• Advanced Composite Materials
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• v.17 no.1
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• pp.25-40
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• 2008

The research and development in soundproof materials for preventing noise have attracted great attention due to their social impact. Noise insulation materials are especially important in the field of soundproofing. Since the insulation ability of most materials follows a mass rule, the heavy weight materials like concrete, lead and steel board are mainly used in the current noise insulation materials. To overcome some weak points in these materials, fiber reinforced composite materials with lightweight and other high performance characteristics are now being used. In this paper, innovative insulation sheet materials with carbon and/or glass fabrics and nano-silica hybrid PU resin are developed. The parameters related to sound performance, such as materials and fabric texture in base fabric, hybrid method of resin, size of silica particle and so on, are investigated. At the same time, the wave analysis code (PZFlex) is used to simulate some of experimental results. As a result, it is found that both bundle density and fabric texture in the base fabrics play an important role on the soundproof performance. Compared with the effect of base fabrics, the transmission loss in sheet materials increased more than 10 dB even though the thickness of the sample was only about 0.7 mm. The results show different values of transmission loss factor when the diameters of silica particles in coating materials changed. It is understood that the effect of the soundproof performance is different due to the change of hybrid method and the size of silica particles. Fillers occupying appropriate positions and with optimum size may achieve a better effect in soundproof performance. The effect of the particle content on the soundproof performance is confirmed, but there is a limit for the addition of the fillers. The optimization of silica content for the improvement of the sound insulation effect is important. It is observed that nano-particles will have better effect on the high soundproof performance. The sound insulation effect has been understood through a comparison between the experimental and analytical results. It is confirmed that the time-domain finite wave analysis (PZFlex) is effective for the prediction and design of soundproof performance materials. Both experimental and analytical results indicate that the developed materials have advantages in lightweight, flexibility, other mechanical properties and excellent soundproof performance.

• Korean Journal of Optics and Photonics
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• v.19 no.5
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• pp.349-359
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• 2008

For an inner-focusing 3-groups zoom lens system, this study suggests a new initial design method which applies the process that changes thin lenses into thick ones effectively and quickly, using the hybrid lens system(thin lens+thick lens). In other words, the hybrid lens system is the semi-automatic design process that makes the thin lens of one group change into a thick one while the other groups are composed of thin lenses. Keeping the total power of the system fixed, the power of each group and the distance between principal planes can be fixed. Of course, the other groups composed of thin lenses could be changed into thick lenses sequentially by this process. This design conception results in the 1/4" 5 M inner-focusing 3-groups 2x zoom lens system satisfying the specifications and performances of zoom lens for phone cameras. Also aspherization on lens elements of glass and plastic material enhanced the resolution and reduced the lens size. As a result, we have an ultra-compact inner-focusing 3-groups 2x zoom lens system for a phone camera, with a slim size with TTL of 9.8 mm.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.56-63
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• 1997

This paper presents a hybrid network model(HY-PIPENET) implementing a minimum cost spanning tree(MCST) network algorithm to be able to determine optimum path and constrained derivative(CD) method to select optimum Pipe diameter. The HY-PIPENET has been validated with the published data of 6-node/7-pipe network. Networking system and also this system has been optimized with MCST-CD method. As a result, it was found that the gas can be sufficiently supplied at the lower pressure with the smaller diameters of pipe compared to the original system in 6-node/7-pipe network. Hence, the construction cost was reduced about $40\%$ in the optimized system. The hybrid networking model has been also applied to a complicated domestic gas pipeline network in metropolitan area, Korea. In this simulation, parametric study was peformed to understand the role of each individual parameter such as source pressure, flow rate, and pipe diameter on the optimized network. From the results of these simulations, we have proposed the optimized network as tree-type structure with optimum pipe diameter and source pressure in metropolitan area, Korea, however, this proposed system does not consider the environmental problems or safety concerns.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.7-17
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• 2007

An analysis and design theory of an aperture-coupled cavity-fed back-to-back microstrip directional coupler is presented for the efficient and optimized design. For this purpose, an equivalent network is developed, and simple but accurate calculations of circuit element values are described. Design equations of the coupler are derived based on the equivalent circuit. In order to determine various structural design parameters, the evolutionary hybrid optimization method based on the genetic algorithm and Nelder-Mead method is invoked. As a validation check of the proposed theory and optimized design method, a 10 dB directional coupler was designed and fabricated. The measured coupling was 10.3 dB at 3 GHz, and the return loss and isolation were 31.8 dB and 30.5 dB, respectively. The directional coupler also showed very good quadrature phase characteristics. Good agreements between the measured and the design specifications fully validate the proposed network analysis and design procedure.

