• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2009.04a
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• pp.11-13
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• 2009

This research is renewal way to get latest digital map, We presented techniques to convert to GIS data for update digital map to utilize completion drawing of CAD data that is used construction and SOC construction. Conversion process is consisted of layer extraction, object transform, coordinate transform, format transform. GIS data that is changed via each process from CAD data can update digital map.

• Journal of Industrial Technology
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• v.19
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• pp.403-408
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• 1999

An efficient method is developed for classifying fingerprint data based on 2-D discrete wavelet transform. Fingerprint data is first converted to a binary image. Then a multi-level 2-D wavelet transform is performed. Vertical and horizontal subbands of the transformed data show typical energy distribution patterns relevant to the fingerprint categories. The proposed method with moderate level of wavelet transform is successful in classifying fingerprints into 5 different types. Finer classification is possible by higher frequency subbands and closer analysis of energy distribution.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.49-51
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• 1996

The z-transform method is a basic mathematical tool in analyzing and designing sampled-data control systems. However, since the z-transform method relates only the sampling-instants signals, another mathematical tool is necessary to describe the continous signals between the sampling instants. For this purpose the delayed and the modi fled z-transform methods were developed. The definition of the modi fled z-transform includes a sample in the interval [-T,0] of the original signal in its series expression, where the signal value is always zero for any physical system. From this reason one step skew of the time index always appears in its application formulas. This introduces an unnecessary operation and a gap in linking the mathematical formula and its physical interpretation. Considering the conceptual difficulty and application inconvenience, a method of using the advanced z-transform in analysis of sampled-data control systems is developed as a replacement of the modi fled z-transform. With one formulation of the advanced z-transform, now it is possible to relate both the signals of the sampling instants and those in between without any complication and conceptual difficulty.

• Journal of the Korean Society for Railway
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• v.9 no.2 s.33
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• pp.187-192
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• 2006

The regularity of railway track alignment is a crucial component fur maintaining travel safety and the smoothness of passenger ride. The conventional spectral analysis has been considered to estimate the severity of the track irregularity from measured data. The time domain data used to be changed into the frequency domain by Fourier transform. Because the measuring points can be regarded as the time points, the spatial-frequency can be introduced instead of the time-frequency. Although FFT(Fast Fourier Transform) and/or PSD(Power Spectral Density) function could provide fairly localized information within frequency domain, but chronical configurations of data could be missed. In this study, we attempt to apply the Morlet wavelet transform for the purpose of a frequency-time-domain analysis rather than a frequency-domain analysis. The applicability of wavelet transform is examined for the estimation of the track irregularity with real measured track data on the section of Kyoung-bu line by EM-120 measuring vehicle. It is shown that the wavelet transform can be an effective tool to manage the track irregularity.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.831-837
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• 2006

The present study applies wavelet transform for the extraction of various periodicities which are included in TOC and pH time series of water quality and rainfall data. The primary objective of the present study is to detect the relationships between the respective data through the correlation analysis using the approximation components which are decomposed by wavelet transform. The results reveal the approximation components of TOC and pH in the 5th level of wavelet transform can explain more than 99% of the whole energy for the raw data respectively and there are considerably high correlation between the approximation components of the respective data used for the study even through no significant correlation between the raw data has been detected.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.429-432
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• 2003

In this study, exterior orientation parameters of one image are determined using linear features of imagery and GIS data based on the Modified Iterative Hough Transform algorithm and the possibility of automatic updating GIS data is presented.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1331-1344
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• 2017

In this paper, data hiding algorithm using Discrete Wavelet Transform (DWT) and Arnold Transform is proposed. The secret data is scrambled using Arnold Transform to make it secure. Wavelet subbands of a cover image are obtained using DWT. The scrambled secret data is embedded into significant wavelet coefficients of subbands of a cover image. The proposed algorithm is robust to a variety of attacks like JPEG and JPEG2000 compression, image cropping and median filtering. Experimental results show that the PSNR of the composite image is 1.05 dB higher than the PSNR of existing algorithms and capacity is 25% higher than the capacity of existing algorithms.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.841-848
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• 2008

This paper examined dynamic characteristics of high speed trains using a time varying frequency transform. Fourier transform based methods are frequently used for the calculation of the dynamic characteristics of trains in the frequency domain, but they cannot represent the time-varying characteristics. Therefore it is necessary to examine their characteristics using a time-varying frequency transform. For the examination, the non-stationary vibration of wheelset, bogie, and carbody are measured using accelerometers and stored in a data aquisition system. They are processed with localization of the data by modulating with a window function, and Fourier transform is taken to each localized data, called the short-time Fourier transform. From the processed results, time varying auto-spectral density, cross-spectral density, frequency response, and coherence functions have been calculated. From the analysis, it is confirmed that the time varying frequency transform is a useful method for analyzing the dynamic characteristics of high speed trains.

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.1-7
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• 2000

This paper presents the approach involving fault detection and diagnosis of gears using pattern recognition and Wavelet transform. It describes result of the comparison between KDI (Kullback Discrimination Information) with the nearest neighbor classification rule as one of pattern recognition methods and Wavelet transform to know a way to detect and diagnosis of gear damages experimentally. To model the damages 1) Normal (no defect), 2) one tooth is worn out, 3) All teeth faces are worn out 4) One tooth is broken. The vibration sensor was attached on the bearing housing. This produced the total time history data that is 20 pieces of each condition. We chose the standard data and measure distance between standard and tested data. In Wavelet transform analysis method, the time series data of magnitude in specified frequency (rotary and mesh frequency) were earned. As a result, the monitoring system using Wavelet transform method and KDI with nearest neighbor classification rule successfully detected and classified the damages from the experimental data.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.