• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1672-1678
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• 2024

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.34-39
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• 2017

Vapor-liquid equilibrium calculation is required to properly design and operation of distillation process. The general calculation method is to use binary interaction parameter. Lower flash points of cyclohexanol+aniline and cyclohexanol+cyclohexanone were measured by using Seta-flash closed cup apparatus. The measured flash points were compared with those calculated by the method based on Raoult's law and the optimization method using Wilson equation. The absolute average errors(A.A.E.) of the results calculated by Raout's law are $0.25^{\circ}C$ and $1.07^{\circ}C$ for cyclohexanol+aniline and cyclohexanol+cyclohexanone, respectively. The absolute average errors of the results calculated by the optimization method are $0.22^{\circ}C$ and $0.65^{\circ}C$ for cyclohexanol+aniline and cyclohexanol+cyclohexanone, respectively. As can be seen from A.A.E., the calculated values based on the optimization method were found to be better than those based on the Raoult's law. The binary interaction parameters calculated by the optimization method are used to predict the dew points of cyclohexanol+aniline and cyclohexanol+cyclohexanone. The A.A.E. for these mixtures show that there is an acceptable agreement between experimental and calculated dew poins.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.6
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• pp.385-393
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• 2017

As the use of mobile devices such as smartphones increases, there is growing interest on the benefits of virtualization in embedded systems. Full virtualization has the advantage of running the guest virtual machine without modifying the guest operating system. However, full virtualization suffers slow execution speed due to the cost of context switching between the virtual machines and the virtual machine monitor. In this paper, we propose a translated block and context switching optimization to improve the guest execution speed in the embedded system. As a result, the improved dynamic binary translator is up to 5.95 times faster than the native execution. Performance degradation is less than that of the other virtualization system.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.39-44
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• 2016

Suitable design and operation of distillation process is very dependent on vapor-liquid equilibrium calculation. The usual calculation method is use binary interaction parameter. Flash points of n-propanol+n-butanol and 2-butanol+n-butanol were measured by Seta-flash closed cup tester. Experimental Flash points were compared with those calculated by the method based on Raoult's law and the optimization method using Wilson equation. The binary interaction parameters obtained by the optimization method are then used to calculate the bubble points of n-propanol+n-butanol and 2-butanol+n-butanol.

• Journal of Astronomy and Space Sciences
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• v.29 no.2
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• pp.123-129
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• 2012

The BINSYN program package, recently expanded to calculate synthetic spectra of cataclysmic variables, is being further extended to include synthetic photometry of ordinary binary stars in addition to binary stars with optically thick accretion disks. The package includes a capability for differentials correction optimization of eclipsing binary systems using synthetic photometry.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.324-329
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• 2005

Dynamic Encoding Algorithm for Searches (DEAS) which is known as a fast and reliable non-gradient optimization method, was proposed [1]. DEAS reaches local or global optimum with binary strings (or binary matrices for multi-dimensional problem) by iterating the two operations; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., 0 or 1), while UDS performs increment or decrement of binary strings in the BSS' result direction with no change of string length. Because the interval of UDS exponentially decreases with increment of bit string length (BSL), DEAS is difficult to escape from local optimum when DEAS falls into local optimum. Therefore, this paper proposes hopping UDS (HUDS) which performs UDS by hopping as many as BSL in the final point of UDS process. HUDS helps to escape from local optimum and enhances a probability searching global optimization. The excellent performance of HUDS will be validated through the well-known benchmark functions.

• Computational Structural Engineering
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• v.8 no.2
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• pp.123-132
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• 1995

Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1382-1389
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• 2007

This paper presents a new approach for solving the problem of maintenance scheduling of generating units using a binary particle swarm optimization (BPSO). In this paper, we find the optimal solution of the maintenance scheduling of generating units within a specific time horizon using a binary particle swarm optimization algorithm, which is the discrete version of a conventional particle swarm optimization. It is shown that the BPSO method proposed in this paper is effective in obtaining feasible solutions in the maintenance scheduling of generating unit. IEEE reliability test systems(1996) including 32-generators are selected as a sample system for the application of the proposed algorithm. From the result, we can conclude that the BPSO can find the optimal solution of the maintenance scheduling of the generating unit with the desirable degree of accuracy and computation time, compared to other heuristic search algorithm such as genetic algorithms. It is also envisaged that BPSO can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain better solutions and improve convergence performance.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.665-676
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• 2022

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.