• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.71-80
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• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

• Conservation Science in Museum
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• v.27
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• pp.125-146
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• 2022

The Wooden Gamsil with Inscription of "Botajeon" in the collection of the Dongguk University Museum was made in imitation of the wooden architecture style of the late Joseon period. The Gamsil had suffered exfoliation in the pigment and loss of components and thus underwent conservation treatment. Prior to the conservation treatment, the damage was classified by type and form, scientific analysis was carried out on the fiber and the species of wood, and portable X-ray fluorescence (P-XRF) analysis was conducted for the pigment component analysis. According to the analyses, Korea Pine(Soft pine) was used for most parts of the Gamsil, Manchurian walnut (Jugalns spp.) was used for the signboard, and the fiber used was identified as rice straw (Oryza sativa). The P-XRF identified white lead and zinc oxide in the white pigment, red lead in the red pigment, ultramarine blue in the blue pigment, and emerald green in the green pigment. For the conservation treatment, contaminants attached to the gamsil were removed by both dry and wet cleaning, detached parts were reattached in their original places, and lost parts were restored.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.69-86
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• 2022

The railway is widely used to transport passengers and freight due to its punctuality and large transport capacity. The recent remarkable development in construction technology enables various subsea railway tunnels for continent-continent or continent-island connectivity. In Korea, design and construction experience is primarily based on the successful completion of the Boryeong subsea tunnel (2021) and the Gadeok subsea tunnel (2010). However, frequent earthquakes with diverse magnitudes, globally induced and continuously increased the awareness of seismic risks and the frequency of domestic earthquakes. The effect of an earthquake on the subsea tunnel is very complicated. However, ground conditions and seismic waves are considered the main factors. This study simulated four ground types of 3-dimensional numerical models, such as soil, rock, composite, and fractured zone, to analyze the effect of ground type and seismic wave. A virtual subsea railway shield tunnel considering external water pressure was modeled. Further, three different seismic waves with long-term, short-term, and both periods were studied. The dynamic analyses by finite difference method were performed to investigate the displacement and stress characteristics. Consequently, the long-term period wave exhibited a predominant lateral displacement response in soil and the short-term period wave in rock. The artificial wave, which had both periodic characteristics, demonstrated predominant in the fractured zone. The effect of an earthquake is more noticeable in the stress of the tunnel segment than in displacement because of confining effect of ground and structural elements in the shield tunnel. 

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.6
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• pp.524-530
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• 1993

The experiment was carried out to investigate the changes in physicochemical properties of milled rice harvested in different year and stored for four to sixty four months. The fat acidity of milled rice increased sharply from four to sixteen months storage, and after then it was increased slowly to sixty four months. Amylose, magnesium, potassium content, and K/Mg ratio of milled rice were not changed by storage duration. Water absorption rates of milled rice at 21$^{\circ}C$ and 77$^{\circ}C$, and alkali digestion value were increased by longer storage duration. Difference of water absorption rate between rice samples was greater during initial forty minutes after soaking at 21$^{\circ}C$ and with longer the soaking time at 77$^{\circ}C$. Shorter gel length of rice flour was found with prolonged storage duration, while peak, minimum, cool, breakdown and setback viscosity of gelatinized rice flour on amylograph were increased along with increasing the storage duration. The volume expansion rate during cooking and degree of iodine coloration of cooking water were higher in the longer stored rice compared with shorter one, and the amount of soluble solid in cooking water was significantly decreased in rice stored longer period.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.43-53
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• 2023

In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.478-478
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• 2022

섬진강은 하굿둑이 없는 열린 하구로서 하구로부터 약 21km까지 조석의 영향을 받아 강물의 염도가 시간에 따라 변하는 환경이다. 오랫동안 섬진강 하구는 다양한 원인으로부터 바다화로 대표되는 염하구 문제가 지역 현안 사항으로 제기되어 왔다. 상류에서의 용수사용 증가로 인한 하천 유하량 감소 또한 그 원인들 중 하나로 판단됨에 따라 실제 하구까지 내려오는 하천유량과 바다로부터 유입되는 해수를 구분하여 정량화하는 연구가 필요한 사안이다. 본 연구의 목적은 섬진강 수계 하구에서의 다양한 생태환경을 보전하기 위한 적정 염분유지가 요구됨에 따라 섬진강하구 염분계측기(섬진강대교)를 설치하여 염분농도를 관측하고 하천유량, 하천취수 및 해양조위에 따른 염분농도 변화를 모의하여 하천유량과 염분과의 관계를 제시하고자 하였다. 본 연구에서는 EFDC(Environmental Fluid Dynamics Code) 수치모델을 이용하여 상류로는 구례군(송정리) 수위관측소부터 하류로는 여수해만 및 문의리까지의 구역을 설정하고 광양조위, 하동수위 및 고정식 염분 계측기 관측염분농도 자료를 이용하여 수치모델링의 재현성을 검증하였다. 검증 결과, 결정계수(R²)는 0.87(하동수위), 0.93(광양조위), 0.56(섬진강대교 염도)를 나타냈다. 모델 검보정 후 하천유량에 따른 염분변화 실험을 실시하여 염분농도 거동을 분석하였다. 모델 결과, 섬진강하구에서의 염분거동은 소조기때 염분체류 현상으로 인해 대조기 거동과는 큰 차이를 나타냈다. 따라서, 모델링 결과를 이용한 유량-염분 조견표는 각각 대조기와 소조기로 구분하여 산정하였다. 대조기때는 송정유량이 10톤/초의 평균갈수량이 흐를 경우 다압에서의 취수량이 2.52톤/초 ~ 4.63/초로 증가할수록 18.8psu ~ 19.9psu로 증가하였다. 소조기의 경우는 25.5psu ~ 25.7psu로 대조기와 비교하여 크게 증가됨을 나타냈다. 본 연구의 결과는 객관적인 생태환경 보전을 위한 적정염분농도 범위가 도출된다면 이를 유지하기 위한 필요유량과 필요유량을 확보하기 위한 장단기적인 대책수립이 가능할 것으로 기대된다.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.