• Journal of Electrochemical Science and Technology
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• v.15 no.3
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• pp.373-387
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• 2024

The layout of the cathode flow field largely determines the net output power of the proton exchange membrane fuel cell (PEMFC). To make the normal mass transfer effect best, the longitudinal channel was waved based on four serpentine flow channels, and the effects of sag depth and longitudinal channel width on the output efficiency of the cell were explored. The results show that the wave channel design systematically enhances the forced convection between adjacent channels, which can prevent a large zone of oxygen starvation zone at the outlet of the channel. The increase of the normal velocity in the gas transmission process will inevitably induce a significant enhancement of the mass transfer effect and obtain a higher current density in the reaction zone. For the longitudinal channel width, it is found that increasing its size in the effective range can greatly reduce the channel pressure drop without reducing the output power, thereby improving the overall efficiency. When the sag depth and longitudinal channel width gradient are 0.6 mm and 0.2 mm respectively, PEMFC can obtain the best comprehensive performance.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1589-1605
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• 2022

Phase unwrapping is an essential procedure for interferometric synthetic aperture radar techniques. Accordingly, a lot of phase unwrapping methods have been developed. Deep-learning-based unwrapping methods have recently been proposed. In this paper, we reviewed state-of-the-art deep-learning-based unwrapping approaches in terms of 1) the approaches to predicting unwrapped phases, 2) deep learning model structures for phase unwrapping, and 3) training data generation. The research trend of the approaches to predicting unwrapped phases was introduced by categorizing wrap count segmentation, phase jump classification, phase regression, and deep-learning-assisted method. We introduced the case studies of deep learning model structure for phase unwrapping, and model structure optimization to relate the overall phase information. In addition, we summarized the research trend of the training data generation approaches in the views of phase gradient and noise in the main. And the future direction in deep-learning-based phase unwrapping was presented. It is expected that this paper is used as guideline for exploring future direction of deep-learning-based phase unwrapping research in Korea.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.3
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• pp.137-145
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• 2014

We present a design sensitivity analysis(DSA) method for multiscale problems based on bridging scale decomposition. In this paper, we utilize a bridging scale method for the coupled system analysis. Since the analysis of full MD systems requires huge amount of computational costs, a coupled system of MD-level and continuum-level simulation is usually preferred. The information exchange between the MD and continuum levels is taken place at the MD-continuum boundary. In the bridging scale method, a generalized Langevin equation(GLE) is introduced for the reduced MD system and the GLE force using a time history kernel is applied at the boundary atoms in the MD system. Therefore, we can separately analyze the MD and continuum level simulations, which can accelerate the computing process. Once the simulation of coupled problems is successful, the need for the DSA is naturally arising for the optimization of macro-scale design, where the macro scale performance of the system is maximized considering the micro scale effects. The finite difference sensitivity is impractical for the gradient based optimization of large scale problems due to the restriction of computing costs but the analytical sensitivity for the coupled system is always accurate. In this study, we derive the analytical design sensitivity to verify the accuracy and applicability to the design optimization of the coupled system.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.2
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• pp.153-163
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• 2010

In this paper a frequency response analysis using Krylov subspace-based model reduction and its design sensitivity analysis with respect to design variables are presented. Since the frequency response and its design sensitivity information are necessary for a gradient-based optimization, problems of high computational cost and resource may occur in the case that frequency response of a large sized finite element model is involved in the optimization iterations. In the suggested method model order reduction of finite element models are used to calculate both frequency response and frequency response sensitivity, therefore one can maximize the speed of numerical computation for the frequency response and its design sensitivity. As numerical examples, a semi-monocoque shell and an array-type $4{\times}4$ MEMS resonator are adopted to show the accuracy and efficiency of the suggested approach in calculating the FRF and its design sensitivity. The frequency response sensitivity through the model reduction shows a great time reduction in numerical computation and a good agreement with that from the initial full finite element model.

• Tunnel and Underground Space
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• v.17 no.2 s.67
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• pp.119-127
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• 2007

Based on the critical plane approach, a methodology far predicting the anisotropic strength ot transversely isotropic rock is Proposed. It is assumed that the rock failure is governed by Hoek-Brown failure criterion. In order to establish an anisotropic failure function, Mohr envelope equivalent to the original Hoek-Brown criterion is used and the strength parameters m, s are expressed as scalar functions of orientation. The conjugate gradient method, which is one of the robust optimization techniques, is applied to the failure function for searching the orientation giving the maximum value of the anisotropic function. While most of the existing anisotropic strength models can be applied only when the stress condition is the same as that of conventional triaxial compression test, the proposed model can be applied to the general 3-dimensional stress conditions. Through the simulation of triaxial compression tests for transversely isotropic rock sample, the validity of the proposed method is investigated by comparing the predicted triaxial strengths and inclinations of failure plane.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.2 s.105
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• pp.158-164
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• 2006

In this paper, we proposed a new algorithm of the inverse scattering for the reconstruction of unknown dielectric scatterers using the finite-difference time-domain method and the design sensitivity analysis. We introduced the design sensitivity analysis based on the gradient information for the fast convergence of the reconstruction. By introducing the adjoint variable method for the efficient calculation, we derived the adjoint variable equation. As an optimal algorithm, we used the steepest descent method and reconstructed the dielectric targets using the iterative estimation. To verify our algorithm, we will show the numerical examples for the two-dimensional $TM^2$ cases.