• 한국수자원학회논문집
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• 제30권6호
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• pp.629-639
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• 1997

1960년대 이래로 우리나라 주요수계에는 이·치수를 목적으로 많은 댐들이 건설되어 운영되어 오고 있다. 계속 증가하는 용수수요를 충족시키기 위한 신규댐 개발이 국가 장기 수자원 개발계획에 포함되어 있어 수자원 시스템은 더욱 복잡해 질 것으로 예상된다. 이와 같은 특징은 장래 수자원 관리 정책 결정문제에서 보다 많은 대안 검토를 요구하게 되므로 이를 해결할 수 있는 합리적인 접근방법의 개발이 요구된다. 본 연구는 10여개 이상의 대구모 저수지 시스템 문제를 해결하기 위한 방법중 문제를 간략화하기 위한 시도로 중·소규모 댐에 대해서는 모의모형(HEC-5)을 적용하고 이의 결과를 대용량의 댐으로 구성된 최적화 모형(IDP)의 입력자료로 이용하여 최종적인 결과를 얻는 2단계 해석 방법을 한강유역 물수지 분석에 적용하였다. 아울러 한강수계의 기존 다목적댐과 장래 개발 계획댐을 하나의 시스템으로 구성하고 DPSA를 이용하여 유역 개념의 수자원 평가에 적용하였다.

• 한국정보통신학회논문지
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• 제23권8호
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• pp.1011-1017
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• 2019

본 논문은 무선 애드혹 네트워크에서 신뢰성이 보장되는 데이터 전송을 위해 다중 경로를 설정하는 노드분리 경로문제를 해결하기 위한 강화학습을 제안한다. 노드분리 경로문제는 소스와 목적지사이에 중간 노드가 중복되지 않게 다수의 경로를 결정하는 문제이다. 본 논문에서는 기계학습 중 하나인 강화학습에서 Q-러닝을 사용하여 노드의 수가 많은 대규모의 무선 애드혹 네트워크에서 전송거리를 고려한 최적화 방법을 제안한다. 특히 대규모의 무선 애드혹 네트워크에서 노드분리 경로 문제를 해결하기 위해서는 많은 계산량이 요구되지만 제안된 강화학습은 효율적으로 경로를 학습함으로써 적절한 결과를 도출한다. 제안된 강화학습의 성능은 2개의 노드분리경로를 설정하기 위한 전송거리 관점에서 평가되었으며, 평가 결과에서 기존에 제안된 시뮬레이티드 어널링과 비교평가하여 전송거리면에서 더 좋은 성능을 보였다.

• Smart Structures and Systems
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• 제25권4호
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• pp.487-499
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• 2020

This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.

• International Journal of Aeronautical and Space Sciences
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• 제8권1호
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• pp.95-104
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• 2007

A new design approach of complex geometries such as wing/body configuration is arranged by using overset mesh techniques under large scale computing environment. For an in-depth study of the flow physics and highly accurate design, several special overlapped structured blocks such as collar grid, tip-cap grid, and etc. which are commonly used in refined drag prediction are adopted to consider the applicability of the present design tools to practical problems. Various pre- and post-processing techniques for overset flow analysis and sensitivity analysis are devised or implemented to resolve overset mesh techniques into the design optimization problem based on Gradient Based Optimization Method (GBOM). In the pre-processing, the convergence characteristics of the flow solver and sensitivity analysis are improved by overlap optimization method. Moreover, a new post-processing method, Spline-Boundary Intersecting Grid (S-BIG) scheme, is proposed by considering the ratio of cell area for more refined prediction of aerodynamic coefficients and efficient evaluation of their sensitivities under parallel computing environment. With respect to the sensitivity analysis, discrete adjoint formulations for overset boundary conditions are derived by a full hand-differentiation. A smooth geometric modification on the overlapped surface boundaries and evaluation of grid sensitivities can be performed by mapping from planform coordinate to the surface meshes with Hicks-Henne function. Careful design works for the drag minimization problems of a transonic wing and a wing/body configuration are performed by using the newly-developed and -applied overset mesh techniques. The results from design applications demonstrate the capability of the present design approach successfully.

• 대한기계학회논문집A
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• 제31권1호
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• pp.26-35
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• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

• Nuclear Engineering and Technology
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• 제52권11호
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• pp.2471-2478
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• 2020

Pumps are essential machinery in the various industries. With the development of high-speed and large-scale pumps, especially high energy density, high requirements have been imposed on the vibration and noise performance of pumps, and cavitation is an important source of vibration and noise excitation in pumps, so it is necessary to improve pumps cavitation performance. The modern pump optimization design method mainly adopts parameterization and artificial intelligence coupling optimization, which requires direct correlation between geometric parameters and pump performance. The existing cavitation performance calculation method is difficult to be integrated into multi-objective automatic coupling optimization. Therefore, a fast prediction method for pump cavitation performance is urgently needed. This paper proposes a novel cavitation prediction method based on impeller pressure isosurface at single-phase media. When the cavitation occurs, the area of pressure isosurface S_iso increases linearly with the NPSH_a decrease. This demonstrates that with the development of cavitation, the variation law of the head with the NPSH_a and the variation law of the head with the area of pressure isosurface are consistent. Therefore, the area of pressure isosurface S_iso can be used to predict cavitation performance. For a certain impeller blade, since the area ratio R_s is proportional to the area of pressure isosurface S_iso, the cavitation performance can be predicted by the R_s. In this paper, a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments, which will greatly accelerate the pump hydraulic optimization design.

