediction quality, while keeping prediction quality as almost the same as that of the full search. The proposed algorithm uses multiple thresholds for partial block sum and times of counting unchanged minimum position for each step. It also calculates the partial block matching error, removes impossible candidates early, implements fast motion estimation by comparing times of keeping the position of minimum error for each step, and controls prediction quality and computation easily by adjusting the thresholds. The proposed algorithm can be combined with conventional fast motion estimation algorithms as well as by itself, further reduce computation while keeping the prediction quality as almost same as the algorithms, and prove it in the experimental results.

• Journal of Korean Society for Quality Management
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• v.35 no.2
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• pp.45-52
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• 2007

The danger of self-ignition of single base propellants will increase with time. Therefore, a good prediction of the safe storage time is very important. In order to determine the remaining shelf-life of the propellants, the content of stabilizer is determined. The propellants stored under normal storage conditions about 10 to 18 years were investigated and accelerated aging test was carried out by storing propellant sample at higher temperature. Finally, we analyzed the results by various methods in order to show the best way to predict the realistic shelf-life. The safe storage life of the propellants will be 24 years, at least 15 years. In case of applying Arrhenius's law, using the reaction rate constant at 28$^{\circ}C$ to 30$^{\circ}C$ to predict the shelf-life by accelerated aging test is reasonable for a good prediction.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.321-326
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• 2006

The danger of self-ignition of single base propellants will increase with time. Therefore, a good prediction of the safe storage time is very important In order to determine the remaining shelf1ife of the propellants, the content of stabilizer is determined. The propellants stored under normal storage conditions about 10 to 18 years were investigated and accelerated aging test was carried out by storing propellant sample at higher temperature. Finally, we analyzed the results by various methods in order to show the best way to predict the realistic shelflife. The safe storage life of the propellants will be 24 years, at least 15 years. In case of applying Arrhenius's law, using the reaction rate constant at $28^{\circ}C$ to $30^{\circ}C$ to predict the shelflife by accelerated aging test is reasonable for a good prediction.

• Food Science and Biotechnology
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• v.14 no.5
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• pp.628-633
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• 2005

Muscle fiber characteristics were evaluated for predictability of meat quality traits using 231 crossbred pigs. Muscle $pH_{45min}$, R-value, and $pH_{24hr}$ were selected to estimate regression equation model of drip loss and lightness, although variances of coefficient estimates could only account for small part of drip loss (about 16.3 to 25.3%) and lightness (about 16.9 to 31.7%). Muscle $pH_{24hr}$ was represented to drip loss and lightness, which explained corresponding 25.3 and 31.7% of estimation in drip loss and lightness, respectively. Area percentage of type IIb fiber significantly contributed to prediction of metabolic rate and meat quality. However, equations predicting meat quality traits based on area percentage of type IIb fiber alone are less useful than ones based on early postmortem parameters. These results suggest estimated model using both metabolic properties of muscle and postmortem metabolic rate could be used for prediction of pork quality traits.

• Journal of Environmental Impact Assessment
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• v.9 no.1
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• pp.13-24
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• 2000

This paper presents a study of water quality model applications in environmental impact statements which were submitted during recent years in Korea. Most of the applications have reported that the development projects would have significant impacts on the water quality, especially, of streams and rivers. The water quality models, however, were hardly used as an impact prediction tool. Even in the cases where models were used, calibration and verification studies were not performed and thus the predicted results would not be reliable. These poor model applications in environmental impact assessment can be attributable to the fact that there were no available model application guidelines as well as no requirements by the review agency. In addition, the expected waste loads were improperly estimated in most cases, especially in non-point sources, and the predicted parameters were not good enough to understand water quality problems expected from the proposed plans. The effects of mitigation measures were not analyzed in most cases. Again, these can be attributed to no formal guidelines available for impact predictions until now. A brief guideline is described in this paper, including model selection, calibration and verification, impact prediction, and analysis of effects of mitigation measures. The results of this study indicate that the model application should be required to overcome the current improper predictions of environmental impacts and the guidelines should be developed in detail and provided.