• Journal of Intelligence and Information Systems
• /
• v.5 no.1
• /
• pp.103-123
• /
• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.27 no.2
• /
• pp.97-104
• /
• 2007

An investigation has been made on the relationship between characteristics of Acoustic Emission (AE) signal in welding flaw and the stress corrosion defect in-service for the high pressure pipe steel. In order to tackle the problem of welding flaw in high pressure pipe, specimens were made by the aid of the application of both corrosion liquid usage and a quenching method after local heating. The amplitude of signal was $60{\sim}75\;dB$ in the territory which is suspected for defect, and the specimens which only have welding flaw showed gradients of 0.034, 0.034, 0.035. Moreover, there is a certain increase in gradient even though the differences are very slight. That is, corrosion specimens showed new gradients of 0.040, 0.039, 0.041 which put welding flaw and corrosion mechanism together. After pressurizing 3 minutes, AE signal has been detected from welding flaw easily in each part of the section. It is possible to predict the occurrence and also prevent the damage of stress corrosion crack which has characteristics of cleavage fracture.

• Journal of Korean Society on Water Environment
• /
• v.24 no.1
• /
• pp.36-43
• /
• 2008

To develop the continuous water quality monitoring system using the daphnid Daphnia sp., the growth of test animal, sensitivity, and behaviour response of toxicants were observed. Growth of test animal significantly increased with increasing the food density under the 90~105 mg/L ($CaCO_3$) hardness, except the concentration of food (Chrollela sp.) was exceeded than optimal food supply. Behaviour responses of test animals were continuously analyzed by changes of fractal dimension value (FDV). The FDV sharply decreased after exposure to the concentrations of 0.13 mg/L copper, 0.06 mg/L lead, and 0.38 mg/L cadmium. In these concentrations, mortality and abnormal behaviour of daphnids exhibited within ca. 1.0-h after exposure. Comparison of 24-h $LC_{50}$ values with other zooplankton species indicated that sensitivity of the Daphnia sp. was higher than most zooplankton for lead, and brain shrimp, rotifer, and water flea (Ceriodaphnia dubia, D. magna) for copper, and brain shrimp, water flea (D. lumholzi), and amphipod for cadmium. Based on the above experimental results, significant relationship between toxicity and behaviour response of Daphnia sp. was supported the high potential of water quality monitoring system. Consequently, behavioural monitoring method in this study suggests a good estimation tool for detection of the discharged toxicants in water body and for ecotoxicological assessment aquatic organisms.

• The KIPS Transactions:PartC
• /
• v.10C no.7
• /
• pp.899-914
• /
• 2003

It is generally accepted that self-similar (or fractal) processes may provide better models for teletraffic in modern telecommunication networks than Poisson Processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. Three generators of pseudo-random self-similar sequences, based on the FFT〔20〕, RMD〔12〕 and SRA methods〔5, 10〕, are compared and analysed in this paper. Properties of these generators were experimentally studied in the sense of their statistical accuracy and times required to produce sequences of a given (long) length. While all three generators show similar levels of accuracy of the output data (in the sense of relative accuracy of the Horst parameter), the RMD- and SRA-based generators appear to be much faster than the generator based on FFT. Our results also show that a robust method for comparative studies of self-similarity in pseudo-random sequences is needed.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.46 no.6
• /
• pp.58-64
• /
• 2009

Digital radiography (DR) images are corrupted by the additive noise, and also distorted by system impulse response. These unwanted phenomena are obstacles to obtain the desired image. To recover the original image, we applied multiscale Wiener filters in wavelet domain for DR images. The multiscale Wiener filter is first proposed by Chen for the restoration of fractal signals which are distorted by the system impulse response and additive noise. In this paper, we extended the multiscale Wiener filter to the two dimensional data. To compare the performance of ours with others, some simulations are given for a couple of wavelet filters with different wavelet levels, system impulse reponses and various noise power. When the addive noise powers are between 20-32 dB, the signal to noise ratio(SNR) of the proposed system is 0.5-2.0 dB better than that of the traditional Wiener filter method.

• Journal of Fashion Business
• /
• v.24 no.1
• /
• pp.1-14
• /
• 2020

Based on the trend of pluralization and globalization the collapse of national borders now is a manifestation of mixed and compromised cultures and societies. It is also emerging as a hybrid fashion in fashion. Hybrid fashion means creating a new image by mixing various cultures beyond the time and space. This study aims to analyze the current state of Chinese fashion design and present its direction by grasping the characteristics of hybrids in the works of rising Chinese fashion designers in the era of pluralization. The research method was literature review and empirical research. According to the selection criteria of new fashion designers, 6 new fashion designers of 5 fashion brands were selected and their total 458 points works were analyzed. The analysis results are as follows. First, most of the time trade-offs were 'past and present' trade-offs that express Chinese traditional culture and the image of the past with modern design. The trade-offs between 'present and future' is expressed by mixing print patterns, colors and light with fractal art. Second, spatial trade-offs was expressed in the way of expressing Chinese themes in the composition of western clothing, expressing the Western themes in oriental colors, and inspired by Japanese culture expressed by deconstructionism, Third, the gender mix mainly used dark embroidery on women's clothing, while the men's wear showed a delicate feminine charm with a surreal pattern on thin and transparent gauze fabric.

• The KIPS Transactions:PartC
• /
• v.12C no.2 s.98
• /
• pp.281-288
• /
• 2005

It is generally accepted that self-similar (or fractal) Processes may provide better models for teletraffic in modem computer networks than Poisson processes. f this is not taken into account, it can lead to inaccurate conclusions about performance of computer networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A generator of pseudo-random self similar sequences, based on the SRA (successive random addition) method, is implemented and analysed in this paper. Properties of this generator were experimentally studied in the sense of its statistical accuracy and the time required to produce sequences of a given (long) length. This generator shows acceptable level of accuracy of the output data (in the sense of relative accuracy of the Hurst parameter) and is fast. The theoretical algorithmic complexity is O(n).

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.6
• /
• pp.1301-1308
• /
• 2018

90/150 CA which are used as key generators of the cipher system have more randomness than LFSRs, but synthesis methods of 90/150 CA are difficult. Therefore, 90/150 CA synthesis methods have been studied by many researchers. In order to synthesize a suitable CA, the analysis of the characteristic polynomial of 90/150 CA should be preceded. In general, the characteristic of polynomial ${\Delta}_n$ of n cell 90/150 CA is obtained by using ${\Delta}_{n-1}$ and ${\Delta}_{n-2}$. Choi et al. analyzed $H_{2^n}(x)$ and $H_{2^n-1}(x)$, where $H_k(x)$ is the characteristic polynomial of k cell 90/150 CA with state transition rule <$10{\cdots}0$>. In this paper, we propose an efficient method to obtain $H_n(x)$ from $H_{n-1}(x)$ and an efficient algorithm to obtain $H_{2^n+i}(x)$ and $H_{2^n-i}(x)$ ($1{\leq}i{\leq}2^{n-1}$) from $H_{2^n}(x)$ by using this method.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 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
• /
• 1999.03a
• /
• pp.175-186
• /
• 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.