• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.233-241
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• 2018

Recently, integrated railway safety monitoring and control system, which is a convergence system based on data distribution service for railway safety monitoring and control, is under development. It collects safety data of vehicle, signal, power and safety monitoring facilities in real time and adopts communication middleware based on distributed service for mass data processing. However, in the case of a server device used as an existing control server, the performance of the distributed service middleware can not be exhibited due to low hardware performance due to safety reasons. In the safety control system, 200,000 packets per second were set as the transmission target, but the performance test of the LAB was not satisfied. In this paper, we analyze the characteristics of railway data to improve the data collection performance of existing equipment and apply DDS-based streaming transmission method to the data model of signal facilities and vehicle facilities with large packet amount according to the analysis result. As a result, it was confirmed that the throughput was improved about 30.4 times when the hardware performance was the same. We plan to improve the data processing performance by applying it to real-time railway safety integrated monitoring and control system in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.8
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• pp.215-222
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• 2021

In order to alleviate the limited resource problem and interference problem in cellular networks, the dual connectivity technology has been introduced with the cooperation of small cell base stations. In this paper, we design a new efficient and fair resource allocation scheme for the dual connectivity technology. Based on two different bargaining solutions - Generalizing Tempered Aspiration bargaining solution and Gupta and Livne bargaining solution, we develop a two-stage radio resource allocation method. At the first stage, radio resource is divided into two groups, such as real-time and non-real-time data services, by using the Generalizing Tempered Aspiration bargaining solution. At the second stage, the minimum request processing speeds for users in both groups are guaranteed by using the Gupta and Livne bargaining solution. These two-step approach can allocate the 5G radio resource sequentially while maximizing the network system performance. Finally, the performance evaluation confirms that the proposed scheme can get a better performance than other existing protocols in terms of overall system throughput, fairness, and communication failure rate according to an increase in service requests.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• KSBB Journal
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• v.22 no.5
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• pp.297-304
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• 2007

For large and rapid screening of high-yielding mutants of lovastatin produced by filamentous fungal cells of Aspergillus terreus, one of the most important stage is to test as large amounts of mutated strains as possible. For this purpose, we intended to develop a miniaturized cultivation method using $7m{\ell}$ culture tube instead of traditional $250m{\ell}$ flask (working volume $50m{\ell}$). For obtaining large amounts of conidiospores to be used as inoculums for miniaturized cultures, 4 components i.e., glucose, sucrose, yeast extract and $KH_2PO_4$ were intensively investigated, which had been observed to show positive effect on enhancement of spore production through Plackett-Burman design experimet. When optimum concentrations of these components that were determined through application of response surface method (RSM) based on central composite design (CCD) were used, maximum spore numbers amounting to $1.9\times10^{10}$ spores/plate were obtained, resulting in approximately 190 fold increase as compared to the commonly used PDA sporulation medium. Using the miniaturized cultures, intensive strain development programs were carried out for screening of lovastatin high-yielding as well as highly reproducible mutants. It was observed that, for maximum production of lovastatin, the producers should be activated through 'PaB' adaptation process during the early solid culture stage. In addition, they should be proliferated in condensed filamentous forms in miniaturized growth cultures, so that optimum amounts of highly active cells could be transferred to the production culture-tube as reproducible inoculums. Under these highly controlled fermentation conditions, compact-pelleted morphology of optimum size (less than 1 mm in diameter) was successfully induced in the miniaturized production cultures, which proved essential for maximal utilization of the producers' physiology leading to significantly enhanced production of lovastatin. As a result of continuous screening in the miniaturized cultures, lovastatin production levels of the 81% of the daughter cells derived from the high-yielding producers turned out to be in the range of 80%$\sim$120% of the lovastatin production level of the parallel flask cultures. These results demonstrate that the miniaturized cultivation method developed in this study is efficient high throughput system for large and rapid screening of highly stable and productive strains.

• Journal of Plant Biotechnology
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• v.33 no.1
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• pp.57-62
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• 2006

Several methods for determining the response of corn to glyphosate were investigated to provide a fast and reliable method for identifying glyphosate-resistant corn in vivo. Two bioassays were developed. One assay is named 'whole plant / leaf growth assay', in which the herbicide glyphosate is applied on the upper part of 3rd leaf and the growth of herbicide-untreated 4th leaf is measured at 3 day after treatment. in this assay, the leaf growth of conventional corn was inhibited in a dose dependent from 50 to $1600{\mu}g/mL$ of glyphosate and growth inhibition at $1600{\mu}g/mL$ was 55% of untreated control. The assay has the potential to be used especially in the case that the primary cause of glyphosate resistance is related with a reduction of the herbicide translocation. Another assay is named 'leaf segment / shikimate accumulation assay', in which the four excised leaf segments ($4{\times}4mm$) are placed in each well of a 48-well microtiter plate containing $200{\mu}L$ test solution and the amount of shikimate is determined after incubation for 24 h in continuous light at $25^{\circ}C$. In this assay, 0.33% sucrose added to basic test solution enhanced a shikimate accumulation by 3 to 4 times and the shikimate accumulation was linearly occurred from 2 to $8{\mu}g/mL$ of glyphosate, showing an improved response to the method described by Shaner et al. (2005). The leaf segment / shikimate accumulation assay is simple and robust and has the potential to be used as a high throughput assay in the case that the primary cause of glyphosate resistance is related with EPSPS, target site of the herbicide. Taken together, these two assays would be highly useful to initially select the lines obtained after transformation, to investigate the migration of glyphosate-resistant gene into other weeds and to detect a weedy glyphosate-resistant corn in field.

