• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.820-828
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• 2008

In order to achieve high coding efficiency, H.264/AVC video coding standard adopts the techniques such as variable block size coding, motion estimation with quarter-pel precision, multiple reference frames, rate-distortion optimization, and etc. However, these coding methods have a defect to greatly increase the complexity for motion estimation. Particularly, from multiple reference frame motion estimation, the computational burden increases in proportion to the number of the searched reference frames. Therefore, we propose the method to reduce the complexity by controlling the number of the searched reference frames in motion estimation. Proposed algorithm uses the optimal reference frame information in both $P16{\times}16$ mode and the adjacent blocks, thus omits unnecessary searching process in the rest of inter modes. Experimental results show the proposed method can save an average of 57.31% of the coding time with negligible quality and bit-rate difference. This method also can be adopted with any of the existing motion estimation algorithm. Therefore, additional performance improvement can be obtained.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.37-44
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• 2003

In this paper, we investigated the performance of both DNA coding method and Genetic Algorithm(GA) in numeric pattern (from 0 to 9) recognition. The performance of the DNA coding method is compared to the that of the GA. GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by Adenine(A), Cytosine(C), Guanine(G) and Thymine(T). To compare the performance of both method, the same genetic operators(crossover and mutation) are applied and the probabilities of crossover and mutation are set the same values. The results show that the DNA coding method has better performance over GA. The reasons for this outstanding performance are multiple candidate solution presentation in one string and variable solution string length.

• Journal of IKEEE
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• v.22 no.3
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• pp.672-679
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• 2018

Recently, microgrid (MG) has been proposed as one of the most critical solutions for various energy problems. For the optimal and economic operation of MGs, it is very important to forecast the load profile. However, it is not easy to predict the load accurately since the load in a MG is small and highly variable. In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings were used. The experimental results show that the proposed approach outperforms the conventional forecasting methods.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.511-525
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• 2021

Aluminium outfitting is widely used in offshore platforms owing to its anti-corrosion ability and its light weight. However, various standards exist (ISO, NORSOK and EN) for the design of handrails used in offshore platforms, and different suppliers have different criteria. This causes great confusion for designers. Moreover, the design load required by the standards is not clearly defined or is uncertain. Thus, many offshore projects reference previous project details or are conservatively designed without additional clarification. In this study, all of the codes and standards were reviewed and analysed through prior studies, and data on variable factors that directly and indirectly affect the handrails applied to offshore platforms were analysed. A total of 50 handrail design load scenarios were proposed through deterministic and probabilistic approaches. To verify the proposed new handrail design load selection scenario, structural analysis was performed using SACS (offshore structural analysis software). This new proposal through deterministic and probabilistic approaches is expected to improve safety by clarifying the purpose of the handrails. Furthermore, the acceptance criteria for probabilistic scenarios for handrails suggest considering the frequency of handrail use and the design life of offshore platforms to prevent excessive design. This study is expected to prevent trial and error in handrail design while maintaining overall worker safety by applying a loading scenario suitable for the project environment to enable optimal handrail design.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.95-102
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• 2015

This paper proposes a simple algorithm for circuit minimization. There are currently two effective heuristics for circuit minimization, namely manual Karnaugh maps and computable Quine-McCluskey algorithm. The latter, however, has a major defect: the runtime and memory required grow $3^n/n$ times for every increase in the number of variables n. The proposed algorithm, however, extracts the prime implicants (PI) that cover minterms of a given Boolean function by deriving an implicants table based on frequency. From a set of the extracted prime implicants, the algorithm then eliminates redundant PIs again based on frequency. The proposed algorithm is therefore capable of minimizing circuits polynomial time when faced with an increase in n. When applied to various 3-variable and 4-variable cases, it has proved to swiftly and accurately obtain the optimal solutions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.699-702
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• 2003

Most of current shopping malls do not satisfy everyone because they present arrangements of goods and suggestions uniformly and comprehensively according to the thinking of their managers. On the other hand not the standard of selection but the comparison of price plays a decisive role of the purchase of goods as similar form each other. When classifying into groups according to generations, gender, income, job, hobby, etc. the propensity of purchase is showed differently and the interest and real purchasing power of the individual is different in shopping malls. It also will maximize the purchasing power of customers to make and implement the sales strategy more quickly as the basis of fashion and season of environmental factors and natural calamity of environmental variable according to the economic principle. This paper concentrates on the design and implementation of intelligent shopping mall that is added the sales strategy according to environmental variable and can not only analysis, update and classify the propensity of purchase continuously but also construct optimal goods automatically.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.3
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• pp.65-77
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• 2015

The urban park has important functions as a habitat for wildlife as well as open space of rest and community for people. This study was carried out to find what factors of structure and vegetation of urban parks could affect forest bird species diversity in Cheonan city. The study surveyed bird and vegetation species in 26 urban parks, Cheonan city. A correlation analysis and multiple linear regressions were performed to test whether habitat structure and vegetation were the major correlate with species diversity. The results showed the Dujeong park was the most high bird species diversity (H' = 2.13), and the Dujeong-8 park (H' = 2.02) and the Cheongsa park (H' = 1.73) were considerably higher than the other urban parks. The variables that were strongly correlated with bird species diversity were park area, number of subtree species, canopy of shrub, number of shrub species, shape index, canopy of subtree, canopy of tree, and impervious surface ratio. The regression of bird species diversity against the environmental variables showed that 3 variables of park area, canopy of subtree, and canopy of tree were included in the best model. Model variable selection was broadly similar for the 5 optimal models. It means park area and multi-layer vegetation were the most consistent and significant predictor of bird species diversity, because urban parks were isolated by built-up areas. Especially the subtree coverage that provides shelter and food for forest birds was an important variable. Therefore, to make parks circular-shaped and abundant multi-layer vegetation, which could be a buffer to external disturbances and improve the quality of habitats, may be used to enhance species diversity in creation and management of urban parks.