• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.3
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• pp.419-429
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• 1999

Identifying the boundary of the effective receiving power of waves is one of the most important factors for cell optimization. In this paper, we introduce a propagation loss prediction model which yields highly accurate prediction in very complex areas as Seoul where a mixture of many large buildings, small buildings, broad streets, narrow alleys, rivers and forests co-exist in an irregular arrangement. This prediction model is based on neural networks trained on field measurement data collected in the past. Using these data along with 3-D digital elevation maps and vector data for building structures, we extract the parameter values which mainly affect the amount of propagation loss. These parameter values are then used as the inputs to the neural network. Trained neural network becomes the approximated function of the propagation loss model which generalizes very well and can predict accurately in the regions not included in training the neural network. The experimental results show a superior performance over the other models in the cells operating in the city of Seoul.

• Journal of Korea Game Society
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• v.18 no.1
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• pp.125-134
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• 2018

This paper presents a practical method to efficiently extract the rotational part of a $3{\times}3$ matrix that changes continuously in time. This is the key technique in the corotational FEM and the shape matching deformation popular in physics-based dynamic deformation. Recently, in contrast to the traditional polar decomposition methods independent of time, an iterative method was proposed that formulates the rotation extraction in a physics-based way and exploits an incremental representation of rotation. We develop an optimization method that reduces the number of iterations under the assumption that the maximum magnitude of the incremental rotation vector is limited within ${\pi}/2$. Realistic simulation of dynamic deformation employs a sufficiently small time step, and thus this assumption is not problematic in practice. We demonstrate the efficiency and practicality of our method in various experiments.

• Mycobiology
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• v.49 no.5
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• pp.491-497
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• 2021

An endolichenic fungus Xylaria grammica EL000614 produces grammicin, a potent nematicidal pyrone derivative that can serve as a new control option for root-knot nematodes. We optimized an Agrobacterium tumefaciens-mediated transformation (ATMT) protocol for X. grammica to support genetic studies. Transformants were successfully generated after co-cultivation of homogenized young mycelia of X. grammica with A. tumefaciens strain AGL-1 carrying a binary vector that contains the bacterial hygromycin B phosphotransferase (hph) gene and the eGFP gene in T-DNA. The resulting transformants were mitotically stable, and PCR analysis showed the integratin of both genes in the genome of transformants. Expression of eGFP was confirmed via fluorescence microscopy. Southern analysis showed that 131 (78.9%) out of 166 transformants contained a single T-DNA insertion. Crucial factors for producing predominantly single T-DNA transformants include 48 h of co-cultivation, pretreatment of A. tumefaciens cells with acetosyringone before co-cultivation, and using freshly prepared mycelia. The established ATMT protocol offers an efficient tool for random insertional mutagenesis and gene transfer in studying the biology and ecology of X. grammica.

• Journal of Veterinary Science
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• v.22 no.6
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• pp.76.1-76.15
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• 2021

Background: The development of a vaccine for Jembrana disease is needed to prevent losses in Indonesia's Bali cattle industry. A DNA vaccine model (pEGFP-C1-tat) that requires a functional delivery system will be developed. Polylactic-co-glycolic acid (PLGA) may have potential as a delivery system for the vaccine model. Objectives: This study aims to evaluate the in vitro potential of PLGA as a delivery system for pEGFP-C1-tat. Methods: Consensus and codon optimization for the tat gene was completed using a bioinformatic method, and the product was inserted into a pEGFP-C1 vector. Cloning of the pEGFP-C1-tat was successfully performed, and polymerase chain reaction (PCR) and restriction analysis confirmed DNA isolation. PLGA-pEGFP-C1-tat solutions were prepared for encapsulated formulation testing, physicochemical characterization, stability testing with DNase I, and cytotoxicity testing. The PLGA-pEGFP-C1-tat solutions were transfected in HeLa cells, and gene expression was observed by fluorescent microscopy and real-time PCR. Results: The successful acquisition of transformant bacteria was confirmed by PCR. The PLGA:DNA:polyvinyl alcohol ratio formulation with optimal encapsulation was 4%:0.5%:2%, physicochemical characterization of PLGA revealed a polydispersity index value of 0.246, a particle size of 925 nm, and a zeta potential value of -2.31 mV. PLGA succeeded in protecting pEGFP-C1-tat from enzymatic degradation, and the percentage viability from the cytotoxicity test of PLGA-pEGFP-C1-tat was 98.03%. The PLGA-pEGFP-C1-tat demonstrated luminescence of the EGFP-tat fusion protein and mRNA transcription was detected. Conclusions: PLGA has good potential as a delivery system for pEGFP-C1-tat.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1765-1794
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• 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

• Genomics & Informatics
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• v.20 no.1
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• pp.11.1-11.20
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• 2022

Vibrio harveyi belongs to the Vibrio genus that causes vibriosis in marine and aquatic fish species through double-stranded DNA virus replication. In humans, around 12 Vibrio species can cause gastroenteritis (gastrointestinal illness). A large amount of virus particles can be found in the cytoplasm of infected cells, which may cause death. Despite these devastating complications, there is still no cure or vaccine for the virus. As a result, we used an immunoinformatics approach to develop a multi-epitope vaccine against most pathogenic hemolysin gene of V. harveyi. The immunodominant T- and B-cell epitopes were identified using the hemolysin protein. We developed a vaccine employing three possible epitopes: cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocyte epitopes, after thorough testing. The vaccine was developed to be antigenic, immunogenic, and non-allergenic, as well as having a better solubility. Molecular dynamics simulation revealed significant structural stiffness and binding stability. In addition, the immunological simulation generated by computer revealed that the vaccination might elicit immune reactions in the actual life after injection. Finally, using Escherichia coli K12 as a model, codon optimization yielded ideal GC content and a higher codon adaptation index value, which was then included in the cloning vector pET2+ (a). Altogether, our experiment implies that the proposed peptide vaccine might be a good option for vibriosis prophylaxis.

• 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 radiological science and technology
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• v.46 no.5
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• pp.379-394
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• 2023

Targeted Alpha Therapy (TAT) is a new method of cancer treatment that protects normal tissues while selectively killing tumor cells using high cytotoxicity and short range of alpha particles, and target alpha therapy is a highly specific and effective cancer treatment strategy, and its potential has been proven through many clinical and experimental studies. This treatment method accurately delivers alpha particles by selecting specific molecules present in cancer tissue, which has an effective destruction and tumor suppression effect on cancer cells, and one of the main advantages of target alpha treatment is the physical properties of alpha particles. Alpha particles have a very high energy and short effective distance, interacting with target molecules in cancer tissues and having a fatal effect on cancer cells, which is known to cause DNA damage and cell death in cancer cells. TAT has shown positive results in preclinical and clinical studies for various types of cancers, especially those that resist or are unresponsive to existing treatments, but there are several challenges and limitations to overcome for successful clinical transition and application. These include the provision and production of suitable alpha radioisotopes, optimization of target vectors and delivery formulations, understanding and regulation of radiological effects, accurate dosage calculation and toxicity assessment. Future research should focus on developing new or improved isotopes, target vectors, transfer formulations, radiobiological models, combination strategies, imaging techniques, etc. for TAT. In addition, TAT has the potential to improve the quality of life and survival of cancer patients due to the possibility of a new treatment for overcoming cancer, and to this end, prospective research on more carcinomas and more diverse patient groups is needed.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.