• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.6 s.109
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• pp.562-573
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• 2006

It is required to reduce the peak-to-average power ratio(PAPR) in an orthogonal frequency division multiplexing system or a multicarrier system. And it is needed to eliminate the transmission of the side information in the Partial Transmit Sequences. So, in this paper, a new technique is proposed, where the subcarriers used for the multiple signal representation are only utilized for the reduction of PAPR to eliminate the burden of transmitting the side information. That is, it is proposed by taking the modified minimization criteria of partial transmit sequences scheme instead of using the convex optimization or the fast algorithm of tone reservation(TR) technique As the result of simulation, the PAPR reduction capability of the proposed method is improved by 3.2 dB dB, 3.4 dB, 3.6 dB with M=2, 4, 8(M is the number of partition in the so-called partial transmit reduction sequences(PTRS)), when the iteration number of fast algorithm of TR is 10 and the data rate loss is 5 %. But it is degraded in the capability of PAPR reduction by 3.4 dB, 3.1 dB, 2.2 dB, comparing to the TR when the data rate loss is 20 %. Therefore, the proposed method is outperformed the TR technique with respect to the complexity and PAPR reduction capability when M=2.

• Journal of the Korean Geotechnical Society
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• v.35 no.11
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• pp.121-129
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• 2019

An Earthquake Early Warning (EEW) system is a technology that alerts people to an incoming earthquake by using P waves that are detected before the arrival of more severe seismic waves. P-wave analysis is therefore an important factor in the production of rapid seismic information as it can be used to quickly estimate the earthquake magnitude and epicenter through the amplitude and predominant period of the observed P-wave. However, when a large-magnitude teleseismic earthquake is observed in a local seismic network, the significantly attenuated P wave phases may be mischaracterized as belonging to a small-magnitude local earthquake in the initial analysis stage. Such a misanalysis may be sent to the public as a false alert, reducing the credibility of the EEW system and potentially causing economic losses for infrastructure and industrial facilities. Therefore, it is necessary to develop methods that reduce misanalysis. In this study, the possibility of seismic misclassifying teleseimic earthquakes as local events was reviewed using the Filter Bank method, which uses the attenuation characteristics of P waves to classify local and outside Korean peninsula (regional and teleseismic) events with filtered waveform depending on frequency and epicenter distance. The data used in our analysis were analyzed for maximum P_v values using 463 events with local magnitudes (2 < M_L ≦ 3), 44 (3 < M_L ≦ 4), 4 (4 < M_L ≦ 5), 3 (M_L > 5), and 89 outside Korean peninsula earthquakes recorded by the KMA seismic network. The results show that local and telesesimic earthquakes can be classified more accurately when combination of filtering bands of No. 3 (6-12 Hz) and No. 6 (0.75-1.5 Hz) is applied.

• Journal of Plant Biotechnology
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• v.33 no.1
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• pp.49-56
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• 2006

Gerber (Gerbera hybrida) is a valuable ornamental species grown as a potted plant and cut flowers. However, genetic variability within the gerbera genus is very limited. So it is absolutely needed to introduce and widen genetic resources into gerbera lines by genetic transformation. For the purpose, 18 Korean gerbera lines were screened to establish Agrobacterium-mediated genetic transformation procedure. In an experiment to select Korean gerbera lines which are amenable to Agrobacterium-inoculation, 12 lines turned out to be positive in Agrobacterium-inoculation. More callus were produced from BA 2ppm, Zeatin 2ppm, IAA 0.2ppm in pre-culture and regeneration medium (2X media) but there was no difference in the frequency of GUS expression rate. In another experiment to find out optimal condition for highly efficient Agrobacterium-inoculation, petiole and leaf explants have been treated with four different pre-culture periods, two different co-culture periods and two different Agrobacterium strains. As a result, high GUS expression has been showed from petiole and leaf explants treated no pre-culture period with LBA4404 Agrobacterium tumerfaciens, 5 day co-culture period and dipping treatment.

