• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1461-1470
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• 2016

In side channel analysis, signal processing techniques can be used as preprocessing to enhance the efficiency and performance of analysis by reducing the noise or compressing the dimension. As signal processing techiniques using singular value decomposition can increase the information of main signal and reduce the noise by using the variance and tendency of signal, it is a great help to improve the performance of analysis. Typical techniques of that are PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis) and SSA(Singular Spectrum Analysis). PCA and LDA can compress the dimension with increasing the information of main signal, and SSA reduces the noise by decomposing the signal into main siganl and noise. When applying each one or combination of these techniques, it is necessary to compare the performance. Therefore, it needs to suggest methodology of that. In this paper, we compare the performance of the three technique and propose using Sinal-to-Noise Ratio(SNR) as the methodology. Through the proposed methodology and various experiments, we confirm the performance and efficiency of each technique. This will provide useful information to many researchers in the field of side channel analysis.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.41-53
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• 2002

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation suggested by the first report of this project. According to the regions and consecutive durations, optimal design rainfalls were derived by the regional frequency analysis for L-moment in the second report of this project. Using the LH-moment ratios and Kolmogorov-Smirnov test, the optimal regional probability distribution was identified to be the Generalized extreme value (GEV) distribution among applied distributions. regional and at-site parameters of the GEV distribution were estimated by the linear combination of the higher probability weighted moments, LH-moment. Design rainfall using LH-moments following the consecutive duration were derived by the regional and at-site analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design rainfall were computed and compared in the regional and at-site frequency analysis. Consequently, it was shown that the regional analysis can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than at-site analysis in the prediction of design rainfall. Relative efficiency (RE) for an optimal order of L-moments was also computed by the methods of L, L1, L2, L3 and L4-moments for GEV distribution. It was found that the method of L-moments is more effective than the others for getting optimal design rainfall according to the regions and consecutive durations in the regional frequency analysis. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.85-92
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• 2018

Reductions of the electricity charge are achieved by demand management of the load. The demand management method of the load using ESS involves peak shifting, which shifts from a high demand time to low demand time. By shifting the load, the peak load can be lowered and the energy charge can be saved. Electricity charges consist of the energy charge and the basic charge per contracted capacity. The energy charge and peak load are minimized by Linear Programming (LP) and Quadratic Programming (QP), respectively. On the other hand, each optimization method has its advantages and disadvantages. First, the LP cannot separate the efficiency of the ESS. To solve these problems, the charge and discharge efficiency of the ESS was separated by Mixed Integer Linear Programming (MILP). Nevertheless, both methods have the disadvantages that they must assume the reduction ratio of peak load. Therefore, QP was used to solve this problem. The next step was to optimize the formula combination of QP and LP to minimize the electricity charge. On the other hand, these two methods have disadvantages in that the charge and discharge efficiency of the ESS cannot be separated. This paper proposes an optimization method according to the situation by analyzing quantitatively the advantages and disadvantages of each optimization method.

• Structural Engineering and Mechanics
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• v.33 no.1
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• pp.79-93
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• 2009

This paper investigates the possibility of controlling the response of typical portal frame structures to blast loading using a combination of semi-active and passive control devices. A one storey reinforced concrete portal frame is modelled using non-linear finite elements with each column discretised into multiple elements to capture the higher frequency modes of column vibration response that are typical features of blast responses. The model structure is subjected to blast loads of varying duration, magnitude and shape, and the critical aspects of the response are investigated over a range of structural periods in the form of blast load response spectra. It is found that the shape or length of the blast load is not a factor in the response, as long as the period is less than 25% of the fundamental structural period. Thus, blast load response can be expressed strictly as a function of the momentum applied to the structure by a blast load. The optimal device arrangements are found to be those that reduce the first peak of the structural displacement and also reduce the subsequent free vibration of the structure. Semi-active devices that do not increase base shear demands on the foundations in combination with a passive yielding tendon are found to provide the most effective control, particularly if base shear demand is an important consideration, as with older structures. The overall results are summarised as response spectra for eventual potential use within standard structural design paradigms.

• Journal of Microbiology and Biotechnology
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• v.15 no.3
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• pp.595-602
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• 2005

A one-step real-time quantitative RT-PCR assay in combination with automated RNA extraction was evaluated for routine testing of HCV RNA in the laboratory. Specific primers and probes were developed to detect 302 bp on 5'-UTR of HCV RNA. The assay was able to quantitate a dynamic linear range of $10^7-10^1$ HCV RNA copies/reaction ($R^2=0.997$). The synthetic HCV RNA standard of $1.84{\pm}0.1\;(mean{\pm}SD)$ copies developed in this study corresponded to 1 international unit (IU) of WHO International Standard for HCV RNA (96/790 I). The detection limit of the assay was 3 RNA copies/reaction (81 IU/ml) in plasma samples. The assay was comparable to the Amplicor HCV Monitor (Monitor) assay with correlation coefficient r=0.985, but was more sensitive than the Monitor assay. The assay could be completed within 3 h from RNA extraction to detection and data analysis for up to 32 samples. It allowed rapid RNA extraction, detection, and quantitation of HCV RNA in plasma samples. The method provided sufficient sensitivity and reproducibility and proved to be fast and labor-saving, so that it was suitable for high throughput HCV RNA test.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.405-415
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• 2000

Time series of wind-induced pressure on a structure are modeled using autoregressive moving average (ARMA) model. In an AR process, the current value of the time series is expressed in terms of a finite, linear combination of the previous values and a white noise. In a MA process, the value of the time series is linearly dependent on a finite number of the previous white noises. The ARMA process is a combination of the AR and MA processes. In this paper, the ARMA models with several different combinations of the AR and MA orders are fitted to the wind-induced pressure time series, and the procedure to select the most appropriate ARMA model to represent the data is described. The maximum likelihood method is used to estimate the model parameters, and the AICC model selection criterion is employed in the optimization of the model order, which is assumed to be a measure of the temporal complexity of the pressure time series. The goodness of fit of the model is examined using the LBP test. It is shown that AR processes adequately fit wind pressure time series.

