• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.41-59
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• 2022

The timely procurement of military supplies is essential to maintain the military's operational capabilities, and contract work is the first step toward timely procurement. In addition, rapid signing of a contract enables consumers to set a leisurely delivery date and increases the possibility of budget execution, so it is essential to improve the contract process to prevent early execution of the budget and transfer or disuse. Recently, research using big data has been actively conducted in various fields, and process analysis using big data and process mining, an improvement technique, are also widely used in the private sector. However, the analysis of contract work in the military is limited to the level of individual analysis such as identifying the cause of each problem case of budget transfer and disuse contracts using the experience and fragmentary information of the person in charge. In order to improve the contract process, this study analyzed using the process mining technique with data on a total of 560 contract tasks directly contracted by the Department of Finance of the Air Force Logistics Command for about one year from November 2019. Process maps were derived by synthesizing distributed data, and process flow, execution time analysis, bottleneck analysis, and additional detailed analysis were conducted. As a result of the analysis, it was found that review/modification occurred repeatedly after request in a number of contracts. Repeated reviews/modifications have a significant impact on the delay in the number of days to complete the cost calculation, which has also been clearly revealed through bottleneck visualization. Review/modification occurs in more than 60% of the top 5 departments with many contract requests, and it usually occurs in the first half of the year when requests are concentrated, which means that a thorough review is required before requesting contracts from the required departments. In addition, the contract work of the Department of Finance was carried out in accordance with the procedures according to laws and regulations, but it was found that it was necessary to adjust the order of some tasks. This study is the first case of using process mining for the analysis of contract work in the military. Based on this, if further research is conducted to apply process mining to various tasks in the military, it is expected that the efficiency of various tasks can be derived.

• Korean Journal of Plant Resources
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• v.35 no.4
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• pp.435-444
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• 2022

Hibiscus hamabo Sieb. et Zucc. (yellow hibiscus) is a deciduous semi-shrub plant and mainly growing in Jeju Island. This is known the unique wild hibiscus genus and classified as an 2nd grade of endangered plant for Korean Red List. In previous studies, properties of germination, ecological, genetical and salt resistance have been reported. In this study, we investigated mass-proliferated adventitious root using bioreactor, antioxidant and whitening effects to conduct functional ingredients. Yellow hibiscus were collected from Gujwa, Jeju by prior permission and they were introduced by explant type and various medium composition after surface sterilization. As a result, seed response rates were evaluated at range of 51.17~51.83%, in terms of comprehensive efficiency of shoot and root formation. In the case of adventitious root propagation condition was confirmed in half strength Murashige and Skoog medium salts, 30 mg/L sucrose, and 2 mg/L indole-3-butyric acid for 8 weeks in 5,000 mL bioreactor. We also compared between relationship with biomass and secondary metabolites accumulation by total phenolics content, the flavonoid content, DPPH free radical scavenging activity and melanin content. The results indicated that adventitious root mass proliferation, antioxidant and whitening effect could develop value of the high-quality cosmeceutical ingredient and further metabolite studies.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.3
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• pp.252-258
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• 1985

An experiment was conducted at the Yeongnam Crop Experiment Station to obtain basic informations about cultural techniques for high yielding by manipulating plant spacing using two rice cultivars, Samgangbyeo (Indica/Japonica type) and Nakdongbyeo (Japonica type), and four plant spacings, 10${\times}$10cm, 20${\times}$20cm 30${\times}$30cm and 40${\times}$40cm, with 4 kinds of seedling number per hill, 1,3,5 and 7, respectively. High photosynthetic efficiency (Eu) exhibited at the Samgangbyeo compared to Nakdongbyeo regardless of plant spacings and seedling numbers. For Samgangbyeo, Eu value was the highest at the 20${\times}$20cm plant spacing and five seedlings and seven seedlings per hill showed high Eu values at 10${\times}$10cm plant spacing and 20${\times}$20cm plant spacing, respectively, while other plant spacings were not significantly differed among seedling numbers. For Nakdongbyeo, however, one seedling plot obtained high Eu value at the 10${\times}$10cm plant spacing while this Eu value increased as the seedling number per hill increased in other plant spacings. There was a high positive correlation between rice grain yield and total competition index for both cultivars while kind of relationships differed in these two cultivars; linear relationship for Samgangbyeo and exponential relationship for Nakdongbyeo, respectively. Competition index between rice hill was more significant than within rice hill for Samgangbyeo while both competition indexs were important for Nakdongbyeo to increase rice yield.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.199-210
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• 2022

