• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.772-785
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• 2021

Container ships has a transverse section in the form of an open profile, making it very sensitive to torsion phenomena. To minimize this effect, a structure known as a torsion box exists, which is subject to high stresses influenced by the fatigue phenomenon and the existence of cut-outs, for the passage of the longitudinal stiffeners, acting as stress concentrators. The aim of this study is to propose a two-stage design methodology to aid designers in satisfying the structural requirements and contribute with to a better understanding of the considered structure. The transverse webs of a torsional box structure are examined by comparing different cut-out geometries from numerical models with different regular load conditions to obtain the variables of the fatigue safety factor through linear regression models. The most appropriate geometry of the torsion box is established in terms of minimum weight, from nonlinear multivariable optimization models.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.35-40
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• 1995

Considering the higher discharge burnup, lower channel refuelling rate, lower linear element rating(LER), lower coolant void reactivity and axial power shape, CANFLEX-RU fuel bundle is optimized for CANDU-6 by grading the fissile composition in the ring-wise of the bundle and by applying fuel management scheme appropriately. The fissile composition of the fuel bundle is graded as the recovered uranium (0.9 w/o U-235) in the outer and intermediate elements, depleted Uranium (0.2 w/o U-235) in the center element, natural uranium (0.71 w/o U-235) in the inner elements. Enrichment is not required for these fuel. The fissile composition is optimized by lattice calculation and by time-averaged reactor simulation. CANFLEX-RU optimized for CANDU-6 resulted to be the 15% lower channel refuelling rate, acceptable axial power profile and power envelope, 70% higher discharge burnup, 15% lower LER and not increase coolant void reactivity compared with the 37-element natural uranium bundle for CANDU-6.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Journal of Pharmaceutical Investigation
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• v.40 no.5
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• pp.313-319
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• 2010

The objectives of this study were to evaluate the effect of formulation ingredients on the drug release and to optimize the novel sustained release matrix tablet formulations of bupropion hydrochloride. A three factor, three-level Box-Behnken design was used for the optimization procedure, with the amounts of PEO ($X_1$), citric acid ($X_2$) and Compritol 888 ATO ($X_3$) as the independent variables. The selected dependent variables were the cumulative percentage values of bupropion hydrochloride that had dissolved after 1, 4 and 8 hr. Various dissolution profiles of the drug from sustained release matrix tablets were obtained. Optimization was performed for $X_1$, $X_2$ and $X_3$ using the following target ranges; $30%{\leq}Y_1{\leq}45%$; $70{\leq}Y_2{\leq}85%$; $85%{\leq}Y_3{\leq}100%$. The optimized formulation for bupropion hydrochloride was achieved with 12.5% PEO ($X_1$), 2.5% citric acid ($X_2$) and 10% Compritol 888 ATO ($X_3$). The sustained release matrix tablets with the optimized formulation provided a release profile that was close to predicted values. In addition, the dissolution profiles of the sustained release matrix tablet with the optimized formulation were similar to those of the commercial product Wellbutrin$^{(R)}$ SR tablets ($f_2$=79.83).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1754-1759
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• 2008

To produce plastic parts that have fine pattern through conventional injection molding, a lot of difficulties follow. Therefore, rapid heating and cooling methods are good candidates for manufacturing injection-molded parts with micro/nano patterns. In this study, we adopted the E-Mold patent technology. The mold for E-Mold technology has a separate heated core with micro heaters. It is very important to optimize the lay-out of the heaters in heated core because it influences both control and distribution of mold temperature. We developed a optimization method of heating line lay-out by using commercial softwares and compared the output with the experimental results. We used Pro-Engineer Wildfire 2.0 for the mold design, ICEMCFD for mesh generation, and FLUENT for heat transfer simulation. The simulation results showed the temperature profile from $60^{\circ}C$ to $120^{\circ}C$ or $180^{\circ}C$ during heating and cooling process which were compared with the injection molding experiments. We concluded that the simulation could well explain the experimental results. It was shown that the E-Mold optimization design for heater lay-out could be available through the simulation.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.749-768
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• 2009

