• 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 Korean Home Economics Education Association
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• v.34 no.3
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• pp.1-23
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• 2022

This study examined the perceptions of Myanmar university students and professors regarding the status and necessity of higher education programs in fashion. Data were collected from professors in textile engineering at Yangon Technological University and Myanmar university students. Closed- and open-ended questions were asked either through interviews or by email. The responses were analyzed using keyword extraction and categorization, and descriptive statistics(closed questions). Generally, the professors perceived higher education, as well as the cultural industries including art and fashion, as important for Myanmar's social and economic development. According to the students interests in pursuing a degree in textile were limited, despite the high interest in fashion. Low wages in the apparel industry and lack of fashion degrees that meet the demand of students were cited as reasons. The demand was high for educational programs in fashion product development, fashion design, pattern-making, fashion marketing, branding, management, costume history, and cultural studies. Students expected to find their future career in textiles and clothing factories. Many students wanted to be hired by global fashion brands for higher salaries and training for advanced knowledge and technical skills. They perceived advanced fashion education programs will have various positive effects on Myanmar's national economy.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.87-101
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• 2022

In addition to administering a questionnaire (J-survey), which questions individuals on subjective vocal fatigue, voice samples were collected before and after speech-language pathology sessions from 50 female speech-language pathologists in their 20s and 30s in the Daejeon and Chungnam areas. We identified significant differences in Korean Vocal Fatigue Index scores between the fatigue and non-fatigue groups, with the most prominent differences in sections one and two. Regarding acoustic phonetic characteristics, both groups showed a pattern in which low-frequency band energy was relatively low, and high-frequency band energy was increased after the treatment sessions. This trend was well reflected in the low-to-high ratio of vowels, slope LTAS, energy in the third formant, and energy in the 4,000-8,000 Hz range. A difference between the groups was observed only in the vowel energy of the low-frequency band (0-4,000 Hz) before treatment, with the non-fatigue group having a higher value than the fatigue group. This characteristic could be interpreted as a result of voice abuse and higher muscle tonus caused by long-term voice work. The perturbation parameter and shimmer local was lowered in the non-fatigue group after treatment, and the noise-to-harmonics ratio (NHR) was lowered in both groups following treatment. The decrease in NHR and the fall of shimmer local could be attributed to vocal cord hypertension, but it could be concluded that the effective voice use of speech-language pathologists also contributed to this effect, especially in the non-fatigue group. In the case of the non-fatigue group, the rhamonics-to-noise ratio increased significantly after treatment, indicating that the harmonic structure was more stable after treatment.

• Atmosphere
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• v.33 no.2
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• pp.223-246
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• 2023

This paper summarized the research papers on weather extremes that occurred in the Republic of Korea, which were published in the domestic and foreign journals during 1963~2022. Weather extreme is defined as a weather phenomenon that causes serious casualty and property loss; here, it includes typhoon, heavy rain, drought, heat wave, cold surge, heavy snow, and strong gust. Based on the 2011~2020 statistics in Korea, above 80% of property loss due to all natural disasters were caused by typhoons and heavy rainfalls. However, the impact of the other weather extremes can be underestimated rather than we have actually experienced; the property loss caused by the other extremes is hard to be quantitatively counted. Particularly, as global warming becomes serious, the influence of drought and heat wave has been increasing. The damages caused by cold surges, heavy snow, and strong gust occurred over relatively local areas on short-term time scales compared to other weather hazards. In particularly, strong gust accompanied with drought may result in severe forest fires over mountainous regions. We hope that the present review paper may remind us of the importance of weather extremes that directly affect our lives.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

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

• Korean Journal of Environmental Agriculture
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• v.42 no.1
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• pp.35-43
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• 2023

The fine particulate structure of biochar limits its use as a heavy metal adsorbent, and makes separation of the biochar from the solution technically challenging, thereby reducing recovery of the heavy metals. To address this issue, this study prepared biochar beads under various mixing conditions and investigated their efficiency in removing Pb from aqueous solutions using adsorption models. The biochar beads were produced by mixing alginate and biochar at different ratios: alginate bead (AB), 1% biochar + bead (1-BB), 2.5% biochar + bead (2.5-BB), and 5% biochar + bead (5-BB). The results revealed that the Freundlich isothermal adsorption pattern of the biochar beads to Pb was of the L-type. The highest Langmuir isothermal adsorption capacity (28.736 mg/g) was observed in the 2.5-BB treatment. The dominant mechanism among the kinetic adsorption characteristics of biochar beads for Pb was chemical adsorption. Additionally, the optimal pH range for Pb adsorption was found to be between 4 and 5.5. The highest Pb removal efficiency (97.9%) was achieved when 26.6 g/L of biochar beads were used. These findings suggest that biochar beads are an economical and highly efficient adsorbent that enables separation and recovery of fine biochar particles.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.267-275
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• 2023

This study predicted rice harvest date in South Korea using 11-year (2012-2022) hindcasts based on dynamically downscaled 2m air temperature at subseasonal (1-month lead) timescale. To obtain high (5 km) resolution meteorological information over South Korea, global prediction obtained from the NOAA Climate Forecast System (CFSv2) is dynamically downscaled using the Weather Research and Forecasting (WRF) double-nested modeling system. To estimate rice harvest date, the growing degree days (GDD) is used, which accumulated the daily temperature from the seeding date (1 Jan.) to the reference temperature (1400℃ + 55 days) for harvest. In terms of the maximum (minimum) temperatures, the hindcasts tends to have a cold bias of about 1. 2℃ (0. 1℃) for the rice growth period (May to October) compared to the observation. The harvest date derived from hindcasts (DOY 289) well simulates one from observation (DOY 280), despite a margin of 9 days. The study shows the possibility of obtaining the detailed predictive information for rice harvest date over South Korea based on the dynamical downscaling method.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Food Science and Preservation
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• v.30 no.6
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• pp.1056-1071
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• 2023

This study aimed to investigate the quality and microbial population changes for 90 days under two fermentation conditions, outdoors and indoors (35℃), with starters (single or mixed) in soybean paste. Bacillus velezensis NY12-2 (S1), Debaryomyces hansenii D5-P5 (S2), Enterococcus faecium N78-11 (S3), and their mixtures (M) were used for the makjang fermentation. The content of amino-type nitrogen among the makjang samples was highly shown in the indoors, followed by M, S3, and S2. The glutamic and aspartic acid contents in the M sample fermented in the indoors showed the highest values of 867.42±77.27 and 243.20±15.79 mg/g, respectively. By the electronic tongue analysis, the M sample fermented in the indoors exhibited lower saltiness and higher umami than the others. Consequently, we expect that using mixed strains, such as Bacillus, Debaryomyces, and Enterococcus, under constant conditions showed potential to the quality improvement of soy products.