• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.410-418
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• 2023

Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.

• Journal of Science Education
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• v.36 no.2
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• pp.381-393
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• 2012

This study was analyzed that what kind of elements for STEAM, except scientific commonsense, are contained in 2009 revised chemistry textbooks I for high school students. So first, elements of STEAM in textbooks were examined by following three sections; by publishing company, each unit and area of textbook. For reference, new sub-elements of STEAM were set because existing elements of STEAM is incongruent with current textbooks. As a result, most chemistry textbooks included elements of STEAM properly for inter-related learning with the other fields. Every textbook had its unique learning methods for utilizing elements of STEAM and they were unified as one way. Depending on textbooks, learning methods were little bit different from the others. Also, detailed elements of STEAM contained in textbooks were classified just 14 types. And they were even focused on a few elements according to sort of textbook. Thus, it seemed that there was a certain limitation of current education of STEAM in chemistry Field. By the unit, according to the curriculum, contained elements of STEAM were different. Almost all elements of STEAM were located in I section. Consequently, it is difficult to include elements of STEAM if mathematics or history were not existed in curriculum. Lastly, by the area, most of all elements of STEAM were included in reference section. Almost all elements of STEAM were focused on art and culture. Thus, STEAM was used for utilization about chemical knowledge in substance. Otherwise, convergence training for approach method was not enough in chemical knowledge.

• Food Science of Animal Resources
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• v.25 no.4
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• pp.365-372
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• 2005

This study was carried out to investigate the Physiochemical Properties of loin and rump in the native horse meat from Jeju. In the analysis of chemical composition of loin and rump, the result showed $72.2\%\;and\;73.8\%$ in moisture content $20.1\%\;and\;21.2\%$ in crude protein, $2.42\%\;and\;3.08\%$ in crude Int and $0.13\%\;and\;0.14\%$ in crude ash respectively. Glutamic acid was 3,275mg/100g and 3,577mg/100g in loin and rump each and it had highest result in amino acid analysis. K content was 388.0mg/100g which showed highest result in mineral analysis and next contents were P>Na>Mg>Ca. Oleic acid had highest result in fatty acid composition which were $62.64\%\;and\;63.77\%$ in loin and rump respectively. Cholesterol content of loin and rump were 43.25 and 43.57 mg/100g but showed no significant differences to the part. pH of loin and rump were 5.60 and 5.75 which had no significant differences. Loin had Higher result than that of rump with no significant differences in WHC and springiness of texture analysis. Redness of rump was higher than that of loin. In the sensory evaluation, there were significant differences in the color and odor. Loin had higher result than that of rump in the overall palatability but showed no significant differences. With the result of this experiment native horse meat from Jeju could be understood as good meat resources.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• 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.