• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.67 no.1
• /
• pp.27-40
• /
• 2022

Building self-sustainable rural infrastructure and environment through smart digital agriculture technology innovation is one of the major goals of the Korean agricultural administration as a part of the nation's 4^th industry revolution. To identify areas for improving and effectively investing in the acceleration of rural development, 207 experts in the areas of crop science and smart digital agriculture technology were interviewed for their opinions and suggestions on 22 questions designed to recognize fundamental agricultural issues to be addressed and solutions to advance technology innovation and rural development. Majority of the participants expected smart digital agriculture technologies to resolve major agricultural issues and help build a better rural environment. To overcome technology gaps and resolve issues more effectively, further investment in training new technology experts and building stronger agricultural technology infrastructure is urgent, and persistent and systematic support from agricultural administration appears to be the key for accelerating the process. While the leading global groups of both public and private sectors have advanced their technologies beyond the field application stage, most of the Korean technologies remain at the early pilot stage. Aging population and lack of labor in rural areas, unknown future climate change, and challenges in sustainable rural development are expected to be resolved by smart digital agriculture technologies. Technological innovations by research institutes should be promptly deployed in the crop production field, and farm training systemically organized by local technology centers can accelerate farming revolution. Standardization of equipment and data systems is another key to the success of digitalization of food crop production and food supply chains nationwide.

• Korean Journal of Cognitive Science
• /
• v.33 no.1
• /
• pp.51-75
• /
• 2022

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation that is able to alter neuronal activity in particular brain regions. Many studies have researched how tDCS modulates neuronal activity and reorganizes neural networks. However it is difficult to conclude the effect of brain stimulation because the studies are heterogeneous with respect to the stimulation parameter as well as individual difference. It is not fully in agreement with the effects of brain stimulation. In particular few studies have researched the reason of variability of brain stimulation in response to time so far. The study investigated individual variability of brain stimulation based on circadian rhythm and chronotype. Participants were divided into two groups which are morning type and evening type. The experiment was conducted by Zoom meeting which is video meeting programs. Participants were sent experiment tool which are Muse(EEG device), tdcs device, cell phone and cell phone holder after manuals for experimental equipment were explained. Participants were required to make a phone in frount of a camera so that experimenter can monitor online EEG data. Two participants who was difficult to use experimental devices experimented in a laboratory setting where experimenter set up devices. For all participants the accuracy of 98% was achieved by SVM using leave one out cross validation in classification in the the effects of morning stimulation and the evening stimulation. For morning type, the accuracy of 92% and 96% was achieved in classification in the morning stimulation and the evening stimulation. For evening type, it was 94% accuracy in classification for the effect of brain stimulation in the morning and the evening. Feature importance was different both in classification in the morning stimulation and the evening stimulation for morning type and evening type. Results indicated that the effect of brain stimulation can be explained with brain state and trait. Our study results noted that the tDCS protocol for target state is manipulated by individual differences as well as target state.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.1
• /
• pp.13-34
• /
• 2022

Soil moisture data have been collected at 11 agrometeorological stations operated by The Korea Meteorological Administration (KMA). This study aimed to verify the accuracy of soil moisture data of KMA and develop a correction formula to be applied to improve their quality. The soil of the observation field was sampled to analyze its physical properties that affect soil water content. Soil texture was classified to be sandy loam and loamy sand at most sites. The bulk density of the soil samples was about 1.5 g/cm³ on average. The content of silt and clay was also closely related to bulk density and water holding capacity. The EnviroSCAN model, which was used as a reference sensor, was calibrated using the self-manufactured "reference soil moisture observation system". Comparison between the calibrated reference sensor and the field sensor of KMA was conducted at least three times at each of the 11 sites. Overall, the trend of fluctuations over time in the measured values of the two sensors appeared similar. Still, there were sites where the latter had relatively lower soil moisture values than the former. A linear correction formula was derived for each site and depth using the range and average of the observed data for the given period. This correction formula resulted in an improvement in agreement between sensor values at the Suwon site. In addition, the detailed approach was developed to estimate the correction value for the period in which a correction formula was not calculated. In summary, the correction of soil moisture data at a regular time interval, e.g., twice a year, would be recommended for all observation sites to improve the quality of soil moisture observation data.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.925-938
• /
• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.295-304
• /
• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.285-294
• /
• 2022

In order to accurately calculate greenhouse gas emissions in the agricultural field, Korea has been developing national-specific emission factors through direct measurement of gas fluxes using the closed-chamber method. In the rice paddy, only national-specific emission factors for methane (CH₄) have been developed. It is thus necessary to develop those for nitrous oxide (N₂O) affected by the application of nitrogen fertilizer. However, since the concentration of N₂O emission from rice cultivation is very low, the QA/QC methods such as method detection and practical quantification limits are important. In this study, N₂O emission from a rice paddy was evaluated affected by the amount of nitrogen fertilizer, by taking into account both method detection and practical quantification limits for N₂O concentration. The N₂O emission from a rice paddy soils affected by the nitrogen fertilizer application was estimated in the following order. The method detection limit (MDL) of N₂O concentration was calculated at 95% confidence level based on the pooled standard deviation of concentration data sets using a standard gas with 98 nmol mol^-1 N₂O 10 times for 3 days. The practical quantification limit (PQL) of the N₂O concentration is estimated by multiplying 10 to the pooled standard deviation. For the N₂O flux data measured during the rice cultivation period in 2021, the MDL and PQL of N₂O concentration were 18 nmol mol^-1 and 87 nmol mol^-1, respectively. The measured values above the PQL were merely about 12% of the total data. The cumulative N₂O emission estimated based on the MDL and PQL was higher than the cumulative emission without nitrogen fertilizer application. This research would contribute to improving the reliability in quantification of the N₂O flux data for accurate estimates of greenhouse gas emissions and uncertainties.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.127-146
• /
• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Science Education
• /
• v.46 no.1
• /
• pp.93-108
• /
• 2022

