• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.2
• /
• pp.108-112
• /
• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• Genomics & Informatics
• /
• v.11 no.4
• /
• pp.200-210
• /
• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.19 no.3
• /
• pp.319-329
• /
• 2016

Current research trends of prediction of possible structures, synthesis and explosive characteristics of polynitrogen molecules(PNs) are reviewed. Theoretically PNs are composed only of nitrogen atoms, in which N-N bonds are either single or double bonds, and thus when these molecules decompose, release of enormous energy is accompanied. From the middle of 20th century energetic material chemists have been seeking possible structures and the methods of synthesis of these new materials. As a results, from $N_4$ to $N_{60}$ together with their ions are predicted, and experimental chemists have been trying to synthesize these materials with a few success, including the famous ${N_5}^+$ ion in 1999. Although experimental successes are very rare beyond $N_5$ until today, the author believes that renovative ideas together with sincere efforts will bring someday next generation of high energy materials such as nitrogen fullerene($N_{60}$) in reality.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.6
• /
• pp.43-51
• /
• 2004

A method for form error prediction in side wall machining with a flat end mill is suggested. Form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. Developed model can predict the surface form error about three hundred times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.555-564
• /
• 2001

Wheel-rail noise is normally classified into three catagories ： rolling, squeal and impact noise. In this paper, rolling noise caused by the irregularity between a wheel and rail is analysed as follows： The irregularity between the wheel and rail is assumed as combination of sinusoidal profiles. Wheel-rail contact stiffness is linearized by using Hertzian contact theory, and then contact force between the wheel and rail is calculated. Vibration of the rail and wheel is calculated theoretically by receptance method or FEM depending on the geometry of wheel or rail for the frequency range of 100-5000Hz, important for noise generation. The radiation caused by those vibration is computed by BEM. To verify this analysis tools, rolling noise is calculated by preceding analysis steps using typical roughness data and it is compared with experimental rolling noise data. This analysis tools show reasonable results and used for the prediction of KTX rolling noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.11a
• /
• pp.512-517
• /
• 2000

The road traffic noise becomes aggravated due to the rapid increase of vehicles. It has a great effect on the dwelling environment. Therefore we investigate the characteristics and sources of the road traffic noise through grasping the status of the road traffic noise. This report is concerned with the description of the various factors affecting the generation and propagation of outdoor traffic noise. It is particularly concerned with the mathematical interpretation of these processes and the resulting development of prediction techniques which are now broadly used for both the environment impact assessment of road traffic noise and the planning and design of roads and adjoining land use.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.1 no.1
• /
• pp.29-38
• /
• 2009

A ship's screw-propeller produces thrust by rotation and, at the same time, generates rotational flow behind the propeller. This rotational flow has no contribution to the generation of thrust, but instead produces energy loss. By recovering part of the lost energy in the rotational flow, therefore, it is possible to improve the propulsion efficiency. The contra-rotating propeller (CRP) system is the representing example of such devices. Unfortunately, however, neither a design method nor a full-scale performance prediction procedure for the CRP system has been well established yet. The authors have long performed studies on the CRP system, and some of the results from the authors' studies shall be presented and discussed.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.287-296
• /
• 2022

Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.2
• /
• pp.15-20
• /
• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• Journal of Broadcast Engineering
• /
• v.23 no.3
• /
• pp.431-436
• /
• 2018

In this paper, a simple but effective algorithm to reduce the computational complexity of thumbnail generation and to improve image quality without aliasing artifacts is proposed. For the high speed decoding, the proposed algorithm performs partial decoding per $4{\times}4$ boundary in TU(Transform Unit), and preforms TU boundary in PU(Prediction Unit). The proposed method defines the weights based on intra prediction directions and estimates the thumbnail pixel by using that weights. this method remains thumbnail extraction time and improves thumbnail image quality compared with conventional algorithms.