• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.27-40
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• 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
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• v.33 no.1
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• pp.51-75
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• 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
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• v.24 no.1
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• pp.13-34
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• 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.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

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

• Economic and Environmental Geology
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• v.54 no.2
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• pp.271-283
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• 2021

The Imjingang Belt in the middle-western Korean Peninsula has tectonically been correlated with the Permo-Triassic Qinling-Dabie-Sulu collisional belt between the North and South China cratons in terms of collisional tectonics. Within the belt, crustal-scale extensional ductile shear zones that were interpreted to be formed during collapsing stage with thrusts and folds were reported as evidence of collisional events by previous studies. In this study, we tried to understand the nature of deformation along the southern boundary of the belt in the Munsan area based on the interpretations of recently conducted structural analyses. To figure out the realistic geometry of the study area, the down-plunge projection was carried out based on the geometric relationships between structural elements from the detailed field investigation. We also conducted kinematic interpretations based on the observed shear sense indicators from the outcrops and the oriented thin-sections made from the mylonite samples. The prominent structures of the Munsan area are the regional-scale ENE-WSW striking thrust and the N-S trending map-scale folds, both in its hanging wall and footwall areas. Shear sense indicators suggest both eastward and westward vergence, showing opposite directions on each limb of the map-scale folds in the Munsan area. In addition, observed deformed microstructures from the biotite gneiss and the metasyenite of the Munsan area suggest that their deformation conditions are corresponding to the typical mid-crustal plastic deformation of the quartzofeldspathic metamorphic rocks. These microstructural results combined with the macro-scale structural interpretations suggest that the shear zones preserved in the Munsan area is mostly related to the development of the N-S trending map-scale folds that might be formed by flexural folding rather than the previously reported E-W trending crustal-scale extensional ductile shear zone by Permo-Triassic collision. These detailed examinations of the structures preserved in the Imjingang Belt can further contribute to solving the tectonic enigma of the Korean collisional orogen.

• Journal of Popular Narrative
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• v.26 no.4
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• pp.9-41
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• 2020

Park Jong-won's debut film "Guro Arirang," based on a short story of the same title by Lee Moon-yeol, is the first commercial film to deal with labor struggles from a worker's point of view in the wake of the 1987 democratic movement, and a pioneering work in terms of representing female workers the Korean cinema has traditionally turned away from. In this film Park Jong-won tried to win the sympathy of the middle class for labor movement in spite of the red scare which still stood firm in the Korean society at that time. To convey its progressive message in a form acceptable to the middle class public, the film portrays labor issues in the light of universal humanity and ethics, not in terms of class hostility or struggle. Park Jong-won calls this point of view "common sense of normal people" and emphasizes its universality and objectivity. This study critically examines the cinematic strategies to deal with labor issues in a form acceptable to the public in a conventional and commercial film and the ideological implications of the "common sense of normal people" reflected in such strategies. The first chapter of the study reveals that the film destroys the irony of the original story and reduces the complex constellation of the characters to the conflict between pure good and evil, creating a melodramatic composition in which the good falls victim to evil. The tragedies suffered by the workers in the film are of course intended to arouse the audience's strong sympathy and solidarity with them. The second chapter shows that the film's various scenes and episodes converge on the them of compassion and grief, and are mostly based on cultural and real experiences and events that caused great public sensations at that time. Especially in the last decisive scene of the movie, the memory of the June 1987 uprising is strongly recalled. So "Guro Arirang" can be seen as a patchwork of proven cases of compassion and grief. The third chapter examines the implications of the scene where the workers turn back demands for wages and put the issues of human treatment and trust to the forefront at the crucial moment of their struggle. It appeals to universal moral values and sentiments that everyone has to acknowledge and removes the political dimension from the workers' campaign. While the film tends to become a pure story of humanity marginalizing irreconcilable conflicts of class interest, the workers fall to the position of passive victims who can be deeply sympathetic on the one hand, and on the other, are idealized as leaders with noble attitude keeping themselves aloof from the hard reality. As a result, the movie loses its realistic ground and weakens its narrative probability. The scenes reminiscent of the 1987 uprising which evoke the solidarity between working and middle class fail to integrate harmoniously into the whole story of the film and remain only as fragmentary parts of the patchwork of compassion and grief.

• Korean Journal of School Psychology
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• v.17 no.2
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• pp.165-180
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• 2020

As a tiered system of supports, School-Wide Positive Behavior Support (SWPBS) is an evidence-based practice in the educational system of Korea. An important aspect of SWPBS is the ongoing progress monitoring and evaluation of implementation fidelity. This study aimed to develop and validate the Korean Tier 3 School-Wide Positive Behavior Support Implementation Fidelity Checklist (KT3-FC). The preliminary KT3-FC consisted of a 37-item, 6-factor checklist. In the first phase of the study, 10 experts reported that the range of content validity of the KT3-FC was adequate. In the second phase of the study, 185 teachers (52 men and 133 women) who implemented SWPBS completed the KT3-FC, Individualized Supports Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and PBS Effectiveness Scale. An exploratory factor analysis resulted in a 5-factor structure, with 20 items, instead of 37 items, consisting of: (a) progress monitoring and evaluation of the individualized supports, (b) provision of supports by aligning and integrating mental health and SWPBS, (c) crisis management planning, (d) problem behavior assessment, and (e) establishment of individualized support team. The internal consistency of the KT3-FC was good (full scale α = .950, sub-factor α = .888 ~ .954). In addition, the KT3-FC showed good convergent validity, having statistically significant correlations with the Individualized Support Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and the PBS Effectiveness Scale. Finally, the confirmatory factor analysis showed that the 5-factor model of the KT3-FC had some good model fits, indicating that the newly developed fidelity measure could be a reliable and valid tool to assess the implementation of Tier 3 supports in Korean schools. Accordingly, the KT3-FC could contribute to implement SWPBS as an evidence-based behavioral intervention for Korean students.

• Journal of Food Hygiene and Safety
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• v.37 no.3
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• pp.136-142
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• 2022

The research aims to develop a rapid and easy analytical method for methoprene using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A simple, highly sensitive, and specific analytical method for the determination of methoprene in livestock products (beef, pork, chicken, milk, eggs, and fat) was developed. Methoprene was effectively extracted with 1% acetic acid in acetonitrile and acetone (1:1), followed by the addition of anhydrous magnesium sulfate (MgSO₄) and anhydrous sodium acetate. Subsequently, the lipids in the livestock sample were extracted by freezing them at -20℃. The extracts were cleaned using MgSO₄, primary secondary amine (PSA), and octadecyl (C₁₈), which were then centrifuged to separate the supernatant. Nitrogen gas was used to evaporate the supernatant, which was then dissolved in methanol. The matrix-matched calibration curves were constructed using 8 levels (1, 2.5, 5, 10, 25, 50, 100, 150 ng/mL) and the coefficient of determination (R²) was above 0.9964. Average recoveries spiked at three levels (0.01, 0.1, and 0.5 mg/kg), and ranged from 79.5-105.1%, with relative standard deviations (RSDs) smaller than 14.2%, as required by the Codex guideline (CODEX CAC/GL 40). This study could be useful for residue safety management in livestock products.

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