• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.791-804
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• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.4
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• pp.447-458
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• 2013

The present operating system for the atmospheric monitoring network in the city of Seoul, Korea, has been established since the late 90s by the Korean Ministry of Environment (KMOE). In this research, it was evaluated by the multi-statistical approaches through combinations of time series analysis, correlation matrix, and multiple cluster analysis. Finally, road traffic including resuspended materials can be one of the main sources of particulate matter in the atmosphere. Based on its importance, it will be significant challenges in quantitative evaluation of its contribution to airborne concentrations. The future directions for their amendments such as a new management plan for the source of road dust (including car emissions) were devised and proposed based on the statistical judgements derived in this research.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.105-115
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• 2013

Increasing the river cross-section by barrage construction causes rises in the average river water levels and discharge rates in the rainy season. The time series patterns for groundwater levels measured at 23 riverside monitoring wells along the lower Nakdong River are compared for two cases: before and after water-filling at the Changnyeong-Haman Barrage. Monthly average groundwater levels indicate a distinct increase in groundwater levels in the upstream riverside close to the barrage. River-water level management by barrage gate control in August, during the rainy season, resulted in a 0.1 m decrease in groundwater levels, while water-filling at the barrage in December caused a 1.3 m increase in groundwater levels. The results of hierarchical cluster analysis indicate that seven groundwater monitoring wells and river water levels were in the same group before barrage construction, but that this number increased to 14 after barrage construction. Principal component analysis revealed that the explanation power of two principal components corresponding to river fluctuation, PC1 and PC2, was approximately 82% before barrage construction but decreased to 45% after construction. This finding indicates that the effect of the river level component that contributes to change in groundwater level, decreases after barrage construction; consequently, other factors, including groundwater pumping, become more important. Continuous surveying and monitoring is essential for understanding change in the hydrological environment. Water policy that takes groundwater-surface water interaction into consideration should be established for riverside areas.

• Journal of Korea Water Resources Association
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• v.46 no.5
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• pp.439-447
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• 2013

The applicability of the parameter map of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) model for the Korean Peninsula was assessed from the perspective of flood prediction. The design rainfalls estimated from the MBLRP model were smaller than those from observed values by 5% to 40%, and the degree of underestimation of design rainfall increases with the increase of the recurrence interval of the design rainfall. The design floods at a virtual watershed estimated using the simulated rainfall time series based on MBLRP model were also smaller than those derived from the observed rainfall time series by 20% to 45%. The degree of underestimation of design flood increases with the increase of the recurrence interval of the design flood.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.14-26
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• 2012

The only extraterrestrial life detection experiments ever conducted were the three which were components of the 1976 Viking Mission to Mars. Of these, only the Labeled Release experiment obtained a clearly positive response. In this experiment $^{14}C$ radiolabeled nutrient was added to the Mars soil samples. Active soils exhibited rapid, substantial gas release. The gas was probably $CO_2$ and, possibly, other radiocarbon-containing gases. We have applied complexity analysis to the Viking LR data. Measures of mathematical complexity permit deep analysis of data structure along continua including signal vs. noise, entropy vs.negentropy, periodicity vs. aperiodicity, order vs. disorder etc. We have employed seven complexity variables, all derived from LR data, to show that Viking LR active responses can be distinguished from controls via cluster analysis and other multivariate techniques. Furthermore, Martian LR active response data cluster with known biological time series while the control data cluster with purely physical measures. We conclude that the complexity pattern seen in active experiments strongly suggests biology while the different pattern in the control responses is more likely to be non-biological. Control responses that exhibit relatively low initial order rapidly devolve into near-random noise, while the active experiments exhibit higher initial order which decays only slowly. This suggests a robust biological response. These analyses support the interpretation that the Viking LR experiment did detect extant microbial life on Mars.

• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.86-97
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• 2007

The principal component analysis was performed to identify the general characteristics of groundwater level changes from 202 deep and 112 shallow wells monitoring data, respectively, which came from the National Groundwater Monitoring Stations operated by KWATER with time spans of 156 continuous weeks from 2003 to 2005. Eight principal components, which accounted for 80% of the variability of the original time series, were extracted for water levels of shallow and deep monitoring wells. As a result of cluster analysis using the loading value of three principal components for shallow wells, shallow monitoring wells were divided into 3 groups which were characterized with a response time to rainfall (Group 1: 4.6 days, Group 2: 24.1 days, Group 3: 1.4 days), average long-term trend of water level (Group 1: $2.05{\times}10^{-4}$ m/day, Group 2: $-7.85{\times}10^{-4}$ m/day, Group 3: $-3.51{\times}10^{-5}$ m/day) and water level difference (Group 1 < Group 2 < Group 3). Additionally, they showed significant differences according to a distance to the nearest stream from well (Group 3 < Group 2 < Group 1), topographic slope of well site (Group 3: plain region, Group 1: mountainous region) and groundwater recharge rate (Group 3 < Group 2 < Group 1) with a p-value of 0.05.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.65-76
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• 2023

This paper identifies the aspects of living environment elements (PM^2.5, PM¹⁰, Noise) throughout Seoul and the urban characteristics that affect them by utilizing the big data of the S-Dot sensors in Seoul, which has recently become a hot topic. In other words, it proposes a big data based urban computing research methodology and research direction to confirm the relationship between urban characteristics and living environments that directly affect citizens. The temporal range is from 2020 to 2021, which is the available range of time series data for S-Dot sensors, and the spatial range is throughout Seoul by 500mX500m GRID. First of all, as part of analyzing specific living environment patterns, simple trends through EDA are identified, and cluster analysis is conducted based on the trends. After that, in order to derive specific urban planning factors of each cluster, basic statistical analysis such as ANOVA, OLS and MNL analysis were conducted to confirm more specific characteristics. As a result of this study, cluster patterns of environment elements(PM^2.5, PM¹⁰, Noise) and urban factors that affect them are identified, and there are areas with relatively high or low long-term living environment values compared to other regions. The results of this study are believed to be a reference for urban planning management measures for vulnerable areas of living environment, and it is expected to be an exploratory study that can provide directions to urban computing field, especially related to environmental data in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.4
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• pp.408-414
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• 2018

Various studies have been conducted to reduce marine accidents. However, research on marine incidents is only marginal. There are many reports of marine incidents, but the main content of existing studies has been qualitative, which makes quantitative analysis difficult. However, quantitative analysis of marine accidents is necessary to reduce marine incidents. The purpose of this paper is to analyze marine incident data quantitatively by applying big data techniques to predict marine incident trends and reduce marine accident. To accomplish this, about 10,000 marine incident reports were prepared in a unified format through pre-processing. Using this preprocessed data, we first derived major keywords for the Marine incidents at sea using text mining techniques. Secondly, time series and cluster analysis were applied to major keywords. Trends for possible marine incidents were predicted. The results confirmed that it is possible to use quantified data and statistical analysis to address this topic. Also, we have confirmed that it is possible to provide information on preventive measures by grasping objective tendencies for marine incidents that may occur in the future through big data techniques.

• Mycobiology
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• v.50 no.1
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• pp.1-11
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• 2022

The ascomycete fungus Cordyceps militaris infects lepidopteran larvae and pupae and forms characteristic fruiting bodies. Owing to its immune-enhancing effects, the fungus has been used as a medicine. For industrial application, this fungus can be grown on geminated soybeans as an alternative protein source. In our study, we performed a comprehensive transcriptomic analysis to identify core gene sets during C. militaris cultivation on germinated soybeans. RNA-Seq technology was applied to the fungal cultures at seven-time points (2, 4, and 7-day and 2, 3, 5, 7-week old cultures) to investigate the global transcriptomic change. We conducted a time-series analysis using a two-step regression strategy and chose 1460 significant genes and assigned them into five clusters. Characterization of each cluster based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases revealed that transcription profiles changed after two weeks of incubation. Gene mapping of cordycepin biosynthesis and isoflavone modification pathways also confirmed that gene expression in the early stage of GSC cultivation is important for these metabolic pathways. Our transcriptomic analysis and selected genes provided a comprehensive molecular basis for the cultivation of C. militaris on germinated soybeans.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.