• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.59-70
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• 2022

Water distribution systems (WDSs) are a representative infrastructure injecting chlorine to disinfect the pathogenic microorganisms and supplying water from sources to consumers. Also, WDSs prescribe to maintain the usual standard (0.1-4.0 mg/L) of residual chlorine. However, the user's usage pattern, water age, network shape, and type affect the hydraulic features (i.e. nodal pressure, pipe velocity) and water quality features (i.e., the residual chlorine concentration). Therefore, this study developed an optimization approach for optimizing WDSs considering water quality-hydraulic factors using Multi-objective Harmony Search (MOHS). The design cost and the system resilience were applied as the design objective functions, and the nodal pressure and the concentration of residual chlorine are used as constraints. The derived optimal designs through this approach were analyzed according to network characteristics such as the network shapes and type. These optimal designs can meet the safety of economic and water quality aspects to increase user acceptance.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.67-72
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• 2023

This paper portrays the design and optimization of a wideband four element triangular dielectric resonator antenna (TDRA) using PSO. The proposed antenna's radiation characteristics were extracted using Ansoft HFSS software. At a resonant frequency of 5-7 GHz, the four element antenna provides nearly 21 percent bandwidth and the optimized gives 5.82 dBi peak gain. The radiation patterns symmetry and uniformity are maintained throughout the operating bandwidth. for WLAN (IEEE 802.16) and WiMAX applications, the proposed antenna exhibits a consistent symmetric monopole type radiation pattern with low cross polarisation. The proposed antenna's performance was compared to that of other dielectric resonator antenna (DRA) shapes, and it was discovered that the TDRA uses a lot less radiation area to provide better performance than other DRA shapes and PSO optimized antenna increases the gain of the antenna

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.7
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• pp.667-675
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• 2012

A damage estimation method for support structure of offshore wind turbine using modal parameters is presented for effective structural health monitoring. Natural frequencies and mode shapes for a support structure with monopile of an offshore wind turbine were calculated considering soil condition and added mass. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. Natural frequencies and mode shapes for 10 prospective damage cases were input to the trained neural network for damage estimation. The identified damage locations and severities agreed reasonably well with the accurate damages. Multi-damage cases could also be successfully estimated. Enhancement of estimation result using another parameters as input to neural network will be carried out by further study. Proposed method could be applied to other type of support structure of offshore wind turbine for structural health monitoring.

• Journal of ILASS-Korea
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• v.29 no.1
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• pp.23-31
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• 2024

This study conducted a 3D thermo-acoustic analysis based on the helmholtz solver to analyze the major resonance modes causing combustion instability in a single-can combustor. The experimental investigations were carried out on a test rig designed by the Korea Institute of Machinery & Materials (KIMM) under various conditions of hydrogen co-firing and fuel staging. Through these experiments, two primary unstable frequencies were identified. To determine the resonance modes of these frequencies, a 3D thermo-acoustic analysis was conducted using temperature information from the test rig. The results confirmed that the unstable frequencies observed in the experiments were all longitudinal modes. Additionally, the mode shapes identified in the analysis facilitated a simplification of the exit geometry for the low-order network model, confirming that this did not significantly affect the fundamental resonance modes.

• Advances in Computational Design
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• v.1 no.1
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• pp.1-25
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• 2016

Domes are architectural and elegant structures which cover a vast area with no interrupting columns in the middle, and with suitable shapes can be also economical. Domes are built in a wide variety of forms and specialized terms are available to describe them. According to their form, domes are given special names such as network, lamella, Schwedler, ribbed, and geodesic domes. In this paper, an optimum topology design algorithm is performed using the enhanced colliding bodies optimization (ECBO) method. The network, lamella, ribbed and Schwedler domes are studied to determine the optimum number of rings, the optimum height of crown and tubular sections of these domes. The minimum volume of each dome is taken as the objective function. A simple procedure is defined to determine the dome structures configurations. This procedure includes calculating the joint coordinates and element constructions. The design constraints are implemented according to the provision of LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Constitution). The wind loading act on domes according to ASCE 7-05 (American Society of Civil Engineers). This paper will explore the efficiency of various type of domes and compare them at the first stage to investigate the performance of these domes under different kind of loading. At the second stage the wind load on optimum design of domes are investigated for Schwedler dome. Optimization process is performed via ECBO algorithm to demonstrate the effectiveness and robustness of the ECBO in creating optimal design for domes.

• Journal of the Korean Society of Safety
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• v.34 no.6
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• pp.50-57
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• 2019

In this study, various types of deep learning models that have been proposed recently are classified according to data input / output types and analyzed to find the deep learning model suitable for constructing a crack detection model. First the deep learning models are classified into image classification model, object segmentation model, object detection model, and instance segmentation model. ResNet-101, DeepLab V2, Faster R-CNN, and Mask R-CNN were selected as representative deep learning model of each type. For the comparison, ResNet-101 was implemented for all the types of deep learning model as a backbone network which serves as a main feature extractor. The four types of deep learning models were trained with 500 crack images taken from real concrete structures and collected from the Internet. The four types of deep learning models showed high accuracy above 94% during the training. Comparative evaluation was conducted using 40 images taken from real concrete structures. The performance of each type of deep learning model was measured using precision and recall. In the experimental result, Mask R-CNN, an instance segmentation deep learning model showed the highest precision and recall on crack detection. Qualitative analysis also shows that Mask R-CNN could detect crack shapes most similarly to the real crack shapes.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.303-308
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• 2022

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.302-312
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• 1994

In this study, a fuzzy theory is introduced to control the cross-sectional strip shape in cold rolling. A fuzzy controller is developed based on the production data and the operational knowledge. The cold rolled products are characterized into several types based on their irregularities. For each type of irregular strip shape, fuzzy controller calculates the changes of bender forces of work and intermediate rolls using fuzzy control algorithm. To simulate the continuous shape control, fuzzy controller is linked with emulator which is developed using neural network. The developed fuzzy controller and emulator simulate the cold rolling process until the irregularities converge to the tolerable range to produce unifrom cross-sectional strip shape. The results from this simulation are reasonable for various irregular strip shapes.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.83.4-83.4
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• 2019

We present multi-color, high-cadence photometric study of a distant SU UMa-type dwarf nova KSP-OT-201611a discovered by the Korea Microlensing Telescope Network (KMTNet) Supernova Program (KSP). From October 2016 to May 2017, two outbursts with an interval of approximately 90 days were detected in the BV I-bands. The shapes and amplitudes of the outbursts reveal the nature of KSP-OT-201611a to be a SU UMa-type dwarf nova of outside-in origin with a superhump and an inferred orbital period of 1.69 h. The two observed bursts show a distinctively different color evolutions during the bursts due most likely to the viscosity different in accretion disk between them. The observed quiescent magnitudes and properties of the source during the outbursts indicate that it is at a large distance (~7.3 kpc) and height (~1.7 kpc) from the Galactic disk, possibly belonging to the group of poorly-studied Population II dwarf novae. The continuous monitoring of this source may offer a rare opportunity to study a PopII dwarf nova.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.67-69
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• 1998

The cogging torque in the small BLDC motors used in the DVD driving system or HDD driving system can cause some serious vibration problem. In this paper, some core shapes that reduce cogging torque are found by using reluctance network method(RNM) for magnetic field analysis and genetic algorithm(GA) for optimization. The outer rotor type BLDC motor for the DVD ROM driving system has been optimized as an sample model.