• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.965-969
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• 1993

In this paper, the GPC algorithm is developed for ACS(advanced control system). ACS equals to DCS(distributed control system) with some advanced control algorithm, for example, fuzzy logic controller, autotuning. By its embedded structural control language, which uses simple function codes corresponding to each function blocks, it is possible to construct multiloop controller. The developed GPC function code is divided by RLS (recursive least square) parameter estimator and GPC controller. Simulation result show the availability of GPC function code using the control language.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.6
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• pp.234-240
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• 2002

This paper treats the problem of estimating the longitudinal velocity of a braking vehicle using measurements from an accelerometer and wheel speed data from standard anti-lock braking wheel speed sensors. We develop and experimentally test three velocity estimation algorithms of increasing complexity. The algorithm that works the best gives peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.58-65
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• 2003

Vehicle acceleration data from an accelerometer and wheel speed data from standard, 50-tooth antilock braking system wheel speed sensors are used to estimate the absolute longitudinal speed of a vehicle. We develop the four velocity estimation algorithms. And we compare experimental results with the Butterworth filtered speed from the fifth wheel and find that it is possible to estimate absolute longitudinal vehicle speed during a hard braking maneuver lasting three seconds.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.11-16
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• 1998

In this paper, simultaneous estimation of rotor speed and time constant for a voltage source inverter (VSI) fed induction motor drive are disccussed. The theory is based on the Model Reference Adaptive System (MRAS). The identifier executes Simultaneous rotor speed and time constant so that vector control of the induction may be achieved in the rotor-flux oriented reference frame. Furthermore, to eliminate the offset error caused by the change in the stator resistance, a fuzzy resistance regulator is also designed which operates in parallel with the rotor speed and time constant identifier

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.360-368
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• 2021

The need for energy harvesting in marine environments is gradually increasing owing to the energy limitation of marine robots. To address this problem, a catamaran-type sail drone (CSD), which can harvest marine energies such as wind and solar, was proposed in a previous study. However, it was designed and manufactured without considering the stability, optimal hull-form, and maintenance. To resolve these problems, a CSD with two keels, a performance estimator, V-shape hulls, and modularized components is proposed and its mechanism is developed in this study. To verify the performance of the CSD, the performance estimation using smoothed-particle hydrodynamics (SPH) and the heading control using fuzzy logic controller (FLC) are performed. Simulation results show the attitude stability of the CSD and the experimental results show the straight path of the CSD according to wind conditions. Therefore, the CSD has potential applications as an energy harvesting system.

• Journal of Power Electronics
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• v.15 no.3
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• pp.730-740
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• 2015

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) - based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.203-209
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• 2015

In this paper, we conducted a conventional regression and fuzzy regression analysis of the core competencies of construction subcontractors. The study was undertaken to check whether these two types of regression core capabilities affect the rating of construction subcontractor. Conventional regression result showed some effect on the rating of construction subcontractors on which core competencies to management and firm contribution were conducted. With fuzzy regression analysis, on the other hand, the rating of construction subcontractors could see the Min and Conjunction problem which utilize 100% reliability of Min. Max and Conjunction. From the above, the dependent variable of conventional regression could determine the evaluation grade of construction subcontractor. The fuzzy regression analysis shows the estimator of evaluation grade of the construction subcontractor including or corresponding to the fuzzy output data.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2188-2195
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• 1999

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neuralnet, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Means) arithmetics, to decide whether a requested call not to be trained in learning phase to be connected or not. The system generates the estimated traffic pattern for the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmetics. The input to the NN is the vector consisted of traffic parameters which are the means and variances of the number of cells arriving in decision as to whether to accept or reject a new call depends on whether the NN is used for decision threshold(+0.5). This method is a new technique for call admission control using the membership values as traffic parameter which declared to CAC at the call set up stage, and this is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simulations, it is founded the performance of the suggested method outperforms compared to the conventional NN method.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.171-182
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• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.