• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.54 no.4
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• pp.315-323
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• 2018

Fossil fuel combustion during fishing activities is a major contributor to climate changes in the fishing industry. The Tier1 methodology calculation and on-site continuous measurements of the greenhouse gas were carried out through the use of fuel by the coastal and offshore gillnet (blue crabs and yellow croaker) and trap (small octopus and red snow crab) fishing boats in Korea. The emission comparison results showed that the field measurements are similar to or slightly higher than the Tier1 estimates for coastal gillnet and trap. In offshore gillnet and trap fisheries, Tier1 estimate of greenhouse gases was about $1,644-13,875kg\;CO_2/L$, which was more than the field measurement value. The $CO_2$ emissions factor based on the fuel usage was $2.49-3.2kg\;CO_2/L$ for coastal fisheries and $1.46-2.24kg\;CO_2/L$ for offshore fisheries. Furthermore, GHG emissions per unit catch and the ratio of field measurement and Tier1 emission estimate were investigated. Since the total catch of coastal fish was relatively small, the emission per unit catch in coastal fisheries was four to eight times larger. The results of this study could be used to determine the baseline data for responding to changes in fisheries environment and reducing greenhouse gas emission.

• Journal of the Korean Society of Systems Engineering
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• v.14 no.2
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• pp.24-32
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• 2018

System Complexity one of the most common cause failure of the projects, it leads to a lack of understanding about the functions of the system. Hence, the model is developed for communication and furthermore modeling help analysis, design, and understanding of the system. On the other hand, the text-based specification is useful and easy to develop but is difficult to visualize the physical composition, structure, and behaviour or data exchange of the system. Therefore, it is necessary to transform system description into a diagram which clearly depicts the behaviour of the system as well as the interaction between components. According to the International Atomic Energy Agency (IAEA) Safety Glossary, The safety system is a system important to safety, provided to ensure the safe shutdown of the reactor or the residual heat removal from the reactor core, or to limit the consequences of anticipated operational occurrences and design basis accidents. Core Protection Calculator System (CPCS) in Advanced Power Reactor 1400 (APR 1400) Nuclear Power Plant is a safety critical system. CPCS was developed using systems engineering method focusing on Departure from Nuclear Boiling Ratio (DNBR) calculation. Due to the complexity of the system, many diagrams are needed to minimize the risk of ambiguities and lack of understanding. Using Model-Based Systems Engineering (MBSE) software for modeling the DNBR algorithm were used. These diagrams then serve as the baseline of the reverse engineering process and speeding up the development process. In addition, the use of MBSE ensures that any additional information obtained from auxiliary sources can then be input into the system model, ensuring data consistency.

• The Korean Journal of Franchise Management
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• v.10 no.4
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• pp.31-42
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• 2019

Purpose : This study can provide various implications for the franchisors to expand activities related to franchise support or to develop andoperate an education program for foodservice franchise owners. Research design, data, and methodology : For those purpose, first, the literatureand literature related to the competency of domestic franchise owner were collected and reviewed through the Korea Education and Research Information Service (RISS). Second, the questionnaire was prepared based on the theoretical basis prepared through previous studies. Based onthe foodservice franchise owner's competency model presented by Kim & Lee (2019b), 13 franchise owner's competencies were marked with both 'What is' levels and 'What should be' levels. Therefore, the total questionnaire consists of 26 questions. Third, questionnaires were distributed and collected. This study used data from 55 surveys which were gathered from foodservice franchise owners in Seongnam-si. SPSS 25.0 was used to analyze the collected survey data. Descriptive and frequency analysis were conducted to analyze the demographic characteristics of the study subjects. Next, we conduct a t-test to analyze the difference between the level of 'What is' competencies by the franchise owners and the level of 'What should be' competencies. Descriptive statistics were used to derive the priorities of the 'What should be' competencies. The Locus for Focus model was used to derive the priorities of the required competencies. Result : Four competencies of team leadership, teamwork and cooperation, customer service, technical·professional·managerial expertise were found to be the first to be developed. Conclusions : The conclusions of this study are as follows. First, teamwork and cooperation competnecy, and team leadership competency, which are derived from the core competencies of foodservice franchise owners, are among the leadership competencies required as managers of organizations. Second, customer service competency and ttechnical·professional·managerial expertise competency derived from the core competencies of restaurant franchise owners belong to the job competencies. Third, the results of this study suggest that the foodservice franchisors will be able that will serve as a baseline to support the foodservice franchisors and franchise owners for sustainable mutual growth by encouraging their will and encouraging them to create results.

