• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.130-135
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• 2008

An aerodynamic optimization design process of multistage axial turbine is presented in this article: first, applying quasi-three dimensional(Q3D) design methods to conduct preliminary design and then adopting modern optimization design methods to implement multistage local optimization. Quasi-three dimensional(Q3D) design methods, which mainly refer to S2 flow surface direct problem calculation, adopt the S2 flow surface direct problem calculation program of Harbin Institute of Technology. Multistage local optimization adopts the software of Numeca/Design3D, which jointly adopts genetic algorithm and artificial neural network. The major principle of the methodology is that the successive design evaluation is performed by using an artificial neural network instead of a flow solver and the genetic algorithms may be used in an efficient way. Flow computation applies three-dimensional viscosity Navier Stokes(N-S) equation solver. Such optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid. Such process was applied to optimize a three-stage axial turbine. During the optimization, blade shape and meridional channel were respectively optimized. Through optimization, the total efficiency increased 1.3% and total power increased 2.4% while total flow rate only slightly changed. Therefore, the total performance was improved and the design objective was achieved. The preliminary design makes use of quasi-three dimensional(Q3D) design methods to achieve most reasonable parameter distribution so as to preliminarily enhance total performance. Then total performance will be further improved by adopting multistage local optimization design. Thus the design objective will be successfully achieved without huge expenditure of manpower and calculation time. Therefore, such optimization design process may be efficiently applied to the aerodynamic design optimization of multistage axial turbine.

• 한국항공운항학회지
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• 제21권1호
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• pp.30-38
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• 2013

This paper describes the design optimization of Unmanned Aerial Vehicles(UAVs). An optimization framework has been developed and implemented for the conceptual design of UAVs. An integrated design analysis program was developed with several analysis modules such as propulsion, performance, mission, weight, and stability and control. A UAV configuration design optimization was performed by implementing the integrated analysis to enhance the endurance of UAVs. A SQP optimizer was utilized to build an optimization module for this program and sensitivity analysis was performed to determine the trends of shape variables for developing optimization objective. In conclusion, the results indicate that the resulting optimized UAVs configurations show performance improvements over the baseline design and reliable analysis results.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.99-102
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• 2003

Computing environments of Embedded Systems are different from those of desktop computers so that they have resource constraints such as CPU processing, memory capacity, power, and etc.. Thus, when a desktop S/W is ported into embedded systems, optimization should be seriously considered. In this paper, we investigate several S/W optimization techniques to be considered for porting H.263 encoder into a high performance DSP, TMS320C6711. Through experiments, it is found that optimization techniques employed can make a big performance improvement.

• Structural Engineering and Mechanics
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• 제53권4호
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• pp.833-845
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• 2015

Natural frequencies of the structural systems should be far away from the excitation frequency in order to avoid or reduce the destructive effects of dynamic loads on structures. To accomplish this goal, a structural optimization on size and shape has been performed considering frequency constraints. Such an optimization problem has highly nonlinear property. Thus, the quality of the solution is not independent of the optimization technique to be applied. This study presents the performance evaluation of the recently proposed meta-heuristic algorithm called Teaching Learning Based Optimization (TLBO) as an optimization engine in the weight optimization of the truss structures under frequency constraints. Some examples regarding the optimization of trusses on shape and size with frequency constraints are solved. Also, the results obtained are tabulated for comparison. The results demonstrated that the performance of the TLBO is satisfactory. Additionally, TLBO is better than other methods in some cases.

• Structural Engineering and Mechanics
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• 제37권5호
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• pp.543-560
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• 2011

In structural engineering there are randomness inherently exist on determination of the loads, strength, geometry, and so on, and the manufacturing of the structural members, workmanship etc. Thus, objective and constraint functions of the optimization problem are functions that depend on those randomly natured components. The constraints being the function of the random variables are evaluated by using reliability index or performance measure approaches in the optimization process. In this study, the minimum weight of a space truss is obtained under the uncertainties on the load, material and cross-section areas with harmony search using reliability index and performance measure approaches. Consequently, optimization algorithm produces the same result when both the approaches converge. Performance measure approach, however, is more efficient compare to reliability index approach in terms of the convergence rate and iterations needed.

• ETRI Journal
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• 제42권4호
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• pp.505-517
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• 2020

The increasing size and complexity of deep neural networks (DNNs) necessitate the development of efficient high-performance accelerators. An efficient memory structure and operating scheme provide an intuitive solution for high-performance accelerators along with dataflow control. Furthermore, the processing of various neural networks (NNs) requires a flexible memory architecture, programmable control scheme, and automated optimizations. We first propose an efficient architecture with flexibility while operating at a high frequency despite the large memory and PE-array sizes. We then improve the efficiency and usability of our architecture by automating the optimization algorithm. The experimental results show that the architecture increases the data reuse; a diagonal write path improves the performance by 1.44× on average across a wide range of NNs. The automated optimizations significantly enhance the performance from 3.8× to 14.79× and further provide usability. Therefore, automating the optimization as well as designing an efficient architecture is critical to realizing high-performance DNN accelerators.

• Journal of applied mathematics & informatics
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• 제15권1_2호
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• pp.313-321
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• 2004

In this paper we study the problem of three-dimensional layout optimization on the simplified rotating vessel of satellite. The layout optimization model with behavioral constraints is established and some effective and convenient conditions of performance optimization are presented. Moreover, we prove that the performance objective function is locally Lipschitz continuous and the results on the relations between the local optimal solution and the global optimal solution are derived.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.56-62
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• 2019

Recently, research using artificial neural networks has further expanded the field of neural network optimization and automatic structuring from improving inference accuracy. The performance of the machine learning algorithm depends on how the hyperparameters are configured. Open-source hyperparameter optimization software can be an important step forward in improving the performance of machine learning algorithms. In this paper, we review open-source hyperparameter optimization softwares.

• 전기학회논문지P
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• 제67권3호
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• 디지털융복합연구
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• 제10권4호
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• pp.207-215
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• 2012

본 논문에서는 무선 모바일 애드 혹 네트워크상에서 스트리밍 미디어 서비스 향상을 위한 프록시 기반 캐싱 최적화 기법을 제안한다. 제안된 기법은 WLAN상에서 미디어 서버와 노드들 간의 통신을 위해 프록시를 사용하며, 프록시는 무선 엑세스 포인터 근처에 설치한다. 그리고 캐싱 최적화가 수행되도록 NFCO(non-full cache optimization)과 CFO(cache full optimization)기법을 제안한다. NFCO와 CFO는 프록시에서 스트리밍이 수행될 때 캐시의 성능을 최적화하기 위해 사용된다. 제안된 기법의 성능을 알아보기 위하여 본 논문에서는 제안된 기법과 서버-중심 스트리밍 기법, 그리고 전송율 왜곡 스트리밍 기법과의 성능을 비교 분석하였다. 그 결과 제안된 기법이 서버-중심 스트리밍 기법과 전송율 왜곡 스트리밍 기법에 비해서 최적화 성능이 우수함을 알게 되었다.