• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4C
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• pp.229-238
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• 2006

Observed field behaviors are frequently different from the behaviors predicted in the design state due to several uncertainties involved in soil properties, numerical modeling, and error of measuring system even though a sophisticated numerical analysis technique is applied to solve the consolidation behavior of drainage-installed soft deposits. In this study, genetic algorithms are applied to back-analyze the soil properties using the observed behavior of soft clay deposit composed of multi layers that shows complex consolidation characteristics. Utilizing the program, one might be able to appropriately predict the subsequent consolidation behavior from the measured data in an early stage of consolidation of multi layered soft deposits. Example analyses for drainage-installed multi-layered soft deposits are performed to examine the applicability of proposed back-analysis method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.264-266
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• 2022

In order to apply the RNN model to the radio fingerprint-based indoor path generation technology, the data set must be continuous and sequential. However, Wi-Fi radio fingerprint data is not suitable as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, continuity information of sequential positions should be given. For this purpose, clustering is possible through classification of each region based on signal data. At this time, the continuity information between the clusters does not contain information on whether actual movement is possible due to the limitation of radio signals. Therefore, correlation information on whether movement between adjacent clusters is possible is required. In this paper, a deep learning network, a recurrent neural network (RNN) model, is used to predict the path of a moving object, and it reduces errors that may occur when predicting the path of an object by generating continuous location information for path generation in an indoor environment. We propose a method of giving correlation between clustering for generating an improved moving path that can avoid erroneous path prediction that cannot move on the predicted path.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.275-276
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• 2022

VTS operators instruct ships to wait for entry and departure to sail in one-way to prevent ship collision accidents in ports with narrow routes. Currently, the instructions are not based on scientific and statistical data. As a result, there is a significant deviation depending on the individual capability of the VTS operators. Accordingly, this study built a 1d-convolutional neural network model by collecting ship and weather data to predict the exact travel time for ship entry/departure waiting for instructions in the port. It was confirmed that the proposed model was improved by more than 4.5% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations, so it is expected that the VTS operators will help provide accurate information to the vessel and determine the waiting order.

• Journal of the Korean Applied Science and Technology
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• v.30 no.4
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• pp.612-621
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• 2013

Microwave-assisted extraction (MAE), which is known as less time and less solvent than current extraction methods, was applied to hydrocarbons extraction from irradiated soybean. Among the transported agricultural products, soybean was selected as representative samples for possible application of irradiated treatment and identification of radiation-induced markers. Using 4 kGy-irradiated soybean, different microwave extraction conditions (extraction time and microwave power) were applied and the changes in hydrocarbon concentrations were monitored. The predicted optimum extracted condition for hydrocarbon analysis of soybean was found to be microwave extraction with a microwave power of 97 W and extraction time of 2.2 min. This extraction time was significantly lower compared to the common extraction time of 12-24hr.

• Journal of Information Management
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• v.25 no.1
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• pp.63-79
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• 1994

Analysis systems and databases covering economics and industrial areas in Japan have long history, but due to considerable depth in specialty those have been used only by limited, certain patrons. It means that there are still few of practical system services at full scale. This paper describes the representative system services. Taking an example of interest prediction the author explains now to utilize systems and some points to be reminded. He also describes how to confirm newspaper information, how to predict economics, how to use various kinds of models based on economic prediction, and industrial analysis. Researches and studies are very often proceeded on economic prediction, and industrial analysis. Researches and studies are very often proceeded through interaction among researchers. So that we should make efforts continuously such as to rountinely get familiar with systems, to exchange information among users, to utilize helpdesks every time we need.

• Applied Chemistry for Engineering
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• v.30 no.5
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• pp.615-619
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• 2019

Statistical design of experiments was performed to optimize MOF-235 synthesis process. Concentrations of terephthalic acid (TPA), iron (III) chloride hexahydrate, N,N-dimethylformamide (DMF) and ethanol were important factors to develop the crystal structure of MOF-235. MOF-235 was synthesized with various concentrations of the listed chemicals above and the crystallinity was measured by XRD. The effect of the composition on the synthesis of MOF-235 was evaluated using a statistical analysis. For the variance analysis using F-test, the concentration of ethanol showed the greatest effect on the crystallinity and TPA the least influential. A regression model for predicting the crystallinity of MOF-235 was derived and the prediction results for two synthetic variables were presented using contour plots. Finally, the crystallinity was predicted by a mixture method with $FeCl_3$, ethanol and DMF.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.553-563
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• 2019

As the number of videos uploaded on live streaming platforms rapidly increases, the demand for providing highlight videos is increasing to promote viewer experiences. In this paper, we present novel methods for predicting highlights using chat logs and audio data in videos. The proposed models employ bi-directional LSTMs to understand the contextual flow of a video. We also propose to use the features over various time-intervals to understand the mid-to-long term flows. The proposed Our methods are demonstrated on e-Sports and baseball videos collected from personal broadcasting platforms such as Twitch and Kakao TV. The results show that the information from multiple time-intervals is useful in predicting video highlights.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1A
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• pp.115-123
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• 2008

The performance prediction of fiber-reinforced composites has attracted engineer's attention in many fields, and the various theoretical and numerical methods have been proposed to predict the behavior of the fiber-reinforced composites. An evolutionary damage model for progressive interfacial debonding between circular fibers and the matrix is newly incorporated into the micromechanics-based elastoplastic model proposed by Ju and Zhang (2001) in this framework. Using the proposed model, a series of numerical simulations are conducted to illustrate the elastoplastic behavior and evolutionary damage of the framework. Furthermore, the influence of the evolutionary interfacial debonding on the behavior of the composites is investigated by comparing it with the result of a stationary damage model.

• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.811-824
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• 2023

The purpose of this study was to investigate the communication confidence, self-efficacy, and self confidence in first aid and to identify the predictors of self confidence in first aid. Subjects were 202 caregivers and data were collected by questionnaires from march 1 to 31, 2023. Data were analyzed using t-test, ANOVA, Scheffe's test, Pearson correlation coefficients and Multiple regression analysis using the SPSS 29.0 program. Self-efficacy was a positive correlation with communication confidence (r=.54, p<.001), and self confidence in first aid was a negative correlation with communication confidence(r=-.18, p<.05) and self-efficacy(r=-.31, p<.001). Predictive factors for self confidence in first aid were absence of nurse's aide(β=-.18, p=.009) and self-efficacy(β=-.30, p<.001), and explanatory power was 11.0%(Adj R2=.110, p<.001). Based on the results of this study, to develop and apply an educational program focusing on emergency problems.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.