• Advances in Energy Research
• /
• v.7 no.1
• /
• pp.35-52
• /
• 2020

The paper proposes a hybrid approach of artificial bee colony (ABC) and grey wolf optimizer (GWO) algorithm for multi-objective and multidimensional engine optimization of a converted plug-in hybrid electric vehicle. The proposed strategy is used to optimize all emissions along with brake specific fuel consumption (FC) for converted parallel operated diesel plug-in hybrid electric vehicle (PHEV). All emissions particulate matter (PM), nitrogen oxide (NOx), carbon monoxide (CO) and hydrocarbon (HC) are considered as optimization parameters with weighted factors. 70 hp engine data of NOx, PM, HC, CO and FC obtained from Oak Ridge National Laboratory is used for the study. The algorithm is initialized with feasible solutions followed by the employee bee phase of artificial bee colony algorithm to provide exploitation. Onlooker and scout bee phase is replaced by GWO algorithm to provide exploration. MATLAB program is used for simulation. Hybrid ABC-GWO algorithm developed is tested extensively for various values of speeds and torque. The optimization performance and its environmental impact are discussed in detail. The optimization results obtained are verified by real data engine maps. It is also compared with modified ABC and GWO algorithm for checking the effectiveness of proposed algorithm. Hybrid ABC-GWO offers combine benefits of ABC and GWO by reducing computational load and complexity with less computation time providing a balance of exploitation and exploration and passes repeatability towards use for real-time optimization.

• Nuclear Engineering and Technology
• /
• v.41 no.8
• /
• pp.1005-1014
• /
• 2009

The Dual Unit Optimizer 2000 MWe (DUO2000) is put forward as a new design concept for large power nuclear plants to cope with economic and safety challenges facing the $21^{st}$ century green and sustainable energy industry. DUO2000 is home to two nuclear steam supply systems (NSSSs) of the Optimized Power Reactor 1000 MWe (OPR1000)-like pressurized water reactor (PWR) in single containment so as to double the capacity of the plant. The idea behind DUO may as well be extended to combining any number of NSSSs of PWRs or pressurized heavy water reactors (PHWRs), or even boiling water reactors (BWRs). Once proven in water reactors, the technology may even be expanded to gas cooled, liquid metal cooled, and molten salt cooled reactors. With its in-vessel retention external reactor vessel cooling (IVR-ERVC) as severe accident management strategy, DUO can not only put the single most querulous PWR safety issue to an end, but also pave the way to very promising large power capacity while dispensing with the huge redesigning cost for Generation III+ nuclear systems. Five prototypes are presented for the DUO2000, and their respective advantages and drawbacks are considered. The strengths include, but are not necessarily limited to, reducing the cost of construction by decreasing the number of containment buildings from two to one, minimizing the cost of NSSS and control systems by sharing between the dual units, and lessening the maintenance cost by uniting the NSSS, just to name the few. The latent threats are discussed as well.

• Progress in Medical Physics
• /
• v.30 no.4
• /
• pp.150-154
• /
• 2019

The objective of this study is to monitor the radiation doses delivered to a cardiac implantable electronic device (CIED) by comparing the absorbed doses calculated by a commercial treatment planning system (TPS) to those measured by an in vivo dosimeter. Accurate monitoring of the radiation absorbed by a CIED during radiotherapy is necessary to prevent damage to the device. We conducted this study on three patients, who had the CIED inserted and were to be treated with radiotherapy. Treatment plans were generated using the Eclipse system, with a progressive resolution photon optimizer algorithm and the Acuros XB dose calculation algorithm. Measurements were performed on the patients using optically stimulated luminescence detectors placed on the skin, near the CIED. The results showed that the calculated doses from the TPS were up to 5 times lower than the measured doses. Therefore, it is recommended that in vivo dosimetry be conducted during radiotherapy for CIED patients to prevent damage to the CIED.

• The Bulletin of The Korean Astronomical Society
• /
• v.41 no.2
• /
• pp.66.2-66.2
• /
• 2016

KASI and NAOJ are making collaborating efforts to implement faster mapping capability into the new 275-500 GHz Atacama Submillimeter Telescope Experiment focal plane array (FPA). Feed horn antenna is one of critical parts of the FPA. Required fractional bandwidth is almost 60 % while that of traditional conical horn is less than 50 %. Therefore, to achieve this wideband performance, we adopted a horn of which the corrugation depths have a longitudinal profile. A profiled horn has features not only of wide bandwidth but also of shorter length compared to a linear-tapered corrugated horn, and lower cost fabrication with less error can be feasible. In our design process the flare region is represented by a cubic splined curve with several parameters. Parameters of the flare region and each dimension of the throat region are optimized by a differential evolution algorithm to keep >20 dB return loss and >30 dB maximum cross-polarization level over the operation bandwidth. To evaluate RF performance of the horn generated by the optimizer, we used a commercial mode matching software, WASP-NET. Also, Gaussian beam (GB) masks to far fields were applied to give better GB behavior over frequencies. The optimized design shows >23 dB return loss and >33 dB maximum cross-polarization level over the whole band. Gaussicity of the horn is over 96.6 %. The length of the horn is 12.5 mm which is just 57 % of the ALMA band 8 feed horn (21.96 mm).

