• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• Structural Engineering and Mechanics
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• v.62 no.5
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• pp.537-550
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• 2017

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.617-635
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• 2021

Internet of Things (IoT) devices connected to the network without appropriate security solutions have become a serious security threat to ICT infrastructure. Moreover, due to the nature of IoT devices, it is difficult to apply currently existing security solutions. As a result, IoT devices have easily become targets for cyber attackers, and malware attacks on IoT devices are actually increasing every year. Even though several security solutions are being developed to protect IoT infrastructure, there is a great risk to apply unverified security solutions to real-world environments. Therefore, verification tools to verify the functionality and performance of the developed security solutions are also needed. Furthermore, just as security threats vary, there are several security solution s that defend against them, requiring suitable verification tools based on the characteristics of each security solution. In this paper, we propose an high-speed malware propagation tool that spreads malware at high speed in the IoT infrastructure. Also, we can verify the functionality of the security solution that detect and quickly block attacks spreading in IoT infrastructure by using the high-speed malware propagation tool.

• The Korean Journal of Franchise Management
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• v.14 no.2
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• pp.49-60
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• 2023

Purpose: NBS (Natural-based Solutions) characteristics as eco-friendly hotels were divided into eco-friendly indoor and outdoor to structurally verify the relationship between customers' perceived eco-friendly value, psychological well-being, customer satisfaction, and pro-environmental behavioral intention. Research design, data and methodology: This survey conducted responses to customers who had experience using eco-friendly hotels for the past two years. SPSS 22.0 and AMOS 22.0 statistical programs were used for the collected questionnaire data. First, frequency analysis and confirmatory factor analysis (CFA) were verified, and structural correlation between variables was verified by covariance matrix structural equation (CB-SEM). Result: First, NBS was found to have a significant positive (+) effect on perceived eco-friendly value and psychological well-being. Second, psychological well-being was found to have a significant positive (+) effect on customer satisfaction and eco-friendly behavioral intention. Finally, Hypothesis 3 was accepted as perceived eco-value showed a significant positive (+) effect on eco-friendly behavioral intention, but Hypothesis 2 was rejected because it did not have a significant effect on customer satisfaction. Conclusions: theoretical and practical implications for the impact of NBS as an eco-friendly hotel on customers' eco-friendly behavior can be provided, as well as basic evidence for establishing efficient management strategies for hotel companies.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.11
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• pp.3031-3040
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• 1995

Two-dimensional incompressible Navier-Stokes equations have been solved by the node-centered finite volume method with the unstructured triangular meshes. The pressure-velocity coupling is handled by the artificial compressibility algorithm due to its computational efficiency associated with the hyperbolic nature of the resulting equations. The convective fluxes are obtained by the Roe's flux difference splitting scheme using edge-based connectivities and higher-order differences are achieved by a reconstruction procedure. The time integration is based on an explicit four-stage Runge-Kutta scheme. Numerical procedures with local time stepping and implicit residual smoothing have been implemented to accelerate the convergence for the steady-state solutions. Comparisons with experimental data and other numerical results have proven accuracy and efficiency of the present unstructured approach.

• Ecology and Resilient Infrastructure
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• v.10 no.2
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• pp.31-39
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• 2023

NNbF is a newly emerging approach to reduce flood risk in coastal and fluvial areas using natural features or engineered nature-based features with the expectation of co-benefits of provisional, regulating, and socio-cultural services provided by the ecosystem. NNbF is not quite different from existing, related terms based on nature, such as NbS, Eco-DRR, NI, GI, EwN, and BwN, for all these terms include expectation of benefits for human societies by directly utilizing or mimicking nature's ecological functions. If we focus on the comprehensiveness of each term's subject and object, we can say that NbS > NNbF > (Eco-DRR, NI/GI). Among the 18 measures introduced in the NNbF International Guideline in the river and floodplain management category, it was found that measures of wash lands and floodplain restoration, including levee setback/removal and side-channel restoration, seemed to be the most applicable to rivers in Korea. These selected measures could be more effective when river managers purchase riparian lands along river courses by relevant laws for river water-quality protection.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.12-18
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• 2024

The accelerated pace of climate crisis due to continuous industrialization and greenhouse gas emissions necessitates sustainable solutions that simultaneously address mitigation and adaptation to climate change. Naturebased Solutions (NbS) have gained prominence as viable approaches, with Green Infrastructure being a representative NbS. Green Infrastructure involves securing green spaces within urban areas, providing diverse climate adaptation functions such as removal of various air pollutants, carbon sequestration, and isolation. The proliferation of Green Infrastructure is influenced by the quantification of improvement effects related to various projects. To support decision-making by assessing the climate vulnerability of Green Infrastructure, the U.S. Department of Agriculture (USDA) has developed i-Tree Tools. This study proposes a comprehensive evaluation approach for climate change adaptation types by quantifying the climate adaptation performance of urban Green Infrastructure. Using i-Tree Canopy, the analysis focuses on five urban green spaces covering more than 30 hectares, considering the tree ratio relative to the total area. The evaluation encompasses aspects of thermal environment, aquatic environment, and atmospheric environment to assess the overall eco-friendliness in terms of climate change adaptation. The results indicate that an increase in the tree ratio correlates with improved eco-friendliness in terms of thermal, aquatic, and atmospheric environments. In particular, it is necessary to prioritize consideration of the water environment sector in order to realize climate change adaptive green infrastructure, such as increasing green space in urban areas, as it has been confirmed that four out of five target sites are specialized in improving the water environment.

• Interaction and multiscale mechanics
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• v.1 no.2
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• pp.191-209
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• 2008

In this paper, we model grain boundary evolution based on a multiple level set method. Grain boundary migration under a curvature-induced driving force is considered and the level set method is employed to deal with the resulting topological changes of grain structures. The complexity of using a level set method for modeling grain structure evolution is due to its N-phase nature and the associated geometry compatibility constraint. We employ a multiple level set method with a predictor-multicorrectors approach to reduce the gaps in the triple junctions down to the grid resolution level. A ghost cell approach for imposing periodic boundary conditions is introduced without solving a constrained problem with a Lagrange multiplier method or a penalty method. Numerical results for both uniform and random grain structures evolution are presented and the results are compared with the solutions based on a front tracking approach (Chen and Kotta et al. 2004b).

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.51-60
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• 2006

When the nature of measuring a performance variable is destructive or very expensive, it is attractive to use a surrogate variable which is highly correlated with the performance variable and less expensive to measure. In this paper, we propose rectifying screening procedures using the performance and surrogate variables. Two screening procedures are considered; a statistically-based screening procedure to reduce the current proportion of nonconforming items to a specified lower value after screening, and an economically-based screening procedure where the screening limit is determined so that the expected cost is minimized. It is assumed that the surrogate variable given the performance variable is normally distributed with known mean and standard deviation. For two screening procedures, methods of finding the optimal solutions are presented and numerical examples are also given.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.70-77
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• 2006

When the nature of measuring a performance variable is destructive or very expensive, it is attractive to use a surrogate variable which is highly correlated with the performance variable and less expensive to measure. In this paper, we propose rectifying screening procedures using the performance and surrogate variables. Two screening procedures are considered; a statistically-based screening procedure to reduce the current proportion of nonconforming items to a specified lower value after screening, and a economically-based screening procedure where the screening limit is determined so that the expected cost is minimized. It is assumed that the surrogate variable given the performance variable is normally distributed with known mean and standard deviation. For two screening procedures, methods of finding the optimal solutions are presented and numerical examples are also given.