• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.1-10
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• 2024

The purpose of this study is to explore the direction of elderly education in the age of science and technology. The research contents to achieve the research purpose are, first, to examine the current status of elderly education, and second, to suggest the direction of elderly education in the age of science and technology. Due to the current status of education for the elderly, education for the elderly continues to change and develop, and various programs and activities are provided to help the elderly enjoy a richer retirement life. According to the survey report on the elderly, 11.9% of all elderly people participate in learning activities. Senior welfare centers accounted for the largest number of institutions conducting learning activities for elderly education learning activities participants at 35.5%. First, in the direction of elderly education in the age of science and technology, education on the use of digital technology and information is necessary in the composition of elderly education contents. Second, in the case of elderly education methods, customized elderly education methods are needed. Third, in the case of operating elderly education institutions, specialized education centers for elderly education must be further strengthened and supported. The international community is already forming a consensus that the establishment of new social systems and financial investment due to the increase in the elderly population should be considered a new growth engine rather than a social crisis. Although there is a burden on the social security sector due to the increase in the elderly population, there is a shift in the direction of recognizing the potential capabilities and experiences of the elderly and returning them to social resources. Elderly education in the age of science and technology needs to change to a direction that can build a healthy and progressive society in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.94-99
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• 2024

Recently, wind power has been gaining attention as a highly efficient renewable energy source, leading to various technological developments worldwide. Typically, wind power is operated in the form of large wind farms with many wind turbines installed in areas rich in wind resources. However, in developing countries or regions isolated from the power grid, off-grid small wind power systems are emerging as an efficient solution. To efficiently operate and expand off-grid small-scale power systems, the development of real-time monitoring systems is required. For the efficient operation of small wind power systems, it is essential to develop real-time monitoring systems that can actively respond to excessive wind speeds and various environmental factors, as well as ensure the stable supply of produced power to small areas or facilities through an Energy Storage System (ESS). The implemented system monitors turbine RPM, power generation, brake operation, and more to create an optimal operating environment. The developed small wind power system can be utilized in remote road lighting, marine leisure facilities, mobile communication base stations, and other applications, contributing to the development of the RE100 industry ecosystem.

• Information Systems Review
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• v.26 no.1
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• pp.337-353
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• 2024

The purpose of this study is to investigate the relationship between consumer reviews and managerial responses, aiming to explore the necessity of webcare for efficiently managing consumer reviews. We intend to propose a methodology for effective webcare and to construct a webcare model using machine learning techniques based on an audiobook platform. In this study, we selected four audiobook platforms and conducted data collection and preprocessing for consumer reviews and managerial responses. We utilized techniques such as topic modeling, topic inconsistency analysis, and DBSCAN, along with various machine learning methods for analysis. The experimental results yielded significant findings in clustering managerial responses and predicting responses to consumer reviews, proposing an efficient methodology considering resource constraints and costs. This research provides academic insights by constructing a webcare model through machine learning techniques and practical implications by suggesting an efficient methodology, considering the limited resources and personnel of companies. The proposed webcare model in this study can be utilized as strategic foundational data for consumer engagement and providing useful information, offering both personalized responses and standardized managerial responses.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.27 no.1
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• pp.21-30
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• 1991

The estimation of the fish biomass density or the size of fish by means of the acoustic equipment is an important part in the quantitative assessment of fisheries resources. The precision of such estimates depend upon the target strength of fish and the accuracy to which the acoustic equipment has been calibrated. This paper examine the accuracy of the digital measurement system which is manufactured by way of trial in order to masure the target strength of fish, and calibrations of that system carry out with an ogive and a ellipsoid made of the aluminum and the epoxy, respectively. Furthermore, measurements of target strength for eight species of fish are made at 25, 50, 100 kHz. The accuracy of the digital measurement system is compared the theory with measurements on ogive and ellipsoid, and the agreement is reasonable. Result of establishments on the target strength to fish length and to fish weight regression obtained from the measurements are available to provide the methods of design for use in interpreting acoustic measurements of fish abundance on the experimented eight species.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.168-187
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• 2006

