• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.86.5-86
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• 2002

The measurement sensor may contain unknown bias in addition to the white noise in the measurement sequence. In this paper, fault detection and diagnosis scheme for the measurement sensor is developed based on the adaptive estimator. The proposed scheme consists of a parallel bank of Kalman-type filters each matched to a set of different possible biases, a mode probability evaluator, an estimate combiner at the outputs of the filters, a bias estimator, and a fault detection and diagnosis logic. Monte Carlo simulations for the PWR steam generator in the nuclear power plant are provided to illustrate the effectiveness of the proposed scheme.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2011-2013
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• 2001

In this paper the MMAE(Multiple Model Adaptive Estimation) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) of which modes are set to be measurement biases is proposed to enhance the performance of target tracking with bearing only measurements. The state are composed of relative position, relative velocity and taregt acceleration. The mode probability is calculated from the bearing only measurements from the HMS(Hull-Mounted Sonar). The proposed algorithm is tested in a series of computer simulation runs.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.2
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• pp.1234-1238
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• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.351-354
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• 1996

A new target adaptive guidance (TAG) algorithm is proposed to engage the aim point formed by adding a bias to the information from an infrared (IR) seeker for improving passive homing guidance effectiveness. The TAG algorithm utilizes an observability enhancing mid-course guidance algorithm to obtain convergent estimates of state variables involved particularly in range channel otherwise unavailable from passive sensors. Simulation results indicate that the TAG algorithm provides improved terminal effectiveness without computational complexities.

• 대기
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• 제29권3호
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• pp.241-255
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• 2019

Bias correction (BC) and quality control (QC) are essential steps for the proper use of satellite observations in data assimilation (DA) system. BC should be calculated over quality controlled observation. And also QC should be performed for bias corrected observation. In the Korea Institute of Atmospheric Prediction Systems (KIAPS) Package for Observation Processing (KPOP), we adopted an adaptive BC method that calculates the BC coefficients with background at the analysis time rather than using static BC coefficients. In this study, we have developed an iterative QC-BC method for Advanced Microwave Sounding Unit-A (AMSU-A) to reduce the negative feedback from the interaction between BC and QC. The new iterative QC-BC is evaluated in the KIAPS 3-dimensional variational (3DVAR) DA cycle for January 2016. The iterative QC-BC method for AMSU-A shows globally significant benefits for error reduction of the temperature. The positive impacts for the temperature were predominant at latitudes of $30^{\circ}{\sim}90^{\circ}$ of both hemispheres. Moreover, the background warm bias across the troposphere is decreased. Even though AMSU-A is mainly designed for atmospheric temperature sounding, the improvement of AMSU-A pre-processing module has a positive impact on the wind component over latitudes of $30^{\circ}S$ near upper-troposphere, respectively. Consequently, the 3-day-forecast-accuracy is improved about 1% for temperature and zonal wind in the troposphere.

• 한국콘텐츠학회논문지
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• 제5권1호
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• pp.1-8
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• 2005

e-러닝은 교육과 학습을 위하여 e-비즈니스 기술 및 서비스를 사용하는 응용프로그램이다. 이는 원격지자원과 서비스에 접근을 수월하게 함으로서 교육의 질을 높이기 위한 새로운 멀티미디어 및 인터넷 기술을 사용한다. 본 논문은 신중하게 설계되고 구현된 인터넷기반의 컴퓨터기반 시험 시스템에 대하여 기술한다. 본 시스템은 콘텐츠 전달 기술, 컴퓨터 적응형 시험 알고리즘, 리뷰엔진으로 구성되어 있다. 본 논문에서는 컴퓨터기반 시험 시스템을 설계하고 구현할 때에 고려되어야 할 요소들에 대하여 서술한다. 또한, 실제 데이터를 이용하여 컴퓨터 적응형 알고리즘을 위한 편향 값을 어떻게 조절하는지를 보인다.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.767-775
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• 2008

Methods used for exploring marine resources and spaces include positioning a probe under water and then recalling it after a specified time. Hydro-acoustic Releasers are commonly used for positioning and retrieving of such exploration equipment. The most important factor in this kind of system is the reliability for recalling the instruments. The frequently used ultrasonic signal detection method can detect ultrasonic signals using a fixed comparator, but because of increased rates of errors due to outside interferences, information is repetitively acquired. This study presents an effective ultrasonic signal detection algorithm using the characteristics of a resonance and adaptive comparator Combined with the FSK+ASK modulator. As a result, approximately 8.8% of ultrasonic wave communication errors caused by background noise and transmission losses were reduced for effectively detecting ultrasonic waves. Furthermore, the resonance circuit's quality factor was enhanced (Q = 120 to 160). As such, the bias voltage of the transistor (Vb= 3.3 to 6.8V) was increased thereby enhancing the frequency's selectivity.

• 한국항해학회지
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• 제23권3호
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
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• pp.525-526
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• 2008

For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

• 대한전자공학회논문지SD
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• 제49권2호
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• pp.23-30
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• 2012

본 논문에서는 음 바이어스 온도 불안정성 (NBTI) 효과에 의해서 야기되는 파워 게이팅 구조의 성능 저하와 증가하는 기상시간을 보상하기위한 새로운 적응형 헤더기반의 파워 게이팅 구조를 제안한다. 제안된 구조는 두 개의 패스 (two-pass)를 가지는 파워 게이팅 구조에 기반을 둔 폭 변화 헤더(header)와 적응형 제어를 위한 새로운 NBTI 센싱 회로로 구성된다. 본 논문의 시뮬레이션 결과는 적응형 제어를 하지 않는 파워 게이팅의 시뮬레이션 결과와 비교되며, 그 결과는 파워 게이팅 구조에서 누설 전력과 돌입 전류(rush current)을 작게 유지하면서 회로 지연과 기상시간에 대한 NBTI 의존성이 단지 3% 와 4% 내로 줄어든다는 것을 보여준다. 본 논문에서는 45nm CMOS 공정과 NBTI 예측 모델이 제안된 회로를 구성하기 위해서 사용된다.