• 대한원격탐사학회지
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• 제37권4호
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• 한국정밀공학회지
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• 제13권11호
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• Smart Structures and Systems
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• 제16권1호
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• pp.213-222
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• 2015

Early detection and precise location of leakage is of great importance for life-cycle maintenance and management of municipal pipeline system. In the past few years, acoustic emission (AE) techniques have demonstrated to be an excellent tool for on-line leakage detection. Regarding the multi-mode and frequency dispersion characteristics of AE signals propagating along a pipeline, the direct cross-correlation technique that assumes the constant AE propagation velocity does not perform well in practice for acoustic leak location. This paper presents an improved cross-correlation method based on wavelet transform, with due consideration of the frequency dispersion characteristics of AE wave and the contribution of different mode. Laboratory experiments conducted to simulate pipeline gas leakage and investigate the frequency spectrum signatures of AE leak signals. By comparing with the other methods for leak location identification, the feasibility and superiority of the proposed method are verified.

• Structural Engineering and Mechanics
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• 제75권6호
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• pp.723-736
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• 2020

In this paper, acoustic emission (AE) and pattern recognition are utilized to identify the AE signal signatures caused by propagation of stress corrosion cracking (SCC) in a 304 stainless steel plate. The surface of the plate is under almost uniform tensile stress at a notch. A corrosive environment is provided by exposing the notch to a solution of 1% Potassium Tetrathionate by weight. The Global b-value indicated an occurrence of the first visible crack and damage stages during the SCC. Furthermore, a method based on linear regression has been developed for damage identification using AE data.

• 한국정보과학회논문지:시스템및이론
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• 제35권4호
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• pp.172-177
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• 2008

본 논문에서는 경량화된 서명의 특징추출기법을 이용하여 사용자의 편의성 및 전자상거래의 효율성을 높일 수 있는 퍼지볼트스킴 기반의 인증 프로토콜을 설계한다. 서명은 간편하고 저렴한 생체정보 중 하나이기 때문에 전자상거래에서 보편적으로 사용되는 인중수단이지만, 전자상거래시 저비용의 안전성을 확보한 프로토콜의 부재라는 취약성을 가진다. 이러한 문제를 해결하기 위하여, 비밀정보를 정확하게 생성해내는 퍼지볼트스킴 및 서명의 특성에 적합한 특징추출기법을 설계하고, 이 특징추출 과정에서 적용된 매개변수들을 활용하여 서명 기반의 효율적인 인증 프로토콜을 설계한다. 본 프로토콜을 적용시의 효과는 1) 간편하고 저렴한 서명의 이용, 2) 서명처리 및 인증에 약 1초의 시간소비로 실시간 사용자 검증 가능, 3) 로그인 및 검증시 통신횟수 1회로 최저, 4) 사용자 인증과 동시에 비밀 값 획득 등이다.

• Journal of Astronomy and Space Sciences
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• 제31권2호
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• pp.159-167
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• 2014

Solar variability is widely known to affect the interplanetary space and in turn the Earth's electromagnetical environment on the basis of common periodicities in the solar and geomagnetic activity indices. The goal of this study is twofold. Firstly, we attempt to associate modes by comparing a temporal behavior of the power of geomagnetic activity parameters since it is barely sufficient searching for common peaks with a similar periodicity in order to causally correlate geomagnetic activity parameters. As a result of the wavelet transform analysis we are able to obtain information on the temporal behavior of the power in the velocity of the solar wind, the number density of protons in the solar wind, the AE index, the Dst index, the interplanetary magnetic field, B and its three components of the GSM coordinate system, $B_X$, $B_Y$, $B_Z$. Secondly, we also attempt to search for any signatures of influence on the space environment near the Earth by inner planets orbiting around the Sun. Our main findings are as follows: (1) Parameters we have investigated show periodicities of ~ 27 days, ~ 13.5 days, ~ 9 days. (2) The peaks in the power spectrum of $B_Z$ appear to be split due to an unknown agent. (3) For some modes powers are not present all the time and intervals showing high powers do not always coincide. (4) Noticeable peaks do not emerge at those frequencies corresponding to the synodic and/or sidereal periods of Mercury and Venus, which leads us to conclude that the Earth's space environment is not subject to the shadow of the inner planets as suggested earlier.