• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.3
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• pp.418-424
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• 2015

The chemical response of energetic materials is analyzed in terms of 1) the thermal decomposition under the thermal stimulus and 2) the reactive flow upon the mechanical impact, both of which give rise to an exothermic thermal runaway or an explosion. The present study aims at building a set of chemical kinetics that can precisely model both thermal and impact initiation of a heavily aluminized cyclotrimethylene-trinitramine (RDX) which contains 35% of aluminum. For a thermal decomposition model, the differential scanning calorimetry (DSC) measurement is used together with the Friedman isoconversional method for defining the frequency factor and activation energy in the form of Arrhenius rate law that are extracted from the evolution of product mass fraction. As for modelling the impact response, a series of unconfined rate stick data are used to construct the size effect curve which represents the relationship between detonation velocity and inverse radius of the sample. For validation of the modeled results, a cook-off test and a pressure chamber test are used to compare the predicted chemical response of the aluminized RDX that is either thermally or mechanically loaded.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.229-254
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• 2011

In this paper, we decomposed Greenhouse-Gas emissions of Korea's manufacturing industry using LMDI (Log Mean Divisia Index) method. Changes in $CO_2$ emissions from 1991 to 2007 studied in 5 different factors, industrial production (production effect), industry production mix (structure effect), sectoral energy intensity (intensity effect), sectoral energy mix (energy-mix effect), and $CO_2$ emission factors (emission-factor effect). By results, the structure effect and intensity effect has a role of reducing GHG emissions and The role of structure effect was bigger than intensity effect. The energy mix effect increased GHG emissions and emission-factor effect decreased GHG emissions. By time series analysis, IMF regime affected the GHG emission pattern. the structure effect and intensity effect in that regime was getting worse. After 2000, in the high oil price period, the structure effect and intensity effect is getting better.

• Bulletin of the Korean Chemical Society
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• v.34 no.1
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• pp.201-206
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• 2013

The second-order rate constants have been measured spectrophotometrically for the reactions of paraoxon 1 and parathion 2 with a series of alicyclic secondary amines, $OH^-$ and $HOO^-$ ions in $H_2O$ at $25.0{\pm}0.1^{\circ}C$. A linear Br${\o}$nsted-type plot with ${\beta}_{nuc}$ = 0.40 was obtained for the reactions of 1 with amines and $OH^-$. The reaction has been concluded to proceed through a concerted mechanism. $HOO^-$ deviates positively from the linear Br${\o}$nsted-type plot, implying that the ${\alpha}$-effect is operative. The magnitude of the ${\alpha}$-effect ($k_{HOO^-}/k_{OH^-}$) was found to be ca. 55 for the reaction of 1 and 290 for that of parathion 2, indicating that $HOO^-$ is highly effective in decomposition of the toxic phosphorus compounds although it is over 4 $pK_a$ units less basic than $OH^-$. Among the theories suggested as origins of the ${\alpha}$-effect (e.g., TS stabilization through an intramolecular Hbonding interaction, solvent effect, and polarizability effect), polarizability effect appears to be the most important factor for the ${\alpha}$-effect in this study, since the polarizable $HOO^-$ exhibits a larger ${\alpha}$-effect for the reaction of the more polarizable substrate 2.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.1
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• pp.120-128
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• 2011

Recently, safety diagnosis of the existing structures has been emerged as important issue. In particular, systematical and precise safety diagnostics for steel structures for power substation, have been required. Steel structures for power substation are under the periodical impact loads from operations of gas insulated switchgear. These loading condition accelerates damage and aging of structure. The objective of this research is to evaluate damage of structure under periodical impact loads. To evaluate the integrity of structures as organizing mathematical models including the dynamic characteristics of structures, Frequency Domain Decomposition method was choiced and an algorism was proposed. For verifying this methods and algorism, a mathematical model is composed of the development of a variety of reverse analysis and a signal processing technology reflecting physical damage of structures. A series of analysis and test results indicatge that proposed method has a confidence for applying a filed test. Therefore, it is expected to be able to take advantage of system identification to detect damage for the maintenance and management of steel structures under periodical impact loads such as power substation.