The purpose of this study is to develop a STEAM program that can be used in the high school credit system to be fully implemented in 2025, and to examine its validity and effectiveness. The STEAM program analyzed the 2015 revised curriculum centering on science, technology, and engineering through the 2015 revised curriculum analysis, and then selected the five latest issues: hydrogen fuel, climate crisis, data science, appropriate technology, and barista. In accordance with this self-developed program development format (frame), it was developed for seven months through a process of group deliberation. The draft of the STEAM program for 29 sessions of five types, developed to indirectly experience the career path and occupation of high school students, was verified through consultation with 2 STEAM education experts. It was applied at five different high schools for a pilot implementation. As a result of the pilot application, it was confirmed that the students' STEAM attitude significantly improved in the post-test than the pre-test, and the students' high satisfaction with the program was confirmed. In addition, through an interview with the pilot application teacher, it was positively evaluated that 'the content and level of the program are suitable and through experience solving real-life problems, you can apply the content knowledge of related subjects and have an opportunity to experience careers.' Based on the results of the pilot application, the high school credit system STEAM program for students and teachers was finally completed in 29 lessons of five types. Through this study, the development and operation of the next-generation STEAM program that can be applied in the high school credit system should be actively developed, and a plan to improve teachers' professionalism so that the high school credit system can be established and operated properly for blended classes triggered by COVID-19. The necessity of design was suggested. This study is expected to be used as basic data for the development and operation of STEAM programs in the high school credit system, which will be fully implemented in 2025.

• 지역과문화
• /
• v.7 no.1
• /
• pp.59-98
• /
• 2020

An empirical study utilizing data was performed with a variable called 'strategic planning' for festivals in order to look into the impact of a cultural tourism festival on urban regeneration. As a success factor of a festival, strategic festival planning was drawn up, and the following hypotheses were set: Seven strategic factors verified through an exploratory factor analysis will have a positive impact on festival satisfaction (festival success) and on urban regeneration, and festival satisfaction will have a positive impact on urban regeneration by having a mediating effect on it. For the analysis, the Gimje Horizon Festival was selected as it was considered as a typical case of urban regeneration through a festival, and the relationship between the festival and urban regeneration was understood by conducting a combined analysis of a quantitative analysis through a survey, a literature search, field investigations and in-depth interviews. The quantitative analysis indicates that strategic planning has a positive impact on festival satisfaction (festival success) and on urban regeneration and that festival satisfaction has a positive impact on urban regeneration. The same study result as the quantitative analysis result was obtained even through a qualitative analysis. This shows that the higher the path coefficient of strategic planning, the higher the path coefficient of festival satisfaction and urban generation and that with better strategic planning, the effects of festival satisfaction and urban regeneration are maximized. In other words, when planning and implementing a festival by actively incorporating the seven strategic planning factors which were suggested as festival success factors earlier in this study beginning from the stage of festival planning, it is likely to have a positive impact not only on the success of the festival but also on urban regeneration. This is an implication that gives a new alternative to software-based urban regeneration through festivals. It is meaningful to present the importance of festival planning and the direction of planning to maximize the effect of urban regeneration to festival planners and urban regeneration experts. This study is believed to serve as a momentum for people to take a new approach to studies on festivals and urban regeneration utilizing software in the future.

• Korean Journal of Ecology and Environment
• /
• v.56 no.1
• /
• pp.45-56
• /
• 2023

In stream ecosystem assessment, RIVPACS, which makes a simple but clear evaluation based on macroinvertebrate community, is widely used. In this study, a preliminary study was conducted to develop a RIVPACS-type model suitable for Korean streams nationwide. Reference streams were classified into two types(upstream and downstream), and a prediction model for macroinvertebrates was developed based on each family. A model for upstream was divided into 7 (train): 3 (test), and that for downstream was made using a leave-one-out method. Variables for the models were selected by non-metric multidimensional scaling, and seven variables were chosen, including elevation, slope, annual average temperature, stream width, forest ratio in land use, riffle ratio in hydrological characteristics, and boulder ratio in substrate composition. Stream order classified 3,224 sites as upstream and downstream, and community compositions of sites were predicted. The prediction was conducted for 30 macroinvertebrate families. Expected (E) and observed fauna (O) were compared using an ASPT biotic index, which is computed by dividing the BMWPK score into the number of families in a community. EQR values (i.e. O/E) for ASPT were used to assess stream condition. Lastly, we compared EQR to BMI, an index that is commonly used in the assessment. In the results, the average observed ASPT was 4.82 (±2.04 SD) and the expected one was 6.30 (±0.79 SD), and the expected ASPT was higher than the observed one. In the comparison between EQR and BMI index, EQR generally showed a higher value than the BMI index.