• Journal of Korean Association for Spatial Structures
• /
• v.5 no.4 s.18
• /
• pp.53-59
• /
• 2005

This paper provides the results of the investigation on the optimum thickness distribution of plate structures with different essential boundary conditions. In this study, the strain energy to be minimized is considered as the objective function and the initial volume of structures is used as the constraint function. The computer-aided geometric design (CAGD) such as Coon's patch representation is used to represent the thickness distribution of plates. A reliable degenerated shell finite element is adopted to calculate the accurate strain energy level of the plates. Robust optimization algorithms provided in the optimizer DOT are adopted to search the optimum thickness values during the optimization iteration. Finally, the square plate is used to find out the optimum thickness distribution of plates according to different essential boundary condition.

• 한국정보통신설비학회:학술대회논문집
• /
• 2003.08a
• /
• pp.253-257
• /
• 2003

CDMA 방식의 디지털 이동통신 망은 기존의 2G 방식에서 IX, IxEV-DO을 거쳐 WCDMA로 비약적으로 발전하고 있으며, 이에 따라 무선망 운용 및 최적화 방법도 진화해가고 있다. 운용자들이 Field Tool을 사용하여 직접 Field 데이터를 측정, 분석하고 조치하는 방식이 가장 기본적인 방법이라면, Field 데이터와 Network 데이터를 함께 수집하여 분석하는, 보다. 발전된 방법도 사용되고 있다. 그러나, 이러한 방법도 여러 Tool에서 데이터를 off-line으로 수집한 후 분석 작업을 수동으로 반복 수행해야하는 번거로움이 있어, 실시간 on-line 무선망 최적화 시스템을 통한 체계적이고 과학적인 운용 방법을 생각해 볼 수 있다. 우선, 타 운용 Tool 들과의 on-line 연동으로 중앙 집중적 데이터베이스를 구축하여, 무선망에 관련된 모든 데이터에 대한 통합적인 관리가 필요하다. 이 데이터베이스를 이용하여, 실시간으로 무선망 성능 및 효율 저하 원인 분석을 실시하며, 분석된 결과는 기지국의 상태 및 문제점 도출에서부터 최종 처방까지 제시해준다. 본 논문에서는 이러한 솔루션을 구축하기 위한 다양한 네트웍 데이터 연동(성능, 장애, 구성, RF, 실측 데이터 등), 주요 KPI (Key Performance Indicator) 모니터링, 통계적 분석, 무선망 분석 등에 대해 고찰해본다.

• The Bulletin of The Korean Astronomical Society
• /
• v.42 no.2
• /
• pp.60.2-61
• /
• 2017

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.77 for flare classification and 0.83 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

• Journal of Mechanical Science and Technology
• /
• v.16 no.2
• /
• pp.203-210
• /
• 2002

For effective response surface modeling during sequential approximate optimization (SAO), the normalized and the augmented D-optimality criteria are presented. The normalized D-optimality criterion uses the normalized Fisher information matrix by its diagonal terms in order to obtain a balance among the linear-order and higher-order terms. Then, it is augmented to directly include other experimental designs or the pre-sampled designs. This augmentation enables the trust region managed sequential approximate optimization to directly use the pre-sampled designs in the overlapped trust regions in constructing the new response surface models. In order to show the effectiveness of the normalized and the augmented D-optimality criteria, following two comparisons are performed. First, the information surface of the normalized D-optimal design is compared with those of the original D-optimal design. Second, a trust-region managed sequential approximate optimizer having three D-optimal designs is developed and three design problems are solved. These comparisons show that the normalized D-optimal design gives more rotatable designs than the original D-optimal design, and the augmented D-optimal design can reduce the number of analyses by 30% - 40% than the original D-optimal design.

• Journal of Mechanical Science and Technology
• /
• v.15 no.8
• /
• pp.1143-1155
• /
• 2001

In order to reduce the expensive CPU time for design sensitivity analysis in dynamic response optimization, this study introduces the design sensitivities approximated within estimated confidence radius in dynamic response optimization with ALM method. The confidence radius is estimated by the linear approximation with Hessian of quasi-Newton formula and qualifies the approximate gradient to be validly used during optimization process. In this study, if the design changes between consecutive iterations are within the estimated confidence radius, then the approximate gradients are accepted. Otherwise, the exact gradients are used such as analytical or finite differenced gradients. This hybrid design sensitivity analysis method is embedded in an in-house ALM based dynamic response optimizer, which solves three typical dynamic response optimization problems and one practical design problem for a tracked vehicle suspension system. The optimization results are compared with those of the conventional method that uses only exact gradients throughout optimization process. These comparisons show that the hybrid method is more efficient than the conventional method. Especially, in the tracked vehicle suspension system design, the proposed method yields 14 percent reduction of the total CPU time and the number of analyses than the conventional method, while giving similar optimum values.

• Advances in aircraft and spacecraft science
• /
• v.4 no.4
• /
• pp.467-486
• /
• 2017

A method for decoupling reliability based design optimization problem into a set of deterministic optimization and performing a reliability analysis is described. The inner reliability analysis and the outer optimization are performed separately in a sequential manner. Since the outer optimizer must perform a large number of iterations to find the optimized shape and size of structure, the computational cost is very high. Therefore, during the course of this research, new multilevel reliability optimization methods are developed that divide the design domain into two sub-spaces to be employed in an iterative procedure: one of the shape design variables, and the other of the size design variables. In each iteration, the probability constraints are converted into equivalent deterministic constraints using reliability analysis and then implemented in the deterministic optimization problem. The framework is first tested on a short column with cross-sectional properties as design variables, the applied loads and the yield stress as random variables. In addition, two cases of curvilinearly stiffened panels subjected to uniform shear and compression in-plane loads, and two cases of curvilinearly stiffened panels subjected to shear and compression loads that vary in linear and quadratic manner are presented.