• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.4
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• pp.324-341
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• 2023

Topological data analysis (TDA) is a data analysis technique, recently developed, that investigates the overall shape of a given dataset. The mapper algorithm is a TDA method that considers the connectivity of the given data and converts the data into a mapper graph. Compared to persistent homology, another popular TDA tool, that mainly focuses on the homological structure of the given data, the mapper algorithm is more of a visualization method that represents the given data as a graph in a lower dimension. As it visualizes the overall data connectivity, it could be used as a prediction method that visualizes the new input points on the mapper graph. The existing mapper packages such as Giotto-TDA, Gudhi and Kepler Mapper provide the descriptive mapper algorithm, that is, the final output of those packages is mainly the mapper graph. In this paper, we develop a simple predictive algorithm. That is, the proposed algorithm identifies the node information within the established mapper graph associated with the new emerging data point. By checking the feature of the detected nodes, such as the anomality of the identified nodes, we can determine the feature of the new input data point. As an example, we employ the fraud credit card transaction data and provide an example that shows how the developed algorithm can be used as a node prediction method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.969-972
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• 2016

In this paper, we propose an algorithm of angular mode selection for high-performance HEVC intra prediction. HEVC intra prediction is used to remove the spatial redundancy. Intra prediction has a total of 35 modes and block size of $64{\times}64$ to $4{\times}4$. Intra prediction has a high amount of calculation and operational time due to performing all 35 modes for each block size for the best cost. The angular mode algorithm proposed has a simple difference between pixels of the original image and the selected angular mode. A decision is made to select one angular mode plus planar mode and DC mode to perform the intra prediction and determine the mode with the best cost. In effect, only three modes are executed compared to the traditional 35 modes. Performance evaluation index used are BD-PSNR and BD-Bitrate. For the proposed algorithm, BD-PSNR results averagely increased by 0.035 and BD-Bitrate decreased by 0.623 relative to the HM-16.9 intra prediction. In addition, the encoding time is decreased by about 6.905%.

• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1355-1369
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• 2021

This study improved the algorithm for the road ice prediction algorithm and analyzed the prediction rate when comparing actual field measurement data and algorithm prediction value. For analysis, road and weather conditions were measured in Geumdong-ri, Sinbuk-myeon, Pocheon-si. First algorithm selected previous research result algorithm. And the 4th algorithm was improved according to the actual freezing conditions and measured values. Finally, five algorithms were developed: freezing by condensation, freezing by precipitation, freezing by snow, continuous freezing, and freezing by wind speed. When forecasting using an algorithm at the Pocheon site, the freezing hit rate was improved to 93.2%. When calculating the combination ratio for the algorithm. the algorithm for freezing due to condensation and the continuation of the frozen state accounted for 95.7%.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2659-2665
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• 2024

This study proposes an algorithm that combines a Kalman Filter method with effective decay constant correction to improve the accuracy of predicting radiation dose rate distribution during emergencies. The algorithm addresses the limitations of relying solely on measurement data by incorporating calculation data and refining the estimations. The effectiveness of algorithm was assessed using hypothetical test scenarios, which demonstrated a significant improvement in the accuracy of dose rate prediction compared to the model predictions. The estimates generated by the algorithm showed a good agreement with the measured data, and the discrepancies tend to decrease over time. Furthermore, the application of the effective decay constant correction accelerated the reduction of prediction errors. In conclusion, it was confirmed that the combined use of the Kalman filter method and effective decay constant correction is an effective approach to improve the accuracy of dose rate prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• Ocean and Polar Research
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• v.23 no.2
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• pp.189-194
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• 2001

A practical algorithm for prediction of the sea surface temperatures (SST)from the satellite remote sensing data is presented in this paper. The fluctuations of SST consist of deterministic normals and stochastic anomalies. Due to large thermal inertia of sea water, the SST anomalies can be modelled by autoregressive or Markov process, and its near future values can be predicted provided the recent values of SST are available. The actual SST is predicted by superposing the pre-known SST normals and the predicted SST anomalies. We applied this prediction algorithm to the NOAA AVHRR weekly SST data for 18 years (1981-1998) in the seas adjacent to Korea (115-$145^{\circ}E$, 20-$55^{\circ}N$). The algorithm is applicable not only for prediction of SST in near future but also for nowcast of SST in the cloud covered regions.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.3
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• pp.123-129
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• 2002

This paper proposes a vehicle trajectory prediction method far application to vehicle-to-vehicle distance control. This method is based on 2-dimensional kinematics and a Kalman filter has been used to estimate acceleration of the object vehicle. The simulation results using the proposed control method show that the relative distance characteristics can be improved via the trajectory prediction method compared to the customary intelligent cruise control algorithm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.5
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• pp.519-526
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• 2018

In this study, an algorithm to predict evacuation routes in support of shipboard lifesaving activities is presented. As the first step of algorithm development, the feasibility and necessity of an evacuation route prediction algorithm are shown numerically. The proposed algorithm can be explained in brief as follows. This system continuously obtains and analyzes passenger movement data from the ship's monitoring system during non-disaster conditions. In case of a disaster, evacuation route prediction information is derived using the previously acquired data and a prediction tool, with the results provided to rescuers to minimize casualties. In this study, evacuation-related data obtained through fire evacuation trials was filtered and analyzed using a statistical method. In a simulation using the conventional evacuation prediction tool, it was found that reliable prediction results were obtained only in the SN1 trial because of the conceptual and structural nature of the tool itself. In order to verify the validity of the algorithm proposed in this study, an industrial engineering tool was adapted for evacuation characteristics prediction. When the proposed algorithm was implemented, the predicted values for average evacuation time and route were very similar to the measured values with error ranges of 0.6-6.9 % and 0.6-3.6 %, respectively. In the future, development of a high-performance evacuation route prediction algorithm is planned based on shipboard data monitoring and analysis.