• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.1
• /
• pp.184-191
• /
• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.1
• /
• pp.15-27
• /
• 2013

While the vehicle detector is collected from a variety of information was mainly used as a real-time data. Recently scheme of application for archived traffic data has become increasingly important. In this background, this research were conducted on the improvement of the preprocessing for archived traffic data application. The purpose of improving specific preprocessing was reflect transportation phenomena by traffic data. As evaluation result, improvement preprocessing was close to the actual value than exist preprocessing.

• KIISE Transactions on Computing Practices
• /
• v.20 no.12
• /
• pp.615-622
• /
• 2014

The Korean film market has rapidly achieved an international scale, and this has led to a need for decision-making based on analytical methods that are more precise and appropriate. In this modern era, a highly advanced information environment can provide an overwhelming amount of data that is generated in real time, and this data must be properly handled and analyzed in order to extract useful information. In particular, the preprocessing of large data, which is the most time-consuming step, should be done in a reasonable amount of time. In this paper, we investigated a big data preprocessing method for predicting movie box office success. We analyzed the movie data characteristics for specialized preprocessing methods, and used the Hadoop MapReduce framework. The experimental results showed that the preprocessing methods using big data techniques are more effective than existing methods.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.141-146
• /
• 2008

As the interest about ubiquitous environment is increasing, there are many researches about the services in this environment. These services have important issues in interpreting the users' context, using many kinds of sensors, like PDA, GPS and accelerometers. Low level raw data, which sensors like accelerometers calibrates, are hard to use, and to provide real-time services preprocessing and interpreting the data into context, in real-time, is important. This paper describes a context integrate system which can integrate these sensors and also sensors which has raw data, like accelerometers and physiological sensors, and define the context interpret rule with XML. The proposing system reduces programming operations when adding a sensor to the sensor network or modifying the context interpreting rule by using XML. By using this system, we implemented a real-time data monitoring system which can describe the numeric data into graphs, and assist the user to validate the data and results of the preprocess phase, and also support the external services and applications to use the context of the user.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.2
• /
• pp.53-60
• /
• 2023

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2002.10a
• /
• pp.62-69
• /
• 2002

With the development of CUP speed and graphic technology, real-time simulation of deformable object is embossed as an essential issue in engineering field. Recently, it has been applied to the surgical training and game animation with haptic force feedback. But real time simulation of deformable objects is not easy because of the conflicting demands of speed and low latency and physical accuracy. In this study, we present the implementation of boundary element method(BEM) which is combined with the nonuniform B-spline surface. It is working together with the real-time simulation technique and the geometry data is altered by handling control points without preprocessing routine.

• Journal of Welding and Joining
• /
• v.17 no.2
• /
• pp.53-60
• /
• 1999

Conventionally, CCD camera and vision sensor using the projected pattern of light is generally used to inspect the weld bead defects. But with this method, a lot of time is needed for image preprocessing, stripe extraction and thinning, etc. In this study, laser vision sensor using the scanning beam of light is used to shorten the time required for image preprocessing. The software for deciding whether the weld bead is in proper shape or not in real time is developed. The criteria are based upon the classification of imperfections in metallic fusion welds(ISO 6520) and limits for imperfections(ISO 5817).

• Journal of Information Technology Services
• /
• v.21 no.4
• /
• pp.91-103
• /
• 2022

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.150-150
• /
• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• International Journal of Contents
• /
• v.13 no.1
• /
• pp.68-75
• /
• 2017

Interactive Typography is a process where a user communicates by interacting with text and a moving factor. This research covers interactive typography using real-time response to a user's gesture. In order to form a language-independent system, preprocessing of entered text data presents image data. This preprocessing is followed by recognizing the image data and the setting interaction points. This is done using computer vision technology such as the Harris corner detector and contour detection. User interaction is achieved using skeleton information tracked by a depth camera. By synchronizing the user's skeleton information acquired by Kinect (a depth camera,) and the typography components (interaction points), all user gestures are linked with the typography in real time. An experiment was conducted, in both English and Korean, where users showed an 81% satisfaction level using an interactive typography system where text components showed discrete movements in accordance with the users' gestures. Through this experiment, it was possible to ascertain that sensibility varied depending on the size and the speed of the text and interactive alteration. The results show that interactive typography can potentially be an accurate communication tool, and not merely a uniform text transmission system.