• Journal of the Korea Society of Computer and Information
• /
• v.3 no.2
• /
• pp.139-146
• /
• 1998

This paper proposes a regularized iterative image restoration using method of conjugate gradient considering a priori information. Compared with conventional regularized method of conjugate gradient, this method has merits to prevent the artifacts by ringing effects and the partial magnification of the noise in the course of restoring the image degraded by blur and additive noise. Proposed method applies the constraints to accelerate the convergence ratio near the edge portions, and the regularized parameter suppresses the magnification of the noise. As experimental results, I show the superior convergence ratio and the suppression by the artifacts of the proposed method compared with conventional methods.

• Journal of Sensor Science and Technology
• /
• v.7 no.6
• /
• pp.418-425
• /
• 1998

Stereo cameras are the most widely used sensing systems for automated machines including robots to interact with their three-dimensional(3D) working environments. The position of a target point in the 3D world coordinates can be measured by the use of stereo cameras and the camera calibration is an important preliminary step for the task. Existing camera calibration techniques can be classified into two large categories - linear and nonlinear techniques. While linear techniques are simple but somewhat inaccurate, the nonlinear ones require a modeling process to compensate for the lens distortion and a rather complicated procedure to solve the nonlinear equations. In this paper, a method employing a neural network for the calibration problem is described for tackling the problems arisen when existing techniques are applied and the results are reported. Particularly, it is shown experimentally that by utilizing the function approximation capability of multi-layer neural networks trained by the back-propagation(BP) algorithm to learn the error pattern of a linear technique, the measurement accuracy can be simply and efficiently increased.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.05a
• /
• pp.164-167
• /
• 2006

본 논문에서는 불확실한 비선형 시스템에 대한 적응 퍼지 슬라이딩 모드 제어기를 설계한다. 불확실한 비선형 시스템에서 발생할 수 있는 파라미터의 변화를 대처하기 위해서 적응 퍼지 이론을 이용하였고, 외란으로 인한 불확실성을 슬라이딩 모드의 제어기를 통해서 해결하였다. 또한 퍼지 튜닝을 통해 슬라이딩 조건을 가변화함으로써 기존의 슬라이딩 모드 제어기에 비해 빠르고 정확하게 추종 가능하도록 제어기의 성능을 향상시킨다. 제안하는 제어기는 정확한 동역학 모델의 구현이 어렵고 복잡한 비선형 시스템에 외란 특성이 우수한 슬라이딩 모드와 실제 시스템을 표현하는 범용 근사자로 유용성이 입증된 퍼지 시스템을 이용하여 간단하고 쉽게 제어할 수 있도록 하였다. Lyapunov이론을 통하여 전역적인 안정화를 보이며, 마지막으로 역진자 시스템에 적용하여 제안된 제어기의 성능을 검증한다.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.12 no.2
• /
• pp.141-148
• /
• 1999

변위 근거 유한요소해석을 사용하는 대부분의 위상 최적화 기법은 요소의 안정성 부족으로 인하여 체스판 무늬가 주기적 형태로 반복하여 설계영역 내부에 나타난다. 본 연구에서는 선형요소를 이용하면서 최적화 알고리즘의 안정성에 영향을 주지 않고 간단하게 모든 최적화 알고리즘에 이용 가능한 체스판무늬 형성 방지책을 개발하였다. 본 연구의 체스판무늬 형성 방치책에서는 먼저 각 선형요소를 구성하는 절점들의 부치분율을 설계변수로 선정하고, 요소내부의 부피분율을 설계변수로 표현하기 위한 선형 보간함수로 선형요소들의 형상함수를 선정하였다. 그리고, 설계변수와 등가 재료상수와의 상관 관계식은 평균장 근사이론을 이용하여 균질화된 재료에 벌칙인자가 도입된 관계식을 이용하였다. 또한, 본 연구에서는 순차이차계획법인 PLBA 알고리즘을 이용하여 위상 최적화문제를 해석하였다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39A no.6
• /
• pp.322-332
• /
• 2014