This study aimed to develop a cropping system to use limited crop-land with optimum efficiency, while considering management from farmers. To establish the cropping system involving a two-year rotation of three crops, three types of cropping system were evaluated in Suwon (Seogcheon series) and Anseong (Geumcheon series) in the middle plain area using six crops from 2018 to 2019: maize-perilla-onion, potato-sesame-garlic, and maize-sesame-onion. The crop productivity and income of the cropping systems involving food-, oilseed-, and horticultural crops were analyzed, and the optimal cropping system was reviewed. The total yield of each crop was as follows: maize 1,281 kg, potato 4,837 kg, perilla 125 kg, sesame 120 kg, onion 6,503 kg, and garlic 1,027 kg per 10a. However, in terms of gross profit, the potato was more than 3.8 times more profitable than corn, sesame was 1.8 times more profitable than perilla, and garlic was more than 2.8 times more profitable than onions. As a result, in terms of net income, the potato-sesame-garlic cropping system produced the highest income per unit area. Sesame seedlings were planted after the potato harvest, thereby solving the problem of competition between the first and last crops. Overall, this study confirmed that the potato-sesame-garlic cropping system, a two-year rotation of three crops, contributed to the improvement of upland crop productivity and farmers' income and was an overall effective cropping system.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• 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.

• Journal of Practical Agriculture & Fisheries Research
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• v.11 no.1
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• pp.79-94
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• 2009

The research project was conducted to develop a paper bagging machine for grape. This technology was aimed to highly reduce a labor for paper bagging in grape and bakery. In agriculture labor and farm population has rapidly decreased since 1980 in Korea so there was so limit in labor. In particular there is highly population in women and old age at rural area and thus labor cost is so high. Therefore a labor saving technology in agricultural sector might be needed to be replaced these old age with mechanical and labor saving tool in agriculture. The following was summarized of the research results for development of a paper bagging machine for grape. 1. Development of a new paper bagging machine for grape - This machine was designed by CATIA VI2/AUTO CAD2000 programme. - A paper bagging machine was mechanically binded a paper bag of grape which should be light and small size. This machine would be designed for women and old age with convenience during bagging work at the field site. - This machine was manufactured with total weight of less than 350g. - An overage bagging operation was more than 99% at the actual field process. - A paper bagging machine was designed with cartridge type which would be easily operated between rows and grape branches under field condition. - The type of cartridge pin was designed as a C-ring type with the length of 500mm which was good for bagging both grape and bakery. - In particular this machine was developed to easily operated among vines of the grape trees. 2. Field trials of a paper bagging machine in grape - There was high in grape quality as compared to the untreated control at the application of paper bagging machine. - The efficiency of paper bagging machine was 102% which was alternative tool for the conventional. - The roll pin of paper bagging machine was good with 5.3cm in terms of bagging precision. - There was no in grape quality between the paper bagging machine and the conventional method. - Disease infection and grape break was not in difference both treatments.

• (The) Research of the performance art and culture
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• no.43
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• pp.357-380
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• 2021

Akjang yoram 『樂章要覽』 is a book that summarizes only the important contents from the Akjang 樂章. Akjang 樂章 is arranged in the first half, and score 樂譜 is arranged in the second half. It seems that Akjang yoram 『樂章要覽』 passed through a total of four stages through the time when the handwriting and the lyrics were written. The presence of various handwriting and traces of modifications means that it has been passed through by several people, so it is not unrelated to the fact that several traces remain on the back of the cover of Akjang yoram 『樂章要覽』. The first part of the Akjang 樂章 is a method of presenting the name and lyrics of the accompanying music based on the ritual procedure, and in particular, the lyrics are written in Chinese characters and Hangeul sounds to improve readability. The score in the second half complies with the ritual procedures, but boldly omits overlapping melodies, and is composed based on the music, and various symbols are used to capture the expression of court music. This structure is a reflection of the direction we practiced to harmonize with the music after prior ritual procedures and diction. This was a device to increase the efficiency of music education and music practice for the court musician. The characteristics of the musical pieces are that they consist of essential musical pieces that must be mastered as musicians. In addition, the name Kim Hyung-sik 金亨植 is noted on the back cover of Akjang yoram 『樂章要覽』, and he was a court musician who was active in the age of King Sunjo 純祖. In other words, the musical pieces included in Akjang yoram 『樂章要覽』 are the core repertoire played by court musicians like Kim Hyung-sik 金亨植. Akjang yoram 『樂章要覽』 is a 'music practice booklet' containing the daily life of court musicians. Akjang yoram 『樂章要覽』 is a booklet designed for the purpose of teaching the court musicians to sing while correctly pronouncing the lyrics in major ceremonies. It is even more noteworthy in that Kim Hyung-sik 金亨植 was an owner. In addition to the fact that Kim Hyung-sik's name remains, and in the practicality of being used by various court musicians reflecting and modifying the changes of the times, it is meaningful in that it contains the path of court musicians who spent a lot of time and time to transmit court music.