• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.29-43
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• 2021

In this paper, a new approach to solve the constrained dynamic programming is proposed by using the constraint programming. While the conventional dynamic programming scheme has the state space augmented with states on constraints, this approach, without state augmentation, represents states of constraints as domains in a contraining programming solver. It has a hybrid computational mechanism in its computation by combining solving the Bellman equation in the dynamic programming framework and exploiting the propagation and inference methods of the constraint programming. In order to portray the differences of the two approaches, this paper solves a simple version of the long-term car rental financing problem. In the conventional scheme, data structures for state on constraints are designed, and a simple inference borrowed from the constraint programming is used to the reduction of violation of constraints because no inference risks failure of a solution. In the hybrid approach, the architecture of interface of the dynamic programming solution method and the constraint programming solution method is shown. It finally discusses the advantages of the proposed method with the conventional method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.497-508
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• 2021

In this paper, we focus on optimizing the performance of CSURF, which uses the tweaked Montgomery curves. The projective version of elliptic curve arithmetic is slower on tweaked Montgomery curves than on Montgomery curves, so that CSURF is slower than the hybrid version of CSIDH. However, as the square-root Velu formula uses less number of ellitpic curve arithmetic than the standard Velu formula, there is room for optimization We optimize the square-root Velu formula and 2-isogeny formula on tweaked Montgomery curves. Our CSURFis 14% faster than the standard CSURF, and 10.8% slower than the CSIDH using the square-root Velu formula. The constant-time CSURF is 6.8% slower than constant-time CSIDH. Compared to the previous implementations, this is a remarkable result.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.1
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• pp.33-44
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• 2012

In this study, the optimal operating conditions for the dual mixed refrigerant(DMR) cycle were determined by considering the power efficiency. The DMR cycle consists of compressors, heat exchangers, seawater coolers, valves, phase separators, tees, and common headers, and the operating conditions include the equipment's flow rate, pressure, temperature, and refrigerant composition per flow. First, a mathematical model of the DMR cycle was formulated in this study by referring to the results of a past study that formulated a mathematical model of the single mixed refrigerant(SMR) cycle, which consists of compressors, heat exchangers, seawater coolers, and valves, and by considering as well the tees, phase separators, and common headers. Finally, in this study, the optimal operating conditions from the formulated mathematical model was obtained using a hybrid optimization method that consists of the genetic algorithm(GA) and sequential quadratic programming(SQP). Moreover, the required power at the obtained conditions was decreased by 1.4% compared with the corresponding value from the past relevant study of Venkatarathnam.

• Smart Structures and Systems
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• v.21 no.4
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• pp.435-448
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• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3046-3070
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• 2017

Nowadays, the utilization of multiprocessor environments has been increased due to the increase in time complexity of application programs and decrease in hardware costs. In such architectures during the compilation step, each program is decomposed into the smaller and maybe dependent segments so-called tasks. Precedence constraints, required execution times of the tasks, and communication costs among them are modeled using a directed acyclic graph (DAG) named task-graph. All the tasks in the task-graph must be assigned to a predefined number of processors in such a way that the precedence constraints are preserved, and the program's completion time is minimized, and this is an NP-hard problem from the time-complexity point of view. The results obtained by different approaches are dominated by two major factors; first, which order of tasks should be selected (sequence subproblem), and second, how the selected sequence should be assigned to the processors (assigning subproblem). In this paper, a hybrid proposed approach has been presented, in which two different artificial ant colonies cooperate to solve the multiprocessor task-scheduling problem; one colony to tackle the sequence subproblem, and another to cope with assigning subproblem. The utilization of background knowledge about the problem (different priority measurements of the tasks) has made the proposed approach very robust and efficient. 125 different task-graphs with various shape parameters such as size, communication-to-computation ratio and parallelism have been utilized for a comprehensive evaluation of the proposed approach, and the results show its superiority versus the other conventional methods from the performance point of view.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.757-764
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• 2017

