• Korean Journal of Agricultural Science
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• v.3 no.1
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• pp.95-104
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• 1976

This study was conducted to test the harvesting operation of two kinds of rice varieties such as Milyang #15 and Tong-il with a imported two furrow Japanese combine and was performed to find out the operational accuracy of it, the adaptability of this machine, and the feasibility of supplying this machine to rural area in Korea. The results obtained in this study are summarized as follows; 1. The harvesting test of the Milyang #15 was carried out 5 times from the optimum harvesting operation was good regardless of its maturity. The field grain loss ratio and the rate of unthreshed paddy were all about 1 percent. 2. The field grain loss of Tong-il harvested was increased from 5.13% to 10.34% along its maturity as shown in Fig 1. In considering this, it was needed that the combine mechanism should be improved mechanically for harvesting of Tong-il rice variety. 3. The rate of unthreshed paddy of Tong-il rice variety of which stem was short was average 1.6 percent, because the sample combine used in this study was developed on basisof the long stem variety in Japan, therefore some ears owing to the uneven stem of Tong-il rice could nat reach the teeth of the threshing drum. 4. The cracking rates of brown rice depending mostly upon the revolution speed of the threshing drum(240-350 rpm) in harvesting of Tong-il and Milyang #15 were all below 1 percent, and there was no significance between two varieties. 5. Since the ears of Tong-il rice variety covered with its leaves, a lots of trashes was produced, especially when threshed in raw materials, and the cleaning and the trashout mechanisms were clogged with those trashes very often, and so these two mechanisms were needed for being improved. 6. The sample combine of which track pressure was $0.19kg/cm^2$ could drive on the soft ground of which sinking was even 25cm as shown in Fig 3. But in considering the reaping height adjustment, 5cm sinking may be afford to drive the combine on the irregular sinking level ground without any readjustment of the resaping height. 7. The harvesting expenses per ha. by the sample combine of which annual coverage area is 4.7 ha. under conditions that the yearly workable days is 40, percentage of days being good for harvesting operation is 60%, field efficiency is 56%, working speed is 0.273m/sec, and daily workable hours is 8 hrs is reasonable to spread this combine to rural area in Korea, comparing to the expenses by the conventional harvesting expenses, if mechanical improvement is supplemented so as to harvest Tong-il rice. 8. In order to harvest Tong-il rice, the two furrow combine should be needed some mechanical improvements that divider can control not to touch ears of paddy, the space between the feeding chain and the thrshing drum is reduced, trash treatment apparatus must be improved, fore and rear adjust-interval is enlarged, and width of track must be enlarged so as to drive on the soft ground.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.171-187
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• 2022

Recently, the demand and development for non-face-to-face services are rapidly progressing due to the pandemic caused by the COVID-19, and attention is focused on the metaverse at the center. Entering the era of the 4th industrial revolution, Metaverse, which means a world beyond virtual and reality, combines various sensing technologies and 3D reconstruction technologies to provide various information and services to users easily and quickly. In particular, due to the miniaturization and economic increase of convergence sensors such as unmanned aerial vehicle(UAV) capable of high-resolution imaging and high-precision LiDAR(Light Detection and Ranging) sensors, research on digital-Twin is actively underway to create and simulate real-life twins. In addition, Game engines in the field of computer graphics are developing into metaverse engines by expanding strong 3D graphics reconstuction and simulation based on dynamic operations. This study constructed a mirror-world type metaverse that reflects real-world coordinate-based reality using Unreal Engine 5, a recently announced metaverse engine, with accurate 3D spatial information data of convergence sensors based on unmanned aerial system(UAS) and LiDAR. and then, spatial information contents and simulations for users were produced based on various public data to verify the accuracy of reconstruction, and through this, it was possible to confirm the construction of a more realistic and highly utilizable metaverse. In addition, when constructing a metaverse that users can intuitively and easily access through the unreal engine, various contents utilization and effectiveness could be confirmed through coordinate-based 3D spatial information with high reproducibility.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.103-117
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• 2022