• 한국철도학회:학술대회논문집
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• 한국철도학회 1999년도 추계학술대회 논문집
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• pp.46-53
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• 1999

As of July 1999, i,185 lomocotives(excluding metropolitan area electric locomotives) are in Korean National Railroad(KNR). With this limited number of resources assigning locomotives to each trains of timetable is very important in the entire railway management point of view because schedule can be regarded as goods in transportation industry. On a simple rail network, it is rather easier to assign proper locomotives to trains with the experience of operating experts and get optimal assignment solution. However, as the network is getting bigger and complicated, the number of trains and corresponding locomotives will be dramatically increased to rover all the demands required to service all of the trains in timetable. There will be also numerous operational constraints to be considered. Assigning proper locomotives to trains and building optimal cyclic rotations of locomotive routings will result in increasing efficiency of schedule and giving a guarantee of more profit. The purpose of this study is two fold: (1) we consider a planning-level locomotive scheduling problem with the objective of minimizing the wasting cost under various practical constraints and (2) development of implementation prototype program of its assigning result. Not like other countries, i.e. Canada, Sweden, Korean railroad operates on n daily schedule basis. The objective is to find optimal assignment of locomotives of different types to each trains, which minimize the wasting cost. This problem is defined on a planning stage and therefore, does not consider operational constraints such as maintenance and emergency cases. Due to the large scale of the problem size and complexity, we approach with heuristic methods and column generation to find optimal solution. The locomotive scheduling prototype consists of several modules including database, optimization engine and diagram generator. The optimization engine solves MIP model and provides an optimal locomotive schedule using specified optimization algorithms. A cyclic locomotive route diagram can be generated using this optimal schedule through the diagram generator.

• Smart Structures and Systems
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• 제31권3호
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• International Journal of Railway
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• 제2권3호
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• pp.124-126
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• 2009

Energy savings can be achieved with optimum energy consumptions, brake energy regeneration, efficient energy storage (onboard, line side), and primarily with light weight vehicles. Over the last few years, the rolling stock industry has experienced a marked increase in eco-awareness and needs for lower life cycle energy consumption costs. For rolling stock vehicle designers and engineers, weight has always been a critical design parameter. It is often specified directly or indirectly as contractual requirements. These requirements are usually expressed in terms of specified axle load limits, braking deceleration levels and/or demands for optimum energy consumptions. The contractual requirements for lower weights are becoming increasingly more stringent. Light weight vehicles with optimized strength to weight ratios are achievable through proven design processes. The primary driving processes consist of: $\bullet$ material selection to best contribute to the intended functionality and performance $\bullet$ design and design optimization to secure the intended functionality and performance $\bullet$ weight control processes to deliver the intended functionality and performance Aluminium has become the material of choice for modern light weight bodyshells. Steel sub-structures and in particular high strength steels are also used where high strength - high elongation characteristics out way the use of aluminium. With the improved characteristics and responses of composites against tire and smoke, small and large composite materials made components are also found in greater quantities in today's railway vehicles. Full scale hybrid composite rolling stock vehicles are being developed and tested. While an "overdesigned" bodyshell may be deemed as acceptable from a structural point of view, it can, in reality, be a weight saving missed opportunity. The conventional pass/fail structural criteria and existing passenger payload definitions promote conservative designs but they do not necessarily imply optimum lightweight designs. The weight to strength design optimization should be a fundamental design driving factor rather than a feeble post design activity. It should be more than a belated attempt to mitigate against contractual weight penalties. The weight control process must be rigorous, responsible, with achievable goals and above all must be integral to the design process. It should not be a mere tabulation of weights for the sole-purpose of predicting the axle loads and wheel balances compliance. The present paper explores and discusses the topics quoted above with a view to strengthen the recommendations and needs for the weight optimization by design approach as a pro-active design activity for the rolling stock industry at large.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2649-2666
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• 2015

In the dynamic cloud computing environment, to ensure, under the terms of service-level agreements, the maximum efficiency of resource utilization, it is necessary to investigate the online dynamic management of virtual machine resources and their operational application systems/components. In this study, the feasibility and properties of the division of virtual machine resources on the cloud platform, using the virtual machine cluster as the management unit, are investigated. First, the definitions of virtual machine clusters are compared, and our own definitions are presented. Then, the feasibility of division using the virtual machine cluster as the management unit is described, and the isomorphism and reconfigurability of the clusters are proven. Lastly, from the perspectives of clustering and cluster segmentation, the dynamics of virtual machines are described and experimentally compared. This study aims to provide novel methods and approaches to the optimization management of virtual machine resources and the optimization configuration of the parameters of virtual machine resources and their application systems/components in large-scale cloud computing environments.