• KIEAE Journal
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• v.10 no.5
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• pp.131-137
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• 2010

The building industry must aim at high-durability and sustainability. A holistic life cycle based approach is recommended to reduce the environmental load. In recent years, technical innovations in the construction industry have advanced to a great extent, and caused the active research and development of high-performance and multifunctional construction materials. Nowadays, various polymer powders have been commercialized to manufacture construction materials in the form of prepackaged-type products, which have rapidly been developed for lack of skilled workmen in construction sites. Recently, terpolymer powders of improved quality have been developed and commercialized as cement modifiers. And, hydrocalumite is a material that can adsorb the chloride ions (Cl-) causing the corrosion of reinforcing bars and liberate the nitrite ions (NO2-) inhibiting the corrosion in reinforced concrete, and can provide a self-corrosion inhibition function to the reinforced concrete. The purpose of this study is to ascertain the self-corrosion inhibition function of polymer-modified mortars using redispersible powders with hydrocalumite. Polymer-modified mortars using VA/E/MMA and VAE redispersible powders are prepared with various calumite contents and polymer-binder ratios, and tested for chloride ion penetration depth, corrosion inhibition. As a result, regardless of the polymer-binder ratio, the replacement of ordinary portland cement with hydrocalumite has a marked effect on the corrosion-inhibiting property of the polymer-modified mortars. Anti-corrosion effect of polymer-modified mortars using VA/E/MMA terpolymer powder with hydrocalumite is higher than that of VAE copolymer powder.

• Smart Media Journal
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• v.12 no.3
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• pp.9-18
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• 2023

Due to the deepening of COVID-19, high-intensity social distancing has been prolonged and many social problems have been cured. In particular, physical and psychological isolation occurred due to the non-face-to-face system and a lot of damage occurred. The various social problems caused by Corona acted as severe stress for all those affected by Corona 19, and eventually acted as a factor threatening mental health such as depression. While the number of people suffering from mental illness is increasing, the actual use of mental health services is low. Therefore, it is necessary to establish a system for people suffering from mental health problems. Therefore, in this study, depression detection and emergency detection models were constructed based on sensor information using smartphones from depressed subjects and general subjects. For the detection of depression and emergencies, VAE, DAGMM, ECOD, COPOD, and LGBM algorithms were used. As a result of the study, the depression detection model had an F1 score of 0.93 and the emergency situation detection model had an F1 score of 0.99. direction.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.221-222
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• 2009

The results showed that the addition of VAE polymer strongly reduces the $Ca(OH)_2$ formation, being this result attributed to reduce degree of cement hydration caused by different ion elution amount of polymer modified cement pastes and interaction between acetate anion from the partial hydrolysis of co-polymer and Ca$^{2+}$ion from OPC hydration.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.11a
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• pp.252-255
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• 2003

Recently, there is a growing trend in the United States toward replacing latex additives in polymer-modified cement mortars with redispersible polymer powders. This movement is relatively new in the U.S. but is further advanced in Europe due to the more extensive use of cement and concrete in residential construction. Hitherto, in Korea - there is a very diminutive movement towards this growing trend. Thus, there is limited availability of data on redispersible polymer powders. This study investigates the effectiveness of redispersible polymer powder on improvement of the mechanical properties of modified mortar. It was concluded from the results of the experiments that the size of the dispersed polymer particles, variations in glass transition points (Tg), and variations in minimum film formation temperature (MFT) influenced the strength development of the modified mortars, and optimum strength in modified mortars using redispersible powders can be achieved when the Tg which accounts for the degree of powder flexibility is considered.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.57-60
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• 2021

본 논문에서는 기지 설비 중 주요 회전기기인 펌프의 이상탐지 알고리즘을 제안한다. 현재 인공지능을 활용하여 생산현장을 혁신하고자 하는 시도가 진행되고 있으나 외산 솔루션에 대한 의존도가 높은 것에 비해 국내 실정에 맞지 않는 경우가 많다. 이에 따라, 선행 연구를 통해 국내 실정에 맞는 인공지능 기술 도입이 필요하다. 본 연구에서는 VAE(Variational Auto Encoder) 알고리즘을 활용해 회전기기의 고장을 진단하는 알고리즘을 개발하였다. 본 연구 수행을 통한 회전기기의 고장 예지·진단 시스템 개발로 설비의 이상 징후 포착, 부품의 교환 시기 등 보수 일정을 예측하고 최종적으로 이를 통한 설비 가동의 효율 증대와 에너지 비용 감소의 효과를 기대한다.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.145-156
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• 2023

