• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.145-152
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• 2007

In this paper, we propose an automatic extraction model for unknown translations and implement an unknown translation extraction system using the proposed model. The proposed model as a phrase-alignment model is incorporated with three models: a phrase-boundary model, a language model, and a translation model. Using the proposed model we implement the system for extracting unknown translations, which consists of three parts: construction of parallel corpora, alignment of Korean and English words, extraction of unknown translations. To evaluate the performance of the proposed system, we have established the reference corpus for extracting unknown translation, which comprises of 2,220 parallel sentences including about 1,500 unknown translations. Through several experiments, we have observed that the proposed model is very useful for extracting unknown translations. In the future, researches on objective evaluation and establishment of parallel corpora with good quality should be performed and studies on improving the performance of unknown translation extraction should be kept up.

• Smart Structures and Systems
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• v.31 no.2
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• pp.131-140
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• 2023

As well-known, the extended Kalman filter (EKF) is a powerful tool for parameter identification with limited measurements. However, traditional EKF is not applicable when the external excitation is unknown. By using least-squares estimation (LSE) for force identification, an EKF with unknown input (EKF-UI) approach was recently proposed by the authors. In this approach, to ensure the influence matrix be of full column rank, the sensors have to be deployed at all the degrees-of-freedom (DOFs) corresponding to the unknown excitation, saying collocated measurements are required. However, it is not easy to guarantee that the sensors can be installed at all these locations. To circumvent this limitation, based on the idea of first-order-holder discretization (FOHD), an improved EKF with unknown input (IEKF-UI) approach is proposed in this study for the simultaneous identification of structural parameters and unknown excitation. By using projection matrix, an improved observation equation is obtained. Few displacement measurements are fused into the observation equation to avoid the so-called low-frequency drift. To avoid the ill-conditioning problem for force identification without collocated measurements, the idea of FOHD is employed. The recursive solution of the structural states and unknown loads is then analytically derived. The effectiveness of the proposed approach is validated via several numerical examples. Results show that the proposed approach is capable of satisfactorily identifying the parameters of linear and nonlinear structures and the unknown excitation applied to them.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.93-98
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• 2006

This paper deals with the estimation of unknown actuator faults for linear dynamic systems with sensor noise. The presented method based on the integral observer permits to achieve good convergence and exact estimation of unknown faults. The validity of proposed method is established by using the simulation results which compare to the existing methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.140-146
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• 2016

This paper proposes a control method of the Segway with unknown control coefficient and input saturation. To design a simple controller for the Segway with the model uncertainty, the prescribed performance function is used. Furthermore, an auxiliary variable is introduced to deal with unknown time-varying control coefficient and input saturation problem. Due to the auxiliary variable, function approximators are not used in this paper although all model uncertainties are unknown. Thus, the controller can be simple. From the Lyapunov stability theory, it is proved that all errors of the proposed control system remain within the prescribed performance bounds. Finally, the simulation results are presented to demonstrate the performance of the proposed scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.229-232
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• 2002

This paper describes the development of robot's finger 3-axis force sensor that detects the Fx, Fy, and Fz simultaneously fur stably grasping an unknown object. In order to safely grasp an unknown object using the robot's fingers, they should detect the force of gripping direction and the force of gravity direction, and perform the force control using the detected farces. The 3-axis force sensor that detects the Fx, Fy, and Fz simultaneously should be used for accurately detecting the weight of an unknown object of gravity direction. Thus, in this paper, robot's finger for stably grasping an unknown object is developed. And, the 3-axis farce sensor that detects the Fx, Fy, and Fz simultaneously fur constructing a robot's finger is newly modeled using several parallel-plate beams, and is fabricated. Also, it is calibrated, and evaluated.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.338-344
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• 2002

In this paper, neural network force tracking control is proposed. The conventional impedance function is reformulated to have direct farce tracking capability. Neural network is used to compensate for all the uncertainties such as unknown robot dynamics, unknown environment stiffness, and unknown environment position. On line training signal of farce error for neural network is formulated. A large x-y table is built as a test-bed and neural network loaming algorithm is implemented on a DSP board mounted in a PC. Experimental studies of farce tracking on unknown environment for x-y table robot are presented to confirm the performance of the proposed technique.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1844-1851
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• 2007

This paper presents methods for the compensation of sinusoidal disturbances with unknown amplitude, phase, and time-varying frequency in nonlinear systems. In the previous disturbance accommodation methods, the sinusoidal disturbance with unknown time-invariant frequency was considered. In the proposed method, the disturbance with unknown time-varying frequency is compensated. As for the control structure, two control inputs are designed separately in such a way that one of them is designed for the nonlinear system control without considering the disturbance, and the other one uses the disturbance estimate obtained from the disturbance accommodating observer. The stability analysis is done considering the disturbance estimation error and the numerical simulation demonstrates the proposed approach.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.275-280
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• 1994

In the case of the image reconstruction from unknown projection data such as imaging the object with opaque obstructions, conventional reconstruction algorithms may reconstruct a degraded image. In this paper, a new method for the estimation of the unknown projection data based on known projection data and the bandwidth of projection data is proposed. The proposed method successfully estimates the unknown projection data through iterative transformation between projection space and frequency space using the known projection data and the bandwidth of the projection data. Computer simulation shows that the proposed method significantly improves image quality and convergence behavior over conventional algorithms. In addition, the proposed method is successfully applied to ultrasound attenuation CT using a sponge phantom.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2319-2324
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• 2005

This paper proposes a intelligent gain and boundary layer based sliding mode control (SMC) method for robotic systems with unknown model uncertainties. For intelligent gain and boundary layer, we employ the self recurrent wavelet neural network (SRWNN) which has the properties such as a simple structure and fast convergence. In our control structure, the SRWNNs are used for estimating the width of boundary layer, uncertainty bound, and nonlinear terms of robotic systems. The adaptation laws for all parameters of SRWNNs and reconstruction error bounds are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with unknown uncertainties. Accordingly, the proposed method can overcome the chattering phenomena in the control effort and has the robustness regardless of unknown uncertainties. Finally, simulation results for the three-link manipulator, one of the robotic systems, are included to illustrate the effectiveness of the proposed method.