• Advances in aircraft and spacecraft science
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• v.2 no.3
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• pp.263-273
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• 2015

Carbon nanotube strain sensors, or so called "fuzzy fiber" sensors have not yet been studied sufficiently. These sensors are composed of a bundle of fiberglass fibers coated with CNT through a thermal chemical vapor deposition process. The characteristics of these fuzzy fiber sensors differ from a conventional nanocomposite in that the CNTs are anchored to a substrate fiber and the CNTs have a preferential orientation due to this bonding to the substrate fiber. A numerical model was constructed to predict the strain response of a composite with embedded fuzzy fiber sensors in order to compare result with the experimental results obtained in an earlier study. A comparison of the numerical and experimental responses was conducted based on this work. The longitudinal sensor output from the model matches nearly perfectly with the experimental results. The transverse and off-axis tests follow the correct trends; however the magnitude of the output does not match well with the experimental data. An explanation of the disparity is proposed based on microstructural interactions between individual nanotubes within the sensor.

• Computers and Concrete
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• v.9 no.5
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• pp.389-402
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• 2012

In this study, the modulus of elasticity of low, normal and high strength steel fiber reinforced concrete has been predicted by developing a fuzzy logic model. The fuzzy models were formed as simple rules using only linguistic variables. A fuzzy logic algorithm was devised for estimating the elastic modulus of SFRC from compressive strength. Fibers used in all of the mixes were made of steel, and they were in different volume fractions and aspect ratios. Fiber volume fractions of the concrete mixtures have changed between 0.25%-6%. The results of the proposed approach in this study were compared with the results of equations in standards and codes for elastic modulus of SFRC. Error estimation was also carried out for each approach. In the study, the lowest error deviation was obtained in proposed fuzzy logic approach. The fuzzy logic approach was rather useful to quickly and easily predict the elastic modulus of SFRC.

• Proceedings of the Korean Fiber Society Conference
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• 2000.04a
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• pp.299-302
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• 2000

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.92-97
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• 2008

This is a research to apply 1310nm single-mode optical fiber to a temperature sensor. The existing study of optical fiber sensor is complicated because it was made with various equipment. To vary scattering, the variation of optical frequency is measured by using Bragg(lattice) or pulse generator and also bulk system is created by YAG laser but there were some difficulties creating experimental environment and it was a problem that the stability of measured data was low. The temperature sensor system using the suggested sBs(stimulated Brillouin scattering:sBs) from this research is much more simplified straight-line system. To improve the trust and accuracy of noises from optical frequency and unclear results, it was analysed by using Neuro-Fuzzy algorithm. we tried to get more correct data than existing system. sBs measure that optical frequency changed due to the variation of temperature. The analyzed change rate of outcome by Fuzzy theory is 1.1 MHz.

• Proceedings of the Korean Fiber Society Conference
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• 1998.04a
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• pp.274-279
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• 1998

주관적인 질감을 표현하는 형용사는 매끄럽기-껄끄럽기, 편편하기-우둘두둘하기, 부드럽기-뻣뻣하기, 폭신하기-딱딱하기로 4개의 상반된 쌍으로 정리 분류할 수 있었다. 직물을 7점 척도에 의해 표현되는 질감을 조사하고, 동일직물의 마찰계수, 표면 거칠기, 마찰력, 밀도, 중량, 두께 등의 역학적, 물리적 값을 객관적으로 측정한 후 상호 상관관계를 구하고 Fuzzy 이론을 이용하여 객관적 등급을 예측하는 모델을 확립하였다.(중략)

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.530-534
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• 2004

We propose a phase stabilisation and control system for the use in fiber-optic ESPI. The fast phase stabilisation against environmental perturbations has been achieved by using Fuzzy PI control. Combined with closed-loop switching, the system showed accurate and fast ${\pi}/2$ phase stepping capability.

• Proceedings of the Korean Fiber Society Conference
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• 2001.04a
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• pp.333-337
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• 2001

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.487-488
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• 2009

In this investigation, the special fiber mixing method was studied for increasing the acoustic absorption properties of porous concrete. The short fibers were mixed as the fuzzy-shape, not as the embedded, and the specimens are measured using the impedance tube method.

• Fibers and Polymers
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• v.7 no.2
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• pp.203-212
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• 2006

The purpose of this study was to examine the effects of the structural properties of plain knit fabrics on the subjective perception of textures, sensibilities, and preference among consumers. This study, then, aimed to provide useful information with respect to planning and designing knitted fabrics by predicting the subjective characteristics analyzed according to their structural properties. For this purpose, we employed statistical analysis tools, such as factor and regression analysis and an adaptive-network-based fuzzy inference system(ANFIS), thereby combining the merits of fuzzy and neural networks and presupposing a non-linear relationship. Through factor analysis, we also categorized the subjective textures into 'roughness', 'softness', 'bulkiness' and 'stretch-ability' with R2=70.32%: and categorized the sensibilities into 'Stable/Neat', 'Natural/Comfortable' and 'Feminine/Elegant' with R2=68.12%. We analyzed subjective textures, sensibilities, and preference with ANFIS, assuming non-linear relationships; consequently, we were able to generate three or four fuzzy rules using wool/rayon fiber content and loop length as input data. The textures of roughness and softness exhibited a linear relationship, but other subjective characteristics demonstrated a non-linear input-output relationship. Compared with linear regression analysis, the ANFIS exhibited had higher predictive power with respect to predicting subjective characteristics.