The recent corporate economy, such as the COVID 19 pandemic that has spread all over the world since the beginning of 2020, the acceleration of the 4th industrial revolution, and supply chain management risks triggered by the US-China conflict and the Ukraine crisis, is more serious than ever before. CEOs who have started and managed small and medium-sized enterprises (SMEs) are more concerned than ever about the sustainability of their businesses in this reality. Nevertheless, there were few empirical studies on the factors that influence the intention of SME CEOs to discontinue business. In this study, the perception of the business environment of SMEs (intensity of competition in key business areas, difficulty in manpower management), corporate competency (employee competency, company product or service competitiveness, supply chain and consumer relations, digital competency and technical expertise), and CEO's competency(trust between employees and the CEO, management competency and perceived health status of CEO) on CEO's intention to discontinue business was discussed. As a result of the study, the intensity of competition in the main business field, and the difficulty in manpower management had a positive (+) effect on the intention to discontinue the business, and the employee competency, product (service) competitiveness, digital competency of the company, and the CEO's Health status had a negative (-) effect on intention to discontinue business. The relationship between these influences was found in the order of CEO's health status, product competitiveness, employee competency, digital competency, competitive strength in the main business, and difficulty in manpower management. It was analyzed that supply chain and consumer relations, trust between employees and the CEO, and management capabilities did not significantly affect the intention to discontinue business. On the other hand, business confidence has a mediating effect between the intensity of competition in the main business field, the difficulty in manpower management, product or service competitiveness, digital competency, trust between employees and the CEO, and the management capability and intention to discontinue business was tested. This study had academic significance in that it empirically analyzed factors related to intention to discontinue business targeting small and medium-sized business CEOs. In practice, as it has been found that business environment awareness, corporate competency, managerial competency, and business confidence are factors that influence the intention to discontinue business, if an action ideas that can reinforce this part can be found, SMEs can achieve sustainable growth or it may help CEO find an meaningful exit.

• Proceedings of the Korean Society of Crop Science Conference
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• 2019.09a
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• pp.5-5
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• 2019

The objectives of this paper are (i) to analyze past trends and future directions of rice production, consumption and trade across the world and (ii) to discuss emerging challenges and future directions in the global rice industry. Rice is a staple food of over half of the world's 7.7 billion people. It is an important economic, social, political, and cultural commodity in most Asian countries. Rice is the $1^{st}$ most widely consumed, $2^{nd}$ largely produced, and $3^{rd}$ most widely grown food crop in the world. It was cultivated by 144 million farms in over 100 countries with harvested area of over 163 million ha producing about 745 million tons paddy in 2018. About 90% of the total rice is produced in Asia. China and India, the biggest rice producers, account for over half of the world's rice production. Between 1960 and 2018, world rice production increased over threefold from 221 to 745 million tons (2.1% per year) due to area expansion from 120 to 163 million ha (0.5% per year) and paddy yield increase from 1.8 to 4.6 t/ha (1.6% per year). The Green Revolution led massive increase in rice production prevented famines, provided food for millions of people, reduced poverty and hunger, and improved livelihoods of millions of Asians. The future increase in rice production must come from yield increase as the scope for area expansion is limited. Rice is the most widely consumed food crop. The world's average per capita milled rice consumption is 64 kilograms providing 19% of daily calories. Asia accounted for 84% of global consumption followed by Africa (7%), South America (3%), and the Middle East (2%). Asia's per capita rice consumption is 100 kilograms per year providing 28% of daily calories. The global and Asian per capita consumption increased from the 1960s to the 1990s but stable afterward. The per capita rice consumption is expected to decline in Asia but increase outside Asia especially in Africa in the future. The total milled rice consumption was about 490 million tons in 2018 and projected to reach 550 million tons by 2030 and 590 million tons by 2040. Rice is thinly traded in international market because it is a highly protected commodity. Only about 9% of the total production is traded in global rice market. However, the volume of global rice trade has increased over six-fold from 7.5 to 46.5 million tons between the 1960s and 2018. A relatively small number of exporting countries interact with a large number of importing countries. The top five rice exporting countries are India, Thailand, Vietnam, Pakistan, and China accounting for 74% of the global rice export. The top five rice importing countries are China, Philippines, Nigeria, European Union and Saudi Arabia accounting for 26% of the global rice import. Within rice varieties, Japonica rice accounts for the highest share of the global rice trade (about 12%) followed by Basmati rice (about 10%). The high concentration of exports to a few countries makes international rice market vulnerable to supply disruptions in exporting countries, leading to higher world prices of rice. The export price of Thai 5% broken rice increased from 198 US$/ton in 2000 to 421 US$/ton in 2018. The volumes of trade and rice prices in the global market are expected to increase in the future. The major future challenges of the rice industry are increasing demand due to population growth, rising demand in Africa, economic growth and diet diversification, competition for natural resources (land and water), labor scarcity, climate change and natural hazards, poverty and inequality, hunger and malnutrition, urbanization, low income in rice farming, yield saturation, aging of farmers, feminization of agriculture, health and environmental concerns, improving value chains, and shifting donor priorities away from agriculture. At the same time, new opportunities are available due to access to new technologies, increased investment by the private sector, and increased global partnership. More investment in rice research and development is needed to develop and disseminate innovative technologies and practices to overcome problems and ensure food and nutrition security of the future population.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.