• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.4
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• pp.408-415
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• 2016

The vehicle attitude and sideslip is critical information to control the vehicle to prevent from unintended motion. Many of estimation strategy use bicycle model or IMU integration, but both of them have limits on application. The main purpose of this paper is development of vehicle orientation estimator which is robust to various vehicle state and road shape. The suggested estimator use 3-axis magnetometer, yaw rate sensor and lateral acceleration sensor to estimate three Euler angles of vehicle. The estimator is composed of two individual observers: First, comparing the known magnetic field and gravity with measured value, the TRIAD algorithm calculates optimal rotational matrix when vehicle is in static or quasi-static condition. Next, merging 3-axis magnetometer with inertial sensors, the extended Kalman filter is used to estimate vehicle orientation under dynamic condition. A validation through simulation tools, Carsim and Simulink, is performed and the results show the feasibility of the suggested estimation method.

• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.6
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• pp.49-56
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• 1995

In this study, self-tuning adaptive control using state observer is developed. Self-tuning adaptive controller that estimates the parameters of the system in real time and generates the optimal control signals has robust characteristic about varying load and external disturbances. In addition, state observer without sensors is applied, thus the control can be performed more quickly and exactly. Since chopper is used commonly in practical drives, the characteristics of the chopper are included in state observer algorithm, which, in turn, makes the system exact estimation. Since series type DC motor has nonlinear models, linearizing approach are investigated. to realize the proposed algorithm it requires fast calculation in real time. TMS320C31, digital signal processor, is applied to realized the adaptive control algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2516-2520
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• 2005

In this paper, finite impulse response (FIR) smoothers are proposed for discrete-time systems. The proposed FIR smoother is designed under the constraints of linearity, unbiasedness, FIR structure, and independence of the initial state information. It is also obtained by directly minimizing the performance criterion with unbiased constraints. The approach to the MVF smoother proposed in this paper is logical and systematic, while existing results have heuristic assumption, such as infinite covariance of the initial state. Additionally, the proposed MVF smoother is based on the general system model that may have the singular system matrix and has both system and measurement noises. Thorough simulation studies, it is shown that the proposed MVF smoother is more robust against modeling uncertainties numerical errors than fixed-lag Kalman smoother which is infinite impulse response (IIR) type estimator.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1976-1978
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• 2001

In this paper, a new robust deadbeat minimax FIR filter (DMFF) is proposed for continuous-time state space signal models. Linearity, deadbeat property, FIR structure, and independence of the initial state information will be required in advance, in addition to a performance index of the worst case gain between the disturbance and the current estimation error. The proposed DMFF is obtained by directly minimizing a performance index with the deadbeat constraint. The proposed DMFF is represented first in a standard FIR form and then in an iterative form. The DMFF will be shown to be used also for the IIR structure. It is shown that the DMFF is similar in form to the existing receding horizon unbiased FIR filter (RHUFF) with some noise covariances. The former is a deterministic filter, while the latter is a stochastic filter.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.53-56
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• 2002

This paper presents neural observer that used to deadbeat load torque observer. Most practical systems are nonlinear, and it is general practice to use linear models to simplify their analysis and design. However, the locally linearized model is invalid for a large signal change. The neural observer is suggested to increase the performance of the load torque observer and main controller The output error and estimeted state is trianed by neural network of neural observer. As a result, the state estimation error is minimised and deadbeat load torque observer make use of corrected esimation state. To reduce of the noise effect of deadbeat load torque observer, the post-filter which is implemented by MA process, is adopted. As a result, the proposed control system becomes a robust and precise system against the load torque. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.15-18
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• 1996

Shewhart control chart is a basic technique to monitor the state of a process. We observe observations of a group of size four or five in a rational way and plot some statistics (e.g., means and ranges) on the chart. When setting up the control chart, the control limits are calculated based on preliminary 20-40 samples, which were supposedly obtained from stable operating conditions. But it may be hard to believe, especially at the beginning of constructing the chart for the first time, whether the process is stable and hence all samples were generated under the homogeneous operating conditions. In this report we suggest a mechanism to obtain robust control limits under self-criticism. When outliers are present in the sample, we obtain tighter control limits and hence increase the sensitivity of the chart. Examples will be given via simulation study.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.330-335
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• 1998

In this paper the robust vector control method of induction motor for the purpose of improving the system performance deterioration caused by parameter variations is proposed. The estimations of the stator current and the rotor flux are obtained by the full order state observer with corrective prediction error feedback. and the adaptive scheme is constructed to estimate the rotor speed with the error signal between real and estimation value of the stator current. Adaptive sliding observer based on the variable structure control is applied to parameter identification. Consequently predictive current control and speed sensorless vector control can be obtained simultaneously regardless of the parameter variations.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.143-157
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• 1994

This paper is concerned with the position control of electrohydraulic servo system under parameter variation. An adaptive sliding mode control which uses the direct parameter estimation scheme, is proposed to design a robust controller for fast and accurate control of the system. It is shown that the adaptive sliding mode control algorithm is robust and effective in attaining fast and accurate position control of system under time-dependent parameter variation. It is also shown experimentally that chattering phenomena in a sliding mode control can significantly be reduced by using boundary layer technique, and that new approach in sliding mode control introducing a term proportional to the distance between the current state and the sliding surface in the control law is effective to obtain fast response and to increase stability of the system. Computer simulation on the dynamic performance of the control system is also presented.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.727-728
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• 2006

This paper is proposed robust control based on the vector controlled induction motor drive with adaptive fuzzy learning control(AFLC). The fuzzy logic principle is first utilized for the control rotor speed. AFLC scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. Also, this paper is proposed estimation of speed of induction motor using ANN Controller. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. This paper is proposed the analysis results to verify the effectiveness of the new method.