• The korean journal of orthodontics
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• v.41 no.1
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• pp.25-35
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• 2011

Objective: The purpose of this study was to optimize the thread pattern of orthodontic microimplants. Methods: In search of an optimal thread for orthodontic microimplants, an objective function stability quotient (SQ) was built and solved which will help increase the stability and torsional strength of microimplants while reducing the bone damage during insertion. Selecting the AbsoAnchor SH1312-7 microimplant (Dentos Inc., Daegu, Korea) as a control, and using the thread height (h) and pitch (p) as design parameters, new thread designs with optimal combination of hand p combination were developed. Design soundness of the new threads were examined through insertion strain analyses using 3D finite element simulation, torque test, and clinical test. Results: Solving the function SQ, four new models with optimized thread designs were developed (h200p6, h225p7, h250p8, and h275p8). Finite element analysis has shown that these new designs may cause less bone damage during insertion. The torsional strength of two models h200p6 and h225p7 were significantly higher than the control. On the other hand, clinical test of models h200p6 and h250p8 had similar success rates when compared to the control. Conclusion: Overall, the new thread designs exhibited better performance than the control which indicated that the optimization methodology may be a useful tool when designing orthodontic microimplant threads.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.4
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• pp.41-47
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• 2012

Railroad rolling stock has long service life and a lot of maintenance cost running on rail by wear and vibration. And it is very important to get optimization of maintenance. This paper want to analyze rolling stock maintenance situation of KORAIL and find out its improvement methods. Especially, the purpose of this paper is to adopt the most effective maintenance period and methods to daily inspection which needs many maintenance manpower in rolling stock. Rolling stock has self-diagnosis function using computer system and the quality of rolling stock has much improved these days but current daily inspection repeat for short period routinely and it is very ineffective. Therefore, the paper adopt improved daily inspection period reflecting the characteristics of rolling stock, and want to secure reliability of rolling stock and minimize maintenance cost.

• Journal of the Korean Solar Energy Society
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• v.34 no.4
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• pp.23-29
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• 2014

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

• Coupled systems mechanics
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• v.9 no.2
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• pp.147-161
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• 2020

Constructive merging of "basic" systems of different behavior creates hybrid systems. In doing so, the structural elements are grouped according to the behavior in carrying the load into a geometric order that provides sufficient load and structure functionality and optimization of the material consumption. Applicable in all materializations and logical geometric forms is a transparent system suitable for the optimization of load-bearing structures. Research by individual authors gave insight into suitable system constellations from the aspect of load capacity and the approximatemethod of estimating the participation of partialstiffnesswithin the rigidity ofthe hybrid system. The obtained terms will continue to be the basisfor our own research of the influence of variable parameters on the behavior of hybrid systemsformed of glued laminated girder and cable of different geometric shapes. Previous research has shown that by applying the strut-type hybrid systems can increase the load capacity and reduce the deformability ofthe free girder.The implemented parametric analysis pointsto the basic parameterin the behavior of these systems-the rigidity ofindividual elements and the overallstiffnessofthe system.The basic idea ofpre-stressing is that, in the load system or individual load-bearing element, prior to application of the exploitation load, artificially challenge the forcesthatshould optimize the finalsystembehaviorin the overall load. Pre-stressing is possible only if the supporting system orsystem's element possesssufficientstrength orstiffness, orreaction to the imposed forces of pre-stressing. In this paper will be presented own research of the relationship of partial stiffness of strut-type hybrid systemsofdifferentgeometric forms.Conducted parametric analysisofhybridsystemswithandwithoutpre-stressing, and on the example of the glulam-steel strut-type hybrid system under realistic conditions of change in the moisture content ofthe wooden girder,resulted in accurate expressions and diagramssuitable for application in practice.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• Journal of Ocean Engineering and Technology
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• v.32 no.6
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• pp.419-426
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• 2018

Recently, in an effort to reduce the energy efficiency design index (EEDI), studies on energy saving devices (ESDs) have been conducted. In this study, we designed a post-device suitable for a KRISO container ship (KCS) using computational fluid dynamics (CFD). In order to increase the efficiency of the post-device, a twisted rudder was used, which has a proven performance (showing a 1.34% reduction in DHP compared to the bare hull at 24 knots) in previous research at Pusan National University. In addition, an increase in efficiency was expected by the use of a rudder bulb, including the discontinuous section of the twisted rudder and a divergent propeller cap to prevent the contraction of the wake. The optimization criterion was the case where the delivery power was the least compared with the bare hull. We analyzed the cause of the efficiency increase through an analysis of the self-propulsion factor. The case study for optimization was divided into 4 types (1. clearance of the bulb and cap, 2. shape of the bulb, 3. size of the bulb and cap, and 4. asymmetric bulb). Finally, with a clearance of 50 mm from the ship, a spherical bulb with the cap having an angle of $5^{\circ}$, and an asymmetric rudder bulb with a bulb diameter of 1.2HH/1.4H (horizontal/vertical) showed a 2.05% reduction in DHP compared to the bare hull at 24 knots. We will fabricate a post-device that will be optimized in the future and verify the performance of the post-device through model tests.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.29-36
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• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4489-4493
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• 2014

LED light driving device has problems in efficiency and heating at higher than 150W. In addition, there is inconvenience in replacing the lighting device to another when W is not the same as the previous one. In this paper, a multi-channel LED light driver, driver embedded driver circuit in a multi-channel structure with a power system in the driver-interlocking structure was designed. With the auto control converter structure with a power efficiency above 93% and power factor above 0.98, the weight of the high efficiency LED lighting-actuating device in driver-interlocking structure, a driver in self-calibrating self-optimization structure. In this paper, at below 10% THD, the existing converter contrast weight was reduced by 40% or more.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.10-15
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• 2015

This paper proposes the parallel implementation of a self-similarity based image SR (super-resolution) algorithm using OpenCL. The SR algorithm requires tremendous computations to search for a similar patch. This becomes a bottleneck for the real-time conversion from a FHD image to UHD. Therefore, it is imperative to accelerate the processing speed of SR algorithms. For parallelization, the SR process is divided into several kernels, and memory optimization is performed. In addition, two GPUs are used for further acceleration. The experimental results shows that a GPGPU implementation can speed up over 140 times compared to a single-core CPU. Furthermore, it was confirmed experimentally that utilizing two GPUs can speed up the execution time proportionally, up to 277 times.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1508-1517
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• 2015

Optimal Power dispatch is the short-term decision of the optimal output of a number of power generation facilities, to meet the system demand, with the objective of Power dispatching at the lowest possible cost, subject to transmission lines power loss and operational constraints. The operational constraint includes power balance constraint, generator limit constraint, and emission dispatch constraint and valve point effects. In this paper, Opposition based Differential Evolution Algorithm (ODEA) has been proposed to handle the objective function and the operational constraints simultaneously. Furthermore, the valve point loading effects and transmission lines power loss are also considered for the efficient and effective Power dispatch. The ODEA has unique features such as self tuning of its control parameters, self acceleration and migration for searching. As a result, it requires very minimum executions compared with other searching strategies. The effectiveness of the algorithm has been validated through four standard test cases and compared with previous studies. The proposed method out performs the previous methods.