• Journal of Korean Society of Rural Planning
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• v.25 no.4
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• pp.1-11
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• 2019

This study conducted a survey on the recognition and demand such as recognition level, policy necessity, service demand and policy demand by supply types in order to provide the basic data for successful settlement of agro-healing services. According to the survey on awareness, 45.2% of respondents were aware of the healing farming, and 31.3% of respondents had experience in participating in the agro-healing services. 63.6% of respondents replied that they were experiencing reasons for participating in agro-healing services. Respondents who had no knowledge of agro-healing services responded that 76.7% of respondents said they would not participate. More than two-thirds of respondents in all types indicated that they needed agro-healing services. As a result of evaluating the maximum willingness to pay, there was a willingness to pay for farm work healing about 15,800 won, horticulture healing about 14,800 won, forest healing about 13,400 won, and animal assisted healing about 17,000 won. Improving accessibility and strengthening awareness were high priorities for inconveniences and improvements. 70.1% of the respondents said that policies for agro-healing services are needed. Development of agro-healing programs and contents was the first priority for support policy. The result of this study is expected to provide reference data that can be suggested for agro-healing policy establishment.

• Proceedings of the Ginseng society Conference
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• 2002.10a
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• pp.456-466
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• 2002

Wild ginseng production is increasing due to forest recovery for last 30 years. Total number of Symmani (traditional mountain ginseng digger) was 558 in 2001. Provincial distribution of Symmani in 2001 was highest in Kangwon $(32\%),$ next in Choongbook $(21\%)$ and least in Jeonnam $(0.7\%)$ and Kyoungnam $(0.9\%).$ Age distribution of Symmani was $33\%\;for\;fourties,\;32\%$ for fifties and $20\%$ for sixties. There were 8 persons in eighties. Symmanies are still keeping traditional ritual for mountain god serving clothes of colored ribbons and foods. Increased production induced open market system from underground dealing of mountain ginseng. Korea Mountain Ginseng Association established mountain ginseng assessment committee with professional Symmanies in 2001. From September to November in 2001, 987 roots were requested for quality assessment to the committee and 476 roots $(48\%)$ were passed and graded and others were rejected. Highest frequency of rejection was foreign origin. Pass rate was highest $(74\%)$ in Choongnam suggesting best place for quality. Number of collected roots in each province was positively correlated (p=0.05) with number of Symmanies. There are 3 quality groups of mountain ginseng, Heaven (pure natural), Earth (from seeding of wild ginseng) and Man (from seeding or seedling of wild ginseng with slight environmental modification). The relationship between price and age was polynomial in high quality root, Heaven, Earth and seed long head of Man group, and linear in low quality group, seedling long head of Man. The best one in 2001 was 26 g, 124 years old and sold with 109 million won. Quality criteria are age, shape, weight, color and healthy outlook. Fine roots are criteria for health status of roots and taproot is criteria for efficacy and called as medicine barrel. The implication is that ginsenosides have rarely been experienced for efficacy. The quality criteria of cultivated ginseng were originated from those of mountain ginseng. It is unique for mountain ginseng that only fresh one can be on market. Since quality criteria of mountain ginseng must be based on the efficacy experience it is well expected that present criteria might almost be established at the age of Shinnong Materia Medica.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.3
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• pp.79-90
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• 2019

There are more and more citizens suffering from severe fatigue, and they wish to escape from it and spend their leisure time for healing. As a result, buildings and complexes are being constructed nationwide with healing as their theme. Particularly, they tend to build facilities with concepts like a spa, beauty, healing, meditation, nature, or forest healing. The purpose of this study is to examine the concept of healing environment and the nationwide tendencies of building facilities with healing as their theme and also investigate the planning characteristics of complexes and architecture with three representative complexes as examples. Complexes intended for healing have immersion into nature being freed from one's routine as their concept. When planning the flow of human traffic within the complexes, they try to obtain the autonomy of choice as well as the diversity of space and experiential factors in order to provide opportunities for experiencing nature. In the complexes selected for a case study here, they have planned the factors of physical environment that are associated with one another based on architectural education programs using red clay, programs specializing poetry, and healing programs using food. Typically, this is centered around outdoor experiential space, indoor meditation and education space, or fitness space. Also, it is characterized by the planning of physical environment and the complex operation of programs. Particularly, public space is divided into communal space, resting space, and health and treatment space, and health/resting space is mainly intended for health and exercise, for example, fitness, spas, or jjimjilbang (Korean dry saunas). Also, it is characterized by the planning of pitched roofs harmonized with nature and also facade planning that can positively adopt the factors of natural environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1363-1372
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• 2021

