• Animal Bioscience
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• v.34 no.9
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• pp.1466-1478
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• 2021

Objective: Ruminants are completely dependent on their microbiota for rumen fermentation, feed digestion, and consequently, their metabolism for productivity. This study aimed to evaluate the rumen bacteria of lactating yaks with different milk protein yields, using high-throughput sequencing technology, in order to understand the influence of these bacteria on milk production. Methods: Yaks with similar high milk protein yield (high milk yield and high milk protein content, HH; n = 12) and low milk protein yield (low milk yield and low milk protein content, LL; n = 12) were randomly selected from 57 mid-lactation yaks. Ruminal contents were collected using an oral stomach tube from the 24 yaks selected. High-throughput sequencing of bacterial 16S rRNA gene was used. Results: Ruminal ammonia N, total volatile fatty acids, acetate, propionate, and isobutyrate concentrations were found to be higher in HH than LL yaks. Community richness (Chao 1 index) and diversity indices (Shannon index) of rumen microbiota were higher in LL than HH yaks. Relative abundances of the Bacteroidetes and Tenericutes phyla in the rumen fluid were significantly increased in HH than LL yaks, but significantly decreased for Firmicutes. Relative abundances of the Succiniclasticum, Butyrivibrio 2, Prevotella 1, and Prevotellaceae UCG-001 genera in the rumen fluid of HH yaks was significantly increased, but significantly decreased for Christensenellaceae R-7 group and Coprococcus 1. Principal coordinates analysis on unweighted UniFrac distances revealed that the bacterial community structure of rumen differed between yaks with high and low milk protein yields. Furthermore, rumen microbiota were functionally enriched in relation to transporters, ABC transporters, ribosome, and urine metabolism, and also significantly altered in HH and LL yaks. Conclusion: We observed significant differences in the composition, diversity, fermentation product concentrations, and function of ruminal microorganisms between yaks with high and low milk protein yields, suggesting the potential influence of rumen microbiota on milk protein yield in yaks. A deeper understanding of this process may allow future modulation of the rumen microbiome for improved agricultural yield through bacterial community design.

• Journal of Family Resource Management and Policy Review
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• v.26 no.2
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• pp.37-51
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• 2022

This study examines the relationship between flexible work and workers' work-life balance using data from the 23rd Korean Labor and Income Panel Study, with a focus on the impact of working from home (WFH) during the COVID-19 pandemic. As a result of the analysis, WFH was found to have a positive effect on the happiness of workers, satisfaction with family relationships, job satisfaction, organizational commitment, and job satisfaction related to job security for women. In addition, we looked at the changes in time use of WFH workers and non-WFH workers before and after COVID-19, with a particular focus on aspects such as time spent sleeping and on self-development, childcare, housework, exercise, and social gatherings. Compared to non-WFH workers, WFH workers increased the time spent on housework and childcare after COVID-19, with this trend more pronounced among women. The results of this study suggest that the effect of utilizing WFH may be halved for workers who have to take care of children and work at the same time, and that this effect may be greater for women. Therefore, it is necessary to strengthen social support measures so that WFH workers who face a work-life conflict due to childcare can maintain work productivity and realize a work-life balance, and to ensure that the flexible work arrangement should not be a medium that reinforces traditional gender roles. Active policy efforts will be required to make sure this happens.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.47-63
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• 2023

