• Journal of Positioning, Navigation, and Timing
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• v.6 no.1
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• pp.17-26
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• 2017

Latency occurs in RTK, where the measured position actually outputs past position when compared to the measured time. This latency has an adverse effect on the navigation accuracy. In the present study, a system that estimates the latency of RTK and compensates the position error induced by the latency was implemented. To estimate the latency, the speed obtained from an odometer and the speed calculated from the position change of RTK were used. The latency was estimated with a modified correlator where the speed from odometer is shifted by a sample until to find best fit with speed from RTK. To compensate the position error induced by the latency, the current position was calculated from the speed and heading of RTK. To evaluate the performance of the implemented method, the data obtained from an actual vehicle was applied to the implemented system. The results of the experiment showed that the latency could be estimated with an error of less than 12 ms. The minimum data acquisition time for the stable estimation of the latency was up to 55 seconds. In addition, when the position was compensated based on the estimated latency, the position error decreased by at least 53.6% compared with that before the compensation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1697-1703
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• 2020

Sensor nodes that directly collect data from the surrounding environment have many constraints, such as power supply and memory size, thus efficient use of resources is required. In this paper, in a sensor node that receives location data of a vehicle on a lane, the data reception period is changed by the target's speed estimated by the Kalman filter and distance weight. For a slower speed of the vehicle, the longer data reception interval of the sensor node can reduce the processing time performed in the entire sensor network. The proposed method was verified through a traffic simulator implemented as MATLAB, and the results achieved that the processing time was reduced in the entire sensor network using the proposed method compared to the baseline method that receives all data from the vehicle.

• Coupled systems mechanics
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• v.3 no.2
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• pp.147-170
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• 2014

In the recent times, dimensions of heavy load carrying vehicle have changed significantly incorporating structural flexibility in vehicle body. The present paper outlines a procedure for the estimation of bridge response statistics considering structural bending modes of the vehicle. Bridge deck roughness has been considered to be non homogeneous random process in space. Influence of pre cambering of bridge surface and settlement of approach slab on the dynamic behavior of the bridge has been studied. A parametric study considering vehicle axle spacing, mass, speed, vehicle flexibility, deck unevenness and eccentricity of vehicle path have been conducted. Dynamic amplification factor (DAF) of the bridge response has been obtained for several of combination of bridge-vehicle parameters. The present study reveals that flexible modes of vehicle can reduce dynamic response of the bridge to the extent of 30-37% of that caused by rigid vehicle model. However, sudden change in the bridge surface profile leads to significant amount of increment in the bridge dynamic response even if flexible bending modes remain active. The eccentricity of vehicle path and flexural/torsional rigidity ratios plays a significant role in dynamic amplification of bridge response.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.8
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• pp.581-588
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• 2002

Precise highway traffic noise simulation and reduction require the accurate data for sound power levels omitted by vehicles, varied to road surface, traffic speed, vehicle types and makers, different from countries to countries. In this study, we have elaboratively measured Korea highway traffic noise and parameters affecting noise levels at the nearside carriageway edge. From numerical simulation using the measured results for highway traffic noise, we propose not only two correction factors to enhance the accuracy of Korea highway traffic sound power estimation using ASJ Model-1998 but also its typical power spectrum according to road surface type. The measured and predicted highway traffic noise levels using the proposed sound power show little difference within 1 dB.

• Journal of the Korean Society for Railway
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• v.10 no.2 s.39
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• pp.146-152
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• 2007

The ride comfort is more important in the train speedup. Generally, it is defined as the vehicle vibration. There are many studies on evaluation method of ride comfort for railway. But the ride comfort for Korean high speed train(HSR 350x) has been assessed by statistical method according to UIC 513R. It is impossible for HSR 350x to run at constant speeds above 310km/h during 5 minutes required in UIC 513R because of the same operational condition as KTX and the design limitation of infrastructures. The ride indices data had been acquired from 80 to 310km/h through the on-line test. The present study has suggested the methodology for the estimation of the ride indices at high speed above 310km/h by using those obtained below 310km/h and reviewed the ride comfort for HSR 350x in these speeds.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.566-569
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• 2005

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 artificial intelligent(AI) controller. 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 AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(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 experimental results to verify the effectiveness of AI controller.

• Journal of Power Electronics
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• v.12 no.3
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.110-114
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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. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. 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 AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(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 ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.2
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• pp.107-113
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• 2019

In this paper, a robust filter based wind velocity estimation algorithm without an air velocity sensor in an air vehicle is presented. The wind velocity is useful information for the air vehicle to perform precise guidance and control. In general, the wind velocity can be obtained by subtracting an air velocity which is obtained by an air velocity sensor such as a pitot-tube, and a ground velocity which is obtained by a navigation equipment. However, in order to simplify the configuration of the air vehicle, the wind estimation algorithm is necessary because the wind velocity can not be directly obtained if the air velocity measurement sensor is not used. At this time, the aerodynamic coefficient of the air vehicle changes due to the turbulence, which causes the uncertainty of the system model of the filter, and the wind estimation performance deteriorates. Therefore, in this study, we propose a wind estimation method using $H{\infty}$ filter to ensure robustness against aerodynamic coefficient uncertainty, and we confirmed through simulation that the proposed method improves the performance in the uncertainty of aerodynamic coefficient.