Red tide, defined as the major reproduction of harmful birds, has the characteristics of being generated and diffused in a wide area. This has limitations in detection only with the existing investigation method. Therefore, in this study, red tide was detected using a remote sensing technique. In addition, it was intended to increase the accuracy of detection by using optical characteristics, not just the concentration of chlorophyll. Red tide mainly occurs on the southern coast where sea signals are complex, and the main red tide control species on the southern coast is Cochlodinium polykirkoides. Therefore, it was intended to secure objectivity by reflecting features that could not be found depending on the researcher's observation and experience, not limited to visual judgment using machine learning techniques. In this study, support background machines and random forest were used among machine learning models, and as a result of calculating accuracy as performance evaluation indicators of the two models, the accuracy was 85.7% and 80.2%, respectively.

• Korean Journal of Applied Biomechanics
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• v.32 no.1
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• pp.1-8
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• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• Journal of People, Plants, and Environment
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• v.24 no.2
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• pp.229-244
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• 2021

Background and objective: The purpose of this study was to comprehensively analyze the user behavior in order to cope appropriately with the increasing demand for mountain usage of those in their 20s and 30s and to allocate resources efficiently. Methods: To analyze the behavior of mountain hiking users, an exploratory data analysis (EDA) was conducted on the data which had been collected in the app Tranggle. The main target are users in their 20s and 30s who visited the mountains in the metropolitan area in 2019-2020. Among them, we have selected data on the top 13 mountains based on the frequency of visits. After data pre-processing, mountain usage patterns were analyzed through statistical analysis and visualization. Results: Compared to 2019, the number of users in 2020 increased 1.36 times. The utilization rate of the well-established hiking trails has also increased. The usage of mountain on weekends (Saturday > Sunday) was still the highest, and the difference in the usage between the days of the week decreased. Outside of work hours, early morning usage has increased and night-time usage has decreased. There was no significant change in usages depending on activity type, level (experience point) and exercise properties. Conclusion: Since the COVID-19 outbreak, the usage of mountains has been changing towards low user density and short-distance trip. in the post-COVID-19 era, the function and role of forests in daily life are expected to increase. To cope with this, further research needs to be carried out with consideration of the wider demographic and social characteristics.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.11-18
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• 2023

Currently, studies to predict the risk of rectal cancer surgery select MRI image slices based on the clinical experience of surgeons. The purpose of this study is to semi-automatically select and classify 2D MRI image slides to predict the risk of rectal cancer surgery using biomarkers. The data used were retrospectively collected MRI imaging data of 50 patients who underwent laparoscopic surgery for rectal cancer at Gachon University Gil Medical Center. Expert-selected MRI image slices and non-selected slices were screened and radiomics was used to extract a total of 102 features. A total of 16 approaches were used, combining 4 classifiers and 4 feature selection methods. The combination of Random Forest and Ridge performed with a sensitivity of 0.83, a specificity of 0.88, an accuracy of 0.85, and an AUC of 0.89±0.09. Differences between expert-selected MRI image slices and non-selected slices were analyzed by extracting the top five significant features. Selected quantitative features help expedite decision making and improve efficiency in studies to predict risk of rectal cancer surgery.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Journal of People, Plants, and Environment
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• v.22 no.1
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• pp.1-14
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• 2019

This study was designed to examine agro-healing services and programs provided and operated by farms in Korea. The results of the analysis of the agro-healing programs and operation of farms were as follows. The purpose of the operation of farms was to raise productivity by managing farms in a cooperative way through agricultural production, education and healing, and to raise income by processing and selling agricultural products. It was difficult to access farms by public transport and thus visitors had to use their own cars. The size of farms varied. The main resources utilized in the surveyed programs were plants, rural environments and landscapes, and agricultural products. The programs were conducted using resources that were commonly found in rural areas. Facilities on each farm were equipped with facilities (indoor and outdoor learning place, gardens, vegetable gardens, orchards, etc.) and convenience facilities (parking lots, drinking fountains, kiosks, etc.) to support program operation. However, facilities for the handicapped and accommodation facilities were insufficient. The programs operated on each farm utilized agricultural resources, farm produce, and rural resources and were classified into activities such as making, feeling, and growing. The average number of people who operated the family-centered program was 2-3, having qualifications such as welfare horticultural therapists, forest interpreters, experience instructors, and social workers. In addition, they had expertise in medicinal food, dietary life, and social welfare, and they also had essential expertise required to operate programs.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.