Unmanned aerial vehicle (UAV) and sensor technologies are rapidly developing and being usefully utilized for spatial information-based agricultural management and smart agriculture. Until now, there have been many difficulties in obtaining production information in a timely manner for large-scale agriculture on reclaimed land. However, smart agriculture that utilizes sensors, information technology, and UAV technology and can efficiently manage a large amount of farmland with a small number of people is expected to become more common in the near future. In this study, we evaluated the productivity of forage maize grown on reclaimed land using UAV and sensor-based technologies. This study compared the plant height, vegetation cover ratio, fresh biomass, and dry biomass of maize grown on general farmland and reclaimed land in South Korea. A biomass model was constructed based on plant height, cover ratio, and volume-based biomass using UAV-based images and Farm-Map, and related estimates were obtained. The fresh biomass was estimated with a very precise model (R² =0.97, root mean square error [RMSE]=3.18 t/ha, normalized RMSE [nRMSE]=8.08%). The estimated dry biomass had a coefficient of determination of 0.86, an RMSE of 1.51 t/ha, and an nRMSE of 12.61%. The average plant height distribution for each field lot was about 0.91 m for reclaimed land and about 1.89 m for general farmland, which was analyzed to be a difference of about 48%. The average proportion of the maize fraction in each field lot was approximately 65% in reclaimed land and 94% in general farmland, showing a difference of about 29%. The average fresh biomass of each reclaimed land field lot was 10 t/ha, which was about 36% lower than that of general farmland (28.1 t/ha). The average dry biomass in each field lot was about 4.22 t/ha in reclaimed land and about 8 t/ha in general farmland, with the reclaimed land having approximately 53% of the dry biomass of the general farmland. Based on these results, UAV and sensor-based images confirmed that it is possible to accurately analyze agricultural information and crop growth conditions in a large area. It is expected that the technology and methods used in this study will be useful for implementing field-smart agriculture in large reclaimed areas.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.87-99
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• 2022

The meat consumption per person has continuously increased in recent years. However, the labor force in the domestic livestock industry has decreased due to the declining and ageing population. In order to increase productivity, the government have developed and distributed design standard of livestock houses. Presently, report showed that the adaptation rate of the developed livestock house design standard on the real farm was still low. Thus, this paper aimed to find ways to improve the utilization of the design standard through surveys. The survey was conducted on 650 farms across the country. Analysis of the result showed that in the poultry house, the unawareness of farmers to the design standard was found to be the biggest reason for not using the design standards. On the other hand, in the swine house, the previously built swine houses do not fit with the design standard. From these result, the following recommendations were suggested: 1) promotion and education are needed to enhance usage of design standard; 2) since it is impossible to make a design standard considering all the farm sites, it is important to consider the conditions of various farm site prior to enhancement of the design standard; 3) improvement factors such as reinforcing the ventilation design, reflecting animal welfare, preventing livestock diseases, and enhancing ICT devices can also be promoted.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.391-396
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• 2021

In this study, items for forest policy and forest resource research in Austria, Japan, New Zealand, and Indonesia, which are major forest advanced countries, were investigated, and the applicability of point cloud data acquired through laser scanning was identified. Through the study, it was found that forest policies in developed countries are being pursued for the purpose of sustainable forest conservation and management, job creation, and timber productivity improvement, and that new technologies are being researched and applied to actual projects. Korea has a high proportion of forests compared to the national land area compared to major forestry developed countries, but the accumulation of trees is relatively low, so it is a time for scientific forest management to improve the accumulation of trees. To understand the applicability of laser scanning technology, a forest resource survey using point cloud data was conducted, and the diameter of breast height, height, number of trees per unit area were calculated, and the shape of the crown was identified. If field experiments and accuracy evaluations applying various laser scanning technologies are carried out in the future, it will be possible to present the quantitative improvement of forest resource survey using foil cloud.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.125-132
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• 2023

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Journal of Service Research and Studies
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• v.10 no.4
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• pp.57-70
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• 2020

Globally, cloud service is a core infrastructure that improves industrial productivity and accelerates innovation through convergence and integration with various industries, and it is expected to continuously expand the market size and spread to all industries. In particular, due to the global pandemic caused by COVID-19, the introduction of cloud services was an opportunity to be recognized as a core infrastructure to cope with the untact era. However, it is still at the preliminary stage for market expansion of cloud service in Korea. This paper aims to empirically analyze how cloud services can be accepted by users by each industry through extended Technology Acceptance Model(TAM), and what factors influence the acceptance and avoidance of cloud services to users. For this purpose, the impact and factors on the acceptance intention of cloud services were analyzed through the hypothesis test through the proposed extended technology acceptance model. The industrial sector selected four industrial sectors of education, finance, manufacturing and health care and derived factors by examining the parameters of TAM, key characteristics of the cloud and other factors. As a result of the empirical analysis, differences were found in the factors that influence the intention to accept cloud services for each of the four industry sectors, which means that there is a difference in perception of the introduction or use of cloud services by industry sector. Eventually it is expected that this study will not only help to understand the intention of using cloud services by industry, but also help cloud service providers expand and provide cloud services to each industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.