• Smart Structures and Systems
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• v.19 no.1
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• pp.39-46
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• 2017

Finite element model updating is very effective procedure to determine the uncertainty parameters in structural model and minimize the differences between experimentally and numerically identified dynamic characteristics. This procedure can be practiced with manual and automatic model updating procedures. The manual model updating involves manual changes of geometry and analyses parameters by trial and error, guided by engineering judgement. Besides, the automated updating is performed by constructing a series of loops based on optimization procedures. This paper addresses the ambient vibration based finite element model updating of long span reinforced concrete highway bridges using manual model updating procedure. Birecik Highway Bridge located on the $81^{st}km$ of Şanliurfa-Gaziantep state highway over Firat River in Turkey is selected as a case study. The structural carrier system of the bridge consists of two main parts: Arch and Beam Compartments. In this part of the paper, the arch compartment is investigated. Three dimensional finite element model of the arch compartment of the bridge is constructed using SAP2000 software to determine the dynamic characteristics, numerically. Operational Modal Analysis method is used to extract dynamic characteristics using Enhanced Frequency Domain Decomposition method. Numerically and experimentally identified dynamic characteristics are compared with each other and finite element model of the arch compartment of the bridge is updated manually by changing some uncertain parameters such as section properties, damages, boundary conditions and material properties to reduce the difference between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of long span highway bridges. Maximum differences between the natural frequencies are reduced averagely from %49.1 to %0.6 by model updating. Also, a good harmony is found between mode shapes after finite element model updating.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3902-3909
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• 2021

SACOS series of subchannel analysis codes have been developed by XJTU-NuTheL for many years and are being used for the thermal-hydraulic safety analysis of various reactor cores. To achieve fine whole core pin-level analysis, the input preprocessing and parallel capabilities of the code have been developed in this study. Preprocessing is suitable for modeling rectangular and hexagonal assemblies with less error-prone input; parallelization is established based on the domain decomposition method with the hybrid of MPI and OpenMP. For domain decomposition, a more flexible method has been proposed which can determine the appropriate task division of the core domain according to the number of processors of the server. By performing the calculation time evaluation for the several PWR assembly problems, the code parallelization has been successfully verified with different number of processors. Subsequent analysis results for rectangular- and hexagonal-assembly core imply that the code can be used to model and perform pin-level core safety analysis with acceptable computational efficiency.

• International Area Studies Review
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• v.13 no.2
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• pp.255-276
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• 2009

This article has examined the international transmission of returns among S&P500, Nikkei225 and SENSEX stock index cash markets using the daily closing prices covered from January 4, 2002 to February 6, 2009. For this purpose we employed dynamic time series models such as the Granger causality analysis and variance decomposition analysis based on VAR model. The main empirical results are as follows; First, according to Granger causality tests we find that S&P500 stock index has a significant prediction power on the changes of SENSEX and Nikkei225 stock index market and vice versa. However, US stock market's influence is dominant to the other stock markets at a significant level statistically. Second, according to variance decomposition, SENSEX stock index is more sensitive to the movement of S&P500 than that of Nikkei225 stock index. These kinds of empirical results shows that the three stock markets are integrated over times and these results will be informative for the international investors to build the world-wide investment portfolio and risk management strategies, etc.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.495-507
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• 2023

Considering the continuing discussion about the Korea-Japan undersea tunnel, it is necessary to conduct a scientific investigation into tunnel deformation associated with large ground movements at fault. This paper presents findings obtained from numerical experiments to investigate a seismic lining that adopts rubber-like material. We utilized the user material subroutine to obtain the deformation gradient of the hyperelastic material. Additionally, polar decomposition is used to analyze the results, where the data is displayed on a series of two-dimensional planes using the principal direction, which facilitates a better insight into the deformation. Tunnel engineers could refer to this paper for the procedure to investigate the deformation of hyperelastic material.

• Journal of Distribution Science
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• v.11 no.10
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.