• IE interfaces
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• v.10 no.3
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• pp.125-132
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• 1997

We proposed a 2-step neural network approach for estimating machining parameters of electric discharge wire cut. The first step net, which is described as a backward neuro-estimation, is designed for estimating coarse cutting parameters while the second phase net, as a polishing forward neuro-estimation, is utilized for determining fine parameters. Sequential estimation procedure, based on backward and forward net, is performed using the net's approximation capability which is M to 1 and 1 to M mapping property. Experimental results an given to evaluate the accuracy of the proposed 2-step neuro-estimation.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.481-488
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• 1994

In this paper a study of neuro-pathway estimation based on visual and somatosensory evoked potential is given. The evoked potentials which are caused by visual and somatosensory stimulation are detected by an average method. The forward problem that is estimating a scalp potential from a given electrical source in the brain is solved by using a triple concentric spherical shell model of the head and a single current dipole model of the neuron activity. The inverse problem which calculates a source position is solved by a least square fit between the model predicted potential and a given evoked potential measurement. The similarities between estimated sensory neuro-pathways and physiological brain function regions are verified.

• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.65-68
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• 1994

시각자극에 의해 머리표면에서 발생하는 Transient Evoked Potential을 검출하여 Source Tracing Method를 이용하여 뇌의 시각인지영역을 추정하였다. 본 과정에서 TEP검출방식은 average method를 이용하였고, 신경흥분에 대한 물리적 모델로 Single Current Dipole Model을 이용하고, 머리기하에 대한 3중구각모델을 이용하여 Forward Problem을 풀었다. Inverse Problem은 current dipole의 6개의 parameter에 대한 Least Square Error Method를 이용하여 신경흥분의 위치를 추정하였다. 이러한 결과와 생리학적으로 밝혀진 시각경로와의 비교결과 유사성이 확인되었다.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.7-13
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• 1998

This paper presents a methodology for determining machining conditions in Electric Discharge Wire-Cut. Unification of two phase neural network approach with an automatic generation of machining parameters is designed. The first phase neural network, which is 1 to M backward-mapping neural net, produces approximate machining conditions. Using approximate conditions, all possible conditions are newly created by the proposed automatic generation procedure. The second phase neural net, which is a M to 1 forward-mapping neural net, determines the best one among the generated candidates. Simulation results with ANN are given to verify that the presenting methodology could apply for determining machining parameters in Electric Discharge Wire-Cut.