• Journal of the Korean Solar Energy Society
• /
• v.36 no.5
• /
• pp.73-89
• /
• 2016

To assure maximum economic benefits and the energy performance of solar water heating systems, the proper sizing of components and operating conditions need to be optimized. In recent years, a number of studies to design optimally solar water heating systems have been tried. This paper presents a design method for optimizing the various capacity-related and installation-related design variables based on life cycle cost using a genetic algorithm. The design variables considered in this study included the types and numbers of solar collector and auxiliary heaters; the types of storage tanks and heat exchangers; the solar collector slope; mass flow rates of the fluid on the hot and cold sides. The suggested method was applied for optimizing a solar water heating system for an elementary school in Seoul, South Korea. In addition, the effectiveness of the proposed optimization method was assessed by analyzing the obtained optimal solutions of six case studies, each of which was simulated with different solar fractions. It is observed that a trade-off between the equipment cost and the energy cost results in an optimal design that yields the lowest life cycle cost. Therefore, it could be helpful to apply the optimal solar water heating system by comparing the various design solutions obtained by using the optimization method instead of the engineer's experience and intuition.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.21 no.2
• /
• pp.27-38
• /
• 2018

Environmental planning includes resource allocation and spatial planning process for the conservation and management of environment. Because the spatialization of the environmental planning is not specifically addressed in the relevant statutes, it actually depends on the qualitative methodology such as expert judgement. The results of the qualitative methodology have the advantage that the accumulated knowledge and intuition of the experts can be utilized. However, it is difficult to objectively judge whether it is enough to solve the original problem or whether it is the best of the possible scenarios. Therefore, this study proposed a methodology to quantitatively and objectively spatialize various environmental planning. At first, we suggested a quantitative spatial planning model based on an optimization algorithm. Secondly, we applied this model to two kinds of environmental planning and discussed about the model performance to present the applicability. Since the models were developed based on conceptual study site, there was a limitation in showing possibility of practical use. However, we expected that this study can contribute to the fields related to environmental planning by suggesting flexible and novel methodology.

• Smart Structures and Systems
• /
• v.26 no.4
• /
• pp.451-468
• /
• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.4
• /
• pp.211-220
• /
• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• Steel and Composite Structures
• /
• v.27 no.4
• /
• pp.427-438
• /
• 2018

In this paper, the single and multi-objective optimization of thin-walled conical tubes with different types of indentations under axial impact has been investigated using surrogate models called metamodels. The geometry of tapered thin-walled tubes has been studied in order to achieve maximum specific energy absorption (SEA) and minimum peak crushing force (PCF). The height, radius, thickness, tapered angle of the tube, and the radius of indentation have been considered as design variables. Based on the design of experiments (DOE) method, the generated sample points are computed using the explicit finite element code. Different surrogate models including Kriging, Feed Forward Neural Network (FNN), Radial Basis Neural Network (RNN), and Response Surface Modelling (RSM) comprised to evaluate the appropriation of such models. The comparison study between surrogate models and the exploration of indentation shapes have been provided. The obtained results show that the RNN method has the minimum mean squared error (MSE) in training points compared to the other methods. Meanwhile, optimization based on surrogate models with lower values of MSE does not provide optimum results. The RNN method demonstrates a lower crashworthiness performance (with a lower value of 125.7% for SEA and a higher value of 56.8% for PCF) in comparison to RSM with an error order of $10^{-3}$. The SEA values can be increased by 17.6% and PCF values can be decreased by 24.63% by different types of indentation. In a specific geometry, higher SEA and lower PCF require triangular and circular shapes of indentation, respectively.

