• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.240-243
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• 2003

Sensorless Vector control of the permanent magnet synchronous motor(PMSM) typically requires knowledge of the PMSM structure and parameters, which in some situations are not readily available or may be difficult to obtain. In this paper, by measuring the currents of the PMSM drive, a neural-network-based rotor position and speed estimation method for PMSM is described. Because the proposed estimator treats the estimated motor speed as the weights, it is possible to estimate motor speed to adapt back propagation algorithm with 2 layered neural network. The proposed control algorithm is applied to PMSM drive system. The operating characteristics controlled by neural networks control are examined in detail.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.414-420
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• 1997

An intelligent speed control system for marine diesel engines is presented. The approach adopt¬ed is to use a conventional PID controller for normal operation and a feedforward controller for adaptive control. The feedforward controller is a neural network. The neural network is the inverse dynamics model of the plant, which is being trained on line. The parametric model of the diesel engine is represented in a linear second-order system, with a first-order combustion part and a revolution part each at a normal operating point. The time delay in the control of the com¬bustion part is approximated to the first-order system. The tuned PID parameters are set based on the model for normal operating point. To obtain the inverse dynamics of the diesel engine system, two neural networks are used, one for inverse, the other for forward dynamics. The former is posi¬tioned across the plant to learn its inverse dynamics during operation, and the latter is placed in series with the controlled plant. Simulation results are presented to illustrate the applicability of the proposed scheme to intelligent adaptive control of diesel engines.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.43-47
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• 1997

Advances in EDM power supplies have made the process competitive in some areas dominated by conventional and numerically controlled machines. This paper will produce more comprehensive data than are presently available and will use this data in applying concepts of optimization based on manufacturer's guide lines utilizing neural networks. A method will be developed for determining the machining parameters of the EDM process considering the conflicting desirability of good surface finish, low tool wear and high rates of metal removal. By the proposed method, one can select machining parameters that can maintain permissible tool wear and obtain maximum machining rates on the system represented by the data.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.14-17
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• 1999

This paper describes the controller for the improving speed control the AC servo motor. The microprocessor provides an output to the difference in command. The servo system improves the characteristics of speed control. When the motor is running at the same speed as set by the reference signal, the speed encoder also provides a signal the same frequency. Thus, the microprocessor controlled digital techniques enable to realize the flexible performance and control which was possible with time constant. We can know that PI control using neural networks by 80196 can control efficiently speed of AC Servo motor. Finally experimental results prove excellent performance of this control system. The system can be adaptable to CNC machine.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.10
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• pp.59-66
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• 1997

Neural networks has widely been be used for several practical applications such as speech, image processing, and pattern recognition. Thus, a approach to the voltage-controlled current source in areas of neural networks, the key features of CNN in locally connected only to its netighbors. Because the architecture of the interconnection elements between cells in very simple and space invariant, CNNs are suitable for VLSI implementation. In this paper, processing element of digital programmable CNN with variable template memory was implemented using CMOS circuit. CNN PE circuit was designe dto control gain for obtaining the optimal solutions in the CNN output. Performance of operation for 4*4 CNN circuit applied for fixed template and variable template analyzed with the result of simulation using HSPICE tool. As a result of simulations, the proposed variable template method verified to improve performance of operation in comparison with the fixed template method.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.546-549
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• 2006

In this paper the neuron operation based on Brillouin-active fiber in optical fiber is described. The inherent optical feedback by the backscattered stokes wave in optical fiber leads to instabilities in the form of optical chaos. Controlling of chaos induced transient instability in Brillouin-active fiber is implemented with Kerr nonlinearity having a non-instantaneous response in network systems. The controlling chaotic instabilities can lead to multistable periodic states; create optical logic 'on' or high level '1' or 'off', or low level '0'. It is theoretically possible to apply the multi-stability regimes as an optical memory device for encoding and decoding series and complex data transmission in optical systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.411-414
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• 2003

Particle swarm optimization (PSO) is employed to train fuzzy-neural networks (FNN), which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. The system has been successfully employed in a real life situation for navigation of a mobile robot.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.3-42
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• 2002

Fuzzy Neural Network (FNN) is a compulsory element in a voice-controlled machine due to its inherent capability of interpreting imprecise natural language commands. To control such a machine, user's perception of imprecise words is very important because the words' meaning is highly subjective. This paper presents a voice based controller centered on an adaptable FNN to capture the user's perception of imprecise words. Conversational interface of the machine facilitates the learning through interaction. The system consists of a dialog manager (DM), the conversational interface, a Knowledge base, which absorbs user's perception and acts as a replica of human understanding of imprecise words,...

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.769-772
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• 1993

This paper presents and adaptive fuzzy controller using fuzzy neural networks(FNNs). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The other FNN is used as a fuzzy controller. The fuzzy controller is designed with the linguistic rules of the fuzzy model. The response of the designed control system is checked with a linguistic response analysis proposed by the authors. An adaptive tuning of the control rules of the FNN controller is made possible utilizing the fuzzy model. Simulations using nonlinear controlled objects were done to verify the proposed control system.

• Genomics & Informatics
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• v.7 no.2
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• pp.85-96
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• 2009

The differentiation of neural precursor cells (NPCs) into neurons and astrocytes is a process that is tightly controlled by complicated and ill-defined gene networks. To extend our knowledge to gene networks, we performed a temporal analysis of gene expression during the differentiation (2, 4, and 8 days) of spinal cord-derived NPCs using oligonucleotide microarray technology. Out of 32,996 genes analyzed, 1878 exhibited significant changes in expression level (fold change>2, p<0.05) at least once throughout the differentiation process. These 1878 genes were classified into 12 groups by k-means clustering, based on their expression patterns. K-means clustering analysis revealed that the genes involved in astrogenesis were categorized into the clusters containing constantly upregulated genes, whereas the genes involved in neurogenesis were grouped to the cluster showing a sudden decrease in gene expression on Day 8. Functional analysis of the differentially expressed genes indicated the enrichment of genes for Pax6- NeuroD signaling.TGFb-SMAD and BMP-SMAD.which suggest the implication of these genes in the differentiation of NPCs and, in particular, key roles for Nova1 and TGFBR1 in the neurogenesis/astrogenesis of mouse spinal cord.