• Fisheries and Aquatic Sciences
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• v.25 no.5
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• pp.276-286
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• 2022

This study is focused on the effect of phycobiliprotein extraction of Gracilaria on the quality of common carp burgers, and the application of nanoliposomes containing pigment in the improvement of its antimicrobial and antioxidant activity of burgers during refrigerated storage in 18 days. Burgers were incorporated with phycobiliprotein and liposomal phycobiliprotein (2.5% and 5% w/w), and their chemical and microbial changes in terms of pH, peroxide value (PV), thiobarbituric acid (TBA), total volatile basic nitrogen (TVB-N), total viable counts (TVC), psychrotrophic bacterial counts (PTC), and sensory characteristics were evaluated. Results presented a nanoliposome size of about 515.5 nm with capable encapsulation efficiency (83.98%). Our results showed non-encapsulated phycobiliprotein could delay the deterioration of common carp burgers, as a reduction in PV, TBA, and TVB-N, TVC, and PTC values in burgers treated with free and nano encapsulated phycobiliprotein. Moreover, the potential of phycobiliprotein was improved when it was encapsulated into chitosan coated liposomes. Burgers treated with 5% nanoliposomes displayed the lowest amount of lipid oxidation and microbial deterioration in comparison to others during storage. According to chemical, microbial and sensory evaluation, the shelf life of common carp burgers was increased in samples treated with encapsulated phycobiliprotein at 2.5% and 5%, as compared to the control (p ≤ 0.05).

• Plant Journal
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• v.18 no.1
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• pp.43-49
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• 2022

In this study, the operational characteristics of the 7.48 MW fuel cell power plant consisting of 17 units of 440 kW Phosphoric Acid Fuel Cell (PAFC) in operation since its commercial operation in December 2017 were explained and the heat recovery process of the plat using Organic Rankine Cycle (ORC)was simulated. The fuel cell system performance improvement and economic assessment were analyzed by calculating the amount of heat recovery and electric power available when connecting a 125 kW XLT Model ORC for hot water heat sources with 105℃, 40.8 t/h. The result of the study shows that integrating the 125 kW ORC to PAFC power plant would improve generating efficiency by about 0.6% through annually 851,472 kWh of electricity produced by ORC, and fuel cell and ORC integrated systems were calculated to have a 0.35% higher Internal Return Ratio and more Net Present Value of 1,249 million KRW than not installing ORC despite installation costs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.1-13
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• 2021

In this study, a time series analysis technique was applied to calibrate and predict traffic data for various purposes, such as planning, design, maintenance, and research. Existing algorithms have limitations in application to data such as traffic data because they show strong periodicity and seasonality or irregular data. To overcome and supplement these limitations, we applied the SARIMA model, an analytical technique that combines the autocorrelation model, the Seasonal Auto Regressive(SAR), and the seasonal Moving Average(SMA). According to the analysis, traffic volume prediction using the SARIMA(4,1,3)(4,0,3) 12 model, which is the optimal parameter combination, showed excellent performance of 85% on average. In addition to traffic data, this study is considered to be of great value in that it can contribute significantly to traffic correction and forecast improvement in the event of missing traffic data, and is also applicable to a variety of time series data recently collected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.1
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• pp.63-72
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• 2022

Objectives This study was conducted to characterize scoliosis patients visiting Korean medicine hospital and to analyze the demands and factors affecting discomfort. Methods This retrospective study analyzed 33 scoliosis patients who visited Korean medicine hospital from March, 2021 to October, 2021. The data analysis consisted of three factors: (1) demographic characteristics, (2) characteristics of demands on Korean medicine (reasons for choosing Korean medical treatment, preferred treatment methods, most uncomfortable part, treatment priorities) and (3) discomfort factors (treatment experiences, diagnosed age and Cobb's angle). Statistical analyses were performed and a p-value≤0.05 was considered to be statistically significant. Results 43.75% of the patients chose 'effectiveness' for the reason why they preferred Korean medicine treatment. 'Chuna treatment' was the most preferred treatment method. The patients chose 'lower back' for the most uncomfortable part and 'pain' for the highest priority of improvement. The Cobb's angle of included patients was 16.02±7.65° and it is not much differ to average of Cobb's angle in Korean. Discomfort was more severe in the patients with treatment-experienced than treatment-naive. The score of discomfort in appearance and psychological were higher in the patients diagnosed in childhood or adolescent period than who were diagnosed after adult. Classification based on Cobb's angle showed no statistical difference. Conclusions Not only Cobb's angle but also other clinical factors should be considered for effective treatment in scoliosis. Also, It is necessary to pay attention to adult scoliosis patients.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.808-811
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• 2022

