• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.391-405
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• 2019

Compared to the ambient vibration test mainly identifying the structural modal parameters, such as frequency, damping and mode shapes, the impact testing, which benefits from measuring both impacting forces and structural responses, has the merit to identify not only the structural modal parameters but also more detailed structural parameters, in particular flexibility. However, in traditional impact tests, an impacting hammer or artificial excitation device is employed, which restricts the efficiency of tests on various bridge structures. To resolve this problem, we propose a new method whereby a moving vehicle is taken as a continuous exciter and develop a corresponding flexibility identification theory, in which the continuous wheel forces induced by the moving vehicle is considered as structural input and the acceleration response of the bridge as the output, thus a structural flexibility matrix can be identified and then structural deflections of the bridge under arbitrary static loads can be predicted. The proposed method is more convenient, time-saving and cost-effective compared with traditional impact tests. However, because the proposed test produces a spatially continuous force while classical impact forces are spatially discrete, a new flexibility identification theory is required, and a novel structural identification method involving with equivalent load distribution, the enhanced Frequency Response Function (eFRFs) construction and modal scaling factor identification is proposed to make use of the continuous excitation force to identify the basic modal parameters as well as the structural flexibility. Laboratory and numerical examples are given, which validate the effectiveness of the proposed method. Furthermore, parametric analysis including road roughness, vehicle speed, vehicle weight, vehicle's stiffness and damping are conducted and the results obtained demonstrate that the developed method has strong robustness except that the relative error increases with the increase of measurement noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.393-402
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• 2012

In this study, we propose a signal processing algorithm to measure the arousal level of a human subject using a PPG(Photoplethysmography) sensor. From the measured PPG signals, the arousal level is determined by PPI(Pulse to Pulse Interval) and discrete-time signal processing. We ran psychophysical experiments displaying visual stimuli on TV display while measuring PPG signal from a finger, where the nature landscape scenes were used for restorative effect, and the urban environments were used to stimulate the stress. However, the measured PPG signals may include noise due to subject movement and measurement error, which results in incorrect detections. In this paper, to mitigate the noise impact on stimulus detection, we propose a detecting algorithm using digital signal processing methods and statistics of measured signals. A filter is adopted to remove a high frequency noise and adaptively designed taking into account the statistics of the measured PPG signals. Moreover we employ a hysteresis method to reduce the distortion of PPI in decision of emotional. Via experiment, we show that the proposed scheme reduces signal noise and improves stimulus detection.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.34-41
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• 2017

Vehicle to Infrastructure(V2I) communication means the technology between the vehicle and the roadside unit to provide the Intelligent Transportation Systems(ITS) and Telematic services. The vehicle collects information about the probe data through the evolved Node B(eNodeB) and after that eNodeB provides road conditions or traffic information to the vehicle. To provide these V2I communication services, we need a link adaptation technology that enables reliable and higher transmission rate. The receiver transmits the estimated Channel State Information(CSI) to transmitter, which uses this information to enable the link adaptation. However, due to the rapid channel variation caused by vehicle speed and the processing delay between the layers, the estimated CSI quickly becomes outdated. For this reason, channel interpolation and prediction scheme are needed to achieve link adaptation in V2I communication system. We propose the Advanced Discrete Prolate Spheroidal Sequence(ADPSS) channel interpolation and prediction scheme. The proposed scheme creates an orthonomal basis, and uses a correlation matrix to interpolate and predict channel. Also, smoothing is applied to frequency domain for noise removal. Simulation results show that the proposed scheme outperforms conventional schemes with the high speed and low speed vehicle in the freeway and urban environment.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1112-1119
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• 1999

Digital watermarking is to embed imperceptible mark into image, video, audio and text data to prevent the illegal copy of multimedia data, arbitrary modification, and also illegal sales of the copes without agreement of copyright ownership. The DCT(discrete Cosine Transforms) transforms of original image is conducted in this research and these DCT coefficients are expanded by Fourier series expansion algorithm. In order to embed the imperceptible and robust watermark, the Fourier coefficients(lower frequency coefficients) can be calculated using sine and cosine function which have a complete orthogonal basis function, and the watermark is embedded into these coefficients, In the experiment, we can show robustness with respect to image distortion such as JPEG compression, bluring and adding uniform noise. The correlation coefficient are in the range from 0.5467 to 0.9507.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.1
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• pp.16-26
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• 2000

The proposed method inserts a watermark information not mto a whole Image region but only into regions of interest(ROIs) To extract the ROIs, we divide an original Image into sub-blocks and use modified Shi-Kuo Chang's PIM(picture information measure) as the criteria to select the ROIs Considering the directional information and frequency bands, we insert the watermark information into sub-blocks m the DCT domain. The proposed method can reduce the distortion in comparison With the other methods which utilize the whole Image as an nor The proposed method makes much less damaged Images m comparison to the other methods And those Images processed by the proposed algorithm are more robust to the changes caused by signal processing operations such as resampling, clipping. noise, and so on Also due to the block-based watermark insertion, the proposed method has the robustness to the Image compression processes such as JPEG and MPEG.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.195-200
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• 2006

The conventional discrete circuit for ABS/TCS system was examined and the problems of the system were analyzed by computer simulation. In order to improve the performance of ABS/TCS system, interface IC which has error compensation, comparator and under voltage lock-out circuit was designed and their electrical characteristics were investigated. The voltage regulator was included to compensate the temperature variation in the temperature range from $-20^{\circ}C$ to $120^{\circ}C$ for automobile environment. ABS and brake signal were separated using the duty factor of same frequency or different frequencies. UVLO(Under Voltage Lock-Out) circuit and constant current circuit were applied for the elimination of noise, and protection circuit was applied to cut the excess current off. Layout for IC fabrication was designed to enhance the electrical performance of ABS/TCS system. Layout was consisted of 11 masks, arrayed effectively 8 pads to reduce the current loss. We can see that the result of layout simulation was better than the result of bread board.

• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.183-196
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• 1996

The purpose of this paper is to explore the possibility of automatic extraction of line feature from Satellite image. The first part reviews the relationship between spatial filtering and cartographic interpretation. The second part describes the principal operations of high frequency filters and their properties, the third part presents the result of filtering application to the SPOT Panchromatic image of the Chinju city. Some experimental results are given here indicating the high feasibility of the filtering technique. The results of the paper is summarized as follows: Firstly the good all-purposes filter dose not exist. Certain laplacian filter and Frei-chen filter were very sensitive to the noise and could not detect line features in our case. Secondly, summary filters and some other filters do an excellent job of identifying edges around urban objects. With the filtered image added to the original image, the interpretation is more easy. Thirdly, Compass gradient masks may be used to perform two-dimensional, discrete differentiation directional edge enhancement, however, in our case, the line featuring was not satisfactory. In general, the wide masks detect the broad edges and narrow masks are used to detect the sharper discontinuities. But, in our case, the difference between the $3{\times}3$ and $7{\times}7$ kernel filters are not remarkable. It may be due to the good spatial resolution of Spot scene. The filtering effect depends on local circumstance. Band or kernel size selection must be also considered. For the skillful geographical interpretation, we need to take account the more subtle qualitative information.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.