• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.298-311
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• 2015

Nowadays, automatic digging operation of an excavator is a big challenge due to the complexity of digging environment, the hardness of soil and buried obstacles into the ground. In order to achieve the maximum soil bucket volume, this paper introduces a novel engineering model that was developed as a virtual excavator in the design phase. Through this model, the designs of mechanical and control systems for autonomous excavator are executed and modified easily before developing in real testbed. Based on a concept of an autonomous excavation, a mechanical system of excavator was first designed in SOLIDWORKS, and a soil model also was modeled by finite-element analysis in ANSYS, both modeled models were then exported to ADAMS environment to investigate the digging behavior through virtual simulation. An intelligent control strategy was generated in MATLAB/Simulink to control the excavator operation. The simulation results were demonstrated by effectiveness of the proposed excavator robot in testing scenarios with many soil types and obstacles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.514-537
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• 2020

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.637-658
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• 2015

The development of multimedia applications has followed the development of high-speed networks. By improving the performance of mobile devices, it is possible to provide high-transfer-speed broadband and seamless mobile multicasting services between indoor and outdoor environments. Multicasting services support efficient group communications. However, mobile multicasting services have two constraints: tunnel convergence and handoff latency. In order to solve these problems, many protocols and handoff methods have been studied. In this paper, we propose inter local mobility anchor (inter-LMA) optimized handoff model for mobile multicasting services in proxy mobility IPv6 based (PMIPv6-based) networks. The proposed model removes the tunnel convergence issue and reduces the router processing costs. Further, it the proposed model allows for the execution of fast handoff operations with adaptive transmission mechanisms. In addition, the proposed scheme exhibits low packet delivery costs and handoff latency in comparison with existing schemes and ensures fast handoff when moving the inter-LMA domain.

• Journal of Power Electronics
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• v.17 no.2
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• pp.398-410
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• 2017

This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate ($LiFePO_4$) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1681-1686
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• 1991

In robotics and other fields of engineering, techniques for artificial reality or virtual reality are focused on and studied extensively, e.g., virtual existence for tele-operator systems in robotics, and virtual reality of designed objects in architecture. In order to realize the system we should create physical stimulations according to internal models created by experiences in a human brain. The internal model does not have to have direct connections to the real world, however, the stimulation must be signals such that the internal model are retrieved in a human brain. In this paper we propose a technique for tele-virtual reality of dynamic mechanical models, which means that one dynamic mechanical model can be shared by peoples in distant places. Since a stability issue due to time delays arises in the system, we employed a scattering technique developed for a tele-operator system and a kind of passive adaptive controllers. Furthermore, restrictions due to a simple digital implementation of the scattering transformation are discussed and some conditions for stability are shown. The proposed method is applied to a remote tug of war system and the effectiveness is verified.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.713-720
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• 2007

In image-guided surgery, automatic bone segmentation of Computed Tomography (CT) images is an important but challenging step. Previous attempts include intensity-, edge-, region-, and deformable curve-based approaches [1], but none claims fully satisfactory performance. Although active contour (AC) techniques possess many excellent characteristics, their applications in CT image segmentation have not worthily exploited yet. In this study, we have evaluated the automaticity and performance of the model of Chan-Vese Multiphase AC Without Edges towards knee bone segmentation from CT images. This model is suitable because it is initialization-insensitive and topology-adaptive. Its segmentation results have been qualitatively compared with those from four other widely used AC models: namely Gradient Vector Flow (GVF) AC, Geometric AC, Geodesic AC, and GVF Fast Geometric AC. To quantitatively evaluate its performance, the results from a commercial software and a medical expert have been used. The evaluation results show that the Chan-Vese model provides superior performance with least user interaction, proving its suitability for automatic bone segmentation from CT images.

• Water for future
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• v.18 no.2
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• pp.153-161
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• 1985

The methods for hydrologic prediction of streamflows for more efficient and functional operations and automation of the flood warning and forecasting system have been studiedand which have been widely used in the control engineering have been studied and investigated for representation of the dynamic behavior of rainfall-runoff precesses, and formulated into mathematical model form. The applicabilities of the model using the adaptive Kalman filter algorithm to the on-line, real-time prediction of river flows have been worked out. The computer programs in FORTRAN which are developed here can be utilized for more efficient operations and better prediction abilities of flood warning and forecasting systems, and also should be modified for better model performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.1
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• pp.1-12
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• 1999

A design and DSP-based implementation of robust nonlinear speed control of a permanent magnet synchronous motor(PMSM) under the unknown parameter variations and speed measurement error is presented. The model reference adaptive system(MRAS) based adaptation mechanisms for the estimation of slowly varying parameters are derived using the MIT rule. For the disturbances or quickly varying parameters, a quasilinearized and decoupled model which includes the influence of parameter variations and speed measurement error on the nonlinear speed control of a PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of the nonlinear speed control of a PMSM is designed and compared with the conventional controller which employs Proportional plus Derivative(PD) control. To show the validity of the proposed scheme, simulations and DSP-based experimental works are carried out and compared with the conventional control scheme.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.