• International Journal of Concrete Structures and Materials
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• v.9 no.4
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• pp.475-486
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• 2015

Mode II fracture toughness ($K_{IIc}$) of fiber reinforced concrete (FRC) has been widely investigated under various patterns of test specimen geometries. Most of these studies were focused on single type fiber reinforced concrete. There is a lack in such studies for hybrid fiber reinforced concrete. In the current study, an experimental investigation of evaluating mode II fracture toughness ($K_{IIc}$) of hybrid fiber embedded in high strength concrete matrix has been reported. Three different types of fibers; namely steel (S), glass (G), and polypropylene (PP) fibers were mixed together in four hybridization patterns (S/G), (S/PP), (G/PP), (S/G/PP) with constant cumulative volume fraction ($V_f$) of 1.5 %. The concrete matrix properties were kept the same for all hybrid FRC patterns. In an attempt to estimate a fairly accepted value of fracture toughness $K_{IIc}$, four testing geometries and loading types are employed in this investigation. Three different ratios of notch depth to specimen width (a/w) 0.3, 0.4, and 0.5 were implemented in this study. Mode II fracture toughness of concrete $K_{IIc}$ was found to decrease with the increment of a/w ratio for all concretes and test geometries. Mode II fracture toughness $K_{IIc}$ was sensitive to the hybridization patterns of fiber. The (S/PP) hybridization pattern showed higher values than all other patterns, while the (S/G/PP) showed insignificant enhancement on mode II fracture toughness ($K_{IIc}$). The four point shear test set up reflected the lowest values of mode II fracture toughness $K_{IIc}$ of concrete. The non damage defect concept proved that, double edge notch prism test setup is the most reliable test to measure pure mode II of concrete.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.08a
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• pp.124-124
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• 2011

This study investigated the interaction of varied plasma power with ultralow-k toluene-tetraethoxysilane (TEOS) hybrid plasma polymer thin films, as well as changing electrical and mechanical properties. The hybrid thin films were deposited on silicon(100) substrates by plasma enhanced chemical vapor deposition (PECVD) system. Toluene and tetraethoxysilane were utilized as organic and inorganic precursors. In order to compare the electrical and the mechanical properties, we grew the hybrid thin films under various conditions such as rf power of plasma, bubbling ratio of TEOS to toluene, and post annealing temperature. The hybrid plasma polymer thin films were characterized by Fourier transform infrared (FT-IR) spectroscopy, atomic force microscopy (AFM), nanoindenter, I-V curves, and capacitance. Also, the hybrid thin films were analyzed by using ellipsometry. The refractive indices varied with the RF power, the bubbling ratio of TEOS to toluene, and the annealing temperature. To analyze their trends of electrical and mechanical properties, the thin films were grown under conditions of various rf powers. The IR spectra showed them to have completely different chemical functionalities from the liquid toluene and TEOS precursors. Also, The SiO peak intensity increased with increasing TEOS bubbling ratio, and the -OH and the CO peak intensities decreased with increasing annealing temperature. The AFM images showed changing of surface roughness that depended on different deposition rf powers. An nanoindenter was used to measure the hardness and Young' modulus and showed that both these values increased as the deposition RF power increased; these values also changed with the bubbling ratio of TEOS to toluene and with the annealing temperature. From the field emission scanning electron microscopy (FE-SEM) results, the thickness of the thin films was determined before and after the annealing, with the thickness shrinkage (%) being measured by using SEM cross-sectional images.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.215-235
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• 2017

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.126-129
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• 1997

This paper propose a new hybrid genetic algorithm for auto-tunig auzzy controller improving the performance. In general, fuzzy controller used pre-determine d moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controller, using hybrid genetic algorithms. The object of the proposed algorithm is to promote search efficiency by overcoming a premature convergence of genetic algorithms. Hybrid genetic algorithm is based on genetic algorithm and modified gradient method. Simulation results verify the validity of the presented method.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.43-45
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• 1994

This paper proposes the new method to calculate the static thrust and normal force of a hybrid type double-sided linear pulse motor by the coenergy which considered the magnetic nonlinealities. In the process of the computation, the nonlinear characteristics of the magnetic material were interpolated by the cubic spline method. And, to investigate the characteristics of the hybrid type DLPM, the static thrust and the normal force is shown as a function of displacement, input current and air airgap length. Also the simulation values are compared with the experimental ones obtained from a hybrid type DLPM.

• ETRI Journal
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• v.35 no.2
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• pp.356-359
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• 2013

We present a method for tracking and interaction based on hybrid sensing for virtual environments. The proposed method is applied to motion tracking of whole areas, including the user's occlusion space, for a high-precision interaction. For real-time motion tracking surrounding a user, we estimate each joint position in the human body using a combination of a depth sensor and a wand-type physical user interface, which is necessary to convert gyroscope and acceleration values into positional data. Additionally, we construct virtual contents and evaluate the validity of results related to hybrid sensing-based whole-body tracking of human motion methods used to compensate for the occluded areas.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2545-2547
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• 2000

In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.