• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6B
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• pp.199-206
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• 2008

Broadcast encryption is a cryptographical primitive which is designed for a content provider to distribute contents to only privileged qualifying users through an insecure channel. Anyone who knows public keys can distribute contents by means of public key broadcast encryption whose technique can also be applicable to many other applications. In order to design public key broadcast encryption scheme, it should devise some methods that convert a broadcast encryption scheme based on symmetric key cryptosystem to a public key broadcast encryption. Up to this point, broadcast encryption scheme on trial for converting from symmetric key setting to asymmetric public key setting has been attempted by employing the Hierarchical Identity Based Encryption (HIBE) technique. However, this converting method is not optimal because some of the properties of HIBE are not quite fitting for public key broadcast schemes. In this paper, we proposed new converting method and an efficient public key broadcast encryption scheme Pub-PI which is obtained by adapting the new converting method to the PI scheme [10]. The transmission overhead of the Pub-PI is approximately 3r, where r is the number of revoked users. The storage size of Pub-PI is O($c^2$), where c is a system parameter of PI and the computation cost is 2 pairing computations.

• Smart Structures and Systems
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• v.26 no.6
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• pp.753-763
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• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.803-810
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• 2022

The small modular liquid-metal fast reactor (SMFR) is an important component of advanced nuclear systems. SMFRs exhibit relatively low breeding capability and constraint space for control rod installation. Consequently, control rods are deeply inserted at beginning and are withdrawn gradually to compensate for large burnup reactivity loss in a long lifetime. This paper is committed to investigating the impact of control rod compensation operation on core neutronics characteristics. This paper presents a whole core fine depletion model of long lifetime SMFR using OpenMC and the influence of depletion chains is verified. Three control rod position schemes to simulate the compensation process are compared. The results show that the fine simulation of the control rod compensation process impacts significantly the fuel burnup distribution and absorber consumption. A control rod equivalent position scheme proposed in this work is an optimal option in the trade-off between computation time and accuracy. The control position is crucial for accurate power distribution and void feedback coefficients in SMFRs. The results in this paper also show that the pin level power distribution is important due to the heterogeneous distribution in SMFRs. The fuel burnup distribution at the end of core life impacts the worth of control rods.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3803-3810
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• 2022

OpenMC is a community-driven open-source Monte Carlo neutron and photon transport simulation code. The Weight Window Mesh (WWM) function and an automatic Global Variance Reduction (GVR) method was recently developed and implemented in a developmental branch of OpenMC. This WWM function and GVR method broaden OpenMC's usage in general purposes deep penetration shielding calculations. However, the Local Variance Reduction (LVR) method, which suits the source-detector problem, is still missing in OpenMC. In this work, the Weight Window Generator (WWG) function has been developed and benchmarked for the same branch. This WWG function allows OpenMC to generate the WWM for the source-detector problem on its own. Single-material cases with varying shielding and sources were used to benchmark the WWG function and investigate how to set up the particle histories utilized in WWG-run and WWM-run. Results show that there is a maximum improvement of WWM generated by WWG. Based on the above results, instructions on determining the particle histories utilized in WWG-run and WWM-run for optimal computation efficiency are given and tested with a few multi-material cases. These benchmarks demonstrate the ability of the OpenMC WWG function and the above instructions for the source-detector problem. This developmental branch will be released and merged into the main distribution in the future.

• Journal of the Korean Chemical Society
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• v.67 no.1
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• pp.13-18
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• 2023

We evaluate electron density functional theories for the computation of 0-1 and 1-2 transition energies of local OH stretching motion of water molecules in condensed phases. By examining thirteen density functionals and nine sets of basis functions, it was found that the optimal combination that predicts the transition energies highly correlated with those calculated by the coupled cluster theory, CCSD(T), is the hybrid density functional theory developed by Head-Gordon group, ωB97X(D)/6-31+G^*.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.6
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• pp.471-480
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• 2022

Inductive power transfer (IPT) is an attractive power transmission solution that is already used in many applications. In the IPT system, optimal coil design is essential to achieve high power efficiency, but the effective design method is yet to be investigated. The inductance formula and finite element method (FEM) are popular means to link the coil geometric parameters and circuit parameters; however, the former lacks generality and accuracy, and the latter consumes much computation time. This study proposes a novel coil design method to achieve speed and generality without much loss of accuracy. By introducing one-turn permeance simulation in each FEM phase combined with curve fitting and optimization by MATLAB in the efficiency calculation phase, the iteration number of FEM can be considerably reduced, and the generality can be retained. The proposed method is verified through a 100 W IPT system experiment.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.575-584
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• 2021

This study contains the process of developing a Mission Planning System (MPS) of an USV that can be applied in real situations and verifying them through HILS. In this study, we set the scenario of a single USV with limited operating time. Since the USV may not perform some missions due to the limited operating time, an objective function was defined to maximize the Mission Achievement Rate (MAR). We used a genetic algorithm to solve the problem model, and proposed a method using a 3-D population. The simulation showed that the probability of deriving the global optimal solution of the mission planning algorithm was 96.6% and the computation time was 1.6 s. Furthermore, USV showed it performs the mission according to the results of the MPS. We expect that the MPS developed in this study can be applied to the real environment where USV performs missions with limited time conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.175-181
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• 2014

Korean government proposed a new initiative 'government 3.0' with which the administration will open its dataset to the public before requests. City of Seoul is the front runner in disclosure of government data. If we know what kind of attributes are governing factors for any given segmentation, these outcomes can be applied to real world problems of marketing and business strategy, and administrative decision makings. However, with respect to city of Seoul, selection of optimal variables from the open dataset up to several thousands of attributes would require a humongous amount of computation time because it might require a combinatorial optimization while maximizing dissimilarity measures between clusters. In this study, we acquired 718 attribute dataset from Statistics Korea and conducted an analysis to select the most suitable variables, which differentiate Gangnam from other districts, using the Genetic algorithm and Dunn's index. Also, we utilized the Microsoft Azure cloud computing system to speed up the process time. As the result, the optimal 28 variables were finally selected, and the validation result showed that those 28 variables effectively group the Gangnam from other districts using the Ward's minimum variance and K-means algorithm.