• 전기학회논문지
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• 제67권7호
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• pp.833-839
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• 2018

This paper presents an analysis and the methodology of optimal operation strategy of the ESS(Energy Storage System) for reduce electricity charges. Electricity charges consist of a basic charge based on the contract capacity and energy charge according to the power usage. In order to use electrical energy at minimal charge, these two factors are required to be reduced at the same time. QP(Quadratic Programming) is appropriate for minimization of the basic charge and LP(Linear Programmin) is adequate to minimize the energy charge. However, the integer variable have to be introduced for modelling of different charge and discharge efficiency of ESS PCS(Power Conversion System), where MILP(Mixed Integer Linear Programming) can be used. In this case, the extent to which the peak load savings is accomplished should be assumed before the energy charge is minimized. So, to minimize the electricity charge exactly, optimization is sequentially performed in this paper, so-called the Two Stage Hybird optimization, where the extent to which the peak load savings is firstly accomplished through optimization of basic charge and then the optimization of energy charge is performed with different charge and discharge efficiency of ESS PCS. Finally, the proposed method is analyzed quantitatively with other optimization methods.

• Advances in aircraft and spacecraft science
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• 제9권5호
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• pp.401-414
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• 2022

In the present paper, the optimal selection of batteries for an electric pump-fed hybrid rocket engine is analyzed. A two-stage Mars Ascent Vehicle, suitable for the Mars Sample Return Mission, is considered as test case. A single engine is employed in the second stage, whereas the first stage uses a cluster of two engines. The initial mass of the launcher is equal to 500 kg and the same hybrid rocket engine is considered for both stages. Ragone plot-based correlations are embedded in the optimization process in order to chose the optimal values of specific energy and specific power, which minimize the battery mass ad hoc for the optimized engine design and ascent trajectory. Results show that a payload close to 100 kg is achievable considering the current commercial battery technology.

• International Journal of Aerospace System Engineering
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• 제2권2호
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• pp.29-33
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• 2015

The multidisciplinary design optimization (MDO) of a launch vehicle (LV) with a hybrid rocket engine (HRE) was carried out to investigate the ability of an HRE for a single-stage LV. The non-dominated sorting genetic algorithm-II (NSGA-II) was employed to solve two design problems. The design problems were formulated as two-objective cases involving maximization of the downrange distance over the target flight altitude and minimization of the gross weight, for two target altitudes: 50.0 km and 100.0 km. Each objective function was empirically estimated. Several non-dominated solutions were obtained using the NSGA-II for each design problem, and in each case, a trade-off was observed between the two objective functions. The results for the two design problem indicate that economical performance of the LV is limited with the HRE in terms of the maximum downrange distances achievable. The LV geometries determined from the non-dominated solutions were examined.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Smart Structures and Systems
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• 제11권1호
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• pp.69-86
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• 2013

An efficient methodology using static test data and changes in natural frequencies is proposed to identify the damages in structural systems. The methodology consists of two main stages. In the first stage, the Damage Signal Match (DSM) technique is employed to quickly identify the most potentially damaged elements so as to reduce the number of the solution space (solution parameters). In the second stage, a particle swarm optimization (PSO) approach is presented to accurately determine the actual damage extents using the first stage results. One numerical case study by using a planar truss and one experimental case study by using a full-scale steel truss structure are used to verify the proposed hybrid method. The identification results show that the proposed methodology can identify the location and severity of damage with a reasonable level of accuracy, even when practical considerations limit the number of measurements to only a few for a complex structure.

• 응용통계연구
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• 제33권6호
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• pp.683-696
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• 2020

표본의 다범주 표현형을 예측하는데 사용되는 최적의 유전자집단이란 적은 수의 유전자로 표현형을 정확히 예측할 수 있는 유전자들의 모임이다. 특이발현유전자를 검색하는 통계량은 이미 여러 가지가 있고, K-평균 군집화를 곁들여 중복성이 적은 특이발현유전자들을 선택 가능하다. 이들을 바탕으로 적은 수로 정확하게 다범주 분류가 가능한 유전자집단을 구성할 수 있도록 수정한 입자최적화 방법을 제안한다. 널리 알려진 ALL 248례와 SRBCT 83례를 이용하여 제안된 방법으로 최적유전자집단을 찾을 수 있음을 보였다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.53-63
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• 2017

The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. Two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.

• Structural Engineering and Mechanics
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• 제70권3호
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• pp.339-350
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• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

• Smart Structures and Systems
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• 제25권5호
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• 한국항공우주학회지
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• 제32권4호
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• pp.53-60
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• 2004

F-4E를 모선으로 하는 초소형 위성을 탑재할 수 있는 공중발사체 1단 부스터용 하이브리드 모터의 최적설계를 실시하였다. 설계변수는 포트개수, 초기 산화제 플럭스, 연소실 압력, 그리고 노즐 팽창비 등을 사용하였다. 또한 서로 다른 최적화 알고리듬의 적용 가능성을 검증하기 위하여 구배법 (GBM)과 유전자 알고리듬 (GA) 방법을 각각 사용하였으며, 목적함수의 선택에 따른 최적화 결과의 변화를 살펴보기 위하여 두 가지 종류의 목적함수 (모터 중량과 모터 길이)를 사용하여 그 결과를 상호 비교하였다. 최적화 알고리듬, 그리고 목적함수의 선택과 무관하게 거의 같은 설계결과로 수렴함을 확인하였다. 최적화결과로 설계요구조건을 만족하는 총중량 704.74kg, 1단 길이 3.74m의 하이브리드 모터를 설계 할 수 있었다.