Windy meteorological conditions and dried fire fuels due to higher atmospheric instability and dryness in the lower troposphere can exacerbate fire controls and result in more losses of forest resources and residential properties due to enhanced large wildland fires. Long-term (1979-2005) climatology of the Haines Index reconstructed in this study reveals that spatial patterns and intra-annual variability of the atmospheric instability and dryness in the lower troposphere affect the frequency of wildland fire incidences over the Korean Peninsula. Exponential regression models verify that daily high Haines Index and its monthly frequency has statistically significant correlations with the frequency of the wildland fire occurrences during the fire season (December-April) in South Korea. According to the climatic maps of the Haines Index created by the Geographic Information System (GIS) using the Digital Elevation Model (DEM), the lowlands below 500m from the mean sea level in the northwestern regions of the Korean Peninsula demonstrates the high frequency of the Haines Index equal to or greater than five in April and May. The annual frequency of the high Haines Index represents an increasing trend across the Korean Peninsula since the mid-1990s, particularly in Gyeongsangbuk-do and along the eastern coastal areas. The composite of synoptic weather maps at 500hPa for extreme events, in which the high Haines Index lasted for several days consecutively, illustrates that the cold low pressure system developed around the Sea of Okhotsk in the extreme event period enhances the pressure gradient and westerly wind speed over the Korean Peninsula. These results demonstrate the need for further consideration of the spatial-temporal characteristics of vertical atmospheric components, such as atmospheric instability and dryness, in the current Korean fire prediction system.

• (The)Study of the Eastern Classic
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• no.69
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• pp.115-146
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• 2017

This paper is about the research on ontology design for a digital archive of seonsaengan(先生案) of the Joseon Period. Seonsaengan is the register of staff officials at each government office, along with their personal information and records of their transfer from one office to another, in addition to their DOBs, family clan, etc. A total of 176 types of registers are known to be kept at libraries and museums in the country. This paper intends to engage in the ontology design of 47 cases of such registers preserved at the Jangseogak Archives of the Academy of Korean Studies (AKS) with a focus on their content and structure including the names of the relevant government offices and posts assumed by the officials, etc. The work for the ontology design was done with a focus on the officials, the offices they belong to, and records about their transfers kept in the registers. The ontology design categorized relevant resources into classes according to the attributes common to the individuals. Each individual has defined a semantic postposition word that can explicitly express the relationship with other individuals. As for the classes, they were divided into eight categories, i.e. registers, figures, offices, official posts, state examination, records, and concepts. For design of relationships and attributes, terms and phrases such as Dublin Core, Europeana Data Mode, CIDOC-CRM, data model for database of those who passed the exam in the past, which are already designed and used, were referred to. Where terms and phrases designed in existing data models are used, the work used Namespace of the relevant data model. The writer defined the relationships where necessary. The designed ontology shows an exemplary implementation of the Myeongneung seonsaengan(明陵先生案). The work gave consideration to expected effects of information entered when a single registered is expanded to plural registers, along with ways to use it. The ontology design is not one made based on the review of all of the 176 registers. The model needs to be improved each time relevant information is obtained. The aim of such efforts is the systematic arrangement of information contained in the registers. It should be remembered that information arranged in this manner may be rearranged with the aid of databases or archives existing currently or to be built in the future. It is expected that the pieces of information entered through the ontology design will be used as data showing how government offices were operated and what their personnel system was like, along with politics, economy, society, and culture of the Joseon Period, in linkage with databases already established.

• Economic and Environmental Geology
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• v.43 no.5
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• pp.467-475
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• 2010

