• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.27-37
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• 2020

The Fukushima Daiichi accident in 2011 revealed some vulnerabilities of existing Nuclear Power Plants (NPPs) under extended Station Blackout (SBO) accident conditions. One of the key Severe Accident Management (SAM) strategies developed post Fukushima accident is the In-Vessel Retention (IVR) Strategy which aims to retain the structural integrity of the Reactor Pressure Vessel (RPV). RELAP/SCDAPSIM/MOD3.4 is selected to predict the thermal-hydraulic response of APR1400 undergoing an extended SBO. To assess the effectiveness of the IVR strategy, it is essential to quantify the underlying uncertainties. In this work, both the epistemic and aleatory uncertainties are considered to identify the success window of the IVR strategy. A set of in-vessel relevant phenomena were identified based on Phenomena Identification and Ranking Tables (PIRT) developed for severe accidents and propagated through the thermal-hydraulic model using Wilk's sampling method. For this work, a Systems Engineering (SE) approach is applied to facilitate the development process of assessing the reliability and robustness of the APR1400 IVR strategy. Specifically, the Kossiakoff SE method is used to identify the requirements, functions and physical architecture, and to develop a design verification and validation plan. Using the SE approach provides a systematic tool to successfully achieve the research goal by linking each requirement to a verification or validation test with predefined success criteria at each stage of the model development. The developed model identified the conditions necessary for successful implementation of the IVR strategy which maintains the vessel integrity and prevents a melt-through.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.6
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• pp.347-353
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• 2021

In this study, we presented a novel size optimization framework to control the linear buckling temperature and several buckling modes of plates, by optimizing thickness values of composite structures for practical engineering applications. Predicting the buckling temperature and mode shape of structures is a vital research topic in engineering to achieve structural stability. However, optimizing designs of engineering structures through engineering intuition is challenging. To address this limitation, we proposed a method that combines finite element simulation and size optimization. Based on the idea that the structural buckling temperature and mode shape of a plate are affected by the thickness of the structure, the thickness values of the nodes of the target structure were set as the design variables in this optimization method; and the buckling temperature values, and buckling mode shapes were set as the objective functions. This size optimization method enabled the determination of optimal thickness distributions, to induce the desired buckling temperature values and mode shapes. The validity of the proposed method was verified in terms of their buckling temperature values and buckling mode shapes, using several numerical examples of rectangular composite structures.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.843-854
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• 2022

For the rip current warning system to reduce rip-current accidents, the implementation method producing the risk index was modified. To produce fast response from the warning system based on real-time observations, the method employed the numerical results (i.e., rip current likelihoods according to the possible scenario) obtained in advance. In this study, instead of using the empirical curve-fitting functions of the previous method, the present modification utilized two-dimensional distributions (i.e., wave height and period, wave height and tidal elevation, wave height and direction, wave height and spreading of frequency-directional spectrum) of rip current likelihoods stacked in a database of the system. The wave and tidal observations in 2021 at the Haeundae coast were applied to the modified system, and its performances at several real events recorded in CCTV images were presented.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.299-307
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• 2022

Harmony Search (HS) is a relatively recently developed meta-heuristic optimization algorithm, and various studies have been conducted on it. HS is based on the musician's improvisational performance, and the objective variables play the role of the instrument. However, each instrument is given only a sound range, and there is no concept of a scale that can be said to be the basis of music. In this study, the performance of the algorithm is improved by introducing a scale to the existing HS and quantizing the bandwidth. The introduced scale was applied to HM initialization instead of the existing method that was randomly initialized in the sound band. The quantization step can be set arbitrarily, and through this, a relatively large bandwidth is used at the beginning of the algorithm to improve the exploration of the algorithm, and a small bandwidth is used to improve the exploitation in the second half. Through the introduction of scale and bandwidth quantization, it was possible to reduce the algorithm performance deviation due to the initial value and improve the algorithm convergence speed and success rate compared to the existing HS. The results of this study were confirmed by comparing examples of optimization values for various functions with the conventional method. Specific comparative values were described in the simulation.

