• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.990-998
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• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.53-60
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• 2007

This paper presents a robust nonlinear autopilot design method based on dynamic inversion and PI(Proportional-Integral) control law. The new controller structure which is different from previous work is composed of classical linear PI control law and nonlinear fast dynamic inversion. A pitch axis model of highly maneuverable missiles and a linearized model for designing Pl controller are presented. The performance of proposed method is illustrated via nonlinear simulations including aerodynamic uncertainties and actuator dynamics.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.54-57
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• 2007

Recently, multidimensional joint inversion of geophysical data based on fundamentally different physical properties has been actively studied. Joint inversion can provide a way to much more accurately image the subsurface structure. Through the joint inversion, furthermore, it is possible to directly estimate non-geophysical material properties from geophysical measurements. In this study, I derive the objective functions and normal equations of three different joint inversion approaches: one approach based on the structural similarity using cross-gradient, and the other two using the a priori information on the model parameters and the correlation between material properties. Since all the equations derived in this study are based on the same inversion method (smoothness constrained least-squares), it is possible to mix the joint inversion methods so as to produce a new joint inversion algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1492-1495
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• 1996

In this paper, an approach to autopilot design based on the robust nonlinear dynamic inversion method is proposed. Both unknown parameters and uncertainty bounds are estimated and parameter estimates are used in the fast inversion. Furthermore, to get more robustness slow inversion is incorporated with MRAC(Model Reference Adaptive Control) and sliding mode control where the estimates of uncertainty bounds are used. The proposed method is applied to the pitch autopilot design of a missile system and excellent performance is shown via computer simulation.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.427-436
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• 2008

This paper presents radar remote sensing of soil moisture and surface roughness for vegetated surfaces. A precise volume scattering model for a vegetated surface is derived based on the first-order radiative transfer technique. At first, the scattering mechanisms of the scattering model are analyzed for various conditions of the vegetation canopies. Then, the scattering model is simplified step by step for developing an appropriate inversion algorithm. For verifying the scattering model and the inversion algorithm, the polarimetric backscattering coefficients at 1.85 GHz, as well as the ground truth data, of a tall-grass field are measured for various soil moisture conditions. The genetic algorithm is employed in the inversion algorithm for retrieving soil moisture and surface roughness from the radar measurements. It is found that the scattering model agrees quite well with the measurements. It is also found that the retrieved soil moisture and surface roughness parameters agree well with the field-measured ground truth data.

• Bulletin of the Korean Chemical Society
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• v.18 no.12
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• pp.1281-1285
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• 1997

The time-dependent tracking inversion method is developed to extract the potential of the excited state from frequency-domain measurements, such as the absorption profile. Based on the relay of the regularized inversion procedure and time-dependent wave-packet propagation, the algorithm extract the underlying potential piece by piece by tracking the time-dependent data which can be synthesized from frequency-domain measurements. We have demonstrated the algorithm to extract the potential of excited state for a model diatomic molecule. Finally, we describe the merits of the time-dependent tracking inversion method compared to the time-dependent inversion and discuss several extensions of the algorithm.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3914-3924
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• 2024

The release source term of radioactivity becomes a critical foundation for emergency response and accident consequence assessment after a nuclear accident Rapidly and accurately inverting the source term remains an urgent scientific challenge. Today source term inversion based on meteorological data and gamma dose rate measurements is a common method. But gamma dose rate actually includes all nuclides information, and the composition of radioactive nuclides is generally uncertain. This paper introduces a novel nuclear accident source term inversion model, which is Improve Snow Ablation Optimizer-Sensitivity Analysis Pruning Extreme Learning Machine (ISAO-SAELM) model. The model inverts the release rates of 11 radioactive nuclides (I-131, Xe-133, Cs-137, Kr-88, Sr-91, Te-132, Mo-99, Ba-140, La-140, Ce-144, Sb-129). It does not require the use of the physical field of the reactor to obtain prior information and establish a dispersion model. And the robustness is validated through noise analysis test. The mean absolute errors of the release rates of 11 nuclides are 15.52 %, 15.28 %, 15.70 %, 14.99 %, 14.85 %, 15.61 %, 15.96 %, 15.42 %, 15.84 %, 15.13 %, 17.72 %, which show the significant superiority of ISAO-SAELM. ISAO-SAELM model not only achieves notable advancements in accuracy but also receives validation in terms of practicality and feasibility.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1111-1123
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• 2023

In recent years, as generative models have developed, research that threatens them has also been actively conducted. We propose a new membership inference attack against text-to-image model. Existing membership inference attacks on Text-to-Image models produced a single image as captions of query images. On the other hand, this paper uses personalized embedding in query images through Textual Inversion. And we propose a membership inference attack that effectively generates multiple images as a method of generating Adversarial Prompt. In addition, the membership inference attack is tested for the first time on the Stable Diffusion model, which is attracting attention among the Text-to-Image models, and achieve an accuracy of up to 1.00.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.57-62
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• 2004

This paper deals with a waypoint trajectory following problem for the tilt-rotor UAV under development in Korea (TR-KUAV). In this problem, dynamic model inversion based on the linearized model and Sigma-Phi neural network with adaptive weight update are involved to realize the waypoint following algorithm for the vehicle in the helicopter flight mode (nacelle angle=0 deg). This algorithms consists of two main parts: outer-loop system as a command generator and inner-loop system as stabilizing controller. In this waypoint following problem, the position information in the inertial axis is given to the outer-loop system. From this information, Attitude Command/Attitude Hold logic in the longitudinal channel and Rate Command/Attitude Hold logic in the lateral channel are realized in the inner-loop part of the overall structure of the waypoint following algorithm. The nonlinear simulation based on the TR-KUAV is carried out to evaluate the stability and performance of the algorithm. From the numerical simulation results, the algorithm shows very good tracking performance of passing the waypoints given. Especially, it is observed that ACAH/RCAH logic in the inner-loop has the satisfactory performance due to adaptive neural network in spite of the model error coming from the linear model based inversion.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.390-397
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• 2003

A three-dimensional (3D) inversion technique has been developed for interpretation of magnetotelluric (MT) data. The inversion method is based on the linearized least-squares (Gauss-Newton) method with smoothness regularization. In addition to the underground 3D resistivity distribution, static shifts are also treated as unknown parameters in the inversion. The forward modeling is by the staggered-grid finite difference method. A Bayesian criterion ABle is applied to search the optimum trade-off among the minimization of the data misfit, model roughness and static shifts. The method has been applied to several MT datasets obtained at geothermal fields in Japan and other Asian countries. In this paper, two examples will be discussed: one is the data at the Ogiri geothermal area, southwestern Japan, and the other is at the Pohang low-enthalpy geothermal field, southeastern Korea. The inversion of the Ogiri data has been performed stably, resulting in a good fitting between the observed and computed apparent resistivities and phases. The recovered 3D resistivity structure is generally similar to the two-dimensional (2D) inversion models, although the deeper portion of the 3D model seems to be more realistic than that of the 2D model. The 3D model is also in a good agreement with the geological model of the geothermal reservoirs. 3D interpretation of the Pohang MT data is still preliminary. Although the fitting to the observed data is very good, the preliminary 3D model is not reliable enough because the station coverage is not sufficient for a 3D inversion.