• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1023-1030
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• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.11
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• pp.2688-2695
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• 2000

In multi-axis force sensor, compliance matrices representing structural behaviour of internal sensor bodies play an important role in decoupled sensing and accuracy, Recently, error propagation through compliance matrices has been studied via approximation approach. However the upper bound of measured force error has not been known. In this paper, error propagation in force sensing is analysed in a unified way when both strain measurement error and compliance matrix error exist, and the upper bound of the measured force error is derived exactly(not approximately). The analysis is examined through a numerical example.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.11
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• pp.650-653
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• 2005

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.507-516
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• 2007

Multinomial logistic regression is probably the most popular representative of probabilistic discriminative classifiers for multiclass classification problems. In this paper, a kernel variant of multinomial logistic regression is proposed by combining a Newton's method with a bound optimization approach. This formulation allows us to apply highly efficient approximation methods that effectively overcomes conceptual and numerical problems of standard multiclass kernel classifiers. We also provide the approximate cross validation (ACV) method for choosing the hyperparameters which affect the performance of the proposed approach. Experimental results are then presented to indicate the performance of the proposed procedure.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.903-905
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• 2003

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control of an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.813-829
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• 2022

In multi-view subspace clustering, how to integrate the complementary information between perspectives to construct a unified representation is a critical problem. In the existing works, the unified representation is usually constructed in the original data space. However, when the data representation in each view is very diverse, the unified representation derived directly in the original data domain may lead to a huge information loss. To address this issue, different to the existing works, inspired by the latest revelation that the data across all perspectives have a very similar or close spectral block structure, we try to construct the unified representation in the spectral embedding domain. In this way, the complementary information across all perspectives can be fused into a unified representation with little information loss, since the spectral block structure from all views shares high consistency. In addition, to capture the global structure of data on each view with high accuracy and robustness both, we propose a novel low-rank approximation via the tight lower bound on the rank function. Finally, experimental results prove that, the proposed method has the effectiveness and robustness at the same time, compared with the state-of-art approaches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1188-1195
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• 2006

This paper presents an efficient design method of 2-D state-space digital filter based on an improved branch-and -bound algorithm. The resultant 2-D state-space digital filters whose coefficients are represented as the sum of two power-of-two terms, are attractive for high-speed operation and simple implementation. The feasibility of the proposed method is demonstrated by several experiments. The results show that the approximation error and group delay characteristic of the resultant filters are similar to those of the digital filters which designed in the continuous coefficient space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2371-2388
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• 2015

In this paper, we investigate the performance evaluation of three dimensional (3D) multiple-input multiple-output (MIMO) systems with an adjustable base station (BS) antenna tilt angle and zero-forcing (ZF) receivers in Ricean/Lognormal fading channels. In particular, we take the lognormal shadow fading, 3D antenna gain with antenna tilt angle and path-loss into account. First, we derive a closed-form lower bound on the sum rate, then we obtain the optimal BS antenna tilt angle based on the derived lower bound, and finally we present linear approximations for the sum rate in high and low-SNR regimes, respectively. Based on our analytical results, we gain valuable insights into the impact of key system parameters, such as the BS antenna tilt angle, the Ricean K-factor and the radius of cell, on the sum rate performance of 3D MIMO with ZF receivers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.395-402
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• 2003

The capacity of a CDMA cellular system is determined by the amount of interference, therefore the exact estimation of interference is important to evaluate the system performance. In this paper, we propose an approximated equation which calculates reverse link other cell interference in the CDMA cellular systems. The equation using Riemann-Zeta function has a property that it is useful in case of any radio propagation loss exponents. And we compare calculation results with simulation results in other to verify it's usefulness. The upper bound of system capacity calculated with the proposed approximated equation gives almost alike result with the simulation. The proposed interference bound is useful to calculate system capacity and to evaluate some algorithm in a hierarchical cellular system which must be considered various propagation exponents.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.5
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• pp.69-78
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• 2005

It is crucial to compute the Euclidean distance between two vectors efficiently in high dimensional space for multimedia information retrieval. In this paper, we propose an effective method for approximating the Euclidean distance between two high-dimensional vectors. For this approximation, a previous method, which simply employs norms of two vectors, has been proposed. This method, however, ignores the angle between two vectors in approximation, and thus suffers from large approximation errors. Our method introduces an additional vector called a reference vector for estimating the angle between the two vectors, and approximates the Euclidean distance accurately by using the estimated angle. This makes the approximation errors reduced significantly compared with the previous method. Also, we formally prove that the value approximated by our method is always smaller than the actual Euclidean distance. This implies that our method does not incur any false dismissal in multimedia information retrieval. Finally, we verify the superiority of the proposed method via performance evaluation with extensive experiments.