• Korean Journal of Optics and Photonics
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• v.12 no.3
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• pp.240-249
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• 2001

A high speed ultra-compact lens with a diffractive optical element (DOE) is designed, which can be applied to mobile communication devices such as IMT2000, PDA, notebook computer, etc. The designed hybrid lens has sufficiently high performance of less than f/2.2, compact size of 3.3 mm (1st surf. to image), and wide field angle of more than 30 deg. compared with the specifications of a single lens. By proper choice of the aspheric and DOE surface which has very large negative dispersion, we can correct chromatic and high order aberrations through the optimization technique. From Seidel third order aberration theory and Sweatt modeling, the initial data and surface configurations, that is, the combination condition of the DOE and the aspherical surface are obtained. However, due to the consideration of diffraction efficiency of a DOE, we can choose only four cases as the optimization input, and present the best solution after evaluating and comparing those four cases. On the other hand, we also report dramatic improvement in optical performance by inserting another refractive lens (so-called, field flattener), that keeps the refractive power of an original DOE lens and makes the petzval sum zero in the original DOE lens system. ystem.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.157-172
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• 2024

Autonomous driving technology, when commercialized, has the potential to improve the safety, mobility, and environmental performance of transportation networks. However, safe autonomous driving may be hindered by poor sensor performance and limitations in long-distance detection. Therefore, cooperative autonomous driving that can supplement information collected from surrounding vehicles and infrastructure is essential. In addition, since HDVs, AVs, and CAVs have different ranges of perceivable information and different response protocols, countermeasures are needed for mixed traffic that occur during the transition period of autonomous driving technology. There is a lack of research on traffic flow optimization that considers the penetration rate of autonomous vehicles and the different characteristics of each road segment. The objective of this study is to develop weights based on safety, operational, and environmental factors for each infrastructure control use case and autonomous vehicle MPR. To develop an integrated evaluation index, infra-guidance AHP and hybrid AHP weights were combined. Based on the results of this study, it can be used to give right of way to each vehicle to optimize mixed traffic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.683-688
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• 2018

Recently, the demand for electric energy has been increasing due to the spread of hybrid electric vehicles. In this study, to meet this demand, the ANSYS MAXWELL electromagnetic simulation system was used to compare the power generation characteristics of three types of suspension system that can generate electricity using energy harvesting technology. Next, the optimal design was determined for each model by using the commercial PIDO (Process Integration and Design Optimization) tool, PIANO (Process Integration, Automation and Optimization). We selected three design variables and constructed an approximate model based on the experimental design method through electromagnetic analysis for 18 experimental points derived from Orthogonal Arrays among the experimental design methods. Then, we determined the optimal design by applying the Evolutionary Algorithm. Finally, the optimal design results were verified by electromagnetic simulation of the optimum design result model using the same analysis conditions as those of the initial model. After comparing the power generation characteristics for the optimal structure for each linear generator model, the maximum power generation amounts in the 8pole-8slot, 12pole-12slot, and 16pole-16slot structures were 366.5W, 466.7W and 579.7W, respectively, and it was found that as the number of slots and poles increases, the power generation increases.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.236-244
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• 2005

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks(FPNN) that is based on fuzzy relation and evolutionally optimized Multi-Layer Perceptron, discuss a comprehensive design methodology and carry out a series of numeric experiments. The construction of the evolutionally optimized FPNN(EFPNN) exploits fundamental technologies of Computational Intelligence. The architecture of the resulting EFPNN results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks(PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the EFPNN. The consequence part of the EFPNN is designed using PNN. As the consequence part of the EFPNN, the development of the genetically optimized PNN(gPNN) dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the EFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed EFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Korean Chemical Engineering Research
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• v.55 no.3
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• pp.332-340
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• 2017

This study aims to propose a new optimization-based approach for design and analysis of the stand-alone hybrid energy supply system using renewable energy sources (RES). In the energy supply system, we include multiple energy production technologies such as Photovoltaics (PV), Wind turbine, and fossil-fuel-based AC generator along with different types of energy storage and conversion technologies such as battery and inverter. We then select six different regions of Korea to represent various characteristics of different RES potentials and demand profiles. We finally designed and analyzed the optimal RES stand-alone energy supply system in the selected regions using multiobjective optimization (MOOP) technique, which includes two objective functions: the minimum cost and the minimum $CO_2$ emission. In addition, we discussed the feasibility and expecting benefits of the systems by comparing to conventional systems of Korea. As a result, the region of the highest RES potential showed the possibility to remarkably reduce $CO_2$ emissions compared to the conventional system. Besides, the levelized cost of electricity (LCOE) of the RES-based energy system is identified to be slightly higher than conventional energy system: 0.35 and 0.46 $/kWh, respectively. However, the total life-cycle emission of $CO_2$ ($LCE_{CO2}$) can be reduced up to 470 g$CO_2$/kWh from 490 g$CO_2$/kWh of the conventional systems.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.