• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.225-234
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• 2000

In this paper, we present an nonlinear PID controller with network based compensator which consists of a conventional PID controller that controls the linear components and neuro-compensator that controls the output errors and nonlinear components. This controller is based on the Harris's concept where he explained that the adaptive controller consists of the PID control term and the disturbance compensating term. The resulting controller's architecture is also found to be very similar to that of Wang's controller. This controller adds a self-tuning ability to the existing PID controller without replacing it by compensating the output errors through the neuro-compensator. Various simulations and comparative studies have proven that the proposed nonlinear PID controller produces superior results to other existing PID controllers. When applied to an actual magnetic levitation system which is known to be very nonlinear, it has also produced an excellent results.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.262-266
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• 1999

In this paper, we consider to apply of 2-DOF (Degree of Freedom) PID controller at D.C servo motor system. Many control system use I-PD, PID control system, but the position control system have difficulty in controling variable load and changing parameter. We propose neural network 2-DOF PID control system having feature for removal disturbrances and tracking function in the target value point. The back propagation algorithm of neural network used for tuning the 2-DOF parameter ($\alpha$, $\beta$, ${\gamma}$, η). We investigate the 2-DOF PID control system in the position control system and verify the effectiveness of proposal method through the result of computer simulation.

• Smart Structures and Systems
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• v.12 no.1
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• pp.23-39
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• 2013

In this article, a smart multifunctional optical system-on-a-chip (SOC) sensor platform is presented and its application for fiber Bragg grating (FBG) sensor interrogation in optical sensor networks is investigated. The smart SOC sensor platform consists of a superluminescent diode as a broadband source, a tunable microelectromechanical system (MEMS) based Fabry-P$\acute{e}$rot filter, photodetectors, and an integrated microcontroller for data acquisition, processing, and communication. Integrated with a wireless sensor network (WSN) module in a compact package, a smart optical sensor node is developed. The smart multifunctional sensor platform has the capability of interrogating different types of optical fiber sensors, including Fabry-P$\acute{e}$rot sensors and Bragg grating sensors. As a case study, the smart optical sensor platform is demonstrated to interrogate multiplexed FBG strain sensors. A time domain signal processing method is used to obtain the Bragg wavelength shift of two FBG strain sensors through sweeping the MEMS tunable Fabry-P$\acute{e}$rot filter. A tuning range of 46 nm and a tuning speed of 10 Hz are achieved. The smart optical sensor platform will open doors to many applications that require high performance optical WSNs.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• The KIPS Transactions:PartC
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• v.12C no.1 s.97
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• pp.121-128
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• 2005

Tho existing TCP(Transmission Control Protocol) is known to be unsuitable for a network with the characteristics of high RDP(Bandwidth-Delay Product) because of the fixed small or large buffer size at the TCP sender and receiver. Thus, some trial cases of adjusting the buffer sizes automatically with respect to network condition have been proposed to improve the end-to-end TCP throughput. ATBT(Automatic TCP fluffer Tuning) attempts to assure the buffer size of TCP sender according to its current congestion window size but the ATBT assumes that the buffer size of TCP receiver is maximum value that operating system defines. In DRS(Dynamic Right Sizing), by estimating the TCP arrival data of two times the amount TCP data received previously, the TCP receiver simply reserves the buffer size for the next arrival, accordingly. However, we do not need to reserve exactly two times of buffer size because of the possibility of TCP segment loss. We propose an efficient TCP buffer tuning technique(called TBT-PLR: TCP buffer tuning algorithm based on packet loss ratio) since we adopt the ATBT mechanism and the TBT-PLR mechanism for the TCP sender and the TCP receiver, respectively. For the purpose of testing the actual TCP performance, we implemented our TBT-PLR by modifying the linux kernel version 2.4.18 and evaluated the TCP performance by comparing TBT-PLR with the TCP schemes of the fixed buffer size. As a result, more balanced usage among TCP connections was obtained.

• Journal of Sensor Science and Technology
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• v.20 no.3
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• pp.151-155
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• 2011

A variety of pattern recognition algorithms including neural networks may be applicable to the identification of odors. In this paper, an identification technique for an electronic odor sensing system applicable to wound state monitoring is presented. The performance of the radial basis function(RBF) network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those parameters are initialized by an ill-conditioned genetic fuzzy c-means algorithm, and the distribution of input patterns in the very first stage, the stochastic gradient(SG), is adapted. The adaptive RBF network with singular value decomposition(SVD), which provides additional adaptation capabilities to the RBF network, is used to process data from array-based gas sensors for early detection of wound infection in burn patients. The primary results indicate that infected patients can be distinguished from uninfected patients.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.221-228
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• 2005

Data storage was thought to be inside of or next to server cases but advances in networking technology make the storage system to be located far away from the main computer. In Internet era with explosive data increases, balanced development of storage and transmission systems is required. SAN(Storage Area Network) and NAS(Network Attached Storage) reflect these requirements. It is important to know the capacity and limit of the complex storage network system to got the optimal performance from it. The capacity data is used for performance tuning and making purchasing decision of storage. This paper suggests an analytic model of storage network system as queuing network and proves the model though simulation model.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.194-201
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• 2005

In this paper, we propose an adaptive mechanism based on humoral immune algorithm and neural network identifier technique. It is also applied for an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To slove this problem, we use the neural network identifier technique for modeling the plant humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. Finally, the experimental results for control of steering and speed of AGV system illustrate the validity of the proposed control scheme. Also, these results for the proposed method show that it has better performance than other conventional controller design method such as PID controller.