• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.9-15
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• 2021

With the recent development of 5G and artificial intelligence technologies, it is interested in AIOT technology to collect, process, and analyze information in cloud edge environments. AIIoT technology is being applied to various smart environments, but research is needed to perform fast response processing through accurate analysis of collected information. In this paper, we propose a technique to minimize bandwidth and processing time by blocking the connection processing between AIOT information through fast processing and accurate analysis/forecasting of information collected in the smart environment. The proposed technique generates seeds for data indexes on AIOT devices by multipointing information collected by blockchain, and blocks them along with collection information to deliver them to the data center. At this time, we deploy Deep Neural Network (DNN) models between cloud and AIOT devices to reduce network overhead. Furthermore, server/data centers have improved the accuracy of inaccurate AIIoT information through the analysis and predicted results delivered to minimize latency. Furthermore, the proposed technique minimizes data latency by allowing it to be partitioned into a layered multilayer network because it groups it into blockchain by applying weights to AIOT information.

• Journal of Korea Foundry Society
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• v.30 no.3
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• pp.100-110
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• 2010

The solidification sequence and formation of intermetallic phase of Fe-rich Al-Si-Cu alloy were investigated by using real-time imaging of synchrotron X-ray radiation. Effects of cooling rate during uni-directional solidification on the resultant solidification behavior was also studied in a specially constructed vacuum chamber in the SPring-8 facility. The series of radiographic images were complementarily analyzed with conventional analysis of OM and SEM/EDX for phase identification. Detailed solidification sequence and formation mechanisms of various phases were discussed based on real-time image analysis. The growth rates of $\alpha$-AlFeMnSi and ${\beta}-Al_5FeSi$ were measured in order to understand the growth behavior of each phase. It is suggested that real-time imaging technique can be a powerful tool for the precise understanding of solidification behavior of various industrial materials.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.2
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• pp.1-9
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• 1985

Two methods for the dynamic analysis of fixed offshore structures subjected to random waves are studied. They are the frequency domain method using the equivalent linearization of the nonlinear drag force, and the time domain method utilizing the Monte Carlo simulation technique for time series of random wave particle velocities and accelerations. Example analyses are carried out for two structures with different structural characteristics and for various wave conditions. A comparison has been made between the results obtained by two methods.

• Ocean Science Journal
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• v.40 no.4
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• pp.213-220
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• 2005

In this paper, image processing technique that reduces video images of buoy motion to yield time series of image coordinates of buoy objects will be investigated. The buoy motion images are noisy due to time-varying brightness as well as non-uniform background illumination. The occurrence of boats, wakes, and wind-induced white caps interferes significantly in recognition of buoy objects. Thus, semi-automated procedures consisting of object recognition and image measurement aspects will be conducted. These offer more satisfactory results than a manual process. Spectral analysis shows that the image coordinates of buoy objects represent wave motion well, indicating its usefulness in the analysis of wave characteristics.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.441-451
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• 2017

In this paper, we review principal component analysis (PCA) procedures in the frequency domain and apply them to analyze sea surface temperature data. The classical PCA defined in the time domain is a popular dimension reduction technique. Extending the conventional PCA to the frequency domain makes it possible to define PCA in the frequency domain, which is useful for dimension reduction as well as a feature extraction of multiple time series. We focus on two PCA methods in the frequency domain, Hilbert PCA (HPCA) and frequency domain PCA (FDPCA). We review these two PCAs in order for potential readers to easily understand insights as well as perform a numerical study for comparison with conventional PCA. Furthermore, we apply PCA methods in the frequency domain to sea surface temperature data on the tropical Pacific Ocean. Results from numerical experiments demonstrate that PCA in the frequency domain is effective for the analysis of time series data.

• The Journal of Information Systems
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• v.18 no.3
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• pp.351-373
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• 2009

An index fund is a collective investment scheme that aims to replicate the movements of an index of a specific financial market regardless of market conditions. An index fund is a popular investment alternative because it is much cheaper to run than an active fund and it performs better than actively managed funds. This paper illustrates the usefulness of wavelet analysis in constructing an index fund. The wavelet analysis can decompose the time series data in frequency domain as well as in time domain. The major findings of this paper are as follows. First, the beta coefficient that represents the systematic risk has the scale dependent property. This result can provide important information to the investors with various investment time frequency. Investors can use the betas corresponding to their investment frequencies among the various scale betas estimated by wavelet analysis. Second, we can find the usefulness of wavelet analysis in constructing index fund because the wavelet technique gives less tracking error(difference between the index performance and the index fund performance) than the traditional constructing techniques. The result of this study implies that the wavelet techniques can be an important analytic method to the other financial markets such as option market, futures market, bond markets and currency market.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.734-741
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• 2003

This paper addresses the vibration mode shape Measurement technique utilizing a Continuous Scanning Laser Doppler Vibrometer (CSLDV). The continuous scanning capability is added to the conventional discrete Laser Doppler Vibrometer by reflecting the laser beams on the surface of the object using two oscillating mirrors. The bow scanning resulted from the proposed scanning method is eliminated by feedback control. The velocity output signal from the CSLDV is modulated to give the spatial velocity distribution in terms of coefficients which are obtained from the Fast Fourier Transformation of the time dependent velocity signal. Using the Chebyshev series from, the analysis of the vibration mode shape techniques for straight line scanning and 2 dimensional scanning are presented and discussed. The performance of the proposed SLDV is presented using the experimental results of the vibration mode shape of a cantilever and plate

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.15-39
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• 2011

This paper deals with the reliability analysis of a complex system with three possibilities at the time of repair. The considered system consists of two subsystems A and Bin series configuration (1-out-of-2: F). Subsystem A has n units which are connected in series whereas subsystem B consists of n units in parallel configuration. The configuration of subsystem A is of 1-out-of-n: F whereas subsystem B is of k-out-of-n: D and k+1-out-of-n: F nature. System has three states: Good, degraded and failed. Supplementary variable technique has been used for mathematical formulation of the model. Laplace transform is being utilized to solve the mathematical equation. Reliability, Availability, M.T.T.F., Busy Period and Cost effectiveness of the system have been computed. The repairs from state $S_7$ to $S_0$, $S_8$ to $S_0$, $S_9$ to $S_0$ and $S_{11}$ to $S_0$ have two types namely exponential and general. Joint probability distribution of repair rate from $S_7$ to $S_0$, $S_8$ to $S_0$, $S_9$ to $S_0$ and $S_{11}$ to $S_0$ is computed by Gumbel-Hougaard family of copula. Some particular cases of the system have also been derived to see the practical importance of the model.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.38.1-38.1
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• 2017

Symbiotic stars are long-orbital-period interacting binaries characterized by extended emission over the whole electromagnetic range and by complex photometric and spectroscopic variability. In this contribution, I will present some high-cadence, long-term optical light curves of confirmed and candidate symbiotic stars in the Magellanic Clouds. By careful visual inspection and combined time series analysis techniques, we investigate for the first time in a systematic way the photometric properties of these astrophysical objects, trying in particular to distinguish the evolutionary status of the cool component, to provide its first-order pulsation ephemeris and to link all this information with the physical parameters of the binary system as a whole. Finally, I will discuss a new, promising photometric technique, potentially able to discover Symbiotic Stars in the Local Group of Galaxies without the recourse to costly spectroscopic follow-up.