• 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.

• Journal of Astronomy and Space Sciences
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• v.22 no.3
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• pp.249-262
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• 2005

Optimization problems are formulated to calculate optimal impulses for deflecting Earth-Crossing Objects using a Nonlinear Programming. This formulation allows us to analyze the velocity changes in normal direction to the celestial body's orbital plane, which is neglected in many previous studies. The constrained optimization in the three-dimensional space is based on a patched conic method including the Earth's gravitational effects, and yields impulsive ${\Delta}V$ to deflect the target's orbit. The optimal solution is dependent on relative positions and velocities between the Earth and the Earth-crossing objects, and can be represented by optimal magnitude and angle of ${\Delta}V $ as a functions of a impulse time. The perpendicular component of ${\Delta}V $ to the orbit plane can sometimes play un-negligible role as the impulse time approaches the impact time. The optimal ${\Delta}V $ is increased when the original orbit of Earth-crossing object is more similar to the Earth's orbit, and is also exponentially increased as the impulse time reaches to the impact time. The analyses performed in present paper can be used to the deflection missions in the future.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.6
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• pp.555-562
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• 2016

Seismic design of braced frames that simultaneously considers economic issues and structural performance represents a rather complicated engineering problem, and therefore, a systematic and well-established methodology is needed. This study proposes a multi-objective seismic design method for an inverted V-braced frame with suspended zipper struts that uses the non-dominated sorting genetic algorithm-II(NSGA-II). The structural weight and the maximum inter-story drift ratio as the objective functions are simultaneously minimized to optimize the cost and seismic performance of the structure. To investigate which of strength- and performance-based design criteria for braced frames is the critical design condition, the constraint conditions on the two design methods are simultaneously considered (i.e. the constraint conditions based on the strength and plastic deformation of members). The linear static analysis method and the nonlinear static analysis method are adopted to check the strength- and plastic deformation-based design constraints, respectively. The proposed optimal method are applied to three- and six-story steel frame examples, and the solutions improved for the considered objective functions were found.

• Applied Chemistry for Engineering
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• v.26 no.3
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• pp.319-325
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• 2015

Submicron-sized $CeO_2:Er^{3+}/Yb^{3+}$ upconversion phosphor particles were synthesized by spray pyrolysis, and their luminescent properties were characterized by changing the concentration of $Er^{3+}$ and $Yb^{3+}$. $CeO_2:Er^{3+}/Yb^{3+}$ showed an intense green and red emission due to the $^4S_{3/2}$ or $^2H_{11/2}{\rightarrow}^4I_{15/2}$ and $^4F_{9/2}{\rightarrow}^4I_{15/2}$ transition of $Er^{3+}$ ions, respectively. In terms of the emission intensity, the optimal concentrations of Er and Yb were 1.0 % and 2.0%, respectively, and the concentration quenching was found to occur via the dipole-dipole interaction. Upconversion mechanism was discussed by using the dependency of emission intensities on pumping powers and considering the dominant depletion processes of intermediate energy levels for the red and green emission with changing the $Er^{3+}$ concentration. An energy transfer from $Yb^{3+}$ to $Er^{3+}$ in $CeO_2$ host was mainly involved in ground-state absorption (GSA), and non-radiative relaxation from $^4I_{11/2}$ to $^4I_{13/2}$ of $Er^{3+}$ was accelerated by the $Yb^{3+}$ co-doping. As a result, the $Yb^{3+}$ co-doping led to greatly enhance the upconversion intensity with increasing ratios of the red to green emission. Finally, it is revealed that the upconversion emission is achieved by two photon processes in which the linear decay dominates the depletion of intermediate energy levels for green and red emissions for $CeO_2:Er^{3+}/Yb^{3+}$ phosphor.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.289-307
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• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1179-1192
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• 2018