• Journal of Korea Trade
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• v.27 no.2
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• pp.1-21
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• 2023

Purpose - The bank payment obligation is a transaction method that combines the certainty of L/C transactions with the speed of remittance payments, so the main purpose of this study is to highlight the superiority of bank payment obligation, noting the difference between bank payment obligation and L/C transactions. In addition, we would like to examine how bank payment obligations can actually be applied to support various valuable proposals such as post-shipment and post-shipment finance according to the payment process.. Design/methodology - This study focused on literature based on data from ICC and SWIFT along with previous domestic and international studies. In terms of a research method, a literature review was adopted with electronic trade-related books and journals and policy-related reports from international trade-related agencies. Findings - Unlike L/C transaction, BPO transaction verify the data inquiry process based only on the combination result of the established baseline and dataset. Accordingly, it is superior to L/C transaction in that there is no confrontation between the parties over the results of the inquiry, and clear transactions are possible according to the principle of proof after prepayment. In addition, unlike credit transactions, data inconsistency acceptance procedures confirm payment obligations in consideration of importers' intentions. As a result, as long as trade documents are in the hands of exporting countries, flexible document disposition is possible in response to the situation after payment, which is more advantageous than L/C transaction. Originality/value - Specifically, from the importer's point of view, BPO transactions have the advantage of reducing the manpower required to prepare and review trade documents and processing transaction negotiations with exporters advantageously due to the strength of payment obligations. From the perspective of the exporter, it has the advantage of enabling rapid recovery of trade payments and reducing the risk of importer's cancellation of transactions or content change. From the perspective of participating banks, it is possible to strengthen relations with importer and obtain high commission income by increasing the role of bank reduced by reducing L/C transaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• The Journal of Bigdata
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• v.9 no.1
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• pp.61-73
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• 2024

Class imbalance is one of the significant challenges in deep learning tasks, particularly pronounced in areas with limited data. This study proposes a new approach that utilizes minimal labeled data for effectively classifying tomato leaf diseases. We introduced a recursive learning method using the YOLOv8 model. By utilizing the detection predictions of images on the training data as additional training data, the number of labeled data is progressively increased. Unlike conventional data augmentation and up-down sampling techniques, this method seeks to fundamentally solve the class imbalance problem by maximizing the utility of actual data. Based on the secured labeled data, tomato leaves were extracted, and diseases were classified using the EfficientNet model. This process achieved a high accuracy of 98.92%. Notably, a 12.9% improvement compared to the baseline was observed in the detection of Late blight diseases, which has the least amount of data. This research presents a methodology that addresses data imbalance issues while offering high-precision disease classification, with the expectation of application to other crops.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.25-30
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• 1998

Time-lapse crosswell seismic data, recorded before and after the cavity filling, showed that the filling increased the velocity at a known cavity zone in an old mine site in Inchon area. The seismic response depicted on the tomogram and in conjunction with the geologic data from drillings imply that the size of the cavity may be either small or filled by debris. In this study, I attempted to evaluate the filling effect by analyzing velocity measured from the time-lapse tomograms. The data acquired by a downhole airgun and 24-channel hydrophone system revealed that there exists measurable amounts of source statics. I presented a methodology to estimate the source statics. The procedure for this method is: 1) examine the source firing-time for each source, and remove the effect of irregular firing time, and 2) estimate the residual statics caused by inaccurate source positioning. This proposed multi-step inversion may reduce high frequency numerical noise and enhance the resolution at the zone of interest. The multi-step inversion with different starting models successfully shows the subtle velocity changes at the small cavity zone. The inversion procedure is: 1) conduct an inversion using regular sized cells, and generate an image of gross velocity structure by applying a 2-D median filter on the resulting tomogram, and 2) construct the starting velocity model by modifying the final velocity model from the first phase. The model was modified so that the zone of interest consists of small-sized grids. The final velocity model developed from the baseline survey was as a starting velocity model on the monitor inversion. Since we expected a velocity change only in the cavity zone, in the monitor inversion, we can significantly reduce the number of model parameters by fixing the model out-side the cavity zone equal to the baseline model.

