• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.311-318
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• 2021

With the rapid development of the Internet of Things(IoT) technology recently, multi-access edge computing(MEC) is emerged as a next-generation technology for real-time and high-performance services. High mobility of users between MECs with limited service areas is considered one of the issues in the MEC environment. In this paper, we consider a vehicle edge computing(VEC) environment which has a high mobility, and propose a task migration algorithm to decide whether or not to migrate and where to migrate using DQN, as a reinforcement learning method. The objective of the proposed algorithm is to improve the system throughput while satisfying QoS(Quality of Service) requirements by minimizing the difference between queueing delays in vehicle edge computing servers(VECSs). The results show that compared to other algorithms, the proposed algorithm achieves approximately 14-49% better QoS satisfaction and approximately 14-38% lower service blocking rate.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.9-16
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• 2021

In this paper, we propose a building exploration drone that can be used for a digital twin-based building control system. The existing building control system using a fixed position sensor box has a problem that a management blind spot occurs. And because people patrol themselves, it takes a lot of human resources. In this paper, a drone equipped with a temperature and humidity sensor and a gas leak detection sensor is used to search the internal path of the building centering on the control blind spot. It also aims to solve the problem of the building control system by transmitting information in real time along with the video. In addition, it has a stable hovering function using an optical floor sensor and can be applied to an existing digital twin-based building control system. The results of this study are believed to be of great help in improving the quality of digital twin control systems using drones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.695-712
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• 2021

As the use of smart devices is being increased rapidly by the development of internet and IT technology, the contents production and utilization rate are showing higher increase, too. In addition, the type of contents also shows very diverse forms such as education, game, video, UCC, etc. In the meantime, the contents are reproduced in diverse forms by reprocessing the original contents, and they are being serviced through the contents service platform. Therefore, the platform to make the contents reprocessing easy and fast is needed. As the diverse contents distribution channels such as YouTube, SNS, App Service, etc, easier contents distribution platform is needed, and the development of the relevant area is expected. In addition, as the selective consumption of the contents having easy accessibility through diverse smart devices is distinguished, the demand for the platform and service that can identify the contents consumption propensity by individual is being increased. Therefore, in this study, to vitalize the online contents distribution, the contents reproduction and publishing platform, was designed and materialized, which can reproduce and distribute the contents based on the real-time contents editing technology in URL unit and the consumer propensity analysis technology using the data management-based broadcasting contents distribution metadata technology and the edited image contents streaming technology. In addition, in the results of comparing with other platforms through the experiment, the performance superiority of the suggested platform was verified. If the suggested platform is applied to the areas of education, broadcasting, press, etc, the multi-media contents can be reproduced and distributed easily, through which the vitalization of contents-related industry is expected.

• Journal of Veterinary Science
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• v.22 no.3
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• pp.35.1-35.13
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• 2021

Background: African swine fever (ASF) is an infectious viral disease of domestic pigs that presents as a hemorrhagic fever, and for which no effective vaccine is available. The disease has a serious negative social and economic impact on pig keepers. There is limited information on the potential risk factors responsible for the spread of ASF in South Kivu. Objective: The aim of this study was to determine the potential risk factors associated with ASF infection in suspected ASF virus (ASFV)-infected pigs. Methods: We sampled whole blood from 391 pigs. Additionally, 300 pig farmers were interviewed using a structured questionnaire. Viral DNA was detected by using the real-time polymerase chain reaction technique. Results: The majority of pigs sampled, 78% (95% confidence interval [CI], 74.4-82.6), were of local breeds. Over half, 60.4% (95% CI, 55.5-65.2), were female, and most of them, 90.5% (95% CI, 87.6-93.4), were adult pigs (> 1 year old). Viral DNA was detected in 72 of the 391 sampled pigs, indicating an overall infection rate of 18.4% (95% CI, 14.5-22.4). Multivariable logistic regression analysis revealed several risk factors positively associated with ASFV infection: feeding with swill in pen (odds ratio [OR], 3.8; 95% CI, 2.12-6.77); mixed ages of pigs in the same pen (OR, 3.3; 95% CI, 1.99-5.57); introduction of new animals to the farm (OR, 5.4; 95% CI, 1.91-15.28). The risk factors that were negatively (protective) correlated with ASFV positivity were the presence of male animals and the use of an in-pen breeding system. Conclusion: Local pig farmers should be encouraged to adopt proper husbandry and feeding practices in order to increase the number of ASF-free farms.

