• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Journal of Microbiology and Biotechnology
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• v.29 no.1
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• pp.160-170
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• 2019

The lactic acid bacteria species Lactobacillus plantarum (L. plantarum) has been used extensively for vaccine delivery. Considering to the critical role of dendritic cells in stimulating host immune response, in this study, we constructed a novel CD11c-targeting L. plantarum strain with surface-displayed variable fragments of anti-CD11c, single-chain antibody (scFv-CD11c). The newly designed L. plantarum strain, named 409-aCD11c, could adhere and invade more efficiently to bone marrow-derived DCs (BMDCs) in vitro due to the specific interaction between scFv-CD11c and CD11c located on the surface of BMDCs. After incubation with BMDCs, the 409-aCD11c strain harboring a eukaryotic vector pValac-GFP could lead to more efficient expression of GFP compared with wild-type strains shown by flow cytometry analysis, indicating the enhanced translocation of pValac-GFP from L. plantarum to BMDCs. Similar results were also observed in an in vivo study, which showed that oral administration resulted in efficient expression of GFP in both Peyer's patches (PP) and mesenteric lymph nodes (MLNs) within 7 days after the last administration. In addition, the CD11c-targeting strain significantly promoted the differentiation and maturation of DCs, the differentiation of $IL-4^+$ and $IL-17A^+$ T helper (Th) cells in MLNs, as well as production of $B220^+$ $IgA^+$ B cells in the PP. In conclusion, this study developed a novel DC-targeting L. plantarum strain which could increase the ability to deliver eukaryotic expression plasmid to host cells, indicating a promising approach for vaccine study.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.47-53
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• 2014

Despite the automatic fire alarm system, according to the national fire data system of national emergency management agency, the fires account for 40,932 incidents, 2,184 injuries and about 430 billion won in property losses in 2013. Since the conventional automatic fire alarm system has several weaknesses related to electrical signal such as noise, surge, lighting, etc. Most fires are mainly caused by electrical faults, mechanical problem, chemical, carelessness and natural. The electrical faults such as line to ground fault, line to line fault, electrical leakage and arc are one of the major problems in fire. This paper describes the development of a novel real-time fire monitoring system algorithm including fault detection function which puts the existing optic smoke and heat detectors for fire detection with current and voltage sensors in order to utility fault monitoring using high accuracy DAQ measurement system with LabVIEW program. The fire detection and electrical fault monitoring with a proposed a new detection algorithm are implemented under several test. The fire detection and monitoring system operates according to the proposed algorithm well.

• Korean Journal of Veterinary Research
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• v.55 no.4
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• pp.227-232
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• 2015

Akabane and bovine ephemeral fever (BEF) viruses cause vector-borne diseases. In this study, inactivated Akabane virus (AKAV)+Bovine ephemeral fever virus (BEFV) vaccines with or without recombinant vibrio flagellin (revibFlaB) protein were expressed in a baculovirus expression system to measure their safety and immunogenicity. Blood was collected from mice, guinea pigs, sows, and cattle that had been inoculated with the vaccine twice. Inactivated AKAV+BEFV vaccine induced high virus neutralizing antibody (VNA) titer against AKAV and BEFV in mice and guinea pigs. VNA titers against AKAV were higher in mice and guinea pigs immunized with the inactivated AKAV+BEFV vaccine than in animals inoculated with vaccine containing revibFlaB protein. Inactivated AKAV+BEFV vaccine elicited slightly higher VNA titers against AKAV and BEFV than the live AKAV and live BEFV vaccines in mice and guinea pigs. In addition, the inactivated AKAV+BEFV vaccine was safe, and induced high VNA titers, ranging from 1 : 64 to 1 : 512, against both AKAV and BEFV in sows and cattle. Moreover, there were no side effects observed in any treated animals. These results indicate that the inactivated AKAV+BEFV vaccine could be used in cattle with high immunogenicity and good safety.

• Journal of Information Science Theory and Practice
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• v.2 no.3
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• pp.29-39
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• 2014

In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.2 s.29
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• pp.169-172
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• 2007

This paper presents circuit models and control algorithms of distributed generation system(DGS) which consists of Z-type converter and PWM inverter. Z-type converter which employs both the L and C passive components and shoot-through zero vectors instead qf the conventional DC/DC converter in order to step up DC-link voltage. Discrete time sliding mode control with the asymptotic observer is used for current control. This system am be used for power conversion of DC renewable energy.

• Journal of the Korea Safety Management & Science
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• v.23 no.3
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• pp.83-88
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• 2021

Recently, due to the aging of workers and the weakening of the labor base in the automobile industry, research on quality inspection methods through ICT(Information and Communication Technology) convergence is being actively conducted. A lot of research has already been done on the development of an automated system for quality inspection in the manufacturing process using image processing. However, there is a limit to detecting defects occurring in the automotive sunroof sealer application process, which is the subject of this study, only by image processing using a general camera. To solve this problem, this paper proposes a system construction method that collects image information using a infrared thermal imaging camera for the sunroof sealer application process and detects possible product defects based on the SVM(Support Vector Machine) algorithm. The proposed system construction method was actually tested and applied to auto parts makers equipped with the sunroof sealer application process, and as a result, the superiority, reliability, and field applicability of the proposed method were proven.

• Journal of Korea Trade
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• v.24 no.4
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• pp.18-33
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• 2020

Purpose - This paper investigated the effect of international trade affects income inequality. It also compares the different effects between developing and developed countries over the period from 2005 to 2014 for 58 countries. Design/methodology - The econometric estimation was used to identify the relationship between export, import, FDI, GDP, unemployment and income inequality. In this empirical analysis, we utilized a Vector Error Correction (VEC) model using panel data. Findings - The findings show that there is a close correlated between trade and income inequality. The higher export ratio of GDP tends to have a 1.79 times more income inequality in developing countries than in developed countries. The higher import ratio of GDP tends to have a 2.44 times higher income inequality in developing countries than in developed countries. Further, Increasing FDI tend to have an approximately 1.43 times higher income inequality in developing countries than in developed countries. Korea is in the middle of developed and developing countries' result. Originality/value - To correct the global income inequality regarding trade, developed countries' proactive trade policies, such as granting preferential tariff benefits to developing countries, are likely to be needed and Income Safety Net in international trade must be taken into account.