• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2A
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• pp.169-176
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• 2010

Generally, poly-urea is widely used as waterproof coating material due to its superior adhesiveness, elongation capacity, and permeability resistance. In addition, it can be quickly and easily applied on structure surfaces using spray application. Since it hardens in about 30 seconds after application, its construction efficiency is very high and its usage as a special functional material is also excellent. However, currently, poly-urea is mostly used as waterproof coating material and the researches on its usage as a retrofitting material is lacking at best. Therefore, basic studies on the use of poly-urea as a general structural retrofitting material are needed urgently. The objective of this study is to develop most optimum poly-urea composition for structure retrofitting purpose. Moreover, the structural strengthening capacity of the developed poly-urea is evaluated through flexural capacity experiments on RC beams and RC slabs. From the results of the flexural test of poly-urea strengthened RC beam and slab specimens, the poly-urea and concrete specimen showed monolithic behavior where ductility and ultimate strength of the poly-urea strengthened specimen showed slight increase. However, the doubly reinforced specimens with FRP sheet and poly-urea showed lower capacity than that of the specimen reinforced only with FRP sheet.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.451-457
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• 2008

Establishing realistic bus service coverage is needed to build optimum city bus line networks and reasonable bus service coverage areas. The purposes of this study are understanding the characteristics of the present walking time and marginal walking time of small-medium cities and constructing an ANFIS (Adaptive Neuro-Fuzzy Inference System) model to estimate the marginal walking time for certain age and income. The cities of Masan, Chongwon and Jinju are selected for study cities. The 80 percentile of present walking time of bus users of these cities are 10.2-11.1 minutes, thus the values are greater than the 5 minutes of the maximum walking time in USA and the marginal walking times of 21.1-21.8 minutes are much greater. An ANFIS model based on pulled data of the cities are constructed to estimate the marginal walking time of small-medium cities. Analyzing the relationship between marginal walking time and age/income by using the model, the marginal walking time decreases as the age increases, but is near constant from the age of 25 to 35. And the marginal walking time is inversely proportional to the income. In comparing the surveyed and the estimated values, as the statistics of coefficient of determination, MSE and MAE are 0.996, 0.163, 0.333 respectively, it may be judged that the explainability of the model is very high. The technique developed in this study can be applied to other cities.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.217-222
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• 2023

The purpose of this paper was to synthesize a polymer organic hard mask that simplifies the manufacturing process, reduces process time significantly, and thereby lowers manufacturing costs. The results of measuring residual metals through vapor refining showed that 9-Naphthalen-1-ylcarbazole(9-NC) measured 101.75ppb in the 4th zone, 2-Naphthol (2-NA) measured 306.98ppb in the 5th zone, and 9-Fluorenone(9-F) measured between 129.05ppb across the 4th and 5th zones. After passing through a filtration system, the synthesized organic hard mask measured residual metals in the range of 9 to 7ppb. Additionally, the thermal analysis indicated a decrease of 2.78%, a molecular weight of 942, carbon content of 89.74%, and a yield of 72.4%. The etching rate was measured at an average of 18.22Å/s, and the coating thickness deviation was averaged at 1.19. For particle sizes below 0.2㎛ in the organic hard mask, no particles were observed. By varying the coating speed at 1,000, 1,500, and 1,800rpm and measuring the resulting coating thickness, the shrinkage rate ranged from 17.9% to 20.8%. The coating results demonstrated excellent adhesion to SiON, and it was evident that the organic hard mask was uniformly applied.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.29-42
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• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.22-29
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• 2023

In the evaluation of seismic performance for structural materials and components, the loading rate and axial force can have a significant impact. Due to time-delay effects between input and output displacements, It is difficult to apply high-rate displacement in cyclic tests and hybrid simulations. Additionally, the difficulty of maintaining a consistent vertical load in the presence of lateral displacement has limited fast and real-time tests performed while maintaining a constant vertical load. In this study, slow, fast cyclic tests and real-time hybrid simulations were conducted to investigate the rate dependency and the influence of vertical loads of Isolation Bearing. In the experiment, the FLB System including an Adaptive Time Series (ATS) compensation and a state estimator was constructed for real-time control of displacement and vertical load. It was found that the vertical load from the superstructure and loading rate can have a significant impact on the strength of the seismic isolation bearing and its behavior during an earthquake. When conducting experiments for seismic performance evaluation, they must be implemented to be similar to reality. This study demonstrates the excellent performance of the system built and used for seismic performance evaluation and enables accurate and efficient seismic performance evaluation.

• Clean Technology
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• v.29 no.4
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• pp.244-248
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• 2023

Recently, displays play an important role in quickly delivering a lot of information. Research is underway to reproduce various colors close to natural colors. In particular, research is being conducted on the light emitting structure of displays as a method of expressing accurate and rich colors. Due to the advancement of technology and the miniaturization of devices, the need for small but high visibility displays with high efficiency in energy consumption continues to increase. Efforts are being made in various ways to improve OLED efficiency, such as improving carrier injection, structuring devices that can efficiently recombine electrons and holes in a numerical balance, and developing materials with high luminous efficiency. In this study, the electrical and optical properties of the seven-layer stacked structure rear-light emitting blue OLED device were analyzed. 4,4'-Bis(carazol-9-yl)biphenyl:Ir(difppy)2(pic), a blue light emitting material that is easy to manufacture and can be highly efficient and brightened, was used. OLED device manufacturing was performed via the in-situ method in a high vacuum state of 5×10-8 Torr or less using a Sunicel Plus 200 system. The experiment was conducted with a seven-layer structure in which an electron or hole blocking layer (EBL or HBL) was added to a five-layer structure in which an electron or hole injection layer (EIL or HIL) or an electron or hole transport layer (ETL or HTL) was added. Analysis of the electrical and optical properties showed that the device that prevented color diffusion by inserting an EBL layer and a HBL layer showed excellent color purity. The results of this study are expected to greatly contribute to the R&D foundation and practical use of blue OLED display devices.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.536-542
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• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.185-205
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• 2023

As the tourism industry recovers post the COVID-19 pandemic, an increasing number of tourists are utilizing various platforms to leave reviews. However, amidst the vast amount of data, finding useful information remains challenging, often leading to time and cost inefficiencies in selecting travel destinations. Despite ongoing research, there are limitations due to the absence of ratings or the presence of different rating formats across platforms. Moreover, inconsistencies between ratings and the content of reviews pose challenges in developing recommendation models. To address these issues, this study utilized 7,104 reviews of tourist spots in Jeju Island to develop a specialized satisfaction index for Jeju tourist attractions and employed this index to construct a 'Rating Prediction Model.' To validate the model's performance, we predicted the ratings of 700 experimental data points using both the developed model and an LSTM approach. The proposed model demonstrated superior performance with a weighted accuracy of 73.87%, which is approximately 4.67% higher than that of the LSTM. The results of this study are expected to resolve the discrepancies between ratings and review contents, standardize ratings in reviews without ratings or in various formats, and provide reliable rating indicators applicable across all areas of travel in different domains.