• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.97-110
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• 2012

High performance sensors and modern data logging technology with real-time telemetry facilitate system fault diagnosis in a very precise manner. Fault detection, isolation and identification in fault diagnosis systems are typical steps to analyze the root cause of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered data (event) logs are too large in general, and further (ii) they usually contain noise and redundant data that make precise analysis difficult. This paper therefore applies suitable pre-processing techniques to data reduction and feature extraction, and then converts the reduced data log into a new format of event sequence information. Finally the event sequence information is decoded to investigate the correlation between specific event patterns and various system faults. The efficiency of the developed pre-filtering procedure is examined with a terminal box data log of a marine diesel engine.

• Wind and Structures
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• v.31 no.4
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• pp.299-309
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• 2020

In this study, wind data such as speeds, loads and potential of Muğla which is located in the southwest of Turkey were statistically analyzed. The wind data which consists of hourly wind speed between 2010 and 2013 years, was measured at the 10-meters height in four different ground stations (Datça, Fethiye, Marmaris, Köyceğiz). These stations are operated by The Turkish State Meteorological Service (T.S.M.S). Furthermore, wind data was analyzed by using Log-Normal and Gamma distributions, since these distributions fit better than Weibull, Normal, Exponential and Logistic distributions. Root Mean Squared Error (RMSE) and the coefficients of the goodness of fit (R²) were also determined by using statistical analysis. According to the results, extreme wind speed in the research area was 33 m/s at the Datça station. The effective wind load at this speed is 0.68 kN/㎡. The highest mean power densities for Datça, Fethiye, Marmaris and Köyceğiz were found to be 46.2, 1.6, 6.5 and 2.2 W/㎡, respectively. Also, although Log-normal distribution exhibited a good performance i.e., lower AD (Anderson - Darling statistic (AD) values) values, Gamma distribution was found more suitable in the estimation of wind speed and power of the region.

• The Journal of Information Systems
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• v.23 no.1
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• pp.185-202
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• 2014

ERP, a typical enterprise application, helps companies to increase their productivity and to support their decision makings. ERP is composed of diverse functions that are optimized under PC environment, whereas the ERP applications on a mobile platform have many constraints such as a small screen, limited resolution, and computing power. Because all the functions of a ERP legacy system are not required for ERP on a mobile device, the core functions of the ERP system should be selected to increase system efficiency. In this study, two main methods were used; interviews and log analyses. The end users using a ERP system were interviewed for their perceptions, and log data analyses were made for the hitting number of specific ERP functions. The differences between the actual usage based on log data and users' cognitive preferences about ERP functions were analysed. Finally, the functional differences between users' perception and actual usage were suggested for some practical implications.

• Bulletin of the Korean Chemical Society
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• v.7 no.1
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• pp.29-35
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• 1986

An empirical formula for semiconductive metal oxides is proposed relating nonstoichiometric value x to a temperature or an oxygen partial pressure such that experimental data can be represented more accurately by the formula than by the well-known Arrhenius-type equation. The proposed empirical formula is log x = A + $B{\cdot}1000/T\;+\;C{\cdot}$exp$(-D{\cdot}1000/T)$ for a temperature dependence and $log\;{\times}\;=a\;+b{\cdot}log\;Po_2\;+\;c{\cdot}$exp$(-d{\cdot}log\;Po_2)$ for an oxygen partial pressure dependence. The A, B, C, D and a, b, c, d are parameters which are evaluated by means of a best-fitting method to experimental data. Subsequently, this empirical formula has been applied to the n-type metal oxides of $Zn_{1+x}O,\; Cd_{1+x}O,\;and\;PrO_{1.8003-x}$, and the p-type metal oxides of $CoO_{1+x},\; FeO_{1+x},\;and\;Cu_2O_{1+x}$. It gives a very good agreement with the experimental data through the best-fitted parameters within 6% of relative error. It is also possible to explain approximately qualitative characters of the parameters A, B, C, D and a, b, c, d from theoretical bases.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.723-732
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• 2022

The success of enterprise information system(EIS) is crucial to align with corporate strategies and eventually attain corporate goals. Since one of the factors to information system success is system use, managerial efforts to measure the level of EIS utilization is vital. In this paper, the EIS utilization level is analyzed using system access log data. In particular, process sequence patterns and clustering of similar functions are identified in more detail based on a process mining method, in addition to basic access log statistics. The result of this research can be used to improve existing information system design by finding real IS usage sequences and function clusters.

• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1231-1239
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• 2012

An estimating procedure is introduced for kernel Poisson regression when the input variables consist of numerical and categorical variables, which is based on the penalized negative log-likelihood and the component-wise product of two different types of kernel functions. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is linearly and/or nonlinearly related to the input variables. Experimental results are then presented which indicate the performance of the proposed kernel Poisson regression.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.613-618
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• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.621-626
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• 2003

Mining association rules in web log files can be divided into two steps: 1) discovering frequent item sets in web data; 2) extracting association rules from the frequent item sets found in the previous step. This paper suggests an algorithm for finding frequent item sets efficiently The essence of the proposed algorithm is to transform transaction data files into matrix format. Our experimental results show that the suggested algorithm outperforms the Apriori algorithm, which is widely used to discover frequent item sets, in terms of scan frequency and execution time.

• Speech Sciences
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• v.9 no.1
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• pp.39-47
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• 2002

This paper proposes an effective method to remedy the acoustic modeling problem inherent in the usual log-normal Parallel Model Composition intended for achieving robust speech recognition. In particular, the Gaussian kernels under the prescribed log-normal PMC cannot sufficiently express the corrupted speech distributions. The proposed scheme corrects this deficiency by judiciously selecting the 'fairly' corrupted component and by re-estimating it as a mixture of two distributions using data-driven PMC. As a result, some components become merged while equal number of components split. The determination for splitting or merging is achieved by means of measuring the similarity of the corrupted speech model to those of the clean model and the noise model. The experimental results indicate that the suggested algorithm is effective in representing the corrupted speech distributions and attains consistent improvement over various SNR and noise cases.