• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.1
• /
• pp.15-33
• /
• 2021

In this study, the possibility of discriminating Fire blight (FB) infection tested using the hyperspectral imagery. The reflectance of healthy and infected leaves and branches was acquired with 5 nm of full width at high maximum (FWHM) and then it was standardized to 10 nm, 25 nm, 50 nm, and 80 nm of FWHM. The standardized samples were divided into training and test sets at ratios of 7:3, 5:5 and 3:7 to find the optimal bands of FWHM by the decision tree analysis. Classification accuracy was evaluated using overall accuracy (OA) and kappa coefficient (KC). The hyperspectral reflectance of infected leaves and branches was significantly lower than those of healthy green, red-edge (RE) and near infrared (NIR) regions. The bands selected for the first node were generally 750 and 800 nm; these were used to identify the infection of leaves and branches, respectively. The accuracy of the classifier was higher in the 7:3 ratio. Four bands with 50 nm of FWHM (450, 650, 750, and 950 nm) might be reasonable because the difference in the recalculated accuracy between 8 bands with 10 nm of FWHM (440, 580, 640, 660, 680, 710, 730, and 740 nm) and 4 bands was only 1.8% for OA and 4.1% for KC, respectively. Finally, adding two bands (550 nm and 800 nm with 25 nm of FWHM) in four bands with 50 nm of FWHM have been proposed to improve the usability of multispectral image sensors with performing various roles in agriculture as well as detecting FB with other combinations of spectral bands.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.33 no.6
• /
• pp.265-274
• /
• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Journal of Bio-Environment Control
• /
• v.31 no.2
• /
• pp.79-89
• /
• 2022

With the wide use of greenhouses, the working hours have been increasing inside the greenhouse for workers. In the closed ventilated greenhouse, the internal environment has less affected to external weather during making a suitable temperature for crop growth. Greenhouse workers are exposed to organic dust including soil dust, pollen, pesticide residues, microorganisms during tillage process, soil grading, fertilizing, and harvesting operations. Therefore, the health status and working environment exposed to workers should be considered inside the greenhouse. It is necessary to secure basic data on particulate matter (PM) concentrations in order to set up dust reduction and health safety plans. To understand the PM concentration of working environment in greenhouse, the PM concnentrations were monitored in the cut-rose and Hallabong greenhouses in terms of PM size, working type, and working period. Compare to no-work (move) period, a significant increase in PM concentration was found during tillage operation in Hallabong greenhouse by 4.94 times on TSP (total suspended particle), 2.71 times on PM-10 (particle size of 10 ㎛ or larger), and 1.53 times on PM-2.5, respectively. During pruning operation in cut-rose greenhouse, TSP concentration was 7.4 times higher and PM-10 concentration was 3.2 times higher than during no-work period. As a result of analysis of PM contribution ratio by particle sizes, it was shown that PM-10 constitute the largest percentage. There was a significant difference in the PM concentration between work and no-work periods, and the concentration of PM during work was significant higher (p < 0.001). It was found that workers were generally exposed to a high level of dust concentration from 2.5 ㎛ to 35.15 ㎛ during tillage operation.

• Korean Chemical Engineering Research
• /
• v.60 no.4
• /
• pp.606-612
• /
• 2022

