• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.391-394
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• 2008

This paper is proposed a high performance speed control of the synchronous reluctance motor through the artificial intelligent SV-PWM. SV-PWM is controlled using fuzzy control that is artificial intelligent control. SV-PWM can be maximum used maximum dc link voltage and is excellent control method due to characteristic to reducing harmonic more than others. Fuzzy control has a advantage which can be robustly controlled. Simulation results are presented to show the validity of the proposed algorithm

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.815-823
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• 1999

This study considers an implementation of artificial neural networks to the receding horizon optimal control and is applications to power systems. The Generalized Backpropagation-Through-Time (GBTT) algorithm is presented to deal with a quadratic cost function defined in a finite-time horizon. A decentralized approach is used to control the complex global system with simpler local controllers that need only local information. A Neural network based Receding horizon Optimal Control (NROC) 1aw is derived for the local nonlinear systems. The proposed NROC scheme is implemented with two artificial neural networks, Identification Neural Network (IDNN) and Optimal Control Neural Network (OCNN). The proposed NROC is applied to a power system to improve the damping of the low-frequency oscillation. The simulation results show that the NROC based power system stabilizer performs well with good damping for different loading conditions and fault types.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.248-250
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• 1986

This paper explain application of expert system techniques from the latest theories of artificial intelligence to industrial process control. This controller continuously monitors a loop's response to disturbances and adapts the tuning parameters (P.I.D) to provide the best response to load upset or set-point change.

• IE interfaces
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• v.20 no.4
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• pp.458-468
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• 2007

Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.223-228
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• 2001

We have been developed an intelligent mobile robot with an artificial olfactory function to recognize odours and to track odour source location. This mobile robot also has ben installed an engine for speech recognition and synthesis and is controlled by wireless communication. An artificial olfactory system based on array of 7 gas sensors has been installed in the mobile robot for odour recognition, and 11 gas sensors also are located in the obttom of robot to track odour sources. 3 optical sensors are also in cluded in the intelligent mobile robot, which is driven by 2 D. C. motors, for clash avoidance in a way of direction toward an odour source. Throughout the experimental trails, it is confirmed that the intelligent mobile robot is capable of not only the odour recognition using artificial neural network algorithm, but also the tracking odour source using the step-by-step approach method. The preliminary results are promising that intelligent mobile robot, which has been developed, is applicable to service robot system for environmental monitoring, localization of odour source, odour tracking of hazardous areas etc.

• YAKHAK HOEJI
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• v.44 no.4
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• pp.347-353
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• 2000

To examine influence of artificial digestive juice on the antitumor activity of Ganoderma lucidum-A(GL-A), protein-bound polysaccharide from Ganoderma lucidum, we compared the digested protein-bound polysaccharide with undigested one both on immunopotentiating activity and influence of digestive juices. Protein-bound polysaccharide GL-B was obtained by digesting the antitumor component GL-A with artificial digestive Juices in vitro. When GL-A was administered orally to sarcoma 180 tumor-bearing ICR mice, the life prolonging effect was exhibited in a dose dependent manner Not only GL-A but GL-B increased the production of colony forming unit (CFU) to 10- and 8-fold of that of the control, respectively. Both of the protein-bound polysaccharides also showed the secretion of nitric oxide in RAW 264.7 cell lines to 3.5-and 3.7-fold of that of the control, respectively: GL-A activated components of the alternative complement pathway, whereas GL-B did not. In humoral immunity GL-A increased the activity of alkaline phosphatase in differentiated B cells to 3 times and GL-B to 4 times of that of the control. These results showed that the artificial digestive juices had no influence on the antitumor activity of the protein-bound polysaccharide from Ganoderma lucidum and that its immunomodulating activity retained after treatment with artificial digestive juice. And this provides a basis of the protein-bound polysaccharide of Ganoderma lucidum as an peroral anticancer drug.

• Smart Media Journal
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• v.13 no.3
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• pp.45-52
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• 2024

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.79-89
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• 1993

This paper deals with a computer aided control of an adjustment process for the complete electronic devices by means of an application of artificial neural network and an implementation of neuro-controller for intelligent control. Multi-layer neural network model is employed as artificial neural network with the learning method of the error back propagation. Information initially available from real plant under control are the initial values of plant output, and the augmented plant input and its corresponding plant output at that time. For the intelligent control of adjustment process utilizing artificial neural network, the neural network emulator (NNE) and the neural network controller(NNC) are developed. The initial weights of each neural network are determined through off line learning for the given product and it is also employed to cope with environments of the another product by on line learning. Computer simulation, as well as the application to the real situation of proposed intelligent control system is investigated.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.