As the reduction of greenhouse gases(GHGs) emission has become a global issue, the microgrid markets are growing rapidly. With the sudden changes in the market, Korean government suggested a new business model called 'Self-Sufficient Energy Islands'. Its main concern is a stand-alone microgrid composed of Distributed Energy Resources(DERs) such as Renewable Energy Sources(RESs), Energy Storage System(ESS) and Fuel Cell, in order to minimize the emission of GHGs. According to these trend, this paper is written to propose an optimal sizing method of DERs in a stand-alone microgrid by using Genetic Algorithm(GA), one of the representative stochastic methods. It is to minimize the net present cost with the variables, size of RESs and ESS. In the process for optimization, the sunless days are considered as additional constraints. Through the case study analysis, the size of DERs installed in a microgrid system has been computed using the proposed method in MATLAB. And the result of MATLAB is compared with that of HOMER(Hybrid Optimization of Multiple Energy Resources), a well-known energy modeling software.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.11
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• pp.989-996
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• 2016

This paper intends to develop the rotor track and balance (RTB) algorithm using the nonlinear RTB models and a real-coded hybrid genetic algorithm. The RTB response data computed using the trim solutions with variation of the adjustment parameters have been used to build nonlinear RTB models based on the quadratic interpolation functions. Nonlinear programming problems to minimize the track deviations and the airframe vibration responses have been formulated to find optimum settings of balance weights, trim-tab deflections, and pitch-link lengths of each blade. The results are efficiently resolved using the real-coded genetic algorithm hybridized with the particle swarm optimization techniques for convergence acceleration. The nonlinear RTB models and the optimized RTB parameters have been compared with those computed using the linear models to validate the proposed techniques. The results showed that the nonlinear models lead to more accurate models and reduced RTB responses than the linear counterpart.

• Composites Research
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• v.24 no.1
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• pp.37-44
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• 2011

In this paper, Particle Swarm Optimization(PSO) is applied to the design of the Frequency Selective Surface(FSS) and residual stresses of hybrid radome is predicted. An equivalent circuit model with Square Loops arrays was derived and then PSO was applied for acquiring the optimized geometrical parameters with proper resonant frequency. Residual stresses occur in the FSS embedded composite structures after cocuring and have a great influence on the strength of the FSS embedded composite structures. They also effect transmission quality because of delamination. Therefore, the thermal residual stresses of FSS embedded composite structures were analyzed using finite element analysis with considering the effects of FSS pattern, and composite stacking sequence.

• Journal of the Korean Society of Radiology
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• v.7 no.6
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• pp.389-395
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• 2013

This study intended to investigate the optimum conditions for lumbar test that has the highest level of irradiation conditions among general test sites. To to this, the most widely used irradiation conditions in terms of statistics were set as standards; test groups applied with DMF were selected; tests groups suitable for clinical trials were selected by using suggested patient dose. Blind tests were conducted by 10 specialists and radiologists. The results suggested that under the optimum conditions, the radiation dose reduction of 2.09 mGy, 4.42 mGy and 3.65 mGy can be achieved in forward-backward test, lateral test and 4-direction test, respectively. There is a need of further studies on the optimization of irradiation conditions in accordance with the conditions of patients.

• Journal of the Korean Magnetics Society
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• v.23 no.3
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• pp.110-116
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• 2013

This paper proposes a sampling-based sensitivity method for dealing with electromagnetic coupled design problems effectively. The black-box modeling technique is basically applied to obtain an optimum regardless of how strong the electromagnetic, thermal and structural analyses are coupled with each other. To achieve this, Kriging surrogate models are produced in a hyper-cubic local window with the center of a current design point. Then design sensitivity values are extracted from the differentiation of basis functions which consist of the models. The proposed method falls under a hybrid optimization method which takes advantages of the sampling-based and the sensitivity-based methods. Owing to the aforementioned feature, the method can be applied even to electromagnetic problems of which the material properties are strongly coupled with thermal or structural outputs. To examine the accuracy and validity of the proposed method, a strongly nonlinear mathematical example and a coil design problem for local induction heating are tested.