• Earthquakes and Structures
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• v.5 no.1
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• pp.83-109
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• 2013

A simple damper optimization method is proposed to find optimal damper allocation for shear buildings under both target added damping ratio and interstorey drift ratio (IDR). The damping coefficients of added dampers are considered as design variables. The cost, which is defined as the sum of damping coefficient of added dampers, is minimized under a target added damping ratio and the upper and the lower constraint of the design variables. In the first stage of proposed algorithm, Simulated Annealing, Nelder Mead and Differential Evolution numerical algorithms are used to solve the proposed optimization problem. The candidate optimal design obtained in the first stage is tested in terms of the IDRs using linear time history analyses for a design earthquake in the second stage. If all IDRs are below the allowable level, iteration of the algorithm is stopped; otherwise, the iteration continues increasing the target damping ratio. By this way, a structural response IDR is also taken into consideration using a snap-back test. In this study, the effects of the selection of upper limit for added dampers, the storey mass distribution and the storey stiffness distribution are all investigated in terms of damper distributions, cost function, added damping ratio and IDRs for 6-storey shear building models. The results of the proposed method are compared with two existing methods in the literature. Optimal designs are also compared with uniform designs according to both IDRs and added damping ratios. The numerical results show that the proposed damper optimization method is easy to apply and is efficient to find optimal damper distribution for a target damping ratio and allowable IDR value.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1540-1555
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• 2021

The plate-type fuel assembly adopted in nuclear research reactor suffers from complicated effect induced by non-uniform irradiation, which might affect its stress conditions, mechanical behavior and thermal-hydraulic performance. A reliable numerical method is of great importance to reveal the complex evolution of mechanical deformation, flow redistribution and temperature field for the plate-type fuel assembly under non-uniform irradiation. This paper is the first part of a two-part study developing the numerical methodology for the thermal-fluid-structure coupling behaviors of plate-type fuel assembly under irradiation. In this paper, the thermal-fluid-structure coupling methodology has been developed for plate-type fuel assembly under non-uniform irradiation condition by exchanging thermal-hydraulic and mechanical deformation parameters between Finite Element Model (FEM) software and Computational Fluid Dynamic (CFD) software with Mesh-based parallel Code Coupling Interface (MpCCI), which has been validated with experimental results. Based on the established methodology, the effects of non-uniform irradiation and fluid were discussed, which demonstrated that the maximum mechanical deformation with irradiation was dozens of times larger than that without irradiation and the hydraulic load on fuel plates due to differential pressure played a dominant role in the mechanical deformation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.215-222
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• 2021

As the importance of human-computer interaction (Human Computer Interface) technology grows and research on HCI is progressing, it is inferred about the research emotion inference or the computer reaction according to the user's intention, not the computer reaction by the standard input of the user. Stress is an unavoidable result of modern human civilization, and it is a complex phenomenon, and depending on whether or not there is control, human activity ability can be seriously changed. In this paper, we propose an intelligent emotional recommendation system using music as a way to relieve stress after measuring heart rate variability (HRV) and acceleration photoplethymogram (APG) increased through stress as part of human-computer interaction. The differential evolution algorithm was used to extract reliable data by acquiring and recognizing the user's biometric information, that is, the stress index, and emotional inference was made through the semantic web based on the obtained stress index step by step. In addition, by searching and recommending a music list that matches the stress index and changes in emotion, an emotional recommendation system suitable for the user's biometric information was implemented as an application.

• Journal of Electrochemical Science and Technology
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• v.12 no.1
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• pp.112-125
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• 2021