• Progress in Medical Physics
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• v.21 no.3
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• pp.261-273
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• 2010

For the use of Indirect-conversion CMOS (complementary metal-oxide-semiconductor) detectors for digital x-ray radiography and their better designs, we have theoretically evaluated the spatial-frequency-dependent detective quantum efficiency (DQE) using the cascaded linear-systems transfer theory. In order to validate the developed model, the DQE was experimentally determined by the measured modulation-transfer function (MTF) and noise-power spectrum, and the estimated incident x-ray fluence under the mammography beam quality of W/Al. From the comparison between the theoretical and experimental DQEs, the overall tendencies were well agreed. Based on the developed model, we have investigated the DQEs values with respect to various design parameters of the CMOS x-ray detector such as phosphor quantum efficiency, Swank noise, photodiode quantum efficiency and the MTF of various scintillator screens. This theoretical approach is very useful tool for the understanding of the developed imaging systems as well as helpful for the better design or optimization for new development.

• 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.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.336-349
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• 2003

Histograms have been getting a lot of attention recently. Histograms are commonly utilized in commercial database systems to capture attribute value distributions for query optimization Recently, in the advent of researches on approximate query answering and stream data, the interests in histograms are widely being spread. The simplest approach assumes that the attributes in relational tables are independent by AVI(Attribute Value Independence) assumption. However, this assumption is not generally valid for real-life datasets. To alleviate the problem of approximation on multi-dimensional data with multiple one-dimensional histograms, several techniques such as wavelet, random sampling and multi-dimensional histograms are proposed. Among them, GENHIST is a multi-dimensional histogram that is designed to approximate the data distribution with real attributes. It uses overlapping buckets that allow more efficient approximation on the data distribution. In this paper, we propose a scheme, OPT that can determine the optimal frequencies of overlapped buckets that minimize the SSE(Sum Squared Error). A histogram with overlapping buckets is first generated by GENHIST and OPT can improve the histogram by calculating the optimal frequency for each bucket. Our experimental result confirms that our technique can improve the accuracy of histograms generated by GENHIST significantly.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.611-625
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• 2018

With innovative advances in wireless communication technology, many researches for extending network lifetime in maximum by using energy harvesting have been actively performed on the area of network resource optimization, QoS-guaranteed transmission, energy-intelligent routing and etc. As known well, it is very hard to guarantee end-to-end network delay due to uncertainty of the amount of harvested energy in multi-hop RF(radio frequency) energy harvesting wireless networks. To minimize end-to-end delay in multi-hop RF energy harvesting networks, this paper proposes an energy efficient routing metric based on interference aware and protocol which takes account of various delays caused by co-channel interference, energy harvesting time and queuing in a relay node. The proposed method maximizes end-to-end throughput by performing avoidance of packet congestion causing load unbalance, reduction of waiting time due to exhaustion of energy and restraint of delay time from co-channel interference. Finally simulation results using ns-3 simulator show that the proposed method outperforms existing methods in respect of throughput, end-to-end delay and energy consumption.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.1 s.53
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• pp.11-19
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• 2007

This paper proposes an optimal design method of distribution and capacities of linear viscous dampers for vibration control of two adjacent buildings. The previous researches have dealt with suboptimal design problem under the assumption that linear viscous dampers are distributed uniformly or proportionally to the sensitivity of the modal damping ratio according to floors, whereas this study deals with global optimization problem in which the damping capacities of each floor are independently selected as design parameters. For this purpose, genetic algorithm to effectively search multiple design variables in large searching domains is adopted and objective function leading to the global optimal solutions is established through the comparison of several optimal design values obtained from different objective functions with control performance and damping capacity. The effectiveness of the proposed method is investigated by comparing the control performance and total damping capacity designed by the proposed method with those of the previous method. In addition, the time history analyses are performed by using three historical earthquakes with different frequency contents, and the simulation results demonstrate that the proposed method is an effective seismic design method for the vibration control of the adjacent structures.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.2
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• pp.179-187
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• 2010

The use of high frequency band, broad band and MIMO antenna is expected in the next generation mobile communication system. By the rapid increase of demand for wireless communications and the explosive increase of the mobile communication services, researches for optimization of next-generation mobile communication system are required. In the existing MIMO channel models, propagation-environments are commonly classified into urban, suburban, rural area, etc. However such approaches can have drawbacks in that many different morphologies may exist even in the urban area, for example. In this paper, we introduced path morphology concept, and proposed the method of morphology classification considering the building height, density, etc. Delay spread(DS), angular spread(AS) of AoD and AoA analyzed for each environment using the ray tracing technique. Based on the analysis, a MIMO channel model appropriate in domestic environment was suggested.