• Horticultural Science & Technology
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• v.28 no.3
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• pp.449-455
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• 2010

'Moulinrouge' was selected as the best regenerating cultivar among 18 different spray-type chrysanthemum cultivars bred in the Gyeongnam Flowers Breeding Research Institute. When the leaf explants from standard- and spray-type chrysanthemum 'Jinba' and 'Moulinrouge' were incubated on MS basal medium supplemented with $0.5mg{\cdot}L^{-1}$ BA and $1.0mg{\cdot}L^{-1}$ NAA, both 'Jinba' and 'Moulinrouge' induced adventitious shoots that can be regenerated into plantlets. Based on these regeneration conditions, we developed an efficient $Agrobacterium$-mediated chrysanthemum 'Moulinrouge' transformation method by using sequential selection of shoots from low ($10mg{\cdot}L^{-1}$) to high ($30mg{\cdot}L^{-1}$) concentrations of kanamycin after co-cultivation of leaf explants with $Agrobacterium$ for 10 days and induction of shoots. All kanamycin resistant plants investigated with genomic PCR analysis carried the report gene, $AtSICKLE$, in their genome. Although expression levels of the report gene in the transgenic plants investigated with RT-PCR were relatively low because of inefficiency of CaMV 35S promoter in chrysanthemum, transgenic lines expressing $AtSICKLE$ efficiently showed leaf epinasty phenotype. We expect that our results will provide a useful method that can perform a high-throughput investigation of genes isolated and studied well in model plants for molecular breeding of chrysanthemum.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.742-757
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• 2001

Since database management systems(DBMSS) have limited lock resources, transactions requesting locks beyond the limit mutt be aborted. In the worst carte, if such transactions are aborted repeatedly, the DBMS can become paralyzed, i.e., transaction execute but cannot commit. Lock escalation is considered a solution to this problem. However, existing lock escalation methods do not provide a complete solution. In this paper, we prognose a new lock escalation method, adaptive lock escalation, that selves most of the problems. First, we propose a general model for lock escalation and present the concept of the unescalatable look, which is the major cause making the transactions to abort. Second, we propose the notions of semi lock escalation, lock blocking, and selective relief as the mechanisms to control the number of unescalatable locks. We then propose the adaptive lock escalation method using these notions. Adaptive lock escalation reduces needless aborts and guarantees that the DBMS is not paralyzed under excessive lock requests. It also allows graceful degradation of performance under those circumstances. Third, through extensive simulation, we show that adaptive lock escalation outperforms existing lock escalation methods. The results show that, compared to the existing methods, adaptive lock escalation reduces the number of aborts and the average response time, and increases the throughput to a great extent. Especially, it is shown that the number of concurrent transactions can be increased more than 16 ~256 fold. The contribution of this paper is significant in that it has formally analysed the role of lock escalation in lock resource management and identified the detailed underlying mechanisms. Existing lock escalation methods rely on users or system administrator to handle the problems of excessive lock requests. In contrast, adaptive lock escalation releases the users of this responsibility by providing graceful degradation and preventing system paralysis through automatic control of unescalatable locks Thus adaptive lock escalation can contribute to developing self-tuning: DBMSS that draw a lot of attention these days.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.6
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• pp.750-763
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• 2018

This study conducted 10 sampling sites survey 4 times to determine the trophic structure and energy flow of marine ecosystems for Uljin marine ranching area, Korean East Sea from March to October 2013. Based on the ecological characteristics of biological species, one used the non-Metric Multidimensional Scaling method based on the similarity of species. A total of 19 classified species groups formed categories including, top predators, seabirds, large pelagic fishes, small pelagic fishes, rockfishes, pleuronectiformes, benthic fishes, semi-benthic fishes, cephalopods, benthic feeders, epifauna, bivalves, abalone, Cnidaria, zooplankton, benthic algae, microalgae, phytoplankton and detritus. The biomass, production/biomass, consumption/biomass, diet composition data of each species groups to input data used in Ecopath mode estimated the trophic structure and energy flow of marine ecosystems in the Uljin marine ranching area. One estimated each species groups on the trophic level from 1 to 5.687. The sum of all consumption was estimated at $229.7t/km^2/yr$ and the sum of all exports was as estimated $3,432.4t/km^2/yr$. Total system throughput was at $6,796.2t/km^2/yr$, and the sum of all production was estimated at $3,613.1t/km^2/yr$. Net system production according to these results was estimated at $3,490.3t/km^2/yr$ and total biomass (excluding detritus) was estimated at $167.3t/km^2/yr$ in the Uljin marine ranching area.

• 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.