• Journal of Web Engineering
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• v.20 no.3
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• pp.795-816
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• 2021

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.240-245
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• 2011

Vegetation changes were studied for 16 yr in clearcut logged Pinus densiflora forests in the southern Gangwon-do province in Korea by applying chronosequence approach. Ambient temperature and relative humidity, Detrended Correspondence Analysis (DCA), Multiple Responses Permutation Procedure (MRPP), Indicator Species Analysis (ISPAN) were used to examine successional trajectory and compositional changes. After clearcutting, canopy openness was increased abruptly at three folds (1yr 68.3% and R1 23.0%) and then decreased, but relative moisture was slightly decreased (6%) compare to control site. In the result of DCA, right after clear cutting, vegetation composition was developed heterogeneously compared to control sites, and then approached to control sites within 16 years. Based on MRPP, species composition of each developmental stages (1yr, 3yr, 10yr and 16yr) revealed signigicant differences to that of control vegetation (R1, R3, R10 and R16). Indicator species in 1yr and 3yr samples included various woody species rather than herbaceous species, but in 10yr and 16yr, herbaceous were more abundant. Earlier succession of pine forests likely can explain to Initial Floristic Composition (IFC) Model.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.31-44
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• 2014

In the present study, a few of recently developed geostatistical models are comparatively studied. The models are two-point statistics based sequential indicator simulation (SISIM) and generalized coupled Markov chain (GCMC), multi-point statistics single normal equation simulation (SNESIM), and object based model of FLUVSIM (fluvial simulation) that predicts structures of target object from the provided geometric information. Out of the models, SNESIM and FLUVSIM require additional information other than conditioning data such as training map and geometry, respectively, which generally claim demanding additional resources. For the comparative studies, three-dimensional fluvial reservoir model is developed considering the genetic information and the samples, as input data for the models, are acquired by mimicking realistic sampling (i.e. random sampling). For SNESIM and FLUVSIM, additional training map and the geometry data are synthesized based on the same information used for the objective model. For the comparisons of the predictabilities of the models, two different measures are employed. In the first measure, the ensemble probability maps of the models are developed from multiple realizations, which are compared in depth to the objective model. In the second measure, the developed realizations are converted to hydrogeologic properties and the groundwater flow simulation results are compared to that of the objective model. From the comparisons, it is found that the predictability of GCMC outperforms the other models in terms of the first measure. On the other hand, in terms of the second measure, the both predictabilities of GCMC and SNESIM are outstanding out of the considered models. The excellences of GCMC model in the comparisons may attribute to the incorporations of directional non-stationarity and the non-linear prediction structure. From the results, it is concluded that the various geostatistical models need to be comprehensively considered and comparatively analyzed for appropriate characterizations.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.79-84
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• 2021

The objective of this paper is to find out the factors for the performance of the Hospital Customer Relationship Management (HCRM) system. Furthermore, the relationships between these factors have been analyzed. In order to analyze the performance of the HCRM system, factor analysis with several Key Performance Indicators (KPIs) was conducted. And also multiple regression analysis and Chi-square test were executed. In this study, several hypotheses were derived to analyze the relationships between factors of performance of HCRM system. These hypotheses were tested by using the Structural Equation Model (SEM). As the result of this study, we discovered the HCRM-Infrastructure has positive effects on the HCRM-Performance. And the HCRM-Performance has also positive influences on the hospital management performance. On the basis of the research result, we proposed some suggestions and guidelines for the successful implementation and improvement of HCRM system.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.229-239
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• 2011

This study aims to suggest profitability models of local cultural festival to be a focal point of local culture to overcome the abuses as an annual event led by the local government agencies have been in force in every local areas currently, to elicit the local residents' voluntary participation and to engage the outside tourists in connection of a more effective promotion and marketing. The festival is attractive to people with that provoke mirth and inspire curiosity, but the festival is not a one-time event ongoing maintenance enlarge: It is absolutely necessary in order to effort to reach at consumer-oriented quality of service. Also this research intends to present a activation scheme by developing profitability models to make profits while holding a number of risks taking into account the characteristics of the local festival service carried out. To this end, based on established research, leading indicator and expert opinion were analyzed through multiple regression analysis.

• Journal of Korea Spatial Information System Society
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• v.10 no.1
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• pp.51-63
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• 2008

Previous studies for routing In USN (Ubiquitous Sensor Networks) appear to be unreliable due to the dependence on non-spatial data and the lack of map overlay analysis. Multiple spatial parameters and radio propagation modeling techniques were integrated to derive RSSI (Received Signal Strength Indicator) value between route nodes and produce a highly reliable path map. It was possible to identify area-wide patterns of USN route subject to many different Influences (e.g. the specific effects of radio blocking factors such as the visible area, road area, cell duplicated area, and building density), which cannot be acquired by traditional non-spatial modeling. The quantitative evidence concerning the USN route for individual cell as well as entire study area would be utilized as major tools to visualize paths in real-time and to select alternative path when failure or audition of route node occurs.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.5
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• pp.376-384
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• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.

• Animal Bioscience
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• v.34 no.1
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• pp.20-25
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• 2021

Objective: The objective of the study was to investigate the possibility of utilizing an early litter size trait as an indirect selection trait for longevity and to estimate genetic parameters between sow stayability and litter size at different parities using a linear-threshold model for longevity in Thai Large White (LW) and Landrace (LR) populations. Methods: The data included litter size at first, second, and third parities (NBA1, NBA2, and NBA3) and sow stayability from first to fourth farrowings (STAY14). The data was obtained from 10,794 LR and 9,475 LW sows. Genetic parameters were estimated using the multiple-trait animal model. A linear-threshold model was used in which NBA1, NBA2, and NBA3 were continuous traits, while STAY14 was considered a binary trait. Results: Heritabilities for litter size were low and ranged from 0.01 to 0.06 for both LR and LW breeds. Similarly, heritabilities for stayability were low for both breeds. Genetic associations between litter size and stayability ranged from 0.43 to 0.65 for LR populations and 0.12 to 0.55 for LW populations. The genetic correlation between NBA1 and STAY14 was moderate and in a favorable direction for both LR and LW breeds (0.65 and 0.55, respectively). Conclusion: A linear-threshold model could be utilized to analyze litter size and sow stayability traits. Furthermore, selection for litter size at first parity, which was the genetic trait correlated with longevity, is possible when one attempts to improve lifetime productivity in Thai swine populations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.220-227
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• 2014

This paper introduces and applies the World Bank's methodology for constructing composite index or aggregating indicators. After recalculating the world competitiveness index of IMD using Unobserved Component Model(UCM) we compare it with the existing index and try to find some implications. We also try to construct the composite index for measuring the performance of local finance. We employ the Principal Component Analysis(PCA) for validating the appropriateness of selected indicators used in making the composite index. We found that the UCM and PCA are very useful and will be used widely in various evaluations such as regional development, local finance, local competitiveness and public enterprise, etc.