• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.265-270
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• 2006

Recently, inertial sensors like gyros and accelerometers have been quite miniaturized by Micro Electro-Mechanical Systems (MEMS) technology. JAXA is developing a MEM-based GPS/INS hybrid navigation system named Micro-GAIA. The navigation performance of Micro-GAIA was evaluated through off-line analysis by using flight test data. The estimation errors of the roll, pitch, and azimuth were $0.03^{\circ}$, $0.05^{\circ}$, $0.05^{\circ}$ $(1{\sigma})$, respectively. he horizontal position errors after 60-second GPS outages were reduced to 25 m CEP. The attitude errors and position errors are nearly half of ones reported previously[2]. Furthermore, using the adaptive Kalman filters, the robustness against the uncertainty of the measurement noise was improved. Comparing the innovation-based and residual-based adaptive Kalman filters, it was confirmed that the latter is robuster than the former.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1456-1463
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• 1999

This paper describes the measurement system of antenna factor using standard antenna method in OATS(Open Area Test Site) of KRISS(Korea Research Institute of Standards and Science) and methods for reducing an environmental noise affecting antenna factor. The range of measurement frequency is 30 - 1000 MHz, all control and data acquisition were done by computer automatically. Measurement results of antenna factors are presented, total uncertainty of antenna factor is $\pm$1 dB.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.366-369
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• 1994

In this paper, reduced-order $H_{\infty}$ robust controller is designed for the drum-type boiler system. From the known nonlinear dynamic model, a linearized multivariable model is obtained. To reduce order of robust controller, observer-based proper $H_{\infty}$ compensator is designed. The designed controller has robust property against the influence of sensor noise, system parameter variation and model uncertainty. A good Performance of the designed controller is shown by simulation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.1
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• pp.45-51
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• 2002

In this paper are presented dynamic modeling and path-tracking of differential drive wheeled-mobile robots(WMRs) having the limited drive-torques. Instantaneously coincident coordinate system, force/torque propagation and Newton's equilibrium law are used to induce the dynamic model. When drive-torques generated by inverse dynamics exceed the limitation, we make wheeled-mobile robots follow the reference path by modifying the planned reference trajectory with time-scaling method. The controller is introduced to compensate for error owing to modeling uncertainty and measurement noise. And simulation results prove that method proposed by this paper is efficient.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.71.5-71
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• 2001

This paper presents the vibration control of a flexible intelligent beam with added mass. The materials which is a glass fiber reinforced(GFR) thermoplastic composite is employed to achieve vibration characteristics according to added mass induced end of composite beam. In the experiments of forced vibration control, the -controller are employed to achieve vibration suppression in forced vibration situations. Also, in the controller design, 1st and 2nd´s natural frequencies are considered in the modeling, because robust control theory which has robustness to structured uncertainty is adopted to suppress the vibration. By designing a controller using mu-synthesis, robust performance against measurement noise, various modeling.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• Earthquakes and Structures
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• v.22 no.4
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• pp.421-430
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• 2022

This paper addresses the stochastic control problem of robots within the framework of parameter uncertainty and uncertain noise covariance. First of all, an open circle deterministic trajectory optimization issue is explained without knowing the unequivocal type of the dynamical framework. Then, a Linear Quadratic Gaussian (LQG) controller is intended for the ostensible trajectory-dependent linearized framework, to such an extent that robust hereditary NN robotic controller made out of the Kalman filter and the fuzzy controller is blended to ensure the asymptotic stability of the non-continuous controlled frameworks. Applicability and performance of the proposed algorithm shown through simulation results in the complex systems which are demonstrate the feasible to improve the performance by the proposed approach.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.260-270
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• 2023

We introduce a novel Visual Inertial Odometry (VIO) algorithm designed to improve the performance of thermal-inertial odometry. Thermal infrared image, though advantageous for feature extraction in low-light conditions, typically suffers from a high noise level and significant information loss during the 8-bit conversion. Our algorithm overcomes these limitations by approximating a 14-bit raw pixel histogram into a Gaussian mixture model. The conversion method effectively emphasizes image regions where texture for visual tracking is abundant while reduces unnecessary background information. We incorporate the robust learning-based feature extraction and matching methods, SuperPoint and SuperGlue, and zero velocity detection module to further reduce the uncertainty of visual odometry. Tested across various datasets, the proposed algorithm shows improved performance compared to other state-of-the-art VIO algorithms, paving the way for robust thermal-inertial odometry.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.157-166
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• 2023

Several experiments were carried out to analyze the impact of the modernized Global Positioning System (GPS) L2C signal on pseudorange-based point positioning. Three dual-frequency positioning algorithms, ionosphere-free linear combination, ionospheric error estimation, and simple integration, were used, and the results were compared with those of Standard Point Positioning (SPP). An analysis was conducted to determine the characteristics of each dual-frequency positioning method, the impact of the magnitude of ionospheric error, and receiver grade. Ionosphere-free and ionospheric error estimation methods can provide improved positioning accuracy relative to SPP because they are able to significantly reduce the ionospheric error. However, this result was possible only when the ionospheric error reduction effect was greater than the disadvantage of these dual-frequency positioning algorithms such as the increment of multipath and noise, impact of uncertainty of unknown parameter estimation. The RMSE of the simple integration algorithm was larger than that of SPP, because of the remaining ionospheric error. Even though the receiver grade was different, similar results were observed.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.