• Journal of Mushroom
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• v.18 no.4
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• pp.357-364
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• 2020

The grade and price of Lentinula edodes largely differs in preference depending on the product area and seasonal factors. The product amount of autumn L. edodes was higher than that of spring L. edodes, but high quality, which is divided into "Hwago" is low in preference. Mostly, the autumn L. edodes is obtained as powder; hence, it is necessary to develop a processing method to utilize its flavor and aroma at an affordable price. Additionally, we estimated the content of 𝛽-glucan, ergosterol, vitamin D₂, reducing sugars, and free amino acids and evaluated the anti-inflammatory activity of saccharification powder of log-cultivated L. edodes. In the saccharification powders obtained via 7 min of UV irradiation of log-cultivated L. edodes, 𝛽-glucan and vitamin D₂ contents were found to be the highest, whereas ergosterol content was found to be the lowest. The content of reducing sugars ranged from 62.4 mg/L to 68.2 mg/L. The free amino acids were higher in these saccharification powders than in the control. Subsequently, RAW 264.7 cells were treated with different concentrations (10, 50, 100, 200, 300, and 500 ㎍/mL) of the saccharification powders of log-cultivated L. edodes obtained via different UV irradiation time applications. The cells showed good viability; the anti-inflammatory effect was found to be the highest at 7 min UV irradiation. Therefore, 7 min of UV irradiation was determined to be the optimum condition for manufacturing saccharification powders of log-cultivated L. edodes. Hence, saccharification powders of log-cultivated L. edodes may be used as a raw material for natural sweeteners, food additives, and in the food industry.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Composites Research
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• v.34 no.4
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• pp.241-248
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• 2021

As the electric vehicle market grows, there is an issue of light weight vehicles to increase battery efficiency. Therefore, it is going to replace the battery module cover that protects the battery module of electric vehicles with high strength/high heat-resistant polymer composite material which has lighter weight from existing aluminum materials. It also aims to respond to the early electric vehicle market where technology changes quickly by combining 3D printing technology that is advantageous for small production of multiple varieties without restrictions on complex shapes. Based on the composite material mechanics, the critical length of glass fibers in short glass fiber (GF)/polycarbonate (PC) composite materials manufactured through extruder was derived as 453.87 ㎛, and the side feeding method was adopted to improve the residual fiber length from 365.87 ㎛ and to increase a dispersibility. Thus, the optimal properties of tensile strength 135 MPa and Young's modulus 7.8 MPa were implemented as GF/PC composite materials containing 30 wt% of GF. In addition, the filament extrusion conditions (temperature, extrusion speed) were optimized to meet the commercial filament specification of 1.75 mm thickness and 0.05 mm standard deviation. Through manufactured filaments, 3D printing process conditions (temperature, printing speed) were optimized by multi-optimization that minimize porosity, maximize tensile strength, and printing speed to increase the productivity. Through this procedure, tensile strength and elastic modulus were improved 11%, 56% respectively. Also, by post-processing, tensile strength and Young's modulus were improved 5%, 18% respectively. Lastly, using the FEA (finite element analysis) technique, the structure of the battery module cover was optimized to meet the mechanical shock test criteria of the electric vehicle battery module cover (ISO-12405), and it is satisfied the battery cover mechanical shock test while achieving 37% lighter weight compared to aluminum battery module cover. Based on this research, it is expected that 3D printing technology of polymer composite materials can be used in various fields in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1627-1642
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• 2021

This study aims to evaluate physical fitness according to the bone age of youth, and ultimately provide basic data for balanced development of youth through physical fitness standard indicators according to the bone age. A total of 730 youth aged 11 to 13 years in bone age and 11 to 13 years in chronological age were selected as subjects; and after taking X-ray films to calculate the bone age, they were evaluated by using the TW3 method. A total of 2 components in physique, which were stature and weight, were measured using a stadiometer(Hanebio, Korea, 2021) and Inbody 270(Biospace, Korea, 2019). A total of 7 components in physical fitness were measured as well, which included muscular strength (Hand Grip Strength), balance (Bass Stick Test), agility (Plate Tapping), power (Standing Long Jump), flexibility (Sit&Reach), muscular endurance (Sit-Up), and cardiovascular endurance (Shuttle Run). Descriptive statistics and independent t-test were conducted for data processing using the SPSS PC/Program(Version 26.0), and it was considered significant at the level of p< .05. The results of this study may be summarized as follow. First, the result of comparing the bone age and the chronological age of 11 to 13 years old in physical fitness, males showed significant difference in muscular strength, power, muscular endurance, and cardiovasular endurance. In females, muscular strength, balance, agility, power, flexibility, muscular endurance, and cardiovascular endurance showed significant difference. Second, physical fitness standard indicators were presented for each gender and age (11-13 years old) of youth according to the bone age; and based on this, physical fitness standard indicators, which are basic data for physical fitness evaluation according to the bone age of youth, were presented.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.575-583
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• 2021

