• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.770-773
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• 2019

본 논문에서는 매년 성장하는 웹툰 시장 속에서 신인 작가들이 성공할 수 있는 성공 요인을 밝히고자 하였다. 국내 1위 웹툰 플랫폼인 네이버 웹툰 중 데뷔작을 기준으로 완결 웹툰 212개, 연재 중인 웹툰 112개, 총 324개의 웹툰을 수집하여 연구를 진행하였다. 기존 선행연구와의 차별화를 두기 위해 독자의 직접적인 반응 중 하나인 댓글을 성공 요인에 포함하였다. 댓글에 담긴 긍정, 부정을 나타내는 주관을 탐지하기 위해 딥러닝을 이용하여 감성 분석을 실시하였다. 각 웹툰에 대한 댓글 반응을 포함하여 평균, '좋아요' 수, 장르 그리고 첫 화 댓글 수와 5화까지 평균 댓글 수를 흥행에 영향을 미치는 독립변수로 사용했다. 댓글 반응이 중요 요인인지를 확인하기 위해 각 모델 생성 시 댓글 반응을 포함한 모델과 포함하지 않은 모델을 생성하여 성능 평가를 실시하였다. 로지스틱 회귀분석, 아다 부스트, 그리고 서포트 벡터 머신 모델을 정확도와 ROC 그래프를 이용해 효율성을 비교하고, 이를 통해 댓글 반응을 활용한 로지스틱 회귀 모델이 가장 적합하다고 판단하였다. 모델 생성 결과 '좋아요' 수, 1화 댓글 수, 댓글 반응 순으로 성공 요인에 많은 영향을 미치는 것을 알 수 있었다.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.92-95
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• 2019

Automation control and the data for control of industrial equipment for the diagnosis and prediction is a key to success in the 4th industrial revolution. It increases process efficiency and productivity through data collection, realtime monitoring, and the data analysis. However, university and research environment are still suffering from logging the data in manual way, and we occasionally loss the equipment data logging due to the lack of automatic data logging system. State variable presents the current condition of the equipment operation which is closely related to process result, and it is valuable to monitor and analyze the data for the equipment health monitoring. In this paper, we demonstrate the collection of equipment state variable data via programmable logic controller (PLC) and the visualization of the collected data over the Web access supervisory control and data acquisition (SCADA). Test vehicle for the implementation of the suggested SCADA system is a relay switched physical vapor deposition system in the university environment.

• Journal of Forest and Environmental Science
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• v.35 no.1
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• pp.54-60
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• 2019

Plant tissue culture techniques offer quick methods of regeneration of plants of medicinal importance but the survival chances of such plants are always questionable when shifted to the in vivo conditions. The present study enumerates the micromorphological developments in the leaves of in vitro regenerated and field transferred plantlets of Oldenlandia umbellata. The leaves developed in vitro after $4^{th}$ subcultures of multiplication phase and after 6 weeks of field transferred plants were used. Statistically significant differences in the number of stomata, veins, raphides, crystals and trichome density per square mm were observed. The improvements in stomatal apparatus and density (decreased from 41.85 to 32.20), developments in leaf architectural parameters and emergence of defense mechanism through increased numbers of raphides (8 to 15), crystals and trichomes (13.5 to 18.2) proved acclimation of tissue culture raised plantlets from in vitro to the in vivo environments lead to 100 % success in field establishment of the plantlets. The in vitro induced foliar abnormalities (changes in stomata, venation pattern, vein density, trichomes, crystals etc.) were repaired while hardening of plantlets in the greenhouse and finally in the field. The observed micromorphological response of leaves under altered environmental conditions could help in determination of proper stage of field transfer and prediction of survival percentage of in vitro regenerated O. umbellata plantlets.

• Journal of Navigation and Port Research
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• v.45 no.5
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• pp.231-237
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• 2021

Enhancing the performance of maritime wireless communication has been highlighted by the issue of cell planning in the sea area because of lack of an appropriate Propagation Loss Model (PLM). To resolve the cell planning issue in vast sea areas, it was essential to develop the (PLM) matching the intended sea area. However, there were considerable gaps between the prediction of legacy PLMs and field measurement in propagation loss and there was a need to develop the adjusted PLM (A-PLM). Therefore, cell planning was performed on this adjusted model, including modification of the base station's location, altitude, and antenna azimuth to meet the quality objectives. Furthermore, in order to verify the availability of the cell planning, Communication Service Quality Monitoring System (CS-QMS) was developed in the LTE-Maritime project to collect LTE signal quality information from the onboard equipment at regular intervals and to ensure that the service quality was high enough to satisfy the goals in each designated grid. As a result of verification, the success rate of RSRP was 95.7% for the intensive management zone (IMZ) and 96.4% for the interested zone (IZ), respectively.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.611-624
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• 2023

Soil properties make it attractive as a building material due to its mechanical strength, aesthetically appearance, plasticity, and low cost. However, it is frequently necessary to improve and stabilize the soil mechanical properties with binders. Soil-cement is applied for purposes ranging from housing to dams, roads and foundations. Unconfined compression strength (UCS) and split tensile strength (CD) are essential mechanical parameters for ascertaining the aptitude of soil-cement for a given application. However, quantifying these parameters requires specimen preparation, testing, and several weeks. Methodologies that allowed accurate estimation of mechanical parameters in shorter time would represent an important advance in order to ensure shorter deliverable timeline and reduce the amount of laboratory work. In this work, an extensive campaign of UCS and CD tests was carried out in a sandy soil from the Leiria region (Portugal). Then, using the machine learning tool Neural Pattern Recognition of the MATLAB software, a prediction of these two parameters based on six input parameters was made. The results, especially those obtained with resource to a Bayesian regularization-backpropagation algorithm, are frankly positive, with a forecast success percentage over 90% and very low root mean square error (RMSE).

