• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.733-741
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• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.213-218
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• 2009

This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All of the test data are taken from OpenGL Simulation. The OpenGL which draws dynamic obstacles environment is used to carry out the experiment for the situation of three-dimension. In order to verify the performance of proposed SVMs, it compares with Back-Propagation algorithm through OpenGL simulation in view of the obstacle recognition accuracy and the time efficiency.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.179-181
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• 2007

This paper presents the desired position control of the mobile inverted pendulum system(MIP). The MIP is required to track the circular trajectory in the xy plane through the kinematic Jacobian relationship between the xy plane and the joint space. The reference compensation technique of the radial basis function(RBF) network is used as a neural network control method. The back-propagation teaming algorithm of the RBF network is derived and embedded on a DSP board. Experimental studies of tracking the circular trajectory are conducted.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.6
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• pp.95-101
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• 2001

This work is concerned with construction of the intelligence stress predictor far compression strength evaluation using neural network-ultrasonic waves. The contact pressure in jointed plates was measured by using ultrasonic technique. Neural network is used to evaluate and predict contact pressure from the results of the calibration curves. The organized neural system was leaned with the accuracy of 99%, as a result of learning the ultrasonic echo ratio to the contact pressure measurement between SM45C and STS410 materials. And it could be evaluated and predicted with the accuracy of 90% in the evaluation of ultrasonic echo ratio difference in the same surface roughness and contact pressure, and 85% in the prediction of virtual ultrasonic echo ratio. Thus the proposed stress predictor is very useful for the evaluation and prediction of the contact pressure between SM45C and STS410 materials.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.93-99
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• 2004

Neural oscillator can be applied to oscillatory systems such as analyses of image information, voice recognition and etc. Conventional EBPA (Error back Propagation Algorithm) is not proper for oscillatory systems with the complicate input`s patterns because of its tedious training procedures and sluggish convergence problems. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(Phase Locked Loop) function and by using a simple Hebbian learning rule. Therefore, in this paper, a technique for Recognition of the Korean Alphabet using Phase Synchronized Neural Oscillator was introduced.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.285-291
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• 2008

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2327-2329
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• 2005

In this paper, we identify the memory effects of the RF high-power base station amplifiers with Vector Signal Analyzer (VSA). It is found that the model of power- amplifier using Tapped Delayed Neural - Networks with back-propagation algorithm shows very accurate modeling performance. Based on this behavioral modeling, we conducted inverse compensation process which also uses Neural Networks.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.571-578
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• 2005

Corporate credit rating analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate credit rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of multiple discriminant analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Journal of the Korean Institute of Navigation
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• v.21 no.4
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• pp.21-28
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• 1997

Along with the rapid growth of shipping and transportation , the size of a ship larger and larger. Low speed maneuverabililty of a full ship has been received a great deal of attention concerting about the navigation safety, especially in the harbour area of waterway. And, the iperation of the full ship in harbour area is one fo tehmost difficult technique. Usually highly experienced experts can make a suitable decision considering various propeller ,rudder actions and environmental conditions. The Artificial Neural Network is applied to the automatic berthing control of a ship. The teaching data are made by the berthing simulation of a ship on the computer. And, the layer neural network is used and the 'Error Back-Propagation Algorithm' is used to teach the neural network. Finally, it is shown that the berthing control is successfully done by the established neural network.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2283-2285
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• 2001

This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.