This study aims to analyze Taekwondo trends according to news articles by year by applying topic modeling. In order to examine the Taekwondo trend through media reports, articles including news articles and Taekwondo specialized media articles were collected through Big Kinds of the Korea Press Foundation. The search period was divided into three sections: before 2000, 2001~2010, and 2011~2020. A total of 12,124 items were selected as research data. For topic analysis, pre-processing was performed, and topic analysis was performed using the LDA algorithm. In this case, python 3 was applied for all analysis. First, as a result of analyzing the topics of media articles by year, 'World' was the most common keyword before 2000. 'South and North Korea' was next common and 'Olympic' was the third commonest topic. From 2001 to 2010, 'World' was the most common topic, followed by 'Association' and 'World Taekwondo'. From 2011 to 2020, 'World', 'Demonstration', and 'Kukkiwon' was the most common topic in that order. Second, as a result of analyzing news articles before 2000 by topic modeling, topics were divided into two categories. Specifically, Topic 1 was selected as 'South-North Korea sports exchange' and Topic 2 was selected as 'Adoption of Olympic demonstration events'. Third, as a result of analyzing news articles from 2001 to 2010 by topic modeling, three topics were selected. Topic 1 was selected as 'Taekwondo Demonstration Performance and Corruption', Topic 2 was selected as 'Muju Taekwondo Park Creation', and Topic 3 was selected as 'World Taekwondo Festival'. Fourth, as a result of analyzing news articles from 2011 to 2020 by topic modeling, three topics were selected. Topic 1 was selected as 'Successful Hosting of the 2018 Pyeongchang Winter Olympics', Topic 2 was selected as 'North-South Korea Taekwondo Joint Demonstration Performance', and Topic 3 was selected as '2017 Muju World Taekwondo Championships'.

• Journal of Food Hygiene and Safety
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• v.37 no.2
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• pp.97-106
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• 2022

Listeria monocytogenes (L. monocytogenes) is one of gram-positive foodborne pathogens with a very high fatality rate. Unlike most foodborne pathogens, L. monocytogenes is capable of growing at low temperatures, such as in refrigerated foods. Thus, various physical and chemical prevention methods are used in the manufacturing, processing and distribution of food. However, there are limitations to the methods such as possible changes to the food quality and the consumer awareness of synthetic preservatives. Thus, the aim of this study was to evaluate the anti-listeria activity of lactic acid bacteria (LAB) isolated from kimchi and characterize the bacteriocin produced by Lactococcuslactis which is one of isolated strains from kimchi. The analysis on the anti-listeria activity of a total of 36 species (Lactobacillus, Weissella, Lactobacillus, and Lactococcus) isolated from kimchi by the agar overlay method revealed that L. lactis NJ 1-10 and NJ 1-16 had the highest anti-listeria activity. For quantitatively analysis on the anti-listeria activity, NJ 1-10 and NJ 1-16 were co-cultured with L. monocytogenes in Brain Heat Infusion (BHI) broth, respectively. As a result, L. monocytogenes was reduced by 3.0 log CFU/mL in 20 h, lowering the number of bacteria to below the detection limit. Both LAB strains showed anti-listeria activity against 24 serotypes of L. monocytogenes, although the sizes of clear zone was slightly different. No clear zone was observed when the supernatants of both LAB cultures were treated with proteinase-K, indicating that their anti-listerial activities might be due to the production of bacteriocins. Heat stability of the partially purified bacteriocins of NJ 1-10 and NJ 1-16 was relatively stable at 60℃ and 80℃. Yet, their anti-listeria activities were completely lost by 60 min of treatment at 100℃ and 15 min of treatment at 121℃. The analysis on the pH stability showed that their anti-listeria activities were the most stable at pH 4.01, and decreased with the increasing pH value, yet, was not completely lost. Partially purified bacteriocins showed relatively stable anti-listeria activities in acetone, ethanol, and methanol, but their activities were reduced after chloroform treatment, yet was not completely lost. Conclusively, this study revealed that the bacteriocins produced by NJ 1-10 and NJ 1-16 effectively reduced L. monocytogenes, and that they were relatively stable against heat, pH, and organic solvents, therefore implying their potential as a natural antibacterial substance for controlling L. monocytogenes in food.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.253-261
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• 2022

The purpose of this study is to investigate the relationship between transformative leadership, team cohesion, and team performance of Chinese university taekwondo leaders. Specifically, it is to investigate the effect of transformative leadership on team cohesion and team performance and to verify the mediating effect of team cohesion in the relationship between transformative leadership and team performance. In order to achieve the research purpose, a total of 350 people were sampled after setting taekwondo leaders at Chinese universities as a population. The measurement tool used in this study was a questionnaire consisting of 5 items on demographic characteristics, a total of 19 questions on transformational leadership, 10 questions on team cohesion, and 4 questions on team performance. The validity of the questionnaire was verified through exploratory factor analysis, and the reliability was verified through reliability analysis. The reliability Cronbach's α of the questionnaire was found to be α=0.755-0.799 for transformative leadership, α=0.848, and α=0.740 for team performance. As the data processing method, exploratory factor analysis and reliability analysis, one-way analysis (one-way ANOVA), and multiple regression analysis were used using SPSS WIN. The conclusions derived through the above research methods and procedures are as follows. First, the transformative leadership of Taekwondo leaders at Chinese universities influenced team cohesion. Second, the transformative leadership of Taekwondo leaders at Chinese universities influenced team performance. Third, the team cohesiveness of Taekwondo leaders at Chinese universities influenced team performance. Fourth, the transformative leadership of Taekwondo leaders at Chinese universities not only directly affects team performance, but also indirectly affects team cohesion. Therefore, it is believed that Chinese Taekwondo players will help improve their performance by affecting team cohesion and team performance for the best games through the leader's variable leadership.