Relatively high indoor CO₂ concentration (>1,000 ppm) has a negative impact on human health. In this work, indoor CO₂ adsorbent was developed by impregnating KOH or K₂CO₃ on commercial activated carbon, named as KOH/AC and K₂CO₃/AC. Commercial activated carbon (AC) showed relatively high BET surface area (929 m²/g) whereas KOH/AC and K₂CO₃/AC presented lower BET surface area of 13.6 m²/g and 289 m²/g. Two experimental methods of TGA (2,000 ppmCO₂, weight basis) and chamber test (initial concentration: 2,000 ppmCO₂, CO₂ IR analyzer) were used to investigate the adsorption capacity. KOH/AC and K₂CO₃/AC exhibited similar adsorption capacities (145~150 mg_CO2/g), higher than K₂CO₃/Al+Si supports adsorbent (84.1 mg_CO2/g_sample). Similarly, chamber test also showed similar trend. Both KOH/AC and K₂CO₃/AC represented higher adsorption capacities (KOH/AC: 93.5 mg_CO2/g K₂CO₃/AC: 94.5 mg_CO2/g_sample) K₂CO₃/Al+Si supports. This is due to the KOH or K₂CO₃ impregnation increased alkaline active sites (chemical adsorption), which is beneficial for CO₂ adsorption. In addition, the regeneration test results showed both K-based adsorbents pose a good regeneration and reusability. Finally, the current study suggested that both KOH/AC and K₂CO₃/AC have a great potential to be used as CO₂ adsorbent for indoor CO₂ adsorption.

• The Mathematical Education
• /
• v.62 no.3
• /
• pp.435-455
• /
• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.3 no.1
• /
• pp.142-157
• /
• 2001

This study was conducted to get the basic information for promoting farm machinery productivity by surveying the regular maintenance and repair status of major farm machinery such as power tiller, farm tractor, rice transplanter and combine harvester. The survey was carried out through 9 provinces including Cheju province by direct visiting farmers with prepared questionnaire. The results of this study can be summarized as follows : 1. The average farming carrier of the surveyed farmers was 25.3 years, and 21-30 years of farming carrier showed the highest portion as 40.7%. The average carrier of using farm machinery was 9.4 years, and that was 14.9 years for power tiller, 8.3 years for farm tractor, 9.0 years for rice transplanter, 7.9 years for combine harvester, 7.5 years for mini tiller, 9.7 years for power sprayer, and 8.2 years for binder etc. 2. The regular maintenance for farm machinery was conducted mainly at repair shop (49.5%) or dealer agency (12.0%) as 61.5%, and 34.9% of farmers conducted the regular maintenance by themselves at their house. 3. The reasons for not-fully recognizing operation manual and insufficient before-, during-, after-maintenance of farm machinery were insufficient time for them (45.8%), troublesome (22.9%), unknown maintenance method (16.3%), unknown the necessity for maintenance (12.4%), and others (2.6%) in order. 4. For the annual exchange of engine oil, 3.2 times is necessary but actually 1.7 times was exchanged for power tiller, 4.3 times is necessary but actually 1.9 times was exchanged for farm tractor, 2.7 times is necessary but actually 1.7 times was exchanged for rice transplanter, 2.2 times is necessary but actually 2.3 times was exchanged combine harvester. 5. For the annual cleanness or exchange of fuel filter, 3.2 times is necessary but actually 1.1 times was done for power tiller, 4.3 times is necessary but actually 1.6 times was done for farm tractor, 2.7 times is necessary but actually 1.7 times was done for rice transplanter, 1.9 times is necessary but actually 0.8 times was done for combine harvester. 6. For the annual cleanness or exchange of air filter, 3.2 times is necessary but actually 1.4 times was done for power tiller, 4.2 times is necessary but actually 2.4 times was done for farm tractor, 2.6 times is necessary but actually 1.6 times was done for rice transplanter, 3.9 times is necessary but actually 7.0 times was done for combine harvester. 7. For the experience of breakdown related to maintenance, 5.3% of farmers experienced breakdown due to the insufficient exchange of engine oil, 7.7% of farmers experienced breakdown due to the insufficient cleanness or exchange of fuel filter, and 2.9% of farmers experienced breakdown due to the insufficient cleanness or exchange of air filter. 8. Most farmers (76.1%) recognized the necessity for agricultural machinery training or education, and most farmers preferred about one week for the training period, simple or ease maintenance for the training level, agricultural technical center or agricultural machinery manufacturer for the training agency. 9. Complete recognition of operation manual and sufficient before-, during-, and after-maintenance for farm machinery can minimize the breakdown as well as conduct suitable period farming, enlarge the endurance, prevent the safety accidents, and promote productivity of farm machinery. Therefore, these can be accomplished by the thorough training or education for agricultural machinery.