In this paper, an intra prediction offset compensation method is proposed to improve intra prediction in H.264/AVC. In H.264/AVC, intra prediction based on various directions improves the coding efficiency by removing spatial correlation between neighboring blocks. In details, neighboring pixels in reconstructed block can be used as intra reference block for the current block to be coded when intra prediction method is used. In order to reduce further the prediction error of the intra reference block, the proposed method introduces an intra prediction offset which is determined in the sense of the rate-distortion optimization and is added to the conventional intra prediction block. Besides the intra prediction offset compensation, the coefficient thresholding method which is used for inter coding in JM 11.0, is used for chroma component in intra block, which leads the improvement of the luma coding efficiency of the proposed method. In experiments, we show that the proposed method achieves average 2.45% in High Profile condition and maximum 4.41% of bitrate reduction relative to JM 11.0.

• Progress in Medical Physics
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• v.18 no.1
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• pp.35-41
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• 2007

In this work practical considerations of a pulsed arterial spin labeling MRI are presented to reliable multi-slice perfusion measurements In the human brain. Three parameters were considered in this study. First, In order to improve slice profile and Inversion efficiency of a labeling pulse a high power Inversion pulse of adiabatic hyperbolic secant was designed. A $900^{\circ}$ rotation of the flip angle was provided to make a good slice profile and excellent Inversion efficiency. Second, to minimize contributions of a residual magnetization be4ween Interleaved scans of control and labeling we tested three different conditions which were applied 1) only saturation pulses, 2) only spotter gradients, and 3) combinations of saturation pulses and spotter gradients Applications of bo4h saturation pulses and spoiler gradients minimized the residual magnetization. Finally, to find a minimum gap between a tagged plane and an imaging plane we tested signal changes of the subtracted image between control and labeled Images with varying the gap. The optimum gap was about 20mm. In conclusion, In order to obtain high quality of perfusion Images In human brain It Is Important to use optimum parameters. Before routinely using In clinical studios, we recommend to make optimizations of sequence parameters.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.3
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• pp.332-336
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• 2024

High-energy bandgap material silicon carbide (SiC) is gaining attention as a next-generation power semiconductor material, and in particular, SiC-based MOSFETs are developed as representative power semiconductors to increase the breakdown voltage (BV) of conventional planar structures. However, as the size of SJ (Super Junction) MOSFET devices decreases and the depth of pillars increases, it becomes challenging to uniformly form the doping concentration of pillars. Therefore, a structure with different doping concentrations segmented within the pillar is being researched. Using Silvaco TCAD simulation, a SJ VVD (vertical variation doping profile) MOSFET with three different doping concentrations in the pillar was studied. Simulations were conducted for the width of the pillar and the doping concentration of N-epi, revealing that as the width of the pillar increases, the depletion region widens, leading to an increase in on-specific resistance (R_on,sp) and breakdown voltage (BV). Additionally, as the doping concentration of N-epi increases, the number of carriers increases, and the depletion region narrows, resulting in a decrease in R_on,sp and BV. The optimized SJ VVD MOSFET exhibits a very high figure of merit (BFOM) of 13,400 KW/cm², indicating excellent performance characteristics and suggesting its potential as a next-generation highperformance power device suitable for practical applications.

• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.474-483
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• 2003

The secret sharing is the basic concept of the threshold cryptosystem and has an important position in the modern cryptography. At 1995, Jarecki proposed the proactive secret sharing to be a solution of existing the mobile adversary and also proposed the share renewal scheme for (k, n) threshold scheme. For n participants in the protocol, his method needs O($n^2$) modular exponentiation per one participant. It is very high computational cost and is not fit for the scalable cryptosystem. In this paper, we propose the efficient share renewal scheme that need only O(n) modular exponentiation per participant. And we prove our scheme is secure if less that ${\frac}\frac{1}{2}n-1$ adversaries exist and they static adversary.