• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.205-215
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• 2015

Common mode (CM) conducted interference are predicted and compared with experiments in a motor drive system of Electric vehicles in this study. The prediction model considers each part as an equivalent circuit model which is represented by lumped parameters and proposes the parameter extraction method. For the modeling of the inverter, a concentrated and equivalent method is used to process synthetically the CM interference source and the stray capacitance. For the parameter extraction in the power line model, a computation method that combines analytical method and finite element method is used. The modeling of the motor is based on measured date of the impedance and vector fitting technique. It is shown that the parasitic currents and interference voltage in the system can be simulated in the different parts of the prediction model in the conducted frequency range (150 kHz-30 MHz). Experiments have successfully confirmed that the approach is effective.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.798-802
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper, the robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is to use different models (CAD model etc.) known a priori. Also fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Because voting is a very simple or no model is needed for fusion, voting-based fusion of cues is applied. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters, namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Structural Engineering and Mechanics
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• v.85 no.2
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• pp.147-161
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• 2023

Based on the ideas of variational differential quadrature (VDQ) and finite element method (FEM), a numerical approach named as VDQFEM is applied herein to study the large deformations of plate-type structures under static loading with arbitrary shape hole made of functionally graded graphene platelet-reinforced composite (FG-GPLRC) in the context of higher-order shear deformation theory (HSDT). The material properties of composite are approximated based upon the modified Halpin-Tsai model and rule of mixture. Furthermore, various FG distribution patterns are considered along the thickness direction of plate for GPLs. Using novel vector/matrix relations, the governing equations are derived through a variational approach. The matricized formulation can be efficiently employed in the coding process of numerical methods. In VDQFEM, the space domain of structure is first transformed into a number of finite elements. Then, the VDQ discretization technique is implemented within each element. As the last step, the assemblage procedure is performed to derive the set of governing equations which is solved via the pseudo arc-length continuation algorithm. Also, since HSDT is used herein, the mixed formulation approach is proposed to accommodate the continuity of first-order derivatives on the common boundaries of elements. Rectangular and circular plates under various boundary conditions with circular/rectangular/elliptical cutout are selected to generate the numerical results. In the numerical examples, the effects of geometrical properties and reinforcement with GPL on the nonlinear maximum deflection-transverse load amplitude curve are studied.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Structural Engineering and Mechanics
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• v.27 no.5
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• pp.625-638
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• 2007

The damage detection process may appear difficult to be implemented for truss structures because not all degrees of freedom in the numerical model can be experimentally measured. In this context, the damage locating vector (DLV) method, introduced by Bernal (2002), is a useful approach because it is effective when operating with an arbitrary number of sensors, a truncated modal basis and multiple damage scenarios, while keeping the calculation in a low level. In addition, the present paper also evaluates the noise influence on the accuracy of the DLV method. In order to verify the DLV behavior under different damages intensities and, mainly, in presence of measurement noise, a parametric study had been carried out. Different excitations as well as damage scenarios are numerically tested in a continuous Warren truss structure subjected to five noise levels with a set of limited measurement sensors. Besides this, it is proposed another way to determine the damage locating vectors in the DLV procedure. The idea is to contribute with an alternative option to solve the problem with a more widespread algebraic method. The original formulation via singular value decomposition (SVD) is replaced by a common solution of an eigenvector-eigenvalue problem. The final results show that the DLV method, enhanced with the alternative solution proposed in this paper, was able to correctly locate the damaged bars, using an output-only system identification procedure, even considering small intensities of damage and moderate noise levels.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.8
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• pp.1275-1283
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• 1990

Perception of surfaces from range images plays a key role in 3-D object recognition. Recognition of 3-D objects from range images is performed by matching the perceived surface descriptions with stored object models. The first step of the 3-d object recognition from range images is image segmentation. In this paper, an approach for segmenting 3-D range images into symbolic surface descriptions using spatial Gabor filter is proposed. Since the phase of data has a lot of important information, the phase information with magnitude information can effectively segment the range imagery into regions satisfying a common homogeneity criterion. The phase and magnitude of Gabor filter can represent a unique featur vector at a point of range data. As a result, range images are trnasformed into feature vectors in 3-parameter representation. The methods not only to extract meaningful features but also to classify a patch information from range images is presented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.240-248
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• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

• Geomechanics and Engineering
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• v.38 no.3
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• pp.231-247
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• 2024

The use of lime stabilization and geosynthetic reinforcement is a common approach to improve the performance of fine-grained soils in geotechnical applications. However, the impact of this combination on the soil-geosynthetic interaction remains unclear. This study addresses this gap by evaluating the interface efficiency and soil-geosynthetic interaction parameters of lime-stabilized clay (2%, 4%, 6%, and 8% lime content) reinforced with geotextile or geogrid using direct shear tests at various curing times (1, 7, 14, and 28 days). Additionally, machine learning algorithms (Support Vector Machine and Artificial Neural Network) were employed to predict soil shear strength. Findings revealed that lime stabilization significantly increased soil shear strength and interaction parameters, particularly at the optimal lime content (4%). Notably, stabilization improved the performance of soil-geogrid interfaces but had an adverse effect on soil-geotextile interfaces. Furthermore, machine learning algorithms effectively predicted soil shear strength, with sensitivity analysis highlighting lime percentage and geosynthetic type as the most significant influencing factors.