• Journal of the Korea Concrete Institute
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• v.15 no.6
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• pp.888-893
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• 2003

The purpose of this study is to make clear the durability of the polymer films formed in the autoclaved polymer-modified mortars and concretes. The polymer films prepared with polymer dispersions such as a styrene-butadiene rubber (SBR) latex, a poly (ethylene-vinyl acetate)(EVA) emulsion and a polyacrylic ester (PAE) emulsion for polymeric admixtures are exposed to autoclaving at 18$0^{\circ}C$ in temperature and 1.01 MPa in vapor pressure, and subjected to tensile test and infrared spectroscopy. The durability of the polymer films is evaluated from the application of autoclaving to the polymer films under saturated Ca(OH)$_2$ solution immersion causes no degradation for SBR films and a significant degradation due to the saponification of the polymers for EVA and PAE films. Accordingly, in the application of autoclaving to polymer-modified mortars and concretes, it is suggested that SBR-modified mortars and concretes are hardly degraded but EVA- and PAE-modified mortars and concretes are markedly degraded by the saponification of the polymers.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.387-394
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• 2019

In this study, a new method of audio high-band coding based on autoencoder with side information is proposed. The proposed method operates in the MDCT domain, and improves the performance by using additional side information consisting of the previous and current low bands, which is different from the conventional autoencoder that only inputs information to be encoded. Moreover, the side information in a time-frequency domain enables the high-band coder to utilize temporal characteristics of the signal. In the proposed method, the encoder transmits a 4-dimensional latent vector computed by the autoencoder and a gain variable using 12 bits for each frame. The decoder reconstructs the high band by applying the decoded low bands in the previous and current frames and the transmitted information to the autoencoder. Subjective evaluation confirms that the proposed method provides equivalent performance to the SBR at approximately half the bit rate of the SBR.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.49-62
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• 2022

Realized volatility is well known to have long memory, strong association with other global financial markets and interdependences among macroeconomic indices such as exchange rate, oil price and interest rates. This paper proposes autoencoder factor-augmented heterogeneous autoregressive (AE-FAHAR) model for realized volatility forecasting. AE-FAHAR incorporates long memory using HAR structure, and exogenous variables into few factors summarized by autoencoder. Autoencoder requires intensive calculation due to its nonlinear structure, however, it is more suitable to summarize complex, possibly nonstationary high-dimensional time series. Our AE-FAHAR model is shown to have smaller out-of-sample forecasting error in empirical analysis. We also discuss pre-training, ensemble in autoencoder to reduce computational cost and estimation errors.

• Defense and Technology
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• no.5 s.159
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• pp.38-45
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• 1992

현존하는 유도탄은 대륙간탄도탄(ICBM)과 같은 유도탄(Ballistic Missile)에서부터 비행기에 흡사한 순항유도탄(Cruise Missile)에 이르기까지 종류가 다양하다. 유도탄은 사용목적에 따라 전략유도탄과 전술유도탄으로 구분되는데, 전략유도탄은 핵탄두를 갖고 있으며 전쟁억제를 목적으로 하고 전술유도탄은 실전에 사용하기 위해 만들어진 것이다. 이 글에서는 주로 전술유도탄을 중심으로 흔히 오토파일럿(Automatic Pilot)이라 부르는 기술현황과 그 개발추세를 살펴보기로 한다

• The Korean Publising Journal, Monthly
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• s.171
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• pp.21-21
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• 1995

처음에는 오토바이가 없어 배낭에 책을 담고 다녔다. 가까운 거리는 뛰어서, 먼 거리는 버스나 택시를 타고 지리도 모르는 서울 시내를 헤매다녔다. 그러나 이제는 누군가 언론매체의 위치를 물으면 그 자리에서 약도를 쓱쓱 그려낼 수 있을 만큼 '도가 텄다.' 오토바이로 도심을 질주할 때면 가슴이 툭 트이기도 한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.27-31
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• 2003

본 논문은 그름 멤버들간의 안전한 통신을 위한 그룹키 관리(Group key management)에 대한 새로운 방법을 제안한다. 제안된 방식은 선형 셀룰러 오토마타를 이용해서 생성된 involution 특성을 갖는 기본 암호 프리미티브를 이용하여 비밀 공유키를 생성한다. 제안된 방식은 공모에 대한 위협을 근본적으로 방지할 수 있는 특징을 가지고 있다.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.23-30
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• 2021

This paper proposes a noise-tolerant image classification system using multiple autoencoders. The development of deep learning technology has dramatically improved the performance of image classifiers. However, if the images are contaminated by noise, the performance degrades rapidly. Noise added to the image is inevitably generated in the process of obtaining and transmitting the image. Therefore, in order to use the classifier in a real environment, we have to deal with the noise. On the other hand, the autoencoder is an artificial neural network model that is trained to have similar input and output values. If the input data is similar to the training data, the error between the input data and output data of the autoencoder will be small. However, if the input data is not similar to the training data, the error will be large. The proposed system uses the relationship between the input data and the output data of the autoencoder, and it has two phases to classify the images. In the first phase, the classes with the highest likelihood of classification are selected and subject to the procedure again in the second phase. For the performance analysis of the proposed system, classification accuracy was tested on a Gaussian noise-contaminated MNIST dataset. As a result of the experiment, it was confirmed that the proposed system in the noisy environment has higher accuracy than the CNN-based classification technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.638-642
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• 2001

In this paper we propose a noise reduction algorithm which based on cellular automata with the local median rule. It is supposed that there is no information about the features of the image that must be improved. The proposed method behavior is to locally increase or decrease the gray level differences of the image without loss of the main characteristics of the image. The dynamical behavior of these automata is completely determined by Lyapunov operators for sequential and parallel update. We have found that the automata present very fast convergence to fixed points, stability in front of random noisy images. Based on the experimental results we discuss the advantage and efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1099-1104
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• 2009

Cellular Automata(CA) has been used as modeling and computing paradigm for a long time. While studying the models of systems, it is seen that as the complexity of the physical system increase, the CA based model becomes very complex and becomes to difficult to track analytically. Also such models fail to recognize the presence of inherent hierarchical nature of a physical system. In this paper we analyze the properties of GF($2^p$) multiplue attractor cellular automata(GF($2^p$) MACA) C and give a method of synthesis of C which is a special class of hierarchical cellular automata proposed as an alternative to solve the problem.