• Journal of Ginseng Research
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• v.43 no.1
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• pp.49-57
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• 2019

Background: Korean Red Ginseng (KRG; Panax ginseng Meyer) is a widely used medicinal herb known to exert various immune modulatory functions. KRG and one of its purified components, ginsenoside Rg3, are known to possess anti-inflammatory activities. How they impact helper T cell-mediated responses is not fully explored. In this study, we attempted to evaluate the effect of KRG extract (KRGE) and ginsenoside Rg3 on Th1 cell responses. Methods: Using well-characterized T cell in vitro differentiation systems, we examined the effects of KRGE or enhanced Rg3 on the Th1-inducing cytokine production from dendritic cells (DC) and the naïve $CD4^+$ T cells differentiation to Th1 cells. Furthermore, we examined the change of Th1 cell population in the intestine after treatment of enhanced Rg3. The influence of KRGE or enhanced Rg3 on Th1 cell differentiation was evaluated by fluorescence-activated cell sorting, enzyme-linked immunosorbent assay, and quantitative real-time polymerase chain reaction. Results: KRGE significantly inhibited the production level of IL-12 from DCs and subsequent Th1 cell differentiation. Similarly, enhanced Rg3 significantly suppressed the expression of interferon gamma ($IFN{\gamma}$) and T-bet in T cells under Th1-skewing condition. Consistent with these effects in vitro, oral administration of enhanced Rg3 suppressed the frequency of Th1 cells in the Peyer's patch and lamina propria cells in vivo. Conclusion: Enhanced Rg3 negatively regulates the differentiation of Th1 cell in vitro and Th1 cell responses in the gut in vivo, providing fundamental basis for the use of this agent to treat Th1-related diseases.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1641-1647
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• 2018

In this paper, we describe a portable potentiostat which is capable of cyclic voltammetry(CV) and amperometry for electrochemical dissolved oxygen sensor. In addition, this portable potentiostat can also transmit the measured data wirelessly to android devices such as smart phone, tablet, etc. through Bluetooth. The potentiostat system consists of three parts; a voltage generator circuit which is controlled by Arduino nano and 12-bit DAC(digital to analog converter) to generate necessary electric potential for operating the electrochemical sensor, an oxidation/reduction current measurement circuit, and a Bluetooth module to transmit data wirelessly to an android device. Once measurements are carried out with the android application, the measured data is transmitted to the android device via Bluetooth and displayed using the android app. in real time. In this paper, we report the measured reduction current with a fabricated dissolved oxygen sensor in both saturated-oxygen state and zero-oxygen states. The results of the developed portable potentiostat system are in good agreement with those of the commercial portable potentiostat (${\mu}stat200$, Dropsens inc.). The measured peak reduction currents using the developed potentiostat and the commercial ${\mu}stat200$ potentiostat were $-0.755{\mu}A$ and $-0.724{\mu}A$, respectively. The reduction currents measured at zero-oxygen state were $-0.005{\mu}A$ and $-0.004{\mu}A$. The discrepancy between those two systems seems very small, which implies successful development of a portable and wireless potentionstat.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.153-169
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• 2018

Previous e-financial anomalies analysis and detection technology collects large amounts of electronic financial transaction logs generated from electronic financial business systems into big-data-based storage space. And it detects abnormal transactions in real time using detection rules that analyze transaction pattern profiling of existing customers and various accident transactions. However, deep analysis such as attempts to access e-finance by insiders of financial institutions with large scale of damages and social ripple effects and stealing important information from e-financial users through bypass of internal control environments is not conducted. This paper analyzes the management status of e-financial security programs of financial companies and draws the possibility that they are allies in security control of insiders who exploit vulnerability in management. In order to efficiently respond to this problem, it will present a comprehensive e-financial security management environment linked to insider threat monitoring as well as the existing e-financial transaction detection system.

• Geomechanics and Engineering
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• v.17 no.4
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• pp.375-383
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• 2019

In underground retreating longwall coal mining, hard roof collapse is one of the most challenging safety problems for mined-out areas. Identifying precursors for hard roof collapse is of great importance for the development of warning systems related to collapse geohazards and ground control. In this case study, the Xinhe mine was chosen because it is a standard mine and the minable coal seam usually lies beneath hard strata. Real-time monitoring of hard roof collapse was performed in longwall face 5301 of the Xinhe mine using support resistance and microseismic (MS) monitoring; five hard roof collapse cases were identified. To reveal the characteristics of MS activity during hard roof collapse development and to identify its precursors, the change in MS parameters, such as MS event rate, energy release, bursting strain energy, b value and the relationships with hard roof collapse, were studied. This research indicates that some MS parameters showed irregularity before hard roof collapse. For the Xinhe coalmine, a substantial decrease in b value and a rapid increase in MS event rate were reliable hard roof collapse precursors. It is suggested that the b value has the highest predictive sensitivity, and the MS event rate has the second highest.