• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.4
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• pp.290-297
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• 1997

Underwater robotic vehicles(URVs) are used for various work assignments such as pipe-lining, inspection, data collection, drill support, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc. As the use of such vehicles increases the development of vehicles having greater autonomy becomes highly desirable. The vehicle control system is one of the most critic vehicle subsystems to increase autonomy of the vehicle. The vehicle dynamics is nonlinear and time-varying. Hydrodynamic coefficients are often difficult to accurately estimate. It was also observed by experiments that the effect of electrically powered thruster dynamics on the vehicle become significant at low speed or stationkeeping. The conventional linear controller with fixed gains based on the simplified vehicle dynamics, such as PID, may not be able to handle these properties and result in poor performance. Therefore, it is desirable to have a control system with the capability of learning and adapting to the changes in the vehicle dynamics and operating parameters and providing desired performance. This paper presents an adaptive and learning control system which estimates a new set of parameters defined as combinations of unknown bounded constants of system parameter matrices, rather than system parameters. The control system is described with the proof of stability and the effect of unmodeled thruster dynamics on a single thruster vehicle system is also investigated.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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• 2006

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 md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming 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 teaming 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 teaming fuzzy neural network and artificial neural network.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.429-436
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• 2005

A simple stereo matching algorithm using population-based incremental learning(PBIL) is proposed in this paper to decrease the general problem of genetic algorithms, such as memory consumption and inefficiency of search. PBIL is a variation of genetic algorithms using stochastic search and competitive teaming based on a probability vector. The structure of PBIL is simpler than that of other genetic algorithm families, such as serial and parallel ones, due to the use of a probability vector. The PBIL strategy is simplified and adapted for stereo matching circumstances. Thus, gene pool, chromosome crossover, and gene mutation we removed, while the evolution rule, that fitter chromosomes should have higher survival probabilities, is preserved. As a result, memory space is decreased, matching rules are simplified and computation cost is reduced. In addition, a scheme controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities, like a result of coarse-to-fine matchers. Because of this scheme, the proposed algorithm can produce a stable disparity map with a small fixed-size window. Finally, an alterative version of the proposed algorithm without using probability vector is also presented for simpler set-ups.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.554-560
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• 2020

In the manufacturing process of flat panel displays, a high-precision planar motion stage is used to position a specimen. Stages of this type typically use frictionless linear motors and air bearings, and laser interferometers. Real-time dynamic correction of the yaw motion error is very important because the inevitable yaw motion error of the stage means a change in the specimen orientation. Gantry control is generally used to compensate for yaw motion errors. Flexure units that allow rotational motion are applied to the stage to apply this method to a stage using an air-bearing guide. This paper proposes a method to improve the constant speed motion performance of a H-type XY stage equipped with air bearing and flexure units. When applying the gantry control to the stage, including the flexure units, the cause of the mutual ripple generated from the linear motors is analyzed, and adaptive learning control is proposed to compensate for the mutual ripple. A simulation was performed to verify the proposed method. The speed ripple was reduced to approximately the 22 % level. The ripple reduction was verified by simulating the stage state where yaw motion error occurs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• Journal of the Korea Computer Graphics Society
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• v.15 no.1
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• pp.7-15
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• 2009

In this paper, a novel framework that synthesizes and clones facial expression in parameter spaces is presented. To overcome the difficulties in manipulating face geometry models with high degrees of freedom, many parameterization methods have been introduced. In this paper, a data-driven parameterization method is proposed that represents a variety of expressions with a small set of fundamental independent movements based on the ICA technique. The face deformation due to the parameters is also learned from the data to capture the nonlinearity of facial movements. With this parameterization, one can control the expression of an animated character's face by the parameters. By separating the parameterization and the deformation learning process, we believe that we can adopt this framework for a variety applications including expression synthesis and cloning. The experimental result demonstrates the efficient production of realistic expressions using the proposed method.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.