• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.6
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• pp.659-665
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• 2010

The finite element method(FEM) is proven to be an effective approximate method of structural analysis if proper element types and meshes are chosen, and recently, the method is often applied to solve complex dynamic and nonlinear problems. A properly chosen element type and mesh yields reliable results for dynamic finite element structural analysis. However, dynamic behavior of a structure may include unpredictably large strains in some parts of the structure, and using the initial mesh throughout the duration of a dynamic analysis may include some elements to go through strains beyond the elements' reliable limits. Thus, the finite element mesh for a dynamic analysis must be dynamically adaptive, and considering the rapid process of analysis in real time, the dynamically adaptive finite element mesh generating schemes must be computationally efficient. In this paper, a computationally efficient dynamically adaptive finite element mesh generation scheme for dynamic analyses of structures is described. The concept of representative strain value is used for error estimates and the refinements of meshes use combinations of the h-method(node movement) and the r-method(element division). The shape coefficient for element mesh is used to correct overly distorted elements. The validity of the scheme is shown through a cantilever beam example under a concentrated load with varying values. The example shows reasonable accuracy and efficient computing time. Furthermore, the study shows the potential for the scheme's effective use in complex structural dynamic problems such as those under seismic or erratic wind loads.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.637-650
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• 2007

A new on-line fault detection and isolation(FDI) scheme has been proposed for engines using an adaptive neural network classifier; this paper investigates the robustness of this scheme by evaluating in a wide range of operational modes. The neural classifier is made adaptive to cope with the significant parameter uncertainty, disturbances, and environmental changes. The developed scheme is capable of diagnosing faults in the on-line mode and can be directly implemented in an on-board diagnosis system(hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes, including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all changes occurring simultaneously. The evaluations are performed using a mean value engine model(MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1679-1687
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• 2016

Mobile robots are effectively used in industrial fields that require flexible manufacturing systems. Mobile robots have to move with mechanical loads such as product parts along the specified paths, and are usually equipped with kinematic controllers. When the loads and nonlinear frictions are too high, satisfactory control performances can not be expected with the kinematic controllers, so some dynamic controllers have been developed. Conventional dynamic controllers require the exact weights and locations of the loads; however, the loads are frequently changed and unknown so that the control performances of the conventional controllers are limited. This paper proposes an adaptive PD control method using gradient descent learning to have sufficient dynamic control performance for unknown loads. Simulation studies have been conducted for various load conditions to verify that the adaptive PD control method have much broader convergence region than the convention method.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.8-16
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• 2007

Finite element analysis (FEA) is an effective means for the analysis of bioelectromagnetism. It has been successfully applied to various problems over conventional methods such as boundary element analysis and finite difference analysis. However, its utilization has been limited due to the overwhelming computational load despite of its analytical power. We have previously developed a novel mesh generation scheme that produces FE meshes that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with far less number of nodes and elements, thus lessen the computational load. In general, the cMesh generation is affected by the quality of feature maps derived from MRI. In this study, we have tested various feature maps created based on the improved differential geometry measures for more effective cMesh head models. As performance indices, correlation coefficient (CC), root mean squared error (RMSE), relative error (RE), and the quality of cMesh triangle elements are used. The results show that there is a significant variation according to the characteristics of specific feature maps on cMesh generation, and offer additional choices of feature maps to yield more effective and efficient generation of cMeshes. We believe that cMeshes with specific and improved feature map generation schemes should be useful in the FEA of bioelectromagnetic problems.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.435-447
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• 2006

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel AEA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2668-2670
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• 2004

This paper presents an advanced load control method in Direct Load Control(DLC) system. It is important to aggregate a various demand side resource which is surely controllable at the peak power time for a successful DLC system. Because the DLC system use simple On/Off control that may cause a harmful effect on a plant to reduce a peak power load, there are some restriction on deriving a voluntary participation of demand side resource. So it needs a new approach to direct load control method, and this paper describes an advanced load control method using control logic which is based on load properties. This method is easy to take account of a various characteristic of load, it can be use as a dynamic control logic which is good for adaptive control. The suggested control logic method is verified by modeling a control logic for a turbo refrigerator which affects on peak power in summer season.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.91-96
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• 2011

In this paper, we present measurement method of the unknown load impedance. Load impedance is continuously varied by external environment conditions. This arbitrarily varied load impedance can be calculated using 3-point voltage measurement on ${\lambda}/4$ sectioned transmission line. We derived several numerical formulas from 3-point voltage measurement results and drew load impedance selection algorithm from calculated load impedance results. These numerical formulas and load impedance selection algorithm are verified by ADS simulation. Simulation results showed that arbitrary load impedance can be correctly measured using above mentioned formulas and selection algorithm.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.612-619
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• 2005

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1103-1109
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• 2003

An adaptive bone remodeling is simulated by using the cellular automata (CA) method. It is assumed that bone tissue consist of bone marrow, osteoclast, osteoblast cell or osteoprogenitor cell. Two types of local rule are adopted; those are the metabolism rule and adaptive bone formation rule. The metabolism rule is based on the interactions of cells and the bone formation rule is based on the adaptation against the mechanical stimulus. The history of load and memory of mechanical stimulus are also considered in the local rules. As a result, the pattern of distribution of the bone tissue is dynamically adequate and it is similar to intact cancellous bone.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.48-55
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• 1997

An adaptive output feedback controller is designed for tracking control of an n-link robot manipulator with unknown load. High-gain obwserver that is used to estimate joint velocities is designed to avoide the restriction of the allowable variation range of unknown parmeters as well as improve the state estimation error. We saturate the control inut outside a domain of interest and use an adaptive law with a parameter projection feature to guarantee boundedness of all the trajectories in the closed-loop system. Simulation resutls on a 2-link manipulator illustrate that when the speed of the high-gain observer is sufficiently high, the proposed controller recovers the performance under state feedback control.