• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.972-980
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• 2022

The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.143-151
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• 2020

The digital content's security and authentication are important in the field of digital content application. There are some methods to perform the authentication. The digital watermarking is one of authentication methods. Paper presents a digital watermark authentication method that works in the application of digital image. The proposed watermark has the triple status information and performs the embedding and the detection without original Content. When authenticating the owner information of digital content, an autocorrelation function is used. In addition, a spread spectrum method is used to be adaptive to the signal of the original content in the frequency domain(DWT Domain). Therefore, the possibility of errors occurring in the detection of hidden information was reduced. it also has a advantage what Watermarking in DWT has faster embedding and detection time than other transformation domains(DFT, DCT, etc.). if it has a an image of size N=mXm, the computational amount can be reduced from O(N·logN) to O(N). The particular advantage is that it can hide more information(bits) per bit.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.262-269
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• 2023

In this paper, we propose a Linear Prediction (LP) analysis-based feature for detecting Snapping Shrimp (SS) Noise (SSN) in underwater acoustic data. SS is a species that creates high amplitude signals in shallow, warm waters, and its frequent and loud sound is a major source of noise. The proposed feature takes advantage of the characteristic of SSN, which is sudden and rapidly disappearing, by using LP analysis to detect the exact noise interval and reduce the effects of SSN. The error between the predicted and measured value is large and results in effective SSN detection. To further improve performance, a constant false alarm rate detector is incorporated into the proposed feature. Our evaluation shows that the proposed methods outperform the state-of-the-art MultiLayer-Wavelet Packet Decomposition (ML-WPD) in terms of receiver operating characteristic curve and Area Under the Curve (AUC), with the LP analysis-based feature achieving a higher AUC by 0.12 on average and lower computational complexity.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.199-210
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• 2023

The physical properties of rocks in reservoirs change after CO₂ injection, we modeled a reservoir with a transition zone within which the physical properties change linearly. The function of the Wolf reflection coefficient consists of the velocity ratio of the upper and lower layers, the frequency, and the thickness of the transition zone. This function can be used to estimate the thickness of a reservoir or seafloor transition zone. In this study, we propose a method for predicting the thickness of the transition zone using deep learning. To apply deep learning, we modeled the thickness-dependent Wolf reflection coefficient on an artificial transition zone formation model consisting of sandstone reservoir and shale cap rock and generated time-frequency spectral images using the continuous wavelet transform. Although thickness estimation performed by comparing spectral images according to different thicknesses and a spectral image from a trace of the seismic stack did not always provide accurate thicknesses, it can be applied to field data by obtaining training data in various environments and thus improving its accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• Smart Structures and Systems
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• v.33 no.3
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.3
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• pp.187-199
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• 2013

Umbrella arch method (UAM) used for improving the stability of the tunnel ground condition has been widely applied in the tunnel construction projects due to the advantage of obtaining both reinforcement and waterproof. The purpose of this study is to develop the evaluation technique of the integrity of bore-hole in UAM by using a non-destructive test and to evaluate the possibility of being applied to the field. In order to investigate the variations of frequency depending on grouted length, the specimens with different grouted ratios are made in the two constraint conditions (free boundary condition and embedded condition). The hammer impact reflection method in which excitation and reception occur simultaneously at the head of pipe was used. The guided waves generated by hitting a pipe with a hammer were reflected at the tip and returned to the head, and the signals were received by an acoustic emission (AE) sensor installed at the head. For the laboratory experiments, the specimens were prepared with different grouted ratios (25 %, 50 %, 75 %, 100 %). In addition, field tests were performed for the application of the evaluation technique. Fast Fourier transform and wavelet transform were applied to analyze the measured waves. The experimental studies show that grouted ratio has little effects on the velocities of guided waves. Main frequencies of reflected waves tend to decrease with an increase in the grouted length in the time-frequency domain. This study suggests that the non-destructive tests using guided ultrasonic waves be effective to evaluate the bore-hole integrity of the UAM in the field.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.240-249
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• 2013

The seismic data quality of marine geological and engineering survey deteriorates because of the sea swell. We often conduct a marine survey when the swell height is about 1 ~ 2 m. The swell effect correction is required to enhance the horizontal continuity of seismic data and satisfy the resolution less than 1 m. We applied the swell correction to the 8 channel high-resolution airgun seismic data and 3.5 kHz subbottom profiler (SBP) data. The correct sea bottom detection is important for the swell correction. To detect the sea bottom, we used maximum amplitude of seismic signal around the expected sea bottom, and picked the first increasing point larger than threshold value related with the maximum amplitude. To find sea bottom easily in the case of the low quality data, we transformed the input data to envelope data or the cross-correlated data using the sea bottom wavelet. We averaged the picked sea bottom depths and calculated the correction values. The maximum correction of the airgun data was about 0.8 m and the maximum correction of two kinds of 3.5 kHz SBP data was 0.5 m and 2.0 m respectively. We enhanced the continuity of the subsurface layer and produced the high quality seismic section using the proper methods of swell correction.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.2
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• pp.89-105
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• 2008

Sea level and atmospheric pressure data of 1999-2005 from four stations along the Korean east coast were analyzed to understand the sea level variability and its causal mechanism. The results of the wavelet and the auto-spectrum analyses indicate that the sea level fluctuations of 3-17 day period are statistically significant at the 95% confidence level, especially in spring to early summer. In this period, the coherency between the sea levels and the atmospheric pressures in a cross-spectrum is high, implying the importance of an inverted barometric effect in generation of the sea level fluctuations. To learn about the sea level variability, the cross-spectrum analyses were applied between the sea levels of the adjacent stations. The results show a case of southward phase propagations along the coast, as in 1999, 2003 and 2005, and an another case of no progressive phase lags between the stations, as in 2000-2002, and 2004. The phase speed in the former case is 12-15 m/s, which is a commonly observed phase speed of coastal Kelvin waves. Generation of such fluctuations seems to be related to low pressure cells developed in the Asian continent in spring and summer and moving eastward over the coastal region north of the stations. The latter case of no progressive phase lag, however, occurs when the low pressure cells developed in the continent move along the region south of the stations. In this case, the northeastward phase propagation with a speed of 5-8 m/s is observed along the southwestern coast of Japan.