• Journal of Korea Water Resources Association
• /
• v.57 no.10
• /
• pp.673-686
• /
• 2024

Groundwater is an important water resource used for various purposes along with surface water. Jeju Island relies on groundwater for most of its water use, so predicting and managing groundwater volume is very important for sustainable use of groundwater. In this study, precipitation data from the Baekrokdam Climate Change Observatory was additionally used to accurately predict groundwater levels. We compared and analyzed the improvement in monthly groundwater level prediction performance of the ANN and LSTM models for two observation wells located in the mid-mountainous area of the Pyoseon watershed in Jeju Island. As a result, when Baekrokdam precipitation data was not used, the NSE values of the two artificial intelligence models were over 0.871, showing very high groundwater level prediction performance. The LSTM model showed relatively higher prediction performance at high and low groundwater levels than the ANN model. We found that the prediction performance decreases as the variation characteristics of the groundwater level become more complex. When Baekrokdam precipitation data was additionally used, the NSE values of the two artificial intelligence models were above 0.907, indicating improved prediction performance, and the NSE value was improved by up to 0.036. This means that when additional rainfall in the upstream area is used, the artificial intelligence model can more appropriately interpret the fluctuating characteristics of the groundwater level. In addition, the additional use of Baekrokdam precipitation data further helped improve groundwater level prediction for observation well, where groundwater level prediction is relatively difficult, and artificial intelligence models, which have relatively low groundwater level prediction performance. In particular, when Baekrokdam precipitation data was additionally used for a specific observation well, the groundwater level prediction performance of the ANN model was improved to a level comparable to that of the LSTM model. The methods and results of this study can be useful in future research using artificial intelligence models.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.29 no.3
• /
• pp.44-51
• /
• 2021

PIP is an abbreviation of 'Performance Improvement Package', which is a package that can improve performance by applying some design changes to existing aircraft. Boeing provides PIP applicable to B777-200, and Airbus provides PIP applicable to A350-900 as standard. PIP provided by Boeing and Airbus is a separate task, but it is expected to reduce fuel consumption by reducing drag through aerodynamic improvements. The PIP applied to the A350-900 includes work such as increasing Winglet Height and re-twisting Outboard Wing. This study is to verify the effect of PIP application of the A350-900 aircraft and use it as basic data for economic analysis. The aerodynamic improvement studies and expected effects of the PIP application were examined, and the actual flight data of the PIP-applied and the non-applied aircraft were compared to confirm the PIP application effect. This paper provides empirical results for the aviation industry on the PIP application efficiency as a method of improving fuel efficiency and reducing carbon emission.

• The Journal of Korean Association of Computer Education
• /
• v.19 no.4
• /
• pp.59-66
• /
• 2016

It has been studied to reflect the number of co-rated items for solving data sparsity problem in collaborative filtering systems. A well-known method of Jaccard index allowed performance improvement, when combined with previous similarity measures. However, the degree of performance improvement when combined with existing similarity measures in various data environments are seldom analyzed, which is the objective of this study. Jaccard index as a sole similarity measure yielded much higher prediction quality than traditional measures and very high recommendation quality in a sparse dataset. In general, previous similarity measures combined with Jaccard index improved performance regardless of dataset characteristics. Especially, cosine similarity achieved the highest improvement in sparse datasets, while similarity of Mean Squared Difference degraded prediction quality in denser sets. Therefore, one needs to consider characteristics of data environment and similarity measures before combining Jaccard index for similarity use.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.6
• /
• pp.608-611
• /
• 2012

This paper focuses on attitude stabilization performance improvement of the quadrotor flying robot. First, the dynamic model of quadrotor flying robot was estimated through PEM (Prediction Error Method) using experimental input/output data. And attitude stabilization performance was improved by increasing the generation frequency of PWM signal from 50 Hz to 500 Hz. Also, the controller is implemented using a standard PID (Proportional-Integral-Derivative) controller augmented with feedback on angular acceleration, allowed the gains to be significantly increased, yielding higher bandwidth. Improved attitude stabilization performance is verified by experiment.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.24 no.5
• /
• pp.581-587
• /
• 2016

Braking is a basic and an important safety feature for all vehicles, and the final braking performance of a vehicle is determined by the vehicle's ABS performance and tire performance. However, the combination of excellent ABS and tires will not always ensure good braking performance. This is due to the fact that tire performance has non-linearity and uncertainty in predicting the repeated increase and decrease of wheel slip when activating the ABS, thus increasing the uncertainty of tire performance prediction. Furthermore, existing studies predicted braking performance after using an ABS that used a wheel slip control as a controller, which was different from an actual vehicle's ABS that controlled angular acceleration, therefore causing a decrease in the prediction accuracy of the braking performance. This paper reverse-designed the ABS that controlled angular acceleration based on the information on brake pressure, etc., which were obtained from vehicle tests, and established a braking performance prediction analysis model by combining a multi-body dynamics(MBD) vehicle model and a magic formula(MF) tire model. The established analysis model was verified after comparing it with the results of the braking tests of an actual vehicle. Using this analysis model, this study analyzed the braking effect by vehicle factor, and finally designed a tire that had optimized braking performance. As a result of this study, it was possible to design the MF tire model whose braking performance improved by 9.2 %.

• Journal of Advanced Navigation Technology
• /
• v.27 no.1
• /
• pp.133-138
• /
• 2023

As a side effect of recent rapid climate change and global warming, the frequency and scale of flood disasters are increasing worldwide. In Korea, the water level of the Han River is a major management target for preventing flood disasters in Seoul, the capital of Korea. In this paper, to improve the water level prediction of the Han River based on machine learning, we perform a comprehensive assessment of the quality of related dataset and propose data preprocessing methods to improve it. Specifically, we improve the dataset in terms of completeness, validity, and accuracy through missing value processing and cross-correlation analysis. In addition, we conduct a performance evaluation using random forest and LightGBM to analyze the effect of the proposed data improvement method on the water level prediction performance of the Han River.

• International Journal of Contents
• /
• v.9 no.4
• /
• pp.11-15
• /
• 2013

"Protein Folding Problem" is considered to be one of the "Great Challenges of Computer Science" and prediction of disordered protein is an important part of the protein folding problem. Machine learning models can predict the disordered structure of protein based on its characteristic of "learning from examples". Among many machine learning models, we investigate the possibility of multilayer perceptron (MLP) as the predictor of protein disorder. The investigation includes a single hidden layer MLP, multi hidden layer MLP and the hierarchical structure of MLP. Also, the target node cost function which deals with imbalanced data is used as training criteria of MLPs. Based on the investigation results, we insist that MLP should have deep architectures for performance improvement of protein disorder prediction.

• Journal of Korean Society for Quality Management
• /
• v.50 no.4
• /
• pp.701-716
• /
• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 2004.07a
• /
• pp.454-454
• /
• 2004

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.4
• /
• pp.143-149
• /
• 2012

MANET is a network that is a very useful application to build network environment in difficult situation to build network infrastructure. But, nodes that configures MANET have difficulties in data retrieval owing to resources which aren't enough and mobility. Therefore, caching scheme is required to improve accessibility and availability for frequently accessed data. In this paper, we proposed a technique that utilize mobility prediction of nodes to retrieve quickly desired information and improve data availability. Mobility prediction of modes is performed through distance calculation using location information. We used technique which global cluster table and local member table is managed by cluster head to reduce data consistency and query latency time. We compared COCA and CacheData and experimented to confirm performance of proposed scheme in this paper and efficiency of the proposed technique through experience was confirmed.