• 한국노년학
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• v.37 no.1
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• pp.55-82
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• 2017

The purpose of this research is to provide implications for the development of age-friendly industry of Korea by analyzing the age-friendly industry activation policy suggested by the Presidential Committee on Ageing Society and Population Policy in response to the population ageing problems. Policy and theoretical implications are derived from an analysis of age-friendly industry activation policy as directly related to the improvement of the three major problems (i.e. health, economy, and loneliness) of Korean older people. This analysis focuses on its specific content, common and core logic, and the validity of the logic. The results show that: 1)the first Plan for Ageing Society and Population suggests a policy for improving poverty, 2)the second Plan for Ageing Society and Population suggests a policy for improving health, and 3)the third Plan for Ageing Society and Population suggests a policy for improving health and loneliness. All policies not only are commonly based on the logically limited concepts of successful ageing and active ageing, but also are characterized by narrow realm, insufficient content, and fragmented policy. The results result in the following policy implications: 1)the age-friendly industry activation policy should be developed with continuity, sufficiency, and diversity, 2)lessons from the limitation related with the de-contextualisation of successful ageing and its elective affinity with consumer capitalism, 3)lessons from the prevention-oriented perspective of active ageing, and 4)lessons from the social characteristics of (non-)psychological resources, limitation of the reablement related with social investment state. The analyses also provide a theoretical implication that a different perspective on the social construct of ageing is needed beyond the dichotomy of ageing based on modelling ageing. Lastly, the paper suggests not only the limitation of the research but also the preferred follow-up studies regarding age-friendly industry activation policy.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.4
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• pp.83-89
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• 2020

For the past few decades, as part of efforts to protect the environment where fossil fuels, which have been a key energy resource for mankind, are becoming increasingly depleted and pollution due to industrial development, ecofriendly secondary batteries, hydrogen generating energy devices, energy storage systems, and many other new energy technologies are being developed. Among them, the lithium-ion battery (LIB) is considered to be a next-generation energy device suitable for application as a large-capacity battery and capable of industrial application due to its high energy density and long lifespan. However, considering the growing battery market such as eco-friendly electric vehicles and drones, it is expected that a large amount of battery waste will spill out from some point due to the end of life. In order to prepare for this situation, development of a process for recovering lithium and various valuable metals from waste batteries is required, and at the same time, a plan to recycle them is socially required. In this study, we introduce a nanoscale pattern transfer printing (NTP) process of Li2CO3, a representative anode material for lithium ion batteries, one of the strategic materials for recycling waste batteries. First, Li2CO3 powder was formed by pressing in a vacuum, and a 3-inch sputter target for very pure Li2CO3 thin film deposition was successfully produced through high-temperature sintering. The target was mounted on a sputtering device, and a well-ordered Li2CO3 line pattern with a width of 250 nm was successfully obtained on the Si substrate using the NTP process. In addition, based on the nTP method, the periodic Li2CO3 line patterns were formed on the surfaces of metal, glass, flexible polymer substrates, and even curved goggles. These results are expected to be applied to the thin films of various functional materials used in battery devices in the future, and is also expected to be particularly helpful in improving the performance of lithium-ion battery devices on various substrates.

• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.82-100
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• 2022

With the crisis of the COVID-19 pandemic and the era of the 4th industrial revolution, archaeological heritage education has entered a new phase. This article responds to the trends in the post-COVID-19 era, seeking ways to develop archaeological heritage education and sustainable strategies necessary in the era of the 4th industrial revolution. The program of archaeological heritage education required in the era of the 4th industrial revolution must cultivate creative talent, solve problems, and improve self-efficacy. It should also draw attention to archaeological heritage maker education. Such maker education should be delivered based on constructivism and be designed by setting specific learning goals in consideration of various age-specific characteristics. Moreover, various ICT-based contents applying VR, AR, cloud, and drone imaging technologies should be developed and expanded, and, above all, ontact digital education(real-time virtual learning) should seek ways to revitalize communities capable of interactive communication in non-face-to-face situations. The development of such ancient heritage content needs to add AI functions that consider learners' interests, learning abilities, and learning purposes while producing various convergent contents from the standpoint of "cultural collage." Online archaeological heritage content education should be delivered following prior learning or with supplementary learning in consideration of motivation or field learning to access the real thing in the future. Ultimately, archaeological ontact education will be delivered using cutting-edge technologies that reflect the current trends. In conjunction with this, continuous efforts are needed for constructive learning that enables discovery and question-exploration.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.63-70
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• 2021

In order to compare and investigate the growth rates of each of the various soils, the soil mixing ratios were varied to four soils (Pitmos, Pearlite, Masato, General Soil, and Cocopeat). Ten were selected for each soil ratio and the average length and weight were compared. As a result, in the ratio of No. 1 pitmos 6.5: Perlite 2: Masato 1.5, it was measured as 16.36cm, 0.60g. In the ratio of No. 2 pitmos 10, 13.74cm, 0.41g. In the ratio of No. 3 general clay 10, it was measured as 12.43cm, 0.26g. 4 general clay 8, 0.39g. The growth rate of each soil was measured to be superior to that of other soil growth environments in the ratio of pitmos 6.5: pearlite 2: masato 1.5 soil. For ginseng plant analysis, 30 ginseng plants grown in the average length and weight of each soil at a ratio of 6.5: pearlite 2: masato 1.5 and relatively low-result general soil were selected and analyzed. As a result, 1,040ppm of nitrite nitrogen(NO₃-N) was higher in ginseng plants grown in general soil. There was no significant difference in phosphoric acid(P), potassium(K), and magnesium(Mg). Ginseng is characterized by poor growth when it exceeds 300ppm by combining ammonia tae (NH₄-N) and nitrate tae (NO₃-N) nitrogen. In addition, nitric acid produced in a part of this nitrite makes the pH reaction of the soil acidic, and the nitrite remaining in the soil evaporates into gas.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.127-136
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• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.191-200
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• 2020

