• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.83-92
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• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.23-27
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• 2009

In this paper, we propose an effective compression method for electrocardiogram (ECG) signals. 1-D ECG signals are reconstructed to 2-D ECG data by period and complexity sorting schemes with image compression techniques to increase inter and intra-beat correlation. The proposed method added block division and mean-period normalization techniques on top of conventional 2-D data ECG compression methods. JPEG 2000 is chosen for compression of 2-D ECG data. Standard MIT-BIH arrhythmia database is used for evaluation and experiment. The results show that the proposed method outperforms compared to the most recent literature especially in case of high compression rate.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.7-13
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• 2019

The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• KSBB Journal
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• v.16 no.4
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• pp.381-388
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• 2001

A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.195-204
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• 2015

This study analyzed the normalization cases of the mutual savings bank insolvency PF (MSBIPF) to suggest the appropriate improvements according to the purpose. The results were as follows. First, the original intention to normalize the MSBIPF was unsuccessful. This may be caused by the daunting situation of the real estate market along with the complex and shared interests. On the other hand, it can be responsible for the lack of evidence and related regulations as well as the lukewarm attitude on public projects. Active institutional settings are warranted to compensate the remaining insolvent businesses to PF even today and in the future. The data related to the recognized sites as the poorest 32 PF sites was compared primarily to normalize by KAMCO and the relevant sites. The area variable was the only significant variable according to the correlation analysis and logit analysis. The direct investment, diverse PF-backed bonds and the activation of the Ritz can be suggested as alternative ways of normalization with respect to the issue of the KAMCO.

• Journal of The Korean Society of Clinical Toxicology
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• v.7 no.2
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• pp.156-163
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• 2009

Purpose: Doxylamine succinate (DS) is frequently used to treat insomnia and it may induce rhabdomyolysis in the overdose cases. The purpose of this study is to evaluate the factors that can predict the serum creatine kinase (CK) level normalization time for patients with rhabdomyolysis due to DS ingestion. Methods: This study was conducted on 71 patients who were admitted with rhabdomyolysis after DS ingestion during the period from January 2000 to July 2009. Rhabdomyolysis was defined as a serum CK level over 1,000 U/L. The collected data included the general characteristics, the anticholinergic symptoms, the ingested dose, the peak serum CK level, the time interval (TI) from the event to the peak CK level and the TI from the event to a CK level below 1,000 U/L. We evaluated the correlation between the patients' variables and the TI from the event to the peak CK level time and the time for a CK level below 1,000 U/L. Results: The mean ingested dose per body weight (BW) was $30.86{\pm}18.63\;mg/kg$ and the mean TI from the event to treatment was $4.04{\pm}3.67$ hours. The TI from the event to the peak CK level was longer for the patients with a larger ingestion dose per BW (r=0.587, p<0.05). The CK normalization time was longer for the patients with a larger ingested dose per BW (r=0.446, p<0.05) and a higher peak CK level (r=0.634, p<0.05). Conclusion: The ingested dose per BW was correlated with the TI from the event to the peak CK level, and the ingested dose per BW and the peak CK level have significant correlations with the CK normalization time. These factors may be used to determine the discharge period of patients who had rhabdomyolysis following a OS overdose.

• Publications of The Korean Astronomical Society
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• v.20 no.1 s.24
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• pp.97-107
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• 2005

We developed a data reduction program (RX) to process BOES data automatically. It processes a whole set of data taken during one night automatically - preprocessing, extraction to one-dimensional spectra and wavelength calibration. The execution is very fast and the performance looks pretty good. We described the performance of this program, comparing its procedure with that of IRAF. RX does not have functions for continuum normalization yet. We will develop those functions in the next works.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.E
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• pp.1-9
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• 1995

It is shown that two step power transformation is more efficient for the normalization of frequency distribution with the coefficient of skewness of zero in comparison with others including SMEMAX and power transformations. It is confirmed that the design low flows calculated using power and two step power transformations used in this study are generally nearer to the observed data as compared with those of SMEMAX transformation at all return periods in the applied watersheds of the Kum, Naktong and Yongsan rivers in Korea.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.663-667
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• 1999

In this paper, we review the conventional speaker verification algorithm and present the text-dependent speaker verification system for application over telephone lines and its result of experiments. We apply blind-segmentation algorithm which segments speech into sub-word unit without linguistic information to the speaker verification system for training speaker model effectively with limited enrollment data. And the World-mode] that is created from PBW DB for score normalization is used. The experiments are presented in implemented system using database, which were constructed to simulate field test, and are shown 3.3% EER.