• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.300-309
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• 2003

The Japanese developed the first Car navigation system in 1981 with the advent of Honda, which was known as the car inertial navigation system. Now days, It is possible to search the shortest route to and from places and arrival time using the internet via cell phone to the driver based on GIS and GPS. However, even with a good navigation system, it losses the shortest route when there is an average speed of the vehicle being between S-15 kilometers. Therefore, in order to improve the vehicle waiting time and average vehicle speed, we are suggesting an optimal green time algorithm using fuzzy adaptive control, where there are different traffic intersection lengths, and lanes. In this paper, to be able to assist the driver and forecast the optimal traffic information with regards to the road conditions; dangerous roads, construction work and estimation of arrival time at their destination using internet.

• Steel and Composite Structures
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• v.51 no.1
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• pp.25-41
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• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.776_777
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using neural network(NN). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism fuzzy-neural networks(ALM-FNN) controller that is implemented using fuzzy control and neural networks. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.76-84
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• 2003

Now days, It is based on GIS and GPS, it can search for the shortest path and estimation of arrival time by using the internet and cell phone to driver. But, even though good car navigation system does not create which is the shortest path when there average vehicle speed is 10 -20 Km. Therefore In order to reduce vehicle waiting time and average vehicle speed, we suggest optimal green time algorithm using fuzzy adaptive control, where there are different traffic intersection length and lane. In this paper, it will be able to forecast the optimal traffic information, estimation of destination arrival time, under construction road, and dangerous road using internet.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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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. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and 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 LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. 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 LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.47-57
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive teaming mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^i{_d}$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.47-54
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• 2006

In order to reduce traffic accidents, many researchers studied a traffic accident model. The Cause of traffic accidents is usually the mis calculation of traffic signals or bad traffic intersection design. Therefore, to analyse the cause of traffic accidents, it takes effort. This paper, it calculates the optimal safe car speed considering intersection conditions and weather conditions. It will recommend calculation of 1/3 in vehicle speed when there are rainy days and snow days. But the problem is that it will always display the same speed limit when whether conditions change. In order to solve these problems, in this paper, it is proposed the calculation of optimal safety speed algorithm uses weather conditions and road conditions. Computer simulations is prove that it computes the traffic speed limit correctly, which proposed considering intelligent traffic accident prediction algorithms.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1369-1378
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• 2017

The present paper reports the development of a computational code based on the Adaptive Group of Ink Drop Spread (AGIDS) for reconstruction of the neutron noise sources in reactor cores. AGIDS algorithm was developed as a fuzzy inference system based on the active learning method. The main idea of the active learning method is to break a multiple input-single output system into a single input-single output system. This leads to the ability to simulate a large system with high accuracy. In the present study, vibrating absorber-type neutron noise source in an International Atomic Energy Agency-two dimensional reactor core is considered in neutron noise calculation. The neutron noise distribution in the detectors was calculated using the Galerkin finite element method. Linear approximation of the shape function in each triangle element was used in the Galerkin finite element method. Both the real and imaginary parts of the calculated neutron distribution of the detectors were considered input data in the developed computational code based on AGIDS. The output of the computational code is the strength, frequency, and position (X and Y coordinates) of the neutron noise sources. The calculated fraction of variance unexplained error for output parameters including strength, frequency, and X and Y coordinates of the considered neutron noise sources were $0.002682{\sharp}/cm^3s$, 0.002682 Hz, and 0.004254 cm and 0.006140 cm, respectively.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.3
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• pp.224-232
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• 2006

The paper proposes the artificial neural network(ANN) sensorless control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper proposes the speed control of induction motor using AFC and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and him controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.