• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.503-509
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• 2022

The purpose of this study is to set the rate of change between the market price of the next day and the previous day to be predicted as the predicted value, and the market price for each section is generated by dividing the stock price ranking of the next day to be predicted at regular intervals, which is different from the previous papers that predict the market price. We would like to propose a new time series data prediction method that predicts the market price change rate of the final next day through a model using the rate of change as the predicted value. The change in the performance of the model according to the degree of subdivision of the predicted value and the type of input data was analyzed.

• Atmosphere
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• v.31 no.1
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• pp.17-28
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• 2021

We investigated the impact of domain size on the simulated summer precipitation over the Korean Peninsula using the Weather Research and Forecasting (WRF) model. Two different domains are integrated up to 72-hours from 29 June 2017 to 28 July 2017 when the Changma front is active. The domain sizes are adopted from previous RDAPS (Regional Data Assimilation and Prediction System) and current LDAPS (Local Data Assimilation and Prediction System) operated by the Korea Meteorological Administration, while other model configurations are fixed identically. We found that the larger domain size showed better prediction skills, especially in precipitation forecast performance. This performance improvement is particularly noticeable over the central region of the Korean Peninsula. Comparisons of physical aspects of each variable revealed that the inflow of moisture flux from the East China Sea was well reproduced in the experiment with a large model domain due to a more realistic North Pacific high compared to the small domain experiment. These results suggest that the North Pacific anticyclone could be an important factor for the precipitation forecast during the summer-time over the Korean Peninsula.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.683-690
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• 2008

The objectives of this study are to provide an observation-based urban flood prediction model and to evaluate their performance on a restored Cheonggye stream. The study area, which has its own unique hydrologic and flooding conditions that can be characterized the standard of flood occurrence by watergate opening and walk lane inundation, measured stream discharges at the 5 sites and watergate opening and walk lane inundation through the main stream since 2006. This study derived the relationship between precipitation intensity and watergate opening and walk lane inundation time by using the observations of 2006 and verified their performance on 2007 flood events. The result showed that the coefficients of determination are ranged on 0.57-0.75, which would be acceptable if considering the complexity of the area and the proposed model simplicity. It also suggested the continuous observation of these properties is required for further improvement of the models.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.1-9
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• 2024

In this paper, we propose BRAIDS, a boot storm mitigation plan consisting of an AI-based VDI usage prediction system and a virtual machine boot scheduler system, to alleviate boot storms and improve service stability. Virtual Desktop Infrastructure (VDI) is an important technology for improving an organization's work productivity and increasing IT infrastructure efficiency. Boot storms that occur when multiple virtual desktops boot simultaneously cause poor performance and increased latency. Using the xgboost algorithm, existing VDI usage data is used to predict future VDI usage. In addition, it receives the predicted usage as input, defines a boot storm considering the hardware specifications of the VDI server and virtual machine, and provides a schedule to sequentially boot virtual machines to alleviate boot storms. Through the case study, the VDI usage prediction model showed high prediction accuracy and performance improvement, and it was confirmed that the boot storm phenomenon in the virtual desktop environment can be alleviated and IT infrastructure can be utilized efficiently through the virtual machine boot scheduler.

• Journal of the Korean GEO-environmental Society
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• v.11 no.2
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• pp.43-52
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• 2010

Shield method is the tunnel boring method that propels a steel cylinder in the ground and excavates tunnels at once. After Marc Isambard Brunel started using the method for the Thames Riverbed Tunnel excavation in London, many kinds of TBM (Tunnel Boring Machine) developed and applied for the construction of road, railway, electricity channel, pipeline, etc. In comparison with NATM concept that allows to observe ground condition and copes with difficulty. The machine selected before starting construction is not able to be changed during construction in shield TBM. Therefore the machine should be designed based on the ground survey result and experiment, so that the tunnel might be excavated effectively by controlling penetration speed, excavation depth and cutter head speed according to the ground condition change. This research was conducted to estimate penetration depth, excavate speed, wear of disc cutter on Boondang Railway of the Han Riverbed Tunnel ground condition by TBM performance prediction models such as NTNU, $Q_{TBM}$, Total Hardness, KICT-SNU and compare the estimated value with the field data. The estimation method is also used to analyze the reason of poor excavation efficiency at south bound tunnel.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.1
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• pp.1-7
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• 2003

When a ship is course-keeping in the open seas, autopilot system is adapted. The design of autopilot system is very important for improvement of ship′s element research. Automatic steering system consists of autopilot device, power unit, steering gear, magnetic or gyro compass and ship dynamics. In order to evaluate automatic steering system of ships in open seas. we need to know the characteristics of each component of the system, and also to know the characteristics of disturbance to ship dynamics. In this paper, I provide evaluation method of autopilot navigation system of the fishing ship. Prediction method based on the principle of linear superposition is introduced for irregular disturbance. For the evaluation of automatic steering system of a ship, "performance index" is introduced from the viewpoint of energy saving and calculation method is frequency response analysis. Finally, I carried out calculation of sensitivity of control constants of autopilot with various conditions of ocean environments.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.267-274
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• 2020

Click-through rate (CTR) prediction is an estimate of the probability that a user will click on a given item and plays an important role in determining strategies for maximizing online ad revenue. Recently, research has been performed to utilize CNN for CTR prediction. Since the CTR data does not have a meaningful order in terms of correlation, the CTR data may be arranged in any order. However, because CNN only learns local information limited by filter size, data arrays can have a significant impact on performance. In this paper, we propose a multi-array CNN model that generates a data array set that can extract all local feature information that CNN can collect, and learns features through individual CNN modules. Experimental results for large data sets show that the proposed model achieves a 22.6% synergy with RI in AUC compared to the existing CNN, and the proposed array generation method achieves 3.87% performance improvement over the random generation method.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.466-470
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• 2024

This study proposes a deep learning-based LSTM model to predict the state of charge (SOC) of lithium-ion batteries. The model was trained using data collected under various temperature and load conditions, including measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. The LSTM model effectively models temporal patterns in the data by learning long-term dependencies. Performance evaluation by epoch showed that the predicted SOC improved from 14.8400 at epoch 10 to 12.4968 at epoch 60, approaching the actual SOC value of 13.5441. The mean absolute error (MAE) and root mean squared error (RMSE) also decreased from 0.9185 and 1.3009 at epoch 10 to 0.2333 and 0.5682 at epoch 60, respectively, indicating continuous improvement in predictive performance. This study demonstrates the validity of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance battery management systems.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.723-724
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• 2006

Exploiting the instruction level parallelism encountered with the limit. Single chip multiprocessor was introduced to overcome the limit of traditional processor using the instruction level parallelism. Also, a branch miss prediction is one of the causes that reduce the processor performance. In order to overcome the problems, in this paper, we make single chip multiprocessor having the idle core execute the two control flow of conditional branch. This scheme is a kind of multi-path execution technique based on single chip multiprocessor architecture.