In order to detect the Antarctic Polar Front (PF) among the main fronts in the Southern Ocean, this study is based on the combinations of satellite-based sea surface temperature (SST) and height (SSH) observations. For accurate PF detection, we classified the signals as front or non-front grids based on the Bayesian decision theory from daily SST and SSH datasets, and then spatio-temporal synthesis has been performed to remove primary noises and to supplement geographical connectivity of the front grids. In addition, sea ice and coastal masking were employed in order to remove the noise that still remains even after performing the processes and morphology operations. Finally, we selected only the southernmost grids, which can be considered as fronts and determined as the monthly PF by a linear smoothing spline optimization method. The mean positions of PF in this study are very similar to those of the PFs reported by the previous studies, and it is likely to be well represents PF formation along the bottom topography known as one of the major influences of the PF maintenance. The seasonal variation in the positions of PF is high in the Ross Sea sector (${\sim}180^{\circ}W$), and Australia sector ($120^{\circ}E-140^{\circ}E$), and these variations are quite similar to the previous studies. Therefore, it is expected that the detection approach for the PF position applied in this study and the final composite have a value that can be used in related research to be carried out on the long term time-scale.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.23-31
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• 2010

Due to the fact that fisheye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Not only use the camera as a viewing system, but also as a camera sensor, camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. In this thesis, we introduce a geometric correction technique to minimize the loss of the image data from a vehicle fish-eye lens having a field of view over $180^{\circ}$, and a asymmetric distortion. Geometric correction is a process in which a camera model with a distortion model is established, and then a corrected view is generated after camera parameters are calculated through a calibration process. First, the FOV model to imitate a asymmetric distortion configuration is used as the distortion model. Then, we need to unify the axis ratio because a horizontal view of the vehicle fish-eye lens is asymmetrically wide for the driver, and estimate the parameters by applying a non-linear optimization algorithm. Finally, we create a corrected view by a backward mapping, and provide a function to optimize the ratio for the horizontal and vertical axes. This minimizes image data loss and improves the visual perception when the input image is undistorted through a perspective projection.

• Resources Recycling
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• v.33 no.3
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• pp.3-11
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• 2024

This study addressed the cost structure of metallurgical plants for vanadium recovery or production, which were previously planned or implemented. Vanadium metallurgy consists of several sub-processes such as such as pretreatment, roasting, leaching, precipitation, and filtration, in order to finally produce vanadium pentoxide. Here, lots of costs should be spent for such plants, in which these costs are largely divided into CAPEX (Capital Expenditure) and OPEX (Operational Expenditure). As a result, the capacities (feed input rates) and vanadium contents are various along the target projects for this study. However, final production rates and grades of vanadium pentoxide showed relatively small differences. In addition, a noticeable correlation is found between capacities and specific operating costs, in that a steadily decreasing trend is described with a non-linear curve with around -0.3 power. Therefore, for the plant capacity below 100,000 tons per year, the specific operating cost rapidly decreases as the capacity increases, whereas the cost remains relatively stable in the range of 0.6 to 1.2 million tons per year of the capacity. From a technical perspective, effective optimization of the metallurgical process plant can be achieved by improving vanadium recovery rate in the pre-treatment and/or roasting-leaching processes. Finally, the results of this study should be updated through future research with on-going field verification and further detailed cost analysis.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.175-195
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• 1998

Automatic lineament extraction algorithms had been developed by various researches for geological purpose using remotely sensed data. However, most of them are designed for a certain topographic model, for instance rugged mountainous region or flat basin. Most of common topographic characteristic in Korea is a mountainous region along with alluvial plain, and consequently it is difficult to apply previous algorithms directly to this area. A new algorithm of automatic lineament extraction from remotely sensed images is developed in this study specifically for geological applications. An algorithm, named as DSTA(Dynamic Segment Tracing Algorithm), is developed to produce binary image composed of linear component and non-linear component. The proposed algorithm effectively reduces the look direction bias associated with sun's azimuth angle and the noise in the low contrast region by utilizing a dynamic sub window. This algorithm can successfully accomodate lineaments in the alluvial plain as well as mountainous region. Two additional algorithms for estimating the individual lineament vector, named as ALEHHT(Automatic Lineament Extraction by Hierarchical Hough Transform) and ALEGHT(Automatic Lineament Extraction by Generalized Hough Transform) which are merging operation steps through the Hierarchical Hough transform and Generalized Hough transform respectively, are also developed to generate geological lineaments. The merging operation proposed in this study is consisted of three parameters: the angle between two lines($\delta$$\beta$), the perpendicular distance($(d_ij)$), and the distance between midpoints of lines(dn). The test result of the developed algorithm using Landsat TM image demonstrates that lineaments in alluvial plain as well as in rugged mountain is extremely well extracted. Even the lineaments parallel to sun's azimuth angle are also well detected by this approach. Further study is, however, required to accommodate the effect of quantization interval(droh) parameter in ALEGHT for optimization.