• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.21-22
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• 2015

The maximum power point of a photovoltaic array alters with changing atmospheric conditions, temperature conditions, shadow conditions, so it is required to track maximum power point. As much as MPPT(Maximum Power Point Tracking) is important in photovoltaic systems, many MPPT techniques have been developed. In this paper, several major existing MPPT methods are comparatively analyzed and novel hybrid MPPT algorithm is proposed. The proposed hybrid MPPT algorithm is developed in combination with traditional MPPT methods to complement each other for improving performance and mitigating partial shading effects. The proposed algorithm is validated by using PISIM simulation tool and experiment in 3kW system.

• Nonlinear Functional Analysis and Applications
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• v.27 no.1
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• pp.203-221
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• 2022

In this paper, we introduce new hybrid inertial contraction projection algorithms for solving variational inequality problems over the intersection of the fixed point sets of demicontractive mappings in a real Hilbert space. The proposed algorithms are based on the hybrid steepest-descent method for variational inequality problems and the inertial techniques for finding fixed points of nonexpansive mappings. Strong convergence of the iterative algorithms is proved. Several fundamental experiments are provided to illustrate computational efficiency of the given algorithm and comparison with other known algorithms

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.161-172
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• 2017

In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.

• Applied Chemistry for Engineering
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• v.17 no.2
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• pp.132-137
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• 2006

Polyurethane acrylate hybrid emulsions were prepared by seeded polymerization techniques. In the synthesis, seeded polyurethane dispersion containing a carboxylic group was used to endow hydrophilicity to the hybrid emulsion and various acrylates such as methyl methacrylate (MMA), 2-hydroxy ethylmethacrylate (2-HEMA), n-butyl acrylate (n-BA) and acrylic acid (AAc) were used to endow hydrophobicity. The particle size and distribution of various emulsion particles such as polyurethane acrylate hybrid emulsion, polyurethane dispersion homopolymer, acrylate emulsion, and physical blending emulsion were measured by a particle size analyzer. The average particle size of hybrid emulsion was greater than physical blending emulsion. And tensile strength, 100% modulus, elongation, and swelling properties of the polyurethane acrylate hybrid emulsion were studied and compared with those of polyurethane homopolymer, acrylate emulsion, and physically blended compositor, respectively. To improve chemical and physical resistance, this paper review a melamine hardener and compares it for effects on the physical properties of cured coating.

• The Journal of the Acoustical Society of Korea
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• v.33 no.6
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• pp.351-357
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• 2014

In this paper, flow noise characteristics of the hybrid vertical-axis wind turbine is investigated. Hybrid vertical-axis wind turbines consisting of two types of vertical-axis wind turbines, Savonius and Darrieus, are devised to maximize merits of one turbine and thus minimize demerits of the other turbine. In order to predict flow noise radiating from hybrid vertical-axis wind turbines, hybrid computatioinal aero acoustic techniques are used. First, unsteady flow fields around the turbine are predicted using computational fluid dynamics method. Then, the flow noise radiations from the turbines are predicted by applying acoustic analogy to the predicted flow fields. Based on numerical results, noise characteristics of a hybrid vertical-axis wind turbine is investigated and is compared with those of Savonius and Darrieus wind turbines.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.684-685
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• 2009

• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.141-162
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• 1996

• Proceedings of the Korean Society of Crop Science Conference
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• 1990.05a
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• pp.128-129
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• 1990