• Imaging Science in Dentistry
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• v.49 no.2
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• pp.123-130
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• 2019

Purpose: This study was conducted to characterize the relationship of the angulation between the tooth root axis and alveolar bone axis with anterior alveolar(AA) arch forms and sagittal root position (SRP) in the anterior esthetic region using cone-beam computed tomography (CBCT) images. Materials and Methods: CBCT images that met the inclusion and exclusion criteria were categorized using a recent classification of AA arch forms and a SRP classification. Then, the angulation of the root axis and the alveolar bone axis was measured using mid-sagittal CBCT images of each tooth. The relationships of the angulation with each AA arch form and SRP classification were evaluated using 1-way analysis of variance and a linear regression model. Results: Ninety-eight CBCT images were included in this study. SRP had a greater influence than the AA arch form on the angulation of the root axis and the alveolar bone axis(P<0.05). However, the combination of AA arch form and SRP was more predictive of the angulation of the root axis and the alveolar bone axis than either parameter individually. Conclusion: The angulation of the root axis and alveolar bone axis demonstrated a relationship with the AA arch form and SRP in teeth in the anterior esthetic region. The influence of SRP was greater, but the combination of both parameters was more predictive of root-to-bone angulation than either parameter individually, implying that clinicians should account for both the AA arch form and SRP when planning implant placement procedures in this region.

• Earthquakes and Structures
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• v.26 no.5
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• pp.401-416
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• 2024

Long-span structures, such as bridges, can experience different seismic excitations at the supports due to spatially variability of ground motion. Regarding current bridge designing codes, it is just EC 2008 that suggested some regulations to consider it and in the other codes almost ignored while based on some previous studies it is found that the effect of mentioned issue could not be neglected. The current study aimed to perform a comprehensive study about the effect of spatially varying ground motions on the dynamic response of a reinforced concrete bridge under asynchronous input motions considering soil-structure interactions. The correlated ground motions were generated by an introduced method that contains all spatially varying components, and imposed on the supports of the finite element model under different load scenarios. Then the obtained results from uniform and non-uniform excitations were compared to each other. In addition, the effect of soil-structure interactions involved and the corresponding results compared to the previous results. Also, to better understand the seismic response of the bridge, the responses caused by pseudo-static components decompose from the total response. Finally, an incremental dynamic analysis was performed to survey the non-linear behavior of the bridge under assumed load scenarios. The outcomes revealed that the local site condition plays an important role and strongly amplifies the responses. Furthermore, it was found that a combination of wave-passage and strong incoherency severely affected the responses of the structure. Moreover, it has been found that the pseudo-static component's contribution increase with increasing incoherent parameters. In addition, regarding the soil condition was considered for the studied bridge, it was found that a combination of spatially varying ground motions and soil-structure interactions effects could make a very destructive scenarios like, pounding and unseating.

• Journal of Ocean Engineering and Technology
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• v.31 no.5
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• pp.357-363
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• 2017

The sloshing pressure acting on a membrane-type LNG CCS is a typical irregular impact load, and the structural response of a tank system induced by sloshing also shows very complex behavior, including fluid structure interaction. Therefore, it is not easy to accurately estimate the sloshing impact pressures and resulting structural response. Moreover, a huge time consuming process to deal with the enormous pressure data obtained during a model tank test and the following structural analysis would be inevitable. To reduce the computation time for structural analysis, in this study, a rational structural modeling strategy was considered, and a simplified scheme to analyze the dynamic structural responses of an LNG CCS was introduced, which was based on the concept of the linear combination of the triangular response functions obtained by a transient response analysis of structures under unit triangular impact pressure. A structural analysis of a real Mark III membrane type insulation system under the sloshing impact pressure time histories obtained by model tests was performed using the various proposed structural models and simplified analysis scheme. The results were investigated in detail, including the elastic support effects of the hull structure.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.135-145
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• 2017

Principal component analysis (PCA) describes the variation of multivariate data in terms of a set of uncorrelated variables. Since each principal component is a linear combination of all variables and the loadings are typically non-zero, it is difficult to interpret the derived principal components. Sparse principal component analysis (SPCA) is a specialized technique using the elastic net penalty function to produce sparse loadings in principal component analysis. When data are structured by groups of variables, it is desirable to select variables in a grouped manner. In this paper, we propose a new PCA method to improve variable selection performance when variables are grouped, which not only selects important groups but also removes unimportant variables within identified groups. To incorporate group information into model fitting, we consider a hierarchical lasso penalty instead of the elastic net penalty in SPCA. Real data analyses demonstrate the performance and usefulness of the proposed method.