• 지질공학
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• 제33권3호
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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.

• 방송공학회논문지
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• 제24권2호
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• pp.227-233
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• 2019

본 논문은 가상현실 음향 구현을 위한 심층신경망 기반 사운드 보간 방법에 관한 것으로서, 이를 통해 두 지점에서 취득한 음향 신호들을 사용하여 두 지점 사이의 음향을 생성한다. 산술평균이나 기하평균 같은 통계적 방법으로 사운드 보간을 수행할 수 있지만 이는 실제 비선형 음향 특성을 반영하기에 미흡하다. 이러한 문제를 해결하기 위해서 본 연구에서는 두 지점과 목표 지점의 음향신호를 기반으로 심층신경망을 훈련하여 사운드 보간을 시도하였으며, 실험결과 통계적 방법에 비해 심층신경망 기반 사운드 보간 방법의 성능이 우수함을 보였다.

• 제어로봇시스템학회논문지
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• 제8권8호
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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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• 제4권5_6호
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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.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2007년도 동계학술발표대회 논문집
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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.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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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.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2004년도 Asia Navigation Conference
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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.

• 한국통신학회논문지
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• 제8권2호
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• pp.43-47
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• 1983

FIR필터와 IIR필터를 각각 독립적으로 설계하여 결합한 혼성 FIR/IIR필터에 의하여 이산신호의 보간을 위한 다상회로를 설계하였다. 제안된 다상회로는 백색 가우시안 잡음을 이용한 평가방법을 통하여 이산신호의 보간 필터로서의 유용성이 확인되었다.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 학술대회 논문집 전문대학교육위원
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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.