Trajectory of ballistic missile is defined by inherent flight dynamics, which decided range and maneuvering characteristics. It is crucial to predict range and maneuvering characteristics of ballistic missile in KAMD (Korea Air and Missile Defense) to minimize damage due to ballistic missile attacks, Nowadays, needs for applying AI(Artificial Intelligence) technologies are increasing due to rapid developments of DNN(Deep Neural Networks) technologies. To apply these DNN technologies amount of data are required for superviesed learning, but trajectory data of ballistic missiles is limited because of security issues. Trajectory data could be considered as multivariate time series including many variables. And augmentation in time series data is a developing area of research. In this paper, we tried to augment trajectory data of ballistic missiles using recently developed methods. We used TimeVAE(Time Variational AutoEncoder) method and TimeGAN(Time Generative Adversarial Networks) to synthesize missile trajectory data. We also compare the results of two methods and analyse for future works.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.2
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• pp.103-112
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• 2011

The purpose of this study is to specify the prior eco-preserve zone by establishing the eco-landscape unit on the stream corridor and evaluating the stream ecosystem in the dam basin. The fundamental ecological data was surveyed and collected through "the ecosystem project on Yongdam multipurpose dam watershed" from 2008 to 2009. The Yongdam Dam Watershed has several streams, Jujacheon, Jeongjacheon and Guryangcheon, of which the area is $930km^2$, stretching to Jinangun, Jangsugun and Mujugun Jellabukdo. In spite of being used for drinking purpose, the dam water quality and ecosystem is threatened by in-watershed pollution produced by development, golf course grounds and sports complex, etc. The landscape unit of stream corridor was zoned across by 250m, 500m, and 750m from the vicinity line of stream, which was decided to the accuracy of mapping and surveying. Types of evaluation are the Stream Corridor Evaluation(SCE) and the Vegetated Area Evaluation(VAE). In the process of SCE, several indices were analysed, fish species diversity, species peculiarity, and stream naturality. Indices for VAE were forest stand map, vegetation protection grade, species diversity and peculiarity for wild bird and mammal life. The importance of the ecological items is categorized into three levels and overlapped for specifying the prior preserve zone. The area at which legally protecting species appeared is categorized as absolute preserve area. This study might be meaningful for proposing the evaluation process of a stream corridor ecosystem, which can synthesize a lot of individual ecological surveys. We hope further research will be actively performed about the ecotope mapping which is based on a individual wildlife territory and habitats and also their relationships.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.1-9
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• 2021

Traditionally, most malicious codes have been analyzed using feature information extracted by domain experts. However, this feature-based analysis method depends on the analyst's capabilities and has limitations in detecting variant malicious codes that have modified existing malicious codes. In this study, we propose a ResNet-Variational AutoEncder-based variant malware classification method that can classify a family of variant malware without domain expert intervention. The Variational AutoEncoder network has the characteristics of creating new data within a normal distribution and understanding the characteristics of the data well in the learning process of training data provided as input values. In this study, important features of malicious code could be extracted by extracting latent variables in the learning process of Variational AutoEncoder. In addition, transfer learning was performed to better learn the characteristics of the training data and increase the efficiency of learning. The learning parameters of the ResNet-152 model pre-trained with the ImageNet Dataset were transferred to the learning parameters of the Encoder Network. The ResNet-Variational AutoEncoder that performed transfer learning showed higher performance than the existing Variational AutoEncoder and provided learning efficiency. Meanwhile, an ensemble model, Stacking Classifier, was used as a method for classifying variant malicious codes. As a result of learning the Stacking Classifier based on the characteristic data of the variant malware extracted by the Encoder Network of the ResNet-VAE model, an accuracy of 98.66% and an F1-Score of 98.68 were obtained.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.