The new emerging "High entropy materials" attract the attention of the scientific society because of their simpler structure and spectacular applications in many fields. A novel nanocrystalline high entropy (Be,Mg,Ca,Sr,Zn,Ni)3O4 oxide has been successfully synthesized through mechanochemical treatment followed by sintering and air quenching. The present research work focuses on the possibility of single-phase formation in the aforementioned high entropy oxide despite the great difference in the atomic sizes of reactant alkaline earth and 3d transition metal oxides. Structural properties of (Be,Mg,Ca,Sr,Zn,Ni)3O4 high entropy oxide were explored by confirmation of its single-phase Fd-3m spinel structure by x-ray diffraction (XRD). Further, nanocrystalline nature and morphology were analyzed by scanning electron microscopy (SEM). Among thermal properties, thermogravimetric analysis (TGA) revealed that the (Be,Mg,Ca,Sr,Zn,Ni)3O4 high entropy oxide is thermally stable up to a temperature of 1200℃. Whereas phase evolution in (Be,Mg,Ca,Sr,Zn,Ni)3O4 high entropy oxide before and after sintering was analyzed through differential scanning calorimetry (DSC). Electrochemical studies of (Be,Mg,Ca,Sr,Zn,Ni)3O4 high entropy oxide consists of a comparison of thermodynamic and kinetic parameters of water and hydrazine hydrate oxidation. Values of activation energy for water oxidation (9.31 kJ mol-1) and hydrazine hydrate oxidation (13.93 kJ mol-1) reveal that (Be,Mg,Ca,Sr,Zn,Ni)3O4 high entropy oxide is catalytically more active towards water oxidation as compared to that of hydrazine hydrate oxidation. Electrochemical impedance spectroscopy is also performed to get insight into the kinetics of both types of reactions.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1087-1098
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• 2023

The rapid advancement of artificial intelligence (AI) technology has led to its proactive utilization across various fields. However, this widespread adoption of AI-based systems has raised concerns about the increasing threat of attacks on these systems. In particular, deep neural networks, commonly used in deep learning, have been found vulnerable to adversarial attacks that intentionally manipulate input data to induce model errors. In this study, we propose a method to protect image classification models from visually imperceptible One-Pixel attacks, where only a single pixel is altered in an image. The proposed defense technique utilizes an autoencoder model to remove potential threat elements from input images before forwarding them to the classification model. Experimental results, using the CIFAR-10 dataset, demonstrate that the autoencoder-based defense approach significantly improves the robustness of pretrained image classification models against One-Pixel attacks, with an average defense rate enhancement of 81.2%, all without the need for modifications to the existing models.

• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.73-90
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• 2023

This research examines the impact of robotics integration on job dynamics in the U.S. manufacturing sector, adding to the critical dialogue on technological evolution and the future of jobs. Anchored in the task-model framework, the study hypothesizes that robotic integration exerts differential influences on diverse occupational clusters, each identified by their unique task-specific attributes. An in-depth examination was undertaken to elucidate the interplay between robotic integration and the occupation clusters. Employing a multilevel growth curve model, our empirical investigation tracked employment dynamics from 2012 to 2022 across 52 U.S. regions, covering 307 manufacturing occupations. The findings suggest a pronounced job decline within occupations necessitating manual dexterity. Nonetheless, the evidence does not conclusively support that the extent of robotics integration exacerbates this trend. These findings imply that the employment shifts in the U.S. manufacturing sector are predominantly driven by long-standing trends of deindustrialization and functional specialization, rather than by the recent diffusion of robotic technologies.

• Journal of Microbiology and Biotechnology
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• v.33 no.12
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• pp.1587-1594
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• 2023

Since its first report in 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a grave threat to public health. Virus-specific countermeasures, such as vaccines and therapeutics, have been developed and have contributed to the control of the viral pandemic, which has become endemic. Nonetheless, new variants continue to emerge and could cause a new pandemic. Consequently, it is important to comprehensively understand viral evolution and the roles of mutations in viral infectivity and transmission. SARS-CoV-2 beta variant encode mutations (D614G, N501Y, E484K, and K417N) in the spike which are frequently found in other variants as well. While their individual role in viral infectivity has been elucidated against various therapeutic antibodies, it still remains unclear whether those mutations may act additively or synergistically when combined. Here, we report that N501Y mutation shows differential effect on two therapeutic antibodies tested. Interestingly, the relative importance of E484K and K417N mutations in antibody evasion varies depending on the antibody type. Collectively, these findings suggest that continuous efforts to develop effective antibody therapeutics and combinatorial treatment with multiple antibodies are more rational and effective forms of treatment.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.