• International Journal of CAD/CAM
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• v.12 no.1
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• pp.9-19
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• 2012

Generation of optimum planar B-spline curve in terms of minimum deviation and required fairness to approximate a target shape defined by a strip-shaped unorganized 2D point cloud is studied. It is proposed to use the location of control points as variables within the geometric optimization framework of point distance minimization. An adaptive simulated annealing heuristic optimization algorithm is developed to iteratively update an initial approximate curve towards the target shape. The new implementation comprises an adaptive cooling procedure in which the temperature change is adaptively dependent on the objective function evolution. It is shown that the proposed method results in an improved convergence speed when compared to the standard simulated annealing method. A couple of examples are included to show the applicability of the proposed method in the surface model reconstruction directly from point cloud data.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.89-118
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• 1996

The development and implementation of a real-time, traffic adaptive control scheme based on fuzzy logic through Video Image Detector systems (VIDs) is presented. Through VIDs based image processing, fuzzy logic can be used for a real-time traffic adaptive signal control scheme. Fuzzy control logic allows linguistic and inexact traffic data to be manipulated as a useful tool in designing signal timing plans. The fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategy based on a priori verbal communication. The implementation of fuzzy logic controller for a traffic network is introduced. Comparisons are made between implementations of the fuzzy logic controller and the actuated controller in an isolated intersection. The results obtained from the application of the fuzzy logic controller are also compared with those corresponding to a pretimed controller for the coordinated intersections. Simulation results from the comparisons indicate the performance of the system is between under the fuzzy logic controller. Integration of the aforementioned schemes into and ATMS framework will lead to real-time adjustment of the traffic control signals, resulting in significant reduction in traffic congestion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1159-1169
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• 2010

This paper introduces the mobility-adaptive QoE provisioning solution. The key is placed on the intelligent selection of access network depending on the QoE criteria classified by the user mobility and the bandwidth demand for service. We further focus on the network-based smart handover scheme using the mobility-adaptive handover decision and the enhanced MIH-FMIP framework. The concept is the network-based calm service and the balance in order to facilitate vertical and seamless handover. In result, it is figured out that our solution improves QoE performance by selecting appropriate access network, repressing handover occurrence, and reducing handover delay as well.

• Journal of Power Electronics
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• v.14 no.3
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• pp.549-563
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• 2014

In this paper, the speed regulation problem of permanent synchronous motor (PMSM) systems under the vector control framework is studied. A model reference adaptive controller (MRAC) based on the Lyapunov stability theory is first designed. Since the standard MRAC method provides poor disturbance rejection performance in the case of strong disturbances, a composite control method which combines the MRAC method and the disturbance estimation method, called the MRAC+ESO method, is proposed. An extended state observer (ESO) is introduced to estimate the lumped disturbances. The obtained estimated value acts as a feedforward compensation term to the MRAC controller. A stability analysis of the composite control method is given. Simulation and experimental results are presented and compared to show the effectiveness of the proposed control method.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.622-631
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• 2011

Corners play an important role in describing object features for pattern recognition and identification. This paper proposed a fast and adaptive corner detector in both coarse and fine scale, followed by the framework of the curvature scale space (CSS). An adaptive curvature threshold and evaluating of angles of corner candidates are added to original CSS to remove round corners and false corners in the detecting process. The efficiency of proposed method is compared to other popular detectors in both accuracy criteria, stability and time consuming. Results illustrate that the proposed method performs extremely surpass in both areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.461-484
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• 2021

Compressed sensing in image reconstruction has attracted attention and many studies are proposed. As we know, adding prior knowledge about the distribution of the support on the original signal to CS can improve the quality of reconstruction. However, it is still difficult for a recovery framework adjusts its strategy for exploiting the prior knowledge efficiently according to the current estimated signals in serial iterations. With the theory of information geometry, we propose an adaptive strategy based on the current estimated signal in each iteration of the recovery. We also improve the performance of existing algorithms through the adaptive strategy for exploiting the prior knowledge according to the current estimated signal. Simulations are presented to validate the results. In the end, we also show the application of the model in the image.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.175-182
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• 2018

In recent years, micro-grid has been widely focused on the fields of renewal energy systems. This paper proposes a novel robust power converter control against arbitrary electric load changes for islanded micro-grid topology. First, we provide a state-space representation of our micro-grid model including power converter and electric load circuit. And then a state feedback control method is applied to construct a nominal control framework. Next, we propose a robust adaptive control law to enhance a control performance against unexpected load perturbation. In addition, we analytically investigate a passivity property for the micro-grid model and carry out computer simulation to demonstrate superiority and reliability of the proposed control methodology.

• 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.

• Structural Engineering and Mechanics
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• v.88 no.6
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• pp.535-549
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• 2023

Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.6
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• pp.597-611
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• 2009

With the advent of ubiquitous computing society, many advanced technologies have enabled wireless sensor networks which consist of small sensor nodes. However, the sensor nodes have limited computing resources such as small size memory, low battery life, short transmission range, and low computational capabilities. Thus, decreasing energy consumption is one of the most significant issues in wireless sensor networks. In addition, numerous applications for wireless sensor networks are recently spreading to various fields (health-care, surveillance, location tracking, unmanned monitoring, nuclear reactor control, crop harvesting control, u-city, building automation etc.). For many of them, supporting security functionalities is an indispensable feature. Especially in case wireless sensor networks should provide a sufficient variety of security functions, sensor nodes are required to have more powerful performance and more energy demanding features. In other words, simultaneously providing security features and saving energy faces a trade-off problem. This paper presents a novel energy-efficient security architecture in an IEEE 802.15.4-based wireless sensor network called the Hybrid Adaptive Security (HAS) framework in order to resolve the trade off issue between security and energy. Moreover, we present a performance analysis based on the experimental results and a real implementation model in order to verify the proposed approach.