• International Journal of Quality Innovation
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• 제5권1호
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• pp.43-51
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• 2004

During process design or process optimization, it is quite common for experimenters to find optimum operating conditions for several responses simultaneously. The traditional multiresponse surfaces optimization methods do not consider the uncertain relationship among these responses sufficiently. For this reason, the authors propose an optimization method based on evidential reasoning theory by Dempster and Shafer. By maximizing the basic probability assignment function, which indicates the degree of belief that certain operating condition is the solution of this multiresponse surfaces optimization problem, the desirable operating condition can be found.

• 융합정보논문지
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• 제7권6호
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• pp.187-192
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• 2017

현재 Android 운영체제에서는 효율성이 검증된 Unity 최적화 방법이 존재한다. 그러나 Tizen 운영체제는 기존의 Unity 최적화 방법이 Tizen 운영 체제에 적용될 수 있는지, 얼마나 효과적인지 알려진 바가 많지 않다. 이 연구에서는 기존에 검증된 Android 운영체제의 Unity 최적화 기법들을 Tizen 운영체제에 적용했을 때의 성능에 대해 분석한다. 이를 위해 기존의 Unity 최적화 방법을 Tizen 운영 체제에 적용할 수 있는지를 조사하고 해당 기법들의 효율성을 Unity Profiler를 통해 평가한다. 이를 통해 향후 기존의 Android 운영체제에 대한 Unity 최적화 방안들에 대한 심층적 평가를 추가적으로 진행하고자 한다.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• International Journal of Naval Architecture and Ocean Engineering
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• 제6권2호
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• pp.496-506
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• 2014

In this paper the scenario based optimization of the bulbous bow of the KRISO Container Ship (KCS) is presented. The optimization of the parametrically modeled vessel is based on a statistically developed operational profile generated from noon-to-noon reports of a comparable 3600 TEU container vessel and specific development functions representing the growth of global economy during the vessels service time. In order to consider uncertainties, statistical fluctuations are added. An analysis of these data lead to a number of most probable upcoming operating conditions (OC) the vessel will stay in the future. According to their respective likeliness an objective function for the evaluation of the optimal design variant of the vessel is derived and implemented within the parametrical optimization workbench FRIENDSHIP Framework. In the following this evaluation is done with respect to vessel's calculated effective power based on the usage of potential flow code. The evaluation shows, that the usage of scenarios within the optimization process has a strong influence on the hull form.

• 한국자동차공학회논문집
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• 제13권2호
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• pp.101-107
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• 2005

In this paper, optimized operating parameters were found using multi-dimensional engine simulation software (KIVA-3V) and micro-genetic algorithm for heavy duty diesel engine. The engine operating condition considered was at 1,737 rev/min and 57 % load. Engine simulation model was validated using an engine equipped with a high pressure electronic unit injector (HEUI) system. Three important parameters were used for the optimization - boost pressure, EGR rate and start of injection timing. Numerical optimization identified HCCI-like combustion characteristics showing significant improvements for the soot and $NO_X$ emissions. The optimized soot and $NO_X$ emissions were reduced to 0.005 g/kW-hr and 1.33 g/kW-hr, respectively. Moreover, the optimum results met EPA 2007 mandates at the operating point considered.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.582-588
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• 1993

A scheme of dynamic optimization for batch reactor his been developed and applied to a semi-batch esterification reactor. To obtain optimal operating conditions for the given semi-batch reactor system with complex reaction kinetic and process constraints, a general nonlinear programming solver and finite element techniques have been introduced. The optimization results for the complex reactor system have been compared with those of Kumar et al. [1984] to show better optimization performance. The proposed optimizing scheme has been applied to the free end time problem to obtain the realistic operating condition. The results can supply valuable information for economic operation of the given batch esterification reactor.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• Journal of Biosystems Engineering
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• 제35권5호
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• pp.287-293
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• 2010

In order to optimize an agricultural tractor clutch mechanism system, its structural static and kinematic mechanism were analyzed. The operating force of the mechanical tractor clutch system is currently not appropriate to drive comfortably. So it is needed to reduce the clutch operating force by applying advanced engineering design techniques. In the present study, an optimization technology is applied to the design of tractor clutch systems to reduce the operating force. As a result of the optimization using 2 link-angles and 1 link-length which are the main design variables of the clutch linkage system, the maximum pushing force of the maximum clutch pedal was found 182.8N, 14% decreased compared to the existing clutch system. The effectiveness of the optimum design is certified by menas of an experiment.

• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.264-273
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• 2017

In order to be able to predict the maximum Annual Energy Production (AEP) for tidal power plants, an AEP optimization tool based on Evolutionary Algorithms was developed by ANDRITZ HYDRO. This tool can simulate all operating modes of the units (bi-directional turbine, pump and sluicing mode) and provide the optimal plant operation that maximizes the AEP to the control system. For the Swansea Bay Tidal Power Plant, the AEP optimization evaluated all different hydraulic and operating concepts and defined the optimal concept that led to a significant AEP increase. A comparison between the optimal plant operation provided by the AEP optimization and the full load operating strategy is presented in the paper, highlighting the advantage of the method in providing the maximum AEP.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1999년도 춘계학술대회 논문집
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• pp.212-216
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• 1999

We propose a method for simulation optimization using stochastic genetic algorithm(GA) to get the best design and operating policies in a Direct-Input-Output Automated Manufacturing System (DIOMS).