Display under strong ambient lighting is perceived darker than it really is. Existing techniques for solving the problem in terms of software show limitations in that image enhancement techniques are applied regardless of ambient lighting or chrominance is not improved compared to luminance. Therefore, this paper proposes a visibility enhancement algorithm using deep learning to adaptively respond to ambient lighting values and an equation to restore optimal chrominance for luminance. The algorithm receives an ambient lighting value with the input image, and then applies a deep learning model and chrominance restoration equation to generate an image to minimize the difference between the degradation modeling of enhanced image and the input image. Qualitative evaluation proves that the algorithm shows excellent performance in improving visibility under strong ambient lighting through comparison of images applied with degradation modeling.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.191-199
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• 2021

Based on survey and data analysis, this paper established a separate and hierarchical design process for lady bags. The design process is divided into four parts: survey, concept extraction, separation and hierarchy design and detail improvement. In light of the influence value grade of lady bag design elements, the data of key elements of lady bags were analyzed, and elements related to new product design were extracted to form conceptual elements, and integrated into design experiments at different levels. Then, their usable proportion in design was measured and applied in design to complete the design of new products. Through the experiment, this design process can provide designers with a new design perspective and improves the timeliness and practicability of fashion design.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.47-53
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• 2021

In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.

• Journal of Korea Trade
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• v.24 no.5
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• pp.87-105
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• 2020

Purpose - The purpose of the study is to propose a theoretical framework of cross-border e-commerce ecosystems based on blockchain technology. The ecosystem includes five systems, namely, crossborder supply chain intelligent system, cross-border logistics system, cross-border payment system, cross-border product quality traceability system and cross-border customs supervision system. Design/methodology - This study firstly derived the main improvement factors for the new ecosystem based on blockchain through prior research and expert interviews on cross-border e-commerce. Then explored the use of virtue of decentralization, anti-counterfeiting traceability, consensus mechanism, smart contract and other means of the core technology of blockchain to overcome the bottleneck of cross-border e-commerce development among Korea, China, and Japan. Finally, proposed valuable implications in both theoretical and practical perspectives. Findings - As a result, we combined with the problems existing in cross-border e-commerce among Korea, China and Japan, this paper proposes a solution based on blockchain. On this basis, it constructs a cross-border e-commerce ecosystem among these three countries, including five systems. In addition, we discuss the main problems existing in the current blockchain, such as low transaction concurrency, security loopholes, and inconsistent standards, the corresponding countermeasures are proposed from the technical level, security level and industry standards. Originality/value - This study is the first to apply the blockchain technology to solve the cross-border e-commerce problems in Korea, China and Japan, which is of pioneering significance in both literature and practice. Block chain technology is in the ascendency. This study provides technical solutions for promoting the development of cross-border e-commerce import and export trade between Korea, China and Japan.

• Animal Bioscience
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• v.34 no.9
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• pp.1451-1459
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• 2021

Objective: The objectives of the study were to evaluate reproductive performance and selection response through genetic trend of community-based breeding programs (CBBPs) of Bonga sheep. Methods: Reproduction traits data were collected between 2012 and 2018 from Bonga sheep CBBPs. Phenotypic performance was analyzed using the general linear model procedures of Statistical Analysis System. Genetic parameters were estimated by univariate animal model for age at first lambing (AFL) and repeatability models for lambing interval (LI), litter size (LS), and annual reproductive rate (ARR) traits using restricted maximum likelihood method of WOMBAT. For correlations bivariate animal model was used. Best model was chosen based on likelihood ratio test. The genetic trends were estimated by the weighted regression of the average breeding value of the animals on the year of birth/lambing. Results: The overall least squares mean±standard error of AFL, LI, LS, and ARR were 375±12.5, 284±9.9, 1.45±0.010, and 2.31±0.050, respectively. Direct heritability estimates for AFL, LI, LS, and ARR were 0.07±0.190, 0.06±0.120, 0.18±0.070, and 0.25±0.203, respectively. The low heritability for both AFL and LI showed that these traits respond little to selection programs but rather highly depend on animal management options. The annual genetic gains were -0.0281 days, -0.016 days, -0.0002 lambs and 0.0003 lambs for AFL, LI, LS, and ARR, respectively. Conclusion: Implications of the result to future improvement programs were improving management of animals, conservation of prolific flocks and out scaling the CBBP to get better results.