This paper proposes a robust lane detection algorithm for non-flat roads by combining a piecewise linear model and dynamic programming. Compared with other lane models, the piecewise linear model can represent 3D shapes of roads from the scenes acquired by monocular camera since it can form a curved surface through a set of planar road. To represent the real road, the planar roads are created by various angles and positions at each section. And dynamic programming determines an optimal combination of planar roads based on lane properties. Experiment results demonstrate the robustness of proposed algorithm against non-flat road, curved road, and camera vibration.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.12
• /
• pp.3185-3199
• /
• 1997

This paper suggests techniques to reduce coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, we choose new approximation coefficients using a non-linear approximation of luminance term. This boosts the fidelity. Our experiment employing the above methods shows enhancement in the coding time more than two times over traditional coding methods and shows improvement in PSNR value by about 1-3dB at the same com- pression rate.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.4
• /
• pp.185-193
• /
• 2022

This paper proposes an adaptive sliding mode control with radial basis function neural network(RBFNN) scheme to enhance the performance of position and attitude tracking control of quadrotor UAV. The RBFNN is utilized on the approximation of nonlinear function in the UAV dynmic model and the weights of the RBFNN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering problems, the sliding mode control term is adjusted by adaptive laws, which can enhance the robust performance of the system. The simulation results of the proposed control method confirm the effectiveness of the proposed controller which applied for a nonlinear quadrotor UAV is presented. Form the results, it's shown that the developed control system is achieved satisfactory control performance and robustness.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.42 no.11
• /
• pp.1-8
• /
• 2005

In this paper, we present design and prototyping of a low-cost, integrated multi-functional micro health sensor chip that can be used or embedded in widely consumer devices, such as cell phone and PDA, for monitoring environmental condition including air pressure, temperature and humidity. This research's scope includes basic individual sensor study, architecture for integrating sensors on a chip, fabrication process compatibility and test/evaluation of prototype sensors. The results show that the integrated TPH sensor has good characteristics of ${\pm}\;1\%FS$ of linearity and hysteresis for pressure sensor and temperature sensor and of ${\pm}\;5\%FS$ of linearity and hysteresis But if we use 3rd order approximation for humidity sensor, full scale error becomes much smaller and this will be one of our future study.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.3
• /
• pp.33-40
• /
• 1998

This paper presents an Modified Orthogonal Neural Network(MONN), new modified model of Orthogonal Neural Network(0NN) based on orthogonal functions, and applies it to nonlinear system approximator. ONN proposed by Yang and Tseng, doesn't have the problems of traditional multilayer feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements. And tranining of ONN converges rapidly. But ONN cannot adapt its orthogonal functions to a given system. The accuracy of ONN, in terms of the minimal possible deviation between system and approximator, is essentially dependent on the choice of basic orthogonal functions. In order to improve ability and effectiveness of approximate nonlinear systems, MONN has an input transformation layer to adapt its basic orthogonal functions to a given nonlinear system. The results show that MONN has the excellent performance of approximate nonlinear systems and the input transfnrmation makes the ability of MONN better than one of ONN.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.17 no.12 s.115
• /
• pp.1164-1171
• /
• 2006

In this paper, a simple and nonlinear MMSE detector is derived from the conventional linear MMSE detector to improve the system performance in turbo processing MIMO system and a new turbo processing MIMO system with nonlinear MMSE detector and Gaussian approximation is proposed. In turbo coded turbo processing MIMO system, the proposed system of M=N=4 performs about 0.5 dB better than the conventional system and the proposed system of M=N=8 performs about 0.4 dB better than conventional system at 1 % FER. In addition, the average number of outer iterations of proposed system shows lower than that of conventional system. This is caused by the fact that the nonlinear MMSE detector of proposed system decides soft-outputs of coded bits based on the observation of received signals, so that it can reduce the uncertainty region in estimating the coded bits.