• 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 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 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 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.

• Journal of the Korean Geographical Society
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• v.43 no.6
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• pp.791-807
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• 2008

The purpose of this study is to analyze landform characteristics and geomorphic processes in Dokdo seamount. For geomorphic analysis, bathymetry data were collected by multi-beam echosounder and the seismic survey was also conducted. Through the detailed analysis of depth, slope, aspect and erosional landform, Dokdo Seamount is characterized by a flat or gently sloping top of $2^{\circ}$ or less and seamount slope with $14{\sim}40^{\circ}$ gradient. There are protrusion of landform around the Dokdo on top of the Dokdo seamount. It is inferred that the features are formed by collapsed debris deposits or remained bedrocks by differential erosion in the past. The massmovement topography including slump and slide is shown on seamount slope with $14{\sim}40^{\circ}$ gradient. In addition, gullies with various length are developed on the Dokdo seamount slope. Slope erosional processes occur more actively along the submarine gullies on the Dokdo seamount. It is inferred that the massmovement processes on the slope of Dokdo seamount are related to earthquake activities and evolution of submarine volcano. Consequently, slope of the Dokdo seamount has retreated by erosional processes of mass-movement and submarine gullies.

• Investigative Magnetic Resonance Imaging
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• v.12 no.2
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• pp.107-114
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• 2008

Purpose : The aim of this study is to determine possibility of application of in vivo proton ($^1H$) magnetic resonance spectroscopy (MRS) in distinguishing cystic mass arising around pancreas by comparison of in vivo MRS, in vitro MRS using 3T MR machine, based on nuclear magnetic resonance (NMR). Materials and Methods : We obtained spectra of in vivo MRS, in vitro MRS and NMR from abdominal mass arising around pancreas (mucinous cystic neoplasm=5, intraductal papillary mucin producing tumor=5, pseudocyst=1, and lymphangioma=1). We estimated existence of peak of in vivo MRS, and in vitro MRS concordant to that of NMR. We also evaluated differential peak for predicting specific disease. Results : Correlation of presence of peak with NMR showed showed sensitivity of 29.6%, specificity of 82.6% and accuracy of 67.7% on in vivo MRS (p = 0.096, McNemar test), sensitivity of 57.1% and specificity of 92.6% and accuracy of 82.3% on in vitro MRS (p = 0.362, McNemar test). The spectra of NMR for IPMT showed more frequent peaks at 3.5-4.0 ppm (p=0.026). Conclusion : Although chemical analysis, using NMR could be regarded as possible tool to differentiate cystic masses, in vivo and in vitro MRS need further technical evolution for clinical application.

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.42.3-43
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• 2009

Triplets of identical cubesats will be built to carry out the following scientific objectives: i) multi-observations of ionospheric ENA (Energetic Neutral Atom) imaging, ii) ionospheric signature of suprathermal electrons and ions associated with auroral acceleration as well as electron microbursts, and iii) complementary measurements of magnetic fields for particle data. Each satellite, a cubesat for ion, neutral, electron, and magnetic fields (CINEMA), is equipped with a suprathermal electron, ion, neutral (STEIN) instrument and a 3-axis magnetometer of magnetoresistive sensors. TRIO is developed by three institutes: i) two CINEMA by Kyung Hee University (KHU) under the WCU program, ii) one CINEMA by UC Berkeley under the NSF support, and iii) three magnetometers by Imperial College, respectively. Multi-spacecraft observations in the STEIN instruments will provide i) stereo ENA imaging with a wide angle in local times, which are sensitive to the evolution of ring current phase space distributions, ii) suprathermal electron measurements with narrow spacings, which reveal the differential signature of accelerated electrons driven by Alfven waves and/or double layer formation in the ionosphere between the acceleration region and the aurora, and iii) suprathermal ion precipitation when the storm-time ring current appears. In addition, multi-spacecraft magnetic field measurements in low earth orbits will allow the tracking of the phase fronts of ULF waves, FTEs, and quasi-periodic reconnection events between ground-based magnetometer data and upstream satellite data.