• 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.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.65-70
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• 2005

Those specifications of the CLIA analytical tolerance limits are consistent with the performance goals in Six Sigma Quality Management. Six sigma analysis determines performance quality from bias and precision statistics. It also shows if the method meets the criteria for the six sigma performance. Performance standards calculates allowable total error from several different criteria. Six sigma means six standard deviations from the target value or mean value and about 3.4 failures per million opportunities for failure. Sigma Quality Level is an indicator of process centering and process variation total error allowable. Tolerance specification is replaced by a Total Error specification, which is a common form of a quality specification for a laboratory test. The CLIA criteria for acceptable performance in proficiency testing events are given in the form of an allowable total error, TEa. Thus there is a published list of TEa specifications for regulated analytes. In terms of TEa, Six Sigma Quality Management sets a precision goal of TEa/6 and an accuracy goal of 1.5 (TEa/6). This concept is based on the proficiency testing specification of target value +/-3s, TEa from reference intervals, biological variation, and peer group median mean surveys. We have found rules to calculate as a fraction of a reference interval and peer group median mean surveys. We studied to develop total error allowable from peer group survey results and CLIA 88 rules in US on 19 items TP, ALB, T.B, ALP, AST, ALT, CL, LD, K, Na, CRE, BUN, T.C, GLU, GGT, CA, phosphorus, UA, TG tests in chematology were follows. Sigma level versus TEa from peer group median mean CV of each item by group mean were assessed by process performance, fitting within six sigma tolerance limits were TP ($6.1{\delta}$/9.3%), ALB ($6.9{\delta}$/11.3%), T.B ($3.4{\delta}$/25.6%), ALP ($6.8{\delta}$/31.5%), AST ($4.5{\delta}$/16.8%), ALT ($1.6{\delta}$/19.3%), CL ($4.6{\delta}$/8.4%), LD ($11.5{\delta}$/20.07%), K ($2.5{\delta}$/0.39mmol/L), Na ($3.6{\delta}$/6.87mmol/L), CRE ($9.9{\delta}$/21.8%), BUN ($4.3{\delta}$/13.3%), UA ($5.9{\delta}$/11.5%), T.C ($2.2{\delta}$/10.7%), GLU ($4.8{\delta}$/10.2%), GGT ($7.5{\delta}$/27.3%), CA ($5.5{\delta}$/0.87mmol/L), IP ($8.5{\delta}$/13.17%), TG ($9.6{\delta}$/17.7%). Peer group survey median CV in Korean External Assessment greater than CLIA criteria were CL (8.45%/5%), BUN (13.3%/9%), CRE (21.8%/15%), T.B (25.6%/20%), and Na (6.87mmol/L/4mmol/L). Peer group survey median CV less than it were as TP (9.3%/10%), AST (16.8%/20%), ALT (19.3%/20%), K (0.39mmol/L/0.5mmol/L), UA (11.5%/17%), Ca (0.87mg/dL1mg/L), TG (17.7%/25%). TEa in 17 items were same one in 14 items with 82.35%. We found out the truth on increasing sigma level due to increased total error allowable, and were sure that the goal of setting total error allowable would affect the evaluation of sigma metrics in the process, if sustaining the same process.

• Child Health Nursing Research
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• v.5 no.1
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• pp.70-83
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• 1999

