• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.285-294
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• 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 Service Research and Studies
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• v.11 no.2
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• pp.37-66
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• 2021

This study aims to provide self-development opportunities to hair salons service workers through e-learning and provide the foundation of sustainable hair salons management by cultivating good talents to hair salons service business executives. In particular, the factors affecting e-learning achievement are identified according to learner characteristics to see whether these factors affect the satisfaction of e-learning learners and also affect the performance of management. The results of the study are summarized as follows. As a result of hypotheses testing on the relationship between e-learning learning environment and e-learning satisfaction, it was found that the higher the level of e-learning content quality is, the higher the satisfaction of e-learning is, the higher the satisfaction of e-learning is, and that the higher the quality level of the support infrastructure is, the higher the satisfaction of e-learning is. The results of the hypotheses testing on the moderating effect of learner factors showed that the influence of the quality of the support infrastructure on the e-learning satisfaction differs according to the level of the learner's goal consciousness. However, it was found that the influence of content quality on e-learning satisfaction according to the level of the learners goal awareness, the influence of content quality on e-learning satisfaction according to the level of the aggressiveness of the learners learning attitude, and the influence of the quality of the support infrastructure on the e-learning satisfaction according to the level of the aggressiveness of learners learning attitude were found to identically demonstrate no moderating effects. The results of hypotheses testing on the relationships among e-Learning performance show that the higher the satisfaction of e-learning was, the higher the customer orientation was, and the higher the satisfaction of e-learning was, the higher the contribution of management performance was, and the higher the customer orientation was, the higher the contribution of management performance was. The implications of this study are as follows. First, the actual path of realiting e-learning performance could be identified that is this study provided organizational decision makers involved in the hair salons service operations with practical guidance for the introduction and expansion of successful educational systems. Second, the e-learning environment derived from the theoretical background is different from the e-learning environment required by the learners.

• Journal of Food Hygiene and Safety
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• v.38 no.3
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• pp.79-88
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• 2023

Trifludimoxazin is a triazinone herbicide that inhibits the synthesis of protoporphyrinogen oxidase (PPO). The lack of PPO damages the cell membranes, leading to plant cell death. An official analytical method for the safety management of trifludimoxazin is necessary because it is a newly registered herbicide in Korea. Therefore, this study aimed to develop a residual analysis method to detect trifludimoxazin in five representative agricultural products. The EN method was established as the final extraction method by comparing the recovery test and matrix effect with those of the QuEChERS method. Various sorbent agents were used to establish the clean-up method, and no differences were observed among them. MgSO₄ and PSA were selected as the final clean-up conditions. We used LC-MS/MS considering the selectivity and sensitivity of the target pesticide and analyzed the samples in the MRM mode. The recovery test results using the established analysis method and inter-laboratory validation showed a valid range of 73.5-100.7%, with a relative standard deviation and coefficient of variation less than 12.6% and 14.5%, respectively. Therefore, the presence of trifludimoxazin can be analyzed using a modified QuEChERS method, which is widely available in Korea to ensure the safety of residual insecticides.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• The Korean Journal of Nuclear Medicine
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• v.37 no.2
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• pp.110-119
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• 2003

Purpose: Radioiodine (I-131) therapy is an effective modality to reduce both recurrence and mortality rates in differentiated thyroid cancer. Whether higher doses shows higher therapeutic responses was still debatable. The purpose of this study was to validate curve-fitting (CF) method measuring maximum permissible dose (MPD) by a biological dosimetry using metaphase analysis of peripheral blood lymphocytes. Materials and Methods: Therapeutic effects of MPD was evaluated in 58 patients (49 females and 9 males, mean age $50{\pm}11$ years) of papillary thyroid cancer. Among them 43 patients were treated with ${\Leq}7.4GBq$, while 15 patients with ${\geq}9.25GBq$. The former was defined as low-dose group, and the latter high-dose group. Therapeutic response was defined as complete response when complete disappearance of lesions on follow-up I-131 scan and undetectable serum thyroglobulin levels were found. Statistical comparison between groups were done using chi-square test. P value less than 0.05 was regarded as statistically significant. Results: MPD measured by CF method using tracer and therapeutic doses were $13.3{\pm}1.9\;and\;13.8{\pm}2.1GBq$, respectively (p=0.20). They showed a significant correlation (r=0.8, p<0.0001). Exposed doses to blood measured by CF and biological methods were $1.54{\pm}0.03\;and\;1.78{\pm}0.03Gy$ (p=0.01). They also showed a significant correlation (r=0.86, p=0.01). High-dose group showed a significantly higher rate of complete response (12/15, 80%) as compared to the low-dose group (22/43, 51.2%) (p=0.05). While occurrence of side effects was not different between two groups (40% vs. 30.2%, p=0.46). Conclusion: Measurement of MPD using CF method is reliable, and the high-dose I-131 therapy using MPD gains significantly higher therapeutic effects as compared with low-dose therapy.

