• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.2
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• pp.77-84
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• 1984

In this paper, the method of the stability test of the Two-Dimensional digital filters is developed. The Liepunov's stability theorem of One-Dimensional discrete system is extended to Two-Dimensional digital filter transfer function which can be transformed twodimensional state space representation. The results developed above is agreed with of mapping method.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.394-397
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• 1987

This paper presents an analysis of the man-machine control system. A man-machine system depends on the performance of a human operator for proper operation. The analysis method is based upon the assumption that human operator will act in a near optimal controller. Optimal control theory and its associated state space representation is used as the basis for the analytic procedure. The computer simulation for a given plant shows that plant parameters have limited range by the human operator.

• Proceedings of the KIEE Conference
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• 1981.07a
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• pp.146-153
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• 1981

A practical and robust control scheme is suggested for MIMO discrete time processes with real simple poles. This type of control scheme, having the advantages of both the adaptiveness and optimality, may be successfully applicable to structured dynamic controllers for plants whose parameters are slowly time-varying. The identification of the process parameters is under-taken in ARMA form and the optimization of the feedback gain matrix is performed in the state space representation with regard to a standard quadratic criterion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.797-804
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• 2013

This paper presents an efficient design method of multiplierless 2-D state space digital filter based on a particle swarm optimization(PSO) algorithm. The design task is reformulated as a constrained minimization problem and is solved by our newly developed PSO algorithm. To ensure the stability of the designed 2-D state space digital filters, a stability strategy is embedded in the basic PSO algorithm. The superiority of the proposed method is demonstrated by several experiments. The results show that the approximation error and roundoff noise of the resultant filters are better than those of the digital filters which designed by recently published filter design methods. In addition, the designed filters with power-of-two coefficients have only about 1/4 computational burden of the 2-D digital filters designed in the 2's complement binary representation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.47-52
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• 2006

Using the equivalent ARMA model representation of the mixed random errors, we propose Klaman filter design methods for aided INS(Inertial Navigation System) which contains the gyroscope mixed random errors. At first step, considering the characteristic of indirect feedback Kalman filter used in the aided INS, we perform the time difference of equivalent ARMA model. Next, according to the order of the time differenced ARMA model, we achieve the state space conversion of that by two methods. If the order of AR part is greater than MA part, we use controllable or observable canonical form. Otherwise, we establish the state apace equation via the method that several step ahead predicts are included in the state variable, where we can derive high and low order models depending on the variable which is compensated from gyroscope output. At final step, we include the state space equation of gyroscope mixed random errors into aided INS Kalman filter model. Through the simulation, we show that both the high and low order filter models proposed give less navigation errors compared to the conventional filter which assume the mixed random errors as white noise.

• Wind and Structures
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• v.39 no.3
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• pp.175-190
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• 2024

Aerodynamic shape optimization is very useful for enhancing the performance of wind-sensitive structures. However, shape parameterization, as the first step in the pipeline of aerodynamic shape optimization, still heavily depends on empirical judgment. If not done properly, the resulting small design space may fail to cover many promising shapes, and hence hinder realizing the full potential of aerodynamic shape optimization. To this end, developing a novel shape parameterization scheme that can reflect real-world complexities while being simple enough for the subsequent optimization process is important. This study proposes a machine learning-based scheme that can automatically learn a low-dimensional latent representation of complex aerodynamic shapes for bluff-body wind-sensitive structures. The resulting latent representation (as design variables for aerodynamic shape optimization) is composed of both discrete and continuous variables, which are embedded in a hierarchy structure. In addition to being intuitive and interpretable, the mixed discrete and continuous variables with the hierarchy structure allow stakeholders to narrow the search space selectively based on their interests. As a proof-of-concept study, shape parameterization examples of tall building cross sections are used to demonstrate the promising features of the proposed scheme and guide future investigations on data-driven parameterization for aerodynamic shape optimization of wind-sensitive structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.2
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• pp.79-86
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• 2017

Precise Point Positioning-Real Time Kinematic (PPP-RTK) refers to a technology that combines PPP with network-RTK in which a user does not directly receive observed data from a reference station but receives State-Space Representation (SSR) messages corrected for error components from a central processing station through Networked Transport of RTCM via Internet Protocol (NTRIP) or Digital Multimedia Broadcasting (DMB) for purposes of positioning. SSR messages, which refer to corrections used in PPP-RTK, are generated by a central processing station using real-time observed data collected from reference stations and account for corrections needed due to the ionosphere, troposphere, satellite orbital errors, satellite time offsets, and satellite biases. This study used a type of SSR message provided in South Korea, known as Korea-SSR (K-SSR), to implement a PPP-RTK algorithm based on code-pseudorange measurements and validated its accuracy within the reference station network. In order to validate the accuracy of the implemented algorithm outside of the network, the K-SSR was extrapolated and applied to positioning in reference stations in Changchun, China (CHAN) and Japan (AIRA). This also entailed a quantitative evaluation that measured improvements in accuracy in comparison with point positioning. The results of the study showed that positioning applied with extrapolated K-SSR correction data was more accurate in both AIRA and CHAN than point positioning with improvements of approximately 20~50%.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.768-773
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• 2010

This paper presents the performance verification of the optimal state feedback controller via inverted pendulum systems. The proposed method generates the optimal control inputs satisfying both the constrained input and the performance specification. In addition, it reduces the steady-state error by adopting the integral control technique. In order to verify the performance of the proposed method, we apply both the proposed method and the general state feedback control to an inverted pendulum, CEM-IP-01 in the experiment.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.155-161
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• 2009

We focus on discrete event systems with a structure of parallel processing, synchronization, and no-concurrency. We use max-plus algebra, which is an effective approach for controller design for this type of system, for modeling and formulation. Since a typical feature of this type of system is that the initial schedule is frequently changed due to unpredictable disturbances, we use a simple model and numerical examples to examine the possibility of applying the concepts of the feeding buffer and the project buffer of critical chain project management (CCPM) on max-plus linear discrete event systems in order to control the occurrence of an undesirable state change. The application of a CCPM-based framework on a max-plus linear discrete event system was proven to be effective.