• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.4
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• pp.216-224
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• 2023

Since maize (Zea mays L.) originated in central and south America, it requires warm climate conditions throughout its growing season. Growth halts when night-time temperatures drop below 10℃, and the plant may die if temperature reach -1.7℃. Thus, temperature should be maintained between 10 and 30℃ from seeding to maturity. The germination temperature for maize should be at least 8-11℃, whit an optimal range 32-34℃. Since temperature significantly affects the germination rate and period, it plays a crucial role in maize growth. In this study, we evaluated the quantity and feed value of 11 major varieties to determine those best suited for maize cultivation as feed in higher latitude, specifically in Democratic People's of Republic of Korea, below 38 degrees north. A cultivation test was also conducted in Suwon in Republic of Korea, to assess adaptability in areas south of Mt. Suyang. Among the varieties tested, Shinhwangok2 reached silking the fastest, in 65 days, while Gwangpyeongok took the longest at 75 days. The stem length of all varieties exceeded 230 cm. Gwangpyeongok had the tallest stems, while Daanok and Shinhwangok2ho displayed the highest ear ratios. Dacheongok presented the highest values in both dry matter and TDN quantity, with 31,420 kg/ha and 21,66 kg/ha respectively. Pyeonggangok had the highest crude protein content at 8.0%. TDN (%) ranged from 57-68%, with Hwangdaok reaching up to 68%. Based on these findings, Dacheongok and Pyeonggangok appear to be the most suitable varieties for cultivation in terms of both quantity and feed value.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.531-538
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• 2023

Recently, many university classes have been changing from instructor-centered classes to learner-centered classes, and universities are trying to establish a new direction for university education, especially to foster talented people suitable for the Fourth Industrial Revolution. To this end, universities are presenting various competencies necessary for students and focusing on research on efficient education plans for each competency. Among them, creativity is considered the most important competency that students should obtain in universities. Developing a creative problem-solving-based subject where various majors gather to produce results while conducting creative team activities away from desk classes is considered a meaningful subject to cultivate capacities suitable for the requirements of the times. Therefore, this study purpose to develop creative problem-solving-based subjects and analyze the results of class progress. This creative problem-solving-based class is an Action Learning class for step-by-step idea development, which starts with a theoretical lecture for creative idea development and then consists of five stages of Action Learning. The tasks of action learning used in this class consisted of ceramic expression to increase the intimacy of the formed group and the group's collective expression, ideas in life to combine and compress individual ideas into one, environmental improvement programs around schools, and finally UCC on various topics. In the theoretical lecture conducted throughout the class, a class was conducted on Scientific Thinking for creative problem solving, and then a group-type action learning class was conducted sequentially. This Action Learnin process gradually increased the difficulty level and led to in-depth learning by increasing the level of difficulty step by step.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.69 no.3
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• pp.163-169
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• 2024

Recent agricultural practices have depleted micronutrients in the soil, exposing approximately two billion people worldwide to "hidden hunger", a condition in which sufficient calories are consumed but there is a deficiency in essential vitamins and minerals. This form of malnutrition occurs not only in developing countries but also in developed nations where staple foods include grains such as wheat and corn. Among cereal crops, the color of wheat grains can vary due to the presence of different pigments in the bran layers. Colored wheat, rich in functional compounds such as anthocyanins, offers various health benefits primarily due to its antioxidant properties. Therefore, this study aims to evaluate the phenotypic and nutritional characteristics of wheat germplasm 'IT016425' compared with domestic wheat cultivars, with the intention of considering the introduction of this germplasm for breeding purposes. In the field trial, 'IT016425' had a heading date and maturity that were 22 and 8 days later than 'Keumgang', respectively. 'IT016425' also presented the tallest plant height (105.1 cm) but the shortest spike length (7.8 cm) and spike number (14 per plant). The thousand grain weight was similar to that of 'Tapdong', measuring 39.0 g. 'IT016425' had the lowest protein content, with a mean value of 12.1%, whereas 'Keumgang' had the highest protein content (15.5%). However, 'Tapdong' and 'IT016425' compensated for their lower protein content by having higher levels of total dietary fiber. These cultivars exhibited the highest total dietary fiber content, with mean values of 3.16 and 3.29 g/100 g, respectively, whereas 'Keumgang' and 'Arijinheuk' had lower values. 'IT016425' also had the highest content of anthocyanin, with a mean value of 1.61 mg/100 g. Additionally, 'IT016425' had the highest levels of minerals such as K (230.64 mg/100 g), P (190.31 mg/100 g), Mg (45.40 mg/100 g), Zn (1.06 mg/100 g), Mn (0.54 mg/100 g), and Cu (0.12 mg/100 g) compared to the other tested cultivars. Cultivating 'IT016425' in Korea may not be suitable due to the common practice of rice-wheat double-cropping, as it has delayed heading and maturity. However, considering its high anthocyanin and mineral content, it is necessary to introduce the beneficial traits of 'IT016425' into breeding programs.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.3
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• pp.261-270
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• 2024