• Steel and Composite Structures
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• v.41 no.6
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• pp.899-910
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• 2021

A 4-unknown shear deformation theory is applied to investigate the vibration of functionally graded plates under thermal environment. The plate is fabricated from a functionally graded material mixed of ceramic and metal with continuously varying material properties through the plate thickness. Three types of thermal loadings, uniform, linear and nonlinear temperature rises along the plate thickness are taken into account. The present theory contains four unknown functions as against five or more in other higher order shear deformation theories. The through-the-thickness distributions of transverse shear stresses of the plate are considered to vary parabolically and vanish at upper and lower surfaces. The present model does not require any problem dependent shear correction factor. Analytical solutions for the free vibration analysis are derived based on Fourier series that satisfy the boundary conditions (Navier's method). Benchmark solutions are firstly considered to evaluate the accuracy of the proposed model. Comparisons with the solutions available in literature revealed the good capabilities of the present model for the simulations of vibration responses of FG plates. Some parametric studies are carried out for the frequency analysis by varying the volume fraction profile and the temperature distribution across the plate thickness.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.905-912
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• 2022

Virtual reality, one of the core technologies of the 4th industrial revolution, is entering a new phase with the spread of low-cost wearable devices represented by Oculus. In the case of disaster evacuation drills, where practical training is almost impossible due to the risk of accidents, virtual reality is becoming a new alternative that enables effective training. In this paper, we propose a smoke modeling method that can be applied to fire evacuation drills implemented with virtual reality technology. In the event of a fire, smoke spreads along the aisle, and the density of the smoke changes over time. The proposed method models the smoke by applying a procedural function that can reflect the density of smoke calculated through simulation to the model in real-time. Implementation results in the background of the factory show that the proposed method produces models that can express the smoke according to the user's movement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3544-3564
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• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_2
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• pp.1071-1081
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• 2022

As the demand for high-rise buildings increases, the demand for high-speed elevators is also increasing. In order to make a high-speed elevator, a method is needed to reduce the weight of the elevator's components, which is a constraint on the increase in speed. As a measure to reduce the weight, it is possible to remove the traveling cable for power and signal supply. Since the weight of the traveling cable varies depending on the position of the carriage, it is difficult to compensate the weight using the counter weight. The power supply is a structure in which a brush-rail type power input terminal is installed in the elevator hoistway to receive power in a contact-type manner while the carriage is moving. If a small-capacity ESS is installed in a passenger car, power can be supplied uninterruptedly inside the passenger car. A small-capacity ESS charging system to be applied to such an elevator system is required to perform several functions. First, the passenger Car must be able to charge as much as possible even during high-speed operation. A control algorithm with high responsiveness is required because charging starts and ends repeatedly by the partially installed input power stage. In addition, if the input-side line impedance is large due to the structure of the system and the response characteristic is increased, the stability of the system may be lowered. Accordingly, in this paper, we propose a control algorithm that has a stable steady-state output while having a fast response in a transient state. To verify the proposed control algorithm, simulation was conducted using PSIM, and the performance of the controller was verified by manufacturing a prototype buck conveter charger.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.176-188
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• 2022

In this paper, we propose a compact method for anthropomorphism that uses Deep Convolutional Neural Networks (DCNN) to detect the similarities between a human face and an animal face. We also apply texture feature-based morphing between them. We propose a basic texture feature-based morphing system for morphing between human faces only. The entire anthropomorphism process starts with the creation of an animal face classifier using a parallel DCNN that determines the most similar animal face to a given human face. The significance of our network is that it contains four sets of convolutional functions that run in parallel, allowing it to extract more features than a linear DCNN network. Our employed texture feature algorithm-based automatic morphing system recognizes the facial features of the human face and takes the Control Points automatically, rather than the traditional human aiding manual morphing system, once the similarity was established. The simulation results show that our suggested DCNN surpasses its competitors with a 92.0% accuracy rate. It also ensures that the most similar animal classes are found, and the texture-based morphing technology automatically completes the morphing process, ensuring a smooth transition from one image to another.