The birth of a premature infant is distressing for its parents. The parents of a premature infant experience stress according to the infant's physical appearance and behavior, the environment of the neonatal intensive care unit (NICU) , and the alteration in the parental role. Especially, a mother of a premature infant feels distressed even after the discharge of the infant : therefore, she has difficulties in maternal role performance. The main purpose of this study is to identify the effects of the planned infant care information program in order to lower the stress level for mothers of premature infants caused by the birth and hospitalization in NICU of premature infants, to reduce the maternal role strain, and to promote the maternal role performance after the infants' discharge. This study employed two methods of research at the same time : quasi -experimental non-equivalent pre and post test to compare : non-equivalent post test to compare. The total number of subjects was 19 who were assigned to the research program : 12 mothers of premature infants at the NICU at the Ch university hospital and 7 at the NICU at the Y general hospital located in Chounju city. The data were collected for 79 days from August 18 to November 5, 1998. The questionnaire method was applied for the data collection, and the measures used in this study were Parental Stressor Scale : NICU(Miles, 1993), the Maternal Role Strain Measures ( Hobbs, 1968 ; Steffensmeier, 1982) , and Self Confidence Scale (Pharis, 1978). Research procedure is as follows : after preliminary examination, the experimental subjects, the mothers of premature infants at the Nl CU at Ch university hospital were provided with slide films and information developed by the researcher based on existing documents and data. It took two 60-minute sessions a week for two weeks, and the mothers' stress level was measured using the same instrument twice one week and two week after the infants' hospitalization. The stress level of the contrast subjects, the mothers at Y general hospital was measured during the same period. The experimental subjects were provided with booklets on matters that require attention after the infants' discharge and on developmental project, and they were educated to play the maternal role in person for 2-3 hours a week : breast-feeding, burping a baby, and changing diapers. One week after the infants' discharge, the maternal role strain and the maternal role performance were examined in two groups of the subjects. The analysis of collected data was done using descriptive statistics including real numbers, percentages, averages, and standard deviations. Mann-Whitney test ; x² test ; Repeated Measures Analysis of Variance ; ANCOVA Spearman's rho correlation coefficients. The results on this study were as follows. (1) The examination of the same quality showed that there were no differences in the general and obstetrical characters between the two groups. However, in terms of the characters of premature infants. just right after their birth, the infants at the contrast group weighed more than those at the experimental group(U=16.5, p=.02), and the former was in mother's womb longer than the latter(U=15.5, p=.02). (2) The stress level of the mothers provided with the plannned nursing intervention program became lower as time passed compared to the others'(F=16.61, p=.00) Even when the influence of weight at birth and the length of gestation was removed among the premature infants' characters, the mothers' stress levels made a statistical difference 2 weeks after the infants' hospitalization depending on treatment (F=8.00, p=.01) (3) The maternal role strain of the mothers provided with the planned nursing intervention program was lower than the others'(U=2.0, p=.00). Even when the influence of weight at birth and the length of gestation was removed among the premature infants' characters, the maternal role strain levels made a statistical difference 2 weeks after the infants' hospitalization, depending on treatment(F=14.72, p=.00). (4) The maternal role performance level of the mothers provided with the planned nursing program was higher than the others'(U=.0, p=.00). Even when the influence of weight at birth and the length of gestation was removed among the premature infants' characters, the mothers' stress levels made a statistical difference 2 weeks after the infants' hospitalization, depending on treatment(F=8.00, p=.01). (5) The correlation between a mother's stress level 2 weeks after her infant's hospitalization, the maternal role strain and the maternal role performance were compared : the stress and the maternal role strain were statistically irrelevant to each other(r=.33, p=.12) : the stress was found to be in inverse proportion to the maternal role performance(r=-.53, p=.02). The maternal role strain was in inverse proportion to the maternal role performance as well(r=-.50, p=.00). In conclusion, for the mothers provided with the planned nursing intervention program, their stress level was getting lower as time passed during the infants' hospitalization, their maternal role strain reduced when they took care of their infants after their discharge, and their maternal role performance level was high compared to the other mothers. Besides, the lower the stress level of mothers of premature infants was during the infants' hospitalization, the higher the maternal role performance after their discharge was. The lower maternal role strain was, the higher the maternal role performance was as well. These results of the study suggested that the nursing intervention program for the mothers of premature infants developed by the researcher would be effectively applied to nursing practice, and it would be a foundation for the development of this kind of program.