The Landsat and ASTER data have been used in mineralogical and lithological studies, and they have also proved to be useful tool in the initial steps for mineral exploration throughout Nevada mining district, US. Huge pyrophyllite quarry mines, including Jungang, Samsung, Kyeongju, and Naenam located in the southeastern part of Gyeongsang Basin. The geology of study area consists mainly of Cretaceous volcanic rocks, which belong into Cretaceous Hayang and Jindong Group. They were intruded by Bulgugsa granites, so called Sannae-Eonyang granites. To extraction of Ratio model for pyrophyllite deposits, tuffaceous rock and pyrophyllite ores from the Jungang mine used in reflectance spectral analysis and these results were re-sampled to Landsat and ASTER bandpass. As a result of these processes, the pyrophyllite ores spectral features show strong reflectance at band 5, whereas strong absorption at band 7 in Landsat data. In the ASTER data, the pyrophyllite ores spectral features show strong absorption at band 5 and 8, whereas strong reflectance at band 4 and 7. Based on these spectral features, as a result of application of $Py_{Landsat}$ model to hydrothermal alteration zone and other exposed sites, the DN values of two different areas are 1.94 and 1.19 to 1.49, respectively. The differences values between pyrophyllite deposits and concrete-barren area are 0.472 and 0.399 for $Py_{ASTER}$ model, 0.452 and 0.371 for OHIb model, 0.365 and 0.311 for PAK model, respectively. Thus, $Py_{ASTER}$ and $Py_{Landsat}$ model proposed from this study proved to be more useful tool for the extraction of pyrophyllite deposits relative to previous models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.215-231
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• 2020

Forest landscape models (FLMs) can be used to investigate the complex interactions of various ecological processes and patterns, which makes them useful tools to evaluate how environmental and anthropogenic variables can influence forest ecosystems. However, due to the large spatio-temporal scales in FLMs studies, parameterization and validation can be extremely challenging when applying to new study areas. To address this issue, we focused on the parameterization and application of a spatially explicit forest landscape model, LANDIS-II, to Mt. Gyebang, South Korea, with the use of the National Forest Inventory (NFI) and long-term ecological research (LTER) site data. In this study, we present the followings for the biomass succession extension of LANDIS-II: 1) species-specific and spatial parameters estimation for the biomass succession extension of LANDIS-II, 2) calibration, and 3) application and validation for Mt. Gyebang. For the biomass succession extension, we selected 14 tree species, and parameterized ecoregion map, initial community map, species growth characteristics. We produced ecoregion map using elevation, aspect, and topographic wetness index based on digital elevation model. Initial community map was produced based on NFI and sub-alpine survey data. Tree species growth parameters, such as aboveground net primary production and maximum aboveground biomass, were estimated from PnET-II model based on species physiological factors and environmental variables. Literature data were used to estimate species physiological factors, such as FolN, SLWmax, HalfSat, growing temperature, and shade tolerance. For calibration and validation purposes, we compared species-specific aboveground biomass of model outputs and NFI and sub-alpine survey data and calculated coefficient of determination (R²) and root mean square error (RMSE). The final model performed very well, with 0. 98 R² and 8. 9 RMSE. This study can serve as a foundation for the use of FLMs to other applications such as comparing alternative forest management scenarios and natural disturbance effects.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.161-176
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• 2021

In this study, one of the distributed hydrologic models, VELAS, was used to analyze the variation of hydrologic elements based on water balance analysis to evaluate the groundwater recharge in more detail than the annual time scale for the past and future. The study area is located in Yanggok-ri, Seobu-myeon, Hongseong-gun, Chungnam-do, which is very vulnerable to drought. To implement the VELAS model, spatial characteristic data such as digital elevation model (DEM), vegetation, and slope were established, and GIS data were constructed through spatial interpolation on the daily air temperature, precipitation, average wind speed, and relative humidity of the Korea Meteorological Stations. The results of the analysis showed that annual precipitation was 799.1-1750.8 mm, average 1210.7 mm, groundwater recharge of 28.8-492.9 mm, and average 196.9 mm over the past 18 years from 2001 to 2018 in the study area. Annual groundwater recharge rate compared to annual precipitation was from 3.6 to 28.2% with a very large variation and average 14.9%. By the climate change RCP 8.5 scenario, the annual precipitation from 2019 to 2100 was 572.8-1996.5 mm (average 1078.4 mm) and groundwater recharge of 26.7-432.5 mm (average precipitation 16.2%). The annual groundwater recharge rates in the future were projected from 2.8% to 45.1%, 18.2% on average. The components that make up the water balance were well correlated with precipitation, especially in the annual data rather than the daily data. However, the amount of evapotranspiration seems to be more affected by other climatic factors such as temperature. Groundwater recharge in more detailed time scale rather than annual scale is expected to provide basic data that can be used for groundwater development and management if precipitation are severely varied by time, such as droughts or floods.