• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1993-1998
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• 2004

This paper proposes a new framework for control system design, called the data-based control approach or data space approach, in which the input and output data of a dynamical system is directly and solely used to analyze or design a control system without the employment of any mathematical models like transfer functions, state space equations, and kernel representations. Since, in this approach, most of the analysis and design processes are carried out in the domain of the data space, we introduce some notions of geometrical objects, e.g., the openloop and closed-loop data spaces, which serve as the system representations in the data space. In addition, we establish a relationship between the open-loop and closed-loop data spaces that the closed-loop data space is contained in the open-loop data space as one of its subspaces. By using this relationship, we can derive the data-based stabilization condition for a linear time-invariant discrete-time system, which leads to a linear matrix inequality with a rank constraint.

• Journal of the Korea Computer Graphics Society
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• v.12 no.2
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• pp.13-18
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• 2006

Color quantization replaces the color of each pixel with the closest representative color, and thus it makes the resulting image partitioned into uniformly-colored regions. As a consequence, continuous, detailed variations of color over the corresponding regions in the original image are lost through color quantization. In this paper, we present a novel scheme for restoring such variations from a color-quantized input image. Our scheme identifies which pairs of uniformly-colored regions in the input image should have continuous variations of color in the resulting image. Then, such regions are seamlessly stitched using the Laplace equation. The user can optionally indicate which regions should be separated or stitched by scribbling constraint brushes across the regions. We demonstrate the effectiveness of our approach through diverse examples, such as photographs, cartoons, and artistic illustrations.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.28-33
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• 1994

An optimal controller, e.g. LQG controller, may not be realistic in the sense that the required control power may not be achieved by existing actuators, and the measured output is not satisfactory. To be realistic, the controller should meet such constraints as sensor or actuator limitation, performance limit, etc. In this paper, the lnput/Output Variance Constrained (IOVC) control problem will be considered from the viewpoint of mathematical programming. A dual version shall be developed to solve the IOVC control problem, whose objective is to find a stabilizing control law attaining a minimum value of a quadratic cost function subject to the inequality constraint on each input and output variance for a stabilizable and detectable plant. One approach to the constrained optimization problem is to use the Kuhn-Tucker necessary conditions for the optimality and to seek an optimal point by an iterative algorithm. However, since the algorithm uses only the necessary conditions, the convergent point may not be optimal solution. Our algorithm will guarantee a sufficiency.

• Journal of Communications and Networks
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• v.8 no.4
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• pp.401-409
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• 2006

Orthogonal frequency division multiplexing (OFDM) and multiple input multiple output (MIMO) technologies provide additional dimensions of freedom with spectral and spatial resources for radio resource management. Multi-dimensional radio resource management has recently been identified to exploit the full dimensions of freedom for more flexible and efficient utilization of scarce radio spectrum while provide diverse quality of service (QoS) guarantees. In this work, a multi-dimensional radio resource scheduling scheme is proposed to achieve above goals in hybrid orthogonal frequency division multiple access (OFDMA) and space division multiple access (SDMA) systems. Cochannel interference (CCI) introduced by frequency reuse under SDMA is eliminated by frequency division and time division between highly interfered users. This scheme maximizes system throughput subjected to the minimum data rate guarantee. for heterogeneous users and transmit power constraint. By numerical examples, system throughput and fairness superiority of the our scheduling scheme are verified.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.89.1-89
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• 2002

Conventional multilayer feedforward artificial neural networks are very effective in dealing with spatial problems. To deal with problems with time dependency, some kinds of memory have to be built in the processing algorithm. In this paper we show how the newly proposed Serial Input Neuron (SIN) convolutional decoders can be derived. As an example, we derive the SIN decoder for \ulcornerrate code with constraint length 3. The SIN is tested in Gaussian channel and the results are compared to the results of the optimal Viterbi decoder. A SIN approach to decode convolutional codes is presented. No supervision is required. The decoder lends itself to pleasing implementations in hardware and processing...

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2010.05a
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• pp.60-65
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• 2010

Language learning involves linguistic environments around the learner. So the variation in training input to which the learner is exposed has been linked to their language learning. We explore how linguistic experiences can cause differences in learning linguistic structural features, as investigate in a probabilistic graphical model. We manipulate the amounts of training input, composed of natural linguistic data from animation videos for children, from holistic (one-word expression) to compositional (two- to six-word one) gradually. The recognition and generation of sentences are a "probabilistic" constraint satisfaction process which is based on massively parallel DNA chemistry. Random sentence generation tasks succeed when networks begin with limited sentential lengths and vocabulary sizes and gradually expand with larger ones, like children's cognitive development in learning. This model supports the suggestion that variations in early linguistic environments with developmental steps may be useful for facilitating language acquisition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.3
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• pp.407-413
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• 2013

This paper proposes anti-swing control of overhead crane system using sum of squares method. The dynamic equations of overhead crane include nonlinear terms, which are transformed into polynomials by using Taylor series expansion. Therefore the dynamic equation of overhead crane can be changed to the system of polynomial equation. On the basis of polynomial dynamics of crane system, we propose the Sum of Squares (SOS) conditions considering the input constraints. In addition, control gains are obtained by numerical tool which is called by SOSTOOL. The effectiveness of the proposed method is demonstrated by numerical simulation.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.777-780
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• 1995

Accounting for actuator nonlinearities in control loops has often been perceived as an implementation issue and usually excluded in the design of controllers. Nonlinearities treated in this paper are saturation, and they are modelled as an inequality constraint. The CRHPC(Constrained Receding Horizon Predictive Control) with inequality constraints algorithm is used to handle actuator rate and amplitude limits simultaneously or respectively. Optimum values of future control signals are obtained by quadratic programming. Simulated examples show that predictive control law with inequality constraints offers good performance as compared with input clipping.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.320-326
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• 2016

In existing literature on multiple-input multiple-output (MIMO) relaying communication systems, antenna selection is often implemented by maximizing the channel capacity or the output single-to-noise ratio (SNR). In this paper, we propose an energy-efficient low-complexity antenna selection scheme for MIMO relaying communication systems. The proposed algorithm is based on beamforming and maximizing the Frobenius norm to jointly optimize the transmit power, number of active antennas, and antenna subsets at the source, relaying and destination. We maximize the energy efficiency between the link of source to relay and the link of relay to destination to obtain the maximum energy efficiency of the system, subject to the SNR constraint. Compared to existing antenna selection methods forMIMO relaying communication systems, simulation results demonstrate that the proposed method can save more power in term of energy efficiency, while having lower computational complexity.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1196-1198
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• 1996

It is well known that the controller output limits have a significant effect on the closed loop system performance. GPC has many tuning-knobs which can he used to minimize actuator activity. Especially, increasing the control weighting $\lambda$ cuts down the controller output variance. Using this property, we propose the GPC with Input constraints(GPCIC) which is based on the relation between control weighting $\lambda$ and optimal solution of the unconstrained GPC. The GPCIC algorithm is the calculation of the optimal $\lambda$ such that the output of the unconstrained GPC is satisfied with the rate Ind the level constraint.