• Journal of the Society of Naval Architects of Korea
• /
• v.52 no.2
• /
• pp.97-103
• /
• 2015

This paper has focused on the middle term process planning around quays based on the prefixed long-term plan of the product mixed ships. Recently, the order rate of high add-value ships in domestic shipyards has been sharply increased and the spending time at quays is accordingly on an increasing trend. For proper and practical process planning related to quays, it has to be closely connected with a long-term plan and product calendar, erection network and result of ship allocation around quays. Moreover, it is also required to include the integrated consideration of the whole process of a yard, each ship, and each team respectively. The most distinguishing feature of this study is that it would run on the ship allocation simulator and GIS framework in order not to be limited to the specific one yard and the readers can figure out the optimization formulation containing the work load leveling and a different approach from PERT/CPM. The proposed approach reflected all requirements from the department of process planning and management in a shipyard, and the analysis of the results has explained its performance of the optimization result with the examples of total 43 ships under construction from 2008 to 2013.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.12
• /
• pp.71-82
• /
• 2012

In this paper, we propose a license plate detection method with improved Adaboost learning and MCT (Modified Census Transform). The MCT represents the local structure patterns as integer numbered feature values which has robustness to illumination change and memory efficiency. However, since these integer values are discrete, a lookup table is needed to design a weak classifier for Adaboost learning. Some previous research efforts have focused on minimization of exponential criterion for Adaboost optimization. In this paper, a method that uses MCT and improved Adaboost learning based on Newton's optimization to exponential criterion is proposed for license plate detection. Experimental results on license patch images and field images demonstrate that the proposed method yields higher performance of detection rates with low false positives than the conventional method using the original Adaboost learning.

• Journal of Ceramic Processing Research
• /
• v.18 no.11
• /
• pp.810-814
• /
• 2017

Proton exchange membrane fuel cells (PEMFCs) are some of the most efficient electrochemical energy sources for transportation applications because of their clean, green, and high efficiency characteristics. The optimization of catalyst layer morphology is considered a feasible approach to achieve high performance of PEMFC membrane electrode assembly (MEA). In this work, we studied the effect of the solvent on the catalyst layer of PEMFC MEAs fabricated using the electrostatic spray deposition method. The catalyst ink comprised of Pt/C, a Nafion ionomer, and a solvent. Two types of solvent were used: isopropyl alcohol (IPA) and dimethylformamide (DMF). Compared with the catalyst layer prepared using IPA-based ink, the catalyst layer prepared with DMF-based ink had a dense structure because the DMF dispersed the Pt/C-Nafion agglomerates smaller and more homogeneously. The size distribution of the agglomerates in catalyst ink was confirmed through Dynamic Light Scattering (DLS) and the microstructure of the catalyst layer was compared using field emission scanning electron microscopy (FE-SEM). In addition, the electrochemical investigation was performed to evaluate the solvent effect on the fuel cell performance. The catalyst layer prepared with DMF-based ink significantly enhanced the cell performance (1.2 A cm-2 at 0.5 V) compared with that fabricated using IPA-based ink (0.5 A cm-2 at 0.5 V) due to the better dispersion and uniform agglomeration on the catalyst layer.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.1
• /
• pp.101-110
• /
• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Smart Structures and Systems
• /
• v.21 no.6
• /
• pp.727-740
• /
• 2018

Tuned mass dampers (TMDs) are passive damping devices widely employed to mitigate the pedestrian-induced vibrations on footbridges. The TMD design must ensure an adequate performance during the overall life-cycle of the structure. Although the TMD is initially adjusted to match the natural frequency of the vibration mode which needs to be controlled, its design must further take into account the change of the modal parameters of the footbridge due to the modification of the operational and environmental conditions. For this purpose, a motion-based design optimization method is proposed and implemented herein, aimed at ensuring the adequate behavior of footbridges under uncertainty conditions. The uncertainty associated with the variation of such modal parameters is simulated by a probabilistic approach based on the results of previous research reported in literature. The pedestrian action is modelled according to the recommendations of the Synpex guidelines. A comparison among the TMD parameters obtained considering different design criteria, design requirements and uncertainty levels is performed. To illustrate the proposed approach, a benchmark footbridge is considered. Results show both which is the most adequate design criterion to control the pedestrian-induced vibrations on the footbridge and the influence of the design requirements and the uncertainty level in the final TMD design.