• Journal of Distribution Science
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• v.12 no.4
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• pp.55-63
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• 2014

Purpose - Brands and products often play key roles in enabling consumers to experience a good attitude, resulting in mentally enacting a specific prototype and reliving the experience by retelling a specific story. Brand storytelling can function as an important tool for managing the brand. To successfully apply a firm's brand storytelling, it is important to prove the effectiveness of storytelling. Therefore, by utilizing the research of Escalas (1998) and Fog et al. (2005), a list of measurements for storytelling component quality (SCQ) was applied. In addition, customer attitudes toward brand storytelling were tested. In particular, if customers encounter a dynamic and interesting story, although the brand is not widely known, they can be in communion with the brand and establish an emotional connection (Hill, 2003). Thus, brand reputation was divided into two levels (high vs. low), and the difference in effectiveness between storytelling component quality and consumers' advertisement attitude, brand attitude, and purchasing intention was examined. Research design, data, and methodology - By using the measurement list used in Choi, Na, and Hwang (2013), 12 categories in the level of message quality, conflict quality, character quality, and plot quality were measured. In addition, categories of brand reputation, advertisement attitude, brand attitude, and purchasing intention were measured. The study was based on 181 final survey samples targeting undergraduate and graduate students in Gwangju Metropolitan City. Results - Consumer responses toward storytelling were researched in the context of brand characteristics or product attributes, such as brand reputation, differentiated from extant simple effects of storytelling. Some brands with high reputation enjoy a halo effect due to prior learning, while other brands with comparatively low reputation have trouble generating positive responses despite attempts to enhance the level of reputation or induce favorable attitudes. Although not all due to the component quality of storytelling, the case of brands with low reputation exerted more positive impact on consumer attitudes than did brands with high reputation. As mentioned earlier, consumer evaluation of the component quality of storytelling was categorized into advertising attitudes, brand attitudes, and purchase intention for this study; this provides managerial implications in other ways. The results imply that an effective application of storytelling could be an important emotional tool for the development of both brands with low brand awareness and of well-known brands. Finally, this study serves to increase consumers' understanding and ability in interpreting brand stories that marketers tell about themselves, as well as to highlight differential experiences with products by level of brand hierarchy. Conclusions - This research aimed to provide an objective guideline for storytelling component quality while considering brand awareness. Thus, brand reputation was considered for proving the baseline effectiveness of storytelling, and this study provided directions for strategic establishment of storytelling. Based on this, we conclude that in further studies, it will be necessary to systematically manage brand story by considering other situation variables and various story patterns, and studying their differences.

• Journal of Korean Academy of Nursing
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• v.30 no.6
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• pp.1437-1454
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• 2000

This study was designed to identify the attitudes of the people on organ donation and transplantation. The purpose of this study was to provide data to help inspire organ donation, and promote registration yield so donor candidates will have more favorable recipients through Q-methodology. A Q-sample was developed through a review of the literature and interviews. Thirty-three statements made up the final Q-sample. The P-sample consisted of twenty-eight subjects, excluding chronic organic disorder. The Q-sorts by each subject were coded and analyzed with the QUNAL computer program. The results were as follows: This study discovered five different types of organ donation and transplantation of twenty- eight subjects. Type I is 'utilitarian.' The people of this type consider human life very valuable and they recognize that organ transplantation is an affirmative medicine that should be performed to extend human life. They believe that are saving others' lives by donating organs. Type II is 'sardonist.' The people of this type approve of organ transplantation usefulness, but they have no intention of participating in the program because of it may trample on human rights. Type III is 'individualist.' The people of this type consider it proper for the activation of organ transplantation by the legal system. They believe that organ donation a valuable too, but needs support through social benefits to donors. Yet, they have not intention of doing what they propose. Type IV is 'familist.' The people of this type have strong attachments to life but they think that organ donation and transplantation should be done between within a family. Type IV is disposition of family intensive consideration rather than altruistic and utilitarianism. Type V is 'deontologist.' The people of this type recognize the benefits of transplantation, but have a negative opinion of activation. They worry about ethical and social problems occurring in the development of modern medicine. They believe that death is the only natural end to life, so they have strong negative opinions of euthanasia and brain death compared to other types. They regard transplantation to be a non-human behavior, because it involves a removing organs and breaking the boundary of death. The findings of this study are only preliminary and serve as a baseline to understanding the subjectivity of individuals on organ donation and transplantation. Therefore, the subjectivity of the five types will be applied to formulate the educational programs and public relations strategies for organ donation because the public's awareness toward organ donation is closely related to their values, beliefs, and attitudes.