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Earthquakes and Structures
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• v.27 no.2
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• pp.83-95
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• 2024

The prediction of seismic behavior of the existing building stock is one of the most impactful and complex problems faced by countries with frequent and intense seismic activities. Human lives can be threatened or lost, the economic life is disrupted and large amounts of monetary reparations can be potentially required. However, authorities at a regional or national level have limited resources at their disposal in order to allocate to preventative measures. Thus, in order to do so, it is essential for them to be able to rank a given population of structures according to their expected degree of damage in an earthquake. In this paper, the authors present a ranking approach, based on Machine Learning (ML) algorithms for pairwise comparisons, coupled with ad hoc ranking rules. The case study employed data from 404 reinforced concrete structures with various degrees of damage from the Athens 1999 earthquake. The two main components of our experiments pertain to the performance of the ML models and the success of the overall ranking process. The former was evaluated using the well-known respective metrics of Precision, Recall, F1-score, Accuracy and Area Under Curve (AUC). The performance of the overall ranking was evaluated using Kendall's tau distance and by viewing the problem as a classification into bins. The obtained results were promising, and were shown to outperform currently employed engineering practices. This demonstrated the capabilities and potential of these models in identifying the most vulnerable structures and, thus, mitigating the effects of earthquakes on society.

• Korean Journal of Cognitive Science
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• v.29 no.2
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• pp.121-135
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• 2018

Dancers' motions are perceived by observers through visual processes with visual information forming the basis for the observers' appreciation and evaluation of the dancers' motions. There have been many discussions as to whether or not observers' personal athletic capabilities form an essential basis for accurate assessment of the motions of others but, so far, no valid conclusions have been reached. The purpose of this study is to investigate how the ability to predict motions of others varies depending on the athletic expertise of the observers. Participants of this research were ballet dancers of varying athletic expertise. Twenty seven participants were divided into three groups with nine in each: beginners, intermediate experts and experts. The participants watched the same dance video and then evaluated whether the motion would be successful or not. The movement related visual information required to evaluate the success of the motion was systematically adjusted by controlling the length of the video. Using the temporal occlusion method, this study measured the response accuracy of the participants by category of expertise. Under the circumstance with insufficient visual information to utilize, the experts showed higher rates of correct response than the intermediate experts and the beginners. The beginners showed higher rates of wrong response than the experts and the intermediate experts. These results showed that the ability to predict success or failure of a dance motion varied depending on motion expertise of the observers, although they had similar level of expertise in perception. Participants considered to have high athletic expertise showed high prediction ability on the result of the motion. In addition, high expertise in perception reduced the likelihood that participants would make hasty responses under the circumstance with insufficient information and helped to reduce wrong response rate. In conclusion, this study showed that motor expertise and perceptual expertise contribute to prediction accuracy of observed motions.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.451-462
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• 2009

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.969-980
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• 2023

Since the 2000s, ensemble streamflow prediction (ESP) has been actively utilized in South Korea, primarily for hydrological forecasting purposes. Despite its notable success in hydrological forecasting, the original objective of enhancing water resources system management has been relatively overlooked. Consequently, this study aims to demonstrate the utility of ESP in water resources management by creating a simple hypothetical exercise for dam operators and applying it to actual multi-purpose dams in South Korea. The hypothetical exercise showed that even when the means of ESP are identical, different costs can result from varying standard deviations. Subsequently, using sampling stochastic dynamic programming (SSDP) and considering the capacity-inflow ratio (CIR), optimal release patterns were derived for Soyang Dam (CIR = 1.345) and Chungju Dam (CIR = 0.563) based on types W and P. For this analysis, Type W was defined with standard deviation equal to the mean inflow, and Type P with standard deviation ten times of the mean inflow. Simulated operations were conducted from 2020 to 2022 using the derived optimal releases. The results indicate that in the case of Dam Chungju, more aggressive optimal release patterns were derived under types with smaller standard deviations, and the simulated operations demonstrated satisfactory outcomes. Similarly, Soyang Dam exhibited similar results in terms of optimal release, but there was no significant difference in the simulation between types W and P due to its large CIR. Ultimately, this study highlights that even with the same mean values, the standard deviation of ESP impacts optimal release patterns and outcomes in simulation. Additionally, it underscores that systems with smaller CIRs are more sensitive to such uncertainties. Based on these findings, there is potential for improvements in South Korea's current operational practices, which rely solely on single representative values for water resources management.