• (The) Research of the performance art and culture
• /
• no.22
• /
• pp.371-411
• /
• 2011

In the twenty first century of rapid cultural change performing art requires new mode of expression based on imaginative power and creativity as well as establishing its own identity. The modern technological environment support this with advanced technology and bring about the expansion of reason from new experience. The introduction of digital media on artistic expression in particular, expands the physical ability of human body which is the main subject of performing art. A virtual body from digital technology is freed from physical boundaries and goes over space and time. It also suggests the possibility of new mode of communication with audience. This study aims at examining the subject of performing art and its digitalized movement focusing on EveR, the world's first professional robot actor. The robot actor which came on stage according to the new expression medium, a digital body, stands in need not only of technological value but also of cultural and artistic application for expression in art. In this endeavor to meet the demand, this study examines the development process and function of 'EveR' the robot actor. Also it searches into the performance of Ever which replaced human being as well as the historical significance of the title:the world's first. To be more specific, there is a example research on two performances:a pansori play "EveR is simply stunning(2009)" and children's play "The Robot Princess and Seven Dwarfs(2009)." Through this example research, it is enabled to anticipate the influence of robot actors on performing arts and to search for the better way of them to evolve. Furthermore, it aims at finding ways to create high value through promoting robot actors to be familiar to the public as well as supporting them to become active cultural contents. The performance with robotic technology is one of the artistic experiment that may cause the change of the future of performing art by actualizing technological imagination together with human body and machinery. As a consequence, it is expected that the meeting of performing art and robotic technology gives positive influence on activating performing art as one of the integrated cultural phenomenon which satisfies the taste of modern era. Moreover, this study may also be the beginning of the expansion of performing art to stretch to diverse field.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.20 no.2
• /
• pp.57-72
• /
• 2018

In order to extract meaningful information from the excellent farming settlement cases of young farmers published by KNCAF, we studied the key words with text mining and created a word cloud for visualization. First, in the text mining results for the entire sample, the words 'CEO', 'corporate executive', 'think', 'self', 'start', 'mind', and 'effort' are the words with high frequency among the top 50 core words. Their ability to think, judge and push ahead with themselves is a result of showing that they have ability of to be managers or managers. And it is a expression of how they manages to achieve their dream without giving up their dream. The high frequency of words such as "father" and "parent" is due to the high ratio of parents' cooperation and succession. Also 'KNCAF', 'university', 'graduation' and 'study' are the results of their high educational awareness, and 'organic farming' and 'eco-friendly' are the result of the interest in eco-friendly agriculture. In addition, words related to the 6th industry such as 'sales' and 'experience' represent their efforts to revitalize farming and fishing villages. Meanwhile, 'internet', 'blog', 'online', 'SNS', 'ICT', 'composite' and 'smart' were not included in the top 50. However, the fact that these words were extracted without omission shows that young farmers are increasingly interested in the scientificization and high-tech of agriculture and fisheries Next, as a result of grouping the top 50 key words by crop, the words 'facilities' in livestock, vegetables and aquatic crops, the words 'equipment' and 'machine' in food crops were extracted as main words. 'Eco-friendly' and 'organic' appeared in vegetable crops and food crops, and 'organic' appeared in fruit crops. The 'worm' of eco-friendly farming method appeared in the food crops, and the 'certification', which means excellent agricultural and marine products, appeared only in the fishery crops. 'Production', which is related to '6th industry', appeared in all crops, 'processing' and 'distribution' appeared in the fruit crops, and 'experience' appeared in the vegetable crops, food crops and fruit crops. To visualize the extracted words by text mining, we created a word cloud with the entire samples and each crop sample. As a result, we were able to judge the meaning of excellent practices, which are unstructured text, by character size.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.427-435
• /
• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.521-535
• /
• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.