Companies are seeking to maximize profits through exports and imports in the ultra-fast, ultra-high-speed modern society. It is only possible to sustain its survival if it targets the global market, not based on any specific region. The K-POP group is also targeting overseas markets in a manner similar to the various global strategies used when companies make inroads into foreign markets, including exports, contracts and direct investment. The K-POP group is engaged in various forms of activities, ranging from simple forms of performance (export) that are visited and staged by an invitation from a certain foreign country to series performances (license) by an invitation from a local promoter and tour performances using its capabilities. The K-POP group is seeking to go beyond the art of single-stage performances and make a systematic plan and make inroads into foreign countries in the form of direct investment suitable for each foreign country. The K-POP group made inroads into overseas markets in the form of simple performances from the late 1990s to 2005, when 'Korean Wave' was first introduced. Group H.O.T., etc. are typical examples. Since then, it has sought to enter overseas markets in the form of franchises by accepting overseas members by 2018, starting with Super Junior in 2005. Since then, the K-POP group in the form of joint investment attempted as group IZ*ONE in 2018 appeared, and a voice story came out in September 2018 when South Korea's JYP Entertainment and Tencent of China joined forces. Unlike K-POP Group, which has entered foreign markets with a global strategy based on the existing export method (H.O.T.), 'Boystory' is a representative group that is made with joint investment, which is a direct investment method. In February 2020, RBW released 'D1Verse,' a five-member group selected by Vietnam's reality show, as a joint investment-type group. This shows the possibility that domestic and foreign companies will release a group in the form of joint investment in order to pursue both globalization and localization.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.28-36
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• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.172-189
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• 2023

This study investigated the potential contribution of ChatGPT, a massive language and information model, in the decision-making process of public design policies, focusing on the characteristics inherent to public design. Public design utilizes the principles and approaches of design to address societal issues and aims to improve public services. In order to formulate public design policies and plans, it is essential to base them on extensive data, including the general status of the area, population demographics, infrastructure, resources, safety, existing policies, legal regulations, landscape, spatial conditions, current state of public design, and regional issues. Therefore, public design is a field of design research that encompasses a vast amount of data and language. Considering the rapid advancements in artificial intelligence technology and the significance of public design, this study aims to explore how massive language and information models like ChatGPT can contribute to public design policies. Alongside, we reviewed the concepts and principles of public design, its role in policy development and implementation, and examined the overview and features of ChatGPT, including its application cases and preceding research to determine its utility in the decision-making process of public design policies. The study found that ChatGPT could offer substantial language information during the formulation of public design policies and assist in decision-making. In particular, ChatGPT proved useful in providing various perspectives and swiftly supplying information necessary for policy decisions. Additionally, the trend of utilizing artificial intelligence in government policy development was confirmed through various studies. However, the usage of ChatGPT also unveiled ethical, legal, and personal privacy issues. Notably, ethical dilemmas were raised, along with issues related to bias and fairness. To practically apply ChatGPT in the decision-making process of public design policies, first, it is necessary to enhance the capacities of policy developers and public design experts to a certain extent. Second, it is advisable to create a provisional regulation named 'Ordinance on the Use of AI in Policy' to continuously refine the utilization until legal adjustments are made. Currently, implementing these two strategies is deemed necessary. Consequently, employing massive language and information models like ChatGPT in the public design field, which harbors a vast amount of language, holds substantial value.

• Research in Plant Disease
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• v.29 no.3
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• pp.243-250
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• 2023

To explore the distribution and the resistance reaction of rice blast pathogens that may occur in North Korea, rice blast pathogens in the North Korean border regions of Dandong and Yeon-gil in China and the North Korean border region of Cheorwon in South Korea were analyzed. In addition, comparative analysis was conducted with rice blast pathogen in Suwon and Jeonju, inland regions of South Korea. Resistance reactions above average were observed in monogenic rice lines (IRBLzt-T, IRBL9-W, IRBL20-IR24, and IRBLta-CP1) in Jeonju, Suwon, and Cheorwon from 2018 to 2020. In Dandong and Yeon-gil, the monogenic lines IRBLz5-CA, IRBL12-M, and IRBL19-A consistently showed resistance reactions for three years. Notably, IRBL19-A exhibited strong resistance. Race distribution analysis in South Korea indicated a shift from KI to KJ dominance from 2018 to 2020, while in the North Korean border regions of Dandong and Yeon-gil, the KI race was dominant in 2021 and 2022. The race distribution of rice blast pathogens in China's North Korean border regions differed significantly from that in South Korea.