• Geotechnical Engineering
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• v.2 no.1
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• pp.27-44
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• 1986

The measured soil data are analyzed to the descriptive statistics and classified into the four models of uncorrelated-normal (UNNO), uncorrelated-nonnormal (VNNN), correlatedonnormal(CONN), and correlated-nonnormal(CONN) . This paper presents the comparisons of reliability index and check points using the advanced first-order second-moment method with respect to the four models as well as BASIC Program. A sin91e-mode Performance function is consisted of the basic design variables of bearing capacity and settlements on shallow foundations and input the above analyzed soil informations. The main conclusions obtained in this study are summarized as follows: 1. In the bearing capacity mode, cohesion and bearing-capacity factors by C-U test are accepted for normal and lognormal distribution, respectively, and negatively low correlated to each other. Since the reliability index of the CONN model is the lowest one of the four model, which could be recommended a reliability.based design, whereas the other model might overestimate the geotechnical conditions. 2. In the case of settlements mode, the virgin compression ratio and preccnsolidation pressure are fitted for normal and lognormal distribution, respectively. Constraining settlements to the lower ones computed by deterministic method, The CONN model is the lowest reliability of the four models.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.469-478
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• 2001

In the virtual memory system, page replacement policy exerts a great influence on the performance of demand paging. There are LRU(Least Recently Used) and LFU (Least Frequently Used) as the typical replacement policies. The LRU policy performs effectively in many cases and adapts well to the changing workloads compared to other policies. It however cannot distinguish well between frequently and infrequently referenced pages. The LFU policy requires that the page with the smallest reference count be replaced. Though it considers all the references in the past, it cannot discriminate between references that occurred far back in the past and the more recent ones. Thus, it cannot adapt well to the changing workload. In this paper, we first analyze memory reference patterns of eight applications. The patterns show that the recently referenced pages or the frequently referenced pages are accessed continuously as the case may be. So it is rather hard to optimize page replacement scheme by using just one of the LRU or LFU policy. This paper makes an attempt to combine the advantages of the two policies and proposes a new page replacement policy. In the proposed policy, paging list is divided into two lists (LRU and LFU lists). By keeping the two lists in recency and reference frequency order respectively, we try to restrain the highly referenced pages in the past from being replaced by the LRU policy. Results from trace-driven simulations show that there exists points on the spectrum at which the proposed policy performs better than the previously known policies for the workloads we considered. Especially, we can see that our policy outperforms the existing ones in such applications that have reference patterns of re-accessing the frequently referenced pages in the past after some time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.164-173
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• 2017

Alternative cropping systems would be one of climate change adaptation options. Suitable areas for a crop could be identified using a climate suitability model. The EcoCrop model has been used to assess climate suitability of crops using monthly climate surfaces, e.g., the digital climate map at high spatial resolution. Still, a high-performance computing approach would be needed for assessment of climate suitability to take into account a complex terrain in Korea, which requires considerably large climate data sets. The objectives of this study were to implement a script for R, which is an open source statistics analysis platform, in order to use the EcoCrop model under a parallel computing environment and to assess climate suitability of maize using digital climate maps at high spatial resolution, e.g., 1 km. The total running time reduced as the number of CPU (Central Processing Unit) core increased although the speedup with increasing number of CPU cores was not linear. For example, the wall clock time for assessing climate suitability index at 1 km spatial resolution reduced by 90% with 16 CPU cores. However, it took about 1.5 time to compute climate suitability index compared with a theoretical time for the given number of CPU. Implementation of climate suitability assessment system based on the MPI (Message Passing Interface) would allow support for the digital climate map at ultra-high spatial resolution, e.g., 30m, which would help site-specific design of cropping system for climate change adaptation.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.681-689
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• 2019

Web usage pattern discovery is an advanced means by using web log data, and it's also a specific application of data mining technology in Web log data mining. In education Data Mining (DM) is the application of Data Mining techniques to educational data (such as Web logs of University, e-learning, adaptive hypermedia and intelligent tutoring systems, etc.), and so, its objective is to analyze these types of data in order to resolve educational research issues. In this paper, the Web log data of a university are used as the research object of data mining. With using the database OLAP technology the Web log data are preprocessed into the data format that can be used for data mining, and the processing results are stored into the MSSQL. At the same time the basic data statistics and analysis are completed based on the processed Web log records. In addition, we introduced the Apriori Algorithm of Web usage pattern mining and its implementation process, developed the Apriori Algorithm program in Python development environment, then gave the performance of the Apriori Algorithm and realized the mining of Web user access pattern. The results have important theoretical significance for the application of the patterns in the development of teaching systems. The next research is to explore the improvement of the Apriori Algorithm in the distributed computing environment.