• Journal of Distribution Research
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• v.17 no.3
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• pp.1-24
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• 2012

Franchises have recently emerged as the most rapidly expanding industry positioned to create a large impact in the domestic economic. The Chinese franchise industry developed rapidly in the period prior and subsequent to WTO accession with more than 50% of new franchises brands emerging since 2000. M&A transactions in the Chinese franchise industry have progressed actively. In the period from 2005-2007, due to the wholesale and retail market opening in accordance with the guidelines laid forth within the MOU by the WTO the Chinese franchise market is now the largest market in the world all despite a short history of only 20 years. The amount of franchise market research on China is disproportional to its current size and development potential. Beginning in the 1990s, market research conducted by the International Franchise Association focused on emerging markets in Eastern Europe and China. While the research dealt with the Chinese investment environment, it insufficiently explained the market region and cultural environment. The purpose of this research is (i) to investigate the determinants of the performance of franchise systems in China and (ii) new contract renewals based on performance factors. This study will complement existing research in terms of the franchisee perspective. This study may also prove of the benefit to the franchise companies entering the Chinese franchise market enabling them to develop an effective strategy. This study shows that support, incentives, and system standardization by franchisor yielded a positive effect on management performance. This is consistent with previous studies by Shin (2000) and Kim (2008) targeting Korean franchises. Therefore, in the Chinese market, the franchisor must focus on support, incentives, and system standardization rather than concentrate only on the recruitment of franchisees in order to improve revenue. Hypotheses regarding franchisor control have been dismissed in existing research, in the opinion of this study, due to their complexity and inability to control the merchant as a one-kind-assessment-standard. Our findings show that the franchisees' financial condition, management ability and entrepreneurial spirit, among franchisee's characteristics, have a positive effect on franchisor's business performance and satisfaction for the franchisee. This is consistent with previous studies on headquarters' management performance of Lussier (1996), Heo and Jang (2008), and franchisees' financial condition, management ability and entrepreneurial spirit effect on franchisor's satisfaction of Weaven and Franzer (2007), Kim (2009), Han (2009), and Yoon etc. (2008). Therefore, when permitting a franchisee, financial condition, management ability, entrepreneurship of the franchisee should be carefully considered. Among relational factors between franchisor and franchisee, trust has the positive influence on the management performance of the franchisor while conflict has a negative effect. However, trust, commitment and conflict factors have been shown not to have any impact on the satisfaction of the franchise headquarters. This result is consistent with the previous studies of Pavlou and Ba (2000), Morrison (1999), Weaven and Frazer (2007), Kim and Park (1994), Sohn (2007) which show that trust between franchisor and the franchisees have a positive effect and that conflict has a negative impact on franchisor's management performance. Other factors causing a negative effective on the franchisor's management performance are a rapid environmental changes and uncertainty in the business. This is consistent with Campbell et al (2007), Kim and Kim (2009), Han and Baek (2008). Finally, the high management performance and satisfaction of the franchise headquarters has a positive effect on the intention of franchise renewal. In the case of large markets such as China, the franchisor's strategy and the role is very important. In this study, we also investigated the characteristics of franchisor and franchisee, relationship, and environmental uncertainty affecting on the management performance and satisfaction of franchisor. Recently, Korean franchises are attempting to enter foreign markets through the rise in popularity of Korean culture and entertainment commonly referred to as the Korean wave. This study provides recommendations for Korean franchises intending on entering the Chinese market. First, in order to achieve stable profits, the franchise corporation needs to support the operation of the individual franchisee through incentives and standardization of services. Second, because trust between the franchisor and franchisee has a positive effect on management performance, on-going discussion and cooperation is necessary to reduce the level of conflict.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.