• The Journal of Engineering Geology
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• v.33 no.3
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• pp.439-459
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• 2023

Numerical analysis has been adopted in nearly all modern scientific and engineering fields due to the rapid and ongoing evolution of computational technology, with the number of grid or mesh points in a given data field also increasing. Some values must be extracted from large data fields to evaluate and supplement numerical analysis results and observational data, thereby highlighting the need for a fast and effective interpolation approach. The quadrilateral irregular network (QIN) proposed in this study is a fast and reliable interpolation method that is capable of sufficiently satisfying these demands. A comparative sensitivity analysis is first performed using known test functions to assess the accuracy and computational requirements of QIN relative to conventional interpolation methods. These same interpolation methods are then employed to produce simple numerical model results for a real-world comparison. Unlike conventional interpolation methods, QIN can obtain reliable results with a guaranteed degree of accuracy since there is no need to determine the optimal parameter values. Furthermore, QIN is a computationally efficient method compared with conventional interpolation methods that require the entire data space to be evaluated during interpolation, even if only a subset of the data space requires interpolation.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.227-233
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• 2019

In this paper, we propose a deep neural network-based sound interpolation method for realizing virtual reality sound. Through this method, sound between two points is generated by using acoustic signals obtained from two points. Sound interpolation can be performed by statistical methods such as arithmetic mean or geometric mean, but this is insufficient to reflect actual nonlinear acoustic characteristics. In order to solve this problem, in this study, the sound interpolation is performed by training the deep neural network based on the acoustic signals of the two points and the target point, and the experimental results show that the deep neural network-based sound interpolation method is superior to the statistical methods.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.8
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• pp.713-718
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• 2002

Motion controllers are essential components for operating industrial equipments. Compared with general industrial controllers, motion controllers allow motion control requiring greater speed and precision. This paper presents a method for controlling multi-axes motors via industrial networks. To achieve a line or arc interpolation, the master system delivers instructions to slave systems connected to the network. The network instruction transmitted from the master controller is re-interpolated by the individual slaves through sub-interpolators. The re-interpolated feedrate information is transmitted to the motion control loop in which the current position and the reference position are then calculated. In this way, the interpolation driving between control units is achieved via industrial networks.

• Journal of Applied and Pure Mathematics
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• v.4 no.5_6
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• pp.269-286
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• 2022

In this article we exhibit univariate and multivariate quantitative approximation by Kantorovich-Choquet type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the first univariate and multivariate moduli of continuity. We approximate continuous and bounded functions on ℝ^N , N ∈ ℕ. When they are also uniformly continuous we have pointwise and uniform convergences. Our activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.29-33
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• 2007

In order to protect the environment from the refrigerant pollution, the $CO_2$ may be regarded as one of the most attractive alternative refrigerants for an automotive air-conditioning system. Control methods for a $CO_2$ system should be different because of $CO_2$'s unique properties as a refrigerant. Especially, the high-side pressure of a $CO_2$ system should be controlled for the effective operation of the system. In this study, the high-side pressure setpoint algorithm was developed by using a neural network and a Lagrange interpolation method. These methods were compared. Simulation results showed that a Lagrange interpolation method was more effective than a neural network in the respect of its easiness of programming and shorter execution time.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.506-507
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• 2007

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.08a
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• pp.37-44
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• 2004

Continuous depth data are often required in applications of both onboard systems and maritime simulation. But data available are usually discrete and irregularly distributed. Based on the neuron network technique, methods of interpolation to the charted depth are suggested in this paper. Two algorithms based on Levenberg-Marquardt back-propaganda and radial-basis function networks are investigated respectively. A dynamic neuron network system is developed which satisfies both real time and mass processing applications. Using hyperbolic paraboloid and typical chart area, effectiveness of the algorithms is tested and error analysis presented. Special process in practical applications such as partition of lager areas, normalization and selection of depth contour data are also illustrated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.8 no.2
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• pp.43-47
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• 1983

The method of designing the polyphase network for the interpolation filter of the discrete signals is designed, the polyphase network consists of the hybrid FIR/IIR digital filter with the FIR filter and IIR filter which are designed by independent methods. It is showed that the proposed polyphase network is useful for the interpolation filter, by the estimation method using the white-gaussian noise.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.188-194
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• 2015

This paper proposes a generalized regression neural network (GRNN) based algorithm for data interpolation and design optimization of brushless dc (BLDC) motor. The procedure makes use of magnet length, stator slot opening and air gap length as design variables. Cogging torque and average torque are treated as performance indices. The optimal design necessitates mitigating the cogging torque and maximizing the average torque by varying design variables. The data set for interpolation and ensuing design optimisation using GRNN is obtained by modeling a standard BLDC motor using finite element analysis (FEA) tool MagNet 7.1.1. The performance indices of the standard motor obtained using FEA are validated with an experimental model and an analytical method. The optimal design is authenticated using particle swarm optimization (PSO) algorithm and the performance indices of the optimal design obtained using GRNN is validated using FEA. The results indicate the suitability of GRNN as an interpolation and design optimization tool for a BLDC motor.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.83-87
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• 2007

Correlation Propagation Neural Networks is proposed for On-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D CPNN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the CPNN hardware.