• Journal of Distribution Science
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• v.14 no.7
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• pp.83-90
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• 2016

Purpose - This study focuses on new type distribution channel that named as Omnichannel. Omnichannel is developed from Multichannel which is used in many distribution channels to buy or selling goods. Omnichannel basically needs an Information and Communications Technologies(ICT) to use, so researcher conduct a Technology Acceptance Model(TAM) to research model. Customer-brand relationship was used as dependent variable to focus on the role of Omnichannel. Research design, data, and methodology - The subject of this study is customer who purchase goods or service through omnichannel. Based on the literature from the preceding research analysis of TAM and customer-brand relationship, this study was constructed by the reference to previous studies, final research model design for figure out casual relationship among perceived ease of use, perceived usefulness, omnichannel use and customer-brand relationship. From 2016 February 3 to March 17, questionnaire survey targeted customers who use online and offline channels. 273 questionnaire survey had conducted, then, 252 survey data were available for empirical analysis. Researcher provide descriptive statistics for checking generality. Cronbach's alpha value was used to check the reliability of data. Exploratory factor analysis was used for purification of values and eigenvalue checking. After EFA, Confirmatory factor analysis was used to prepare structural equation modeling with executing structural equation modeling for confirming hypothesis which developed by researcher. Results - The main results of this empirical study are as follows. First, omnichannel's perceived ease of use has positive significant effect on perceived usefulness(estimate: 0.579). Moreover, omnichannel's perceived ease of use and perceived usefulness has positive significant effect on omnichannel use(estimate: 0.325,0.648). Second, using omnichannel has positive significant effect on brand-customer relationship(estimate: 0.521). Every hypothesis adopted as researcher designed. This study found out the intermediate relationship between perceived ease of use and omnichannel use by investigating hypothesis. Conclusions - Base on the empirical result, this study confirmed that TAM theory perceived has relation with omnichannel. First, factors of TAM has positive effect on omnichannel use, so it highlights the important role of customer based interface and usefulness. Especially, perceived usefulness has high indirect influence on ease of use and use of omnichannel. It seems that when customers try to decide use or not use omnichannel, customers focus on percept benefits from omnichannel. Thus, a provider should applicate attractive price table, accurate product or service information and high switching cost strategy to emphasize the usefulness of omnichannel. Second, using omnichannel enhances the relationship between customers and brand, because there are more time and frequency to serve customers. It is important because good relationship between customers can increase the future's financial performance through word of mouse, positive brand image and loyalty to brand or company. Finally, despite of empirical result and implications, this study has limitations. First, there are only a few previous studies about omnicahnnel, so literature reviews are restricted. While set up the factors which can affect the use of omnichannel, next study should be considered with broader theories or models(ex: contingency theory). Second, omnichannel has developed from multichannel, so comparative analysis is needed between these methods because there is a possibility about different forte character of each distribution system on customer's consuming patterns.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.673-679
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• 2017

In this paper a method for estimating the 'service life' and 'residual life' of a water pipe based on the Water Pipe Network Performance Evaluation(WPNPE) results of Water Supply Technical Diagnosis was developed for efficient maintenance of water pipes. The residual life of a pipe was defined as the difference between the service life and elapsed time since installation. The service life was defined as the time when a pipe reaches the reference score for determining deteriorated pipes that was used in the WPNPE. The pipe evaluation criteria and deterioration scores used in the WPNPE for the case study area were considered as independent variables in the multiple regression model for estimating the service life and residual life of the pipes in the area. To estimate the service life for the pipes the reference scores for determining deteriorated pipes were used as the values of the variables that represent the deterioration scores in the constructed regression models. Subsequently, the statistics of the service life and residual life of the pipes in the case study area were presented and analyzed in comparison with the service life defined by the Local Public Enterprizes Act.