In this study, the effects of relieving itching and improving the barrier due to moisturizing human skin were evaluated for the cream containing Siraitia grosvenorii extract, which is effective in suppressing histamine and improving skin inflammation in the study subjects with itching. 43 female volunteers aged 21 ~ 59 years old (experimental group: 21 and control group 22) suitable for the purpose of the study were used for 4 weeks on the anterior gourd. The evaluation measured the amount of skin moisture and transepidermal moisture loss (TEWL) in the left or right anterior gourd before and after use of the product, and the visual analogue scale (VAS) evaluation due to skin drying, the efficacy and usability questionnaire evaluation of the product, and skin safety evaluation were conducted. As a result of the analysis, compared to before product use, both the experimental group and the control group showed significant improvement effects according to product use 4 weeks after use on the evaluation items of skin moisture and transepidermal moisture loss and the VAS due to skin dryness. In particular, the experimental group showed a significant improvement effect compared to the control group. As a result of the survey on the efficacy and usability of the product, the subjects of the study in the test group showed higher satisfaction than the control group in the case of the items "relieving itching (suitable)", "moisturizing" and "smoothing" after four weeks of use. In terms of usability, the subjects of the study in the experimental group showed higher satisfaction than the control group in the case of the "scent" and "feeling" items. Based on the above results, it is believed that "cream containing S. grosvenorii extract" has an itch relief (barrier improvement) effect due to skin moisturizing and can be used as a functional product for itching and barrier function improvement.

• Horticultural Science & Technology
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• v.32 no.4
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• pp.499-506
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• 2014

This research was conducted to develop root media containing ground and aged pine bark (GAPB) and ground and raw pine bark (GRPB). After analysis of physico chemical properties, the pine barks were blended with peat moss (PM) or coir dust (CD) in various ratios to formulate 12 root media. Then, two out of 12 root media were chosen based on the physical properties for further experiments. The pre-planting nutrient charge fertilizers (PNCF) were incorporated into two root media and chemical properties were analysed again. The total porosity (TP), container capacity (CC), and air-filled porosity (AFP) of GAPB were 78.7%. 39.4%, and 38.3%, respectively, while those of GRPB were 74.7%, 41.2%, and 33.4%, respectively. The percentage of easily available water (EAW, from CC to 4.90 kPa tension) and buffering water (BW, 4.91-9.81 kPa tension) in GAPB were 12.7% and 8.5%, respectively, which were a little lower than the 13.5% and 8.8% in GRPB. The pH and EC were not different significantly, but cation exchange capacity was different between the two pine barks (GAPB: pH 5.26, EC $0.61dS{\cdot}m^{-1}$, CEC $15.7meq{\cdot}100g^{-1}$; GRPB: pH 5.19, EC $0.32dS{\cdot}m^{-1}$, CEC $9.32meq{\cdot}100g^{-1}$). The concentrations of exchangeable cations in GAPB were Ca 0.32, K 0.05, Mg 0.27 and $0.12cmol+{\cdot}kg^{-1}$, whereas those in GRPB were Ca 0.28, K 0.08, Mg 0.25 and $0.09cmol+{\cdot}kg^{-1}$. The concentrations of $PO_4$-P, $NH_4$-N and $NO_3$-N were 485.8, 0.62 and $0.91mg{\cdot}L^{-1}$ in GAPB and 578, 1.00 and $0.82mg{\cdot}L^{-1}$ in GRPB, respectively, when those were analyzed in the solution of the saturated paste. The TP, CC and AFP in the two selected media were 89.3 and 76.3, and 13.0% in PM+GAPB (8:2, v/v) and 88.2, 68.2 and 20.0% in CD+GRPB (8:2), respectively. The pHs and ECs were 3.8 and $0.24dS{\cdot}m^{-1}$ in PM+GAPB which were a little lower than 5.8 and $0.65dS{\cdot}m^{-1}$ in CD+GRPB. However, the pHs analysed before and after incorporation of PNCF in the two root media did not show large differences. This is because the solubility of dolomitic lime is very low, and the pH it is expected to rise gradually when crops are cultivated int he root media. The information obtained in this study should facilitate effective formulation of root media containing pine bark.

• Journal of Food Hygiene and Safety
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• v.26 no.4
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• pp.322-329
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• 2011

This study was done to analyze the contents of minerals and vitamins to compare the measured values of minerals, vitamins with labeled values of them in food labeling and to investigate the ratio of measured values to labeled values in 437 specimen with minerals and vitamins - fortified commercial beverages and liquid teas. Content of calcium and sodium in samples after microwave digestion was analyzed with an ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometer) and vitamins were determined using by HPLC (High Performance Liquid Chromatography). The measured values of calcium were ranged 80.3~142.6% of the labeled values in 21 samples composed calcium - fortified commercial beverages and liquid teas. In case of sodium, measured values were investigated 33.9~48.5% of the labeled values in 21 sports beverages. The measured values of vitamin C, vitamin $B_2$ and niacin were ranged 99.7~2003.6, 81.1~336.7, 90.7~393.2% of the labeled values in vitamins - fortified commercial beverages and liquid teas, 57, 12, 11 samples. To support achievement of the accurate nutrition label, there must be program and initiatives for better understanding and guidances on food labelling and nutrition for food manufacture.