• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.279-287
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• 2010

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.485-490
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• 1999

Wind vector information over the ocean is currently obtained using multiple beam scatterometer data. The scatterometers on ERS-1/2 generate wind vector information with a spatial resolution of 50km and accuracies of $\pm$2m/s in wind speed and $\pm$20$^{\circ}$ in wind direction. Synthetic aperture radar (SAR) data over the ocean have the potential of providing wind vector information independent of weather conditions with finer resolution. Finer resolution wind vector information can often be useful particularly in coastal regions where the scatterometer wind information is often corrupted because of the lower resolution system characteristics which is often contaminated by the signal returns from the coastal areas or ice in the case of arctic environments. In this paper we tested CMOD_4 and CMOD_IFR2 algorithms for extracting the wind vector from SAR data. These algorithms require precise estimation of normalized radar cross-section and wind direction from the SAR data and the local incidence angle. The CMOD series algorithms were developed for the C-band, VV-Polarized SAR data, typically for the ERS SAR data. Since RADARSAT operates at the same C-band but with HH-Polarization, the CMOD series algorithms should not be used directly. As a preliminary approach of resolving with this problem, we applied the polarization ratio between the HH and VV polarizations in the wind vectors estimation. Two test areas, one in front of Inchon and several sites around Jeju island were selected and investigated for wind vector estimation. The new results were compared with the wind vectors obtained from CMOD algorithms. The wind vector results agree well with the observed wind speed data. However the estimation of wind direction agree with the observed wind direction only when the wind speed is greater than approximately 3.0m/s.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.233-240
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• 2015

In this paper, we present an algorithm that estimates the speed of a moving object only using its sound information. In general, the speed gun projects the incident light onto a moving object and measures the frequency variation of the scattered light. Then the speed is measured by this frequency difference. In our study, instead of light information, we measure the speed by sound frequency difference when the object is coming and moving away. In our experiments on the speed measurement, on average the error of 6.08% was obtained. Utilizing this algorithm for smart device, we can measure the speed of a moving object without sensor that measures the frequency of the light.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.6
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• pp.671-679
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• 2012

This paper presents an estimation procedure for axial displacement in spindle equipped with angular contact ball bearings due to rotational speed. High-speed spindle-bearing system experiences axial displacement due to thermal expansion and rotational speed-dependent characteristics of angular contact ball bearings. This paper deals with the axial displacement caused by the rotational speed-dependent effects such as centrifugal force and gyroscopic moments. To this end, a bearing dynamic model is established that includes all the static and dynamic properties of angular contact ball bearing. An analytical formula to calculate the axial displacement based on contact angles between ball and races is derived to discuss the physics regarding the axial displacement in spindle. The proposed dynamic model is compared with a reference and a commercial program. Numerical examples are presented to show the effects of centrifugal force and gyroscopic moment on the axial displacement. The proposed model is also validated with an experimental result.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.6
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• pp.695-704
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• 2002

Generally, induction motor controller requires rotor speed sensor for commutation and current control, but it increases cost and size of the motor. So in these days, various researches including speed sensorless vector control have been reported and some of them have been put to practical use. In this paper a new speed estimation method using neural networks is proposed. The optimal neural network structure was tracked down by trial and error, and it was found that the 8-16-1 neural network has given correct results for the instantaneous rotor speed. Supervised learning methods, through which the neural network is trained to learn the input/output pattern presented, are typically used. The back-propagation technique is used to adjust the neural network weights during training. The rotor speed is calculated by weights and eight inputs to the neural network. Also, the proposed method has advantages such as the independency on machine parameters, the insensitivity to the load condition, and the stability in the low speed operation.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.27-36
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• 2024

The towing distance, which is speed over the ground, and the water flow quantity, which is speed through the water, were used when estimating the amount of Metapenaeus joyneri resources that rose to the surface at night using the swept area method in order to compare and analyze the difference. It was conducted using a shrimp dredge, trial fishing gear for catching Metapenaeus joyneri. Catch during the entire survey period was 188.9 kg. Monthly catch ranged from 3.1 to 109.2 kg, highest in June and lowest in September. The swept volume calculated using the speed over the ground was about 13% higher than using the speed through the water. Metapenaeus joyneri resources estimated using the towing distance ranged from 320.1 to 14,649.8 kg. Resources estimated using the water flow quantity ranged from 278.5 to 12,886.3 kg. Therefore, the amount of Metapenaeus joyneri resources estimated using the speed over the ground was about 14% higher than the method using the speed through the water, indicating that the amount of resources was overestimated.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.7
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• pp.532-538
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• 2013

This paper describes a vision-based only attitude estimation technique for the leader in the formation flight. The feature points in image obtained from the X-PLANE simulator are extracted by the SURF(Speed Up Robust Features) algorithm. We use POSIT(Pose from Orthography and Scaling with Iteration) algorithm to estimate attitude. Finally we verify that attitude estimation using vision only can yield small estimated error of $1.1{\sim}1.76^{\circ}$.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.182-188
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• 2023

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.4
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• pp.313-320
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• 2018

As ships operating on the Arctic route are exposed to various ice environments such as level ice, pre-swan, pack ice, ice ridge and brash ice, it is essential to estimate the ice resistance according to the ice environment. Methods for estimating the ice resistance include a method using mathematical model, numerical simulation, and a method using empirical formula. In this study, empirical formulas are used to estimate the ice resistance. The purpose of this study is to develop the ice resistance and attainable speed estimation program(I-RES) for brash ice. To develop the Brash ice attainable speed estimation algorithm, the environmental characteristics of the brash ice were analyzed, and the results of I-RES were evaluated by comparing the model test results of brash ice. The accuracy of I-RES for brash ice is around 20% in this study but it will be more developed near future with accumulating more model test results and calculation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4240-4258
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• 2016

A practical iterative channel estimation technique is proposed for the multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the high-speed mobile environment, such as high speed railway scenario. In the iterative algorithm, the Kalman filter and data detection are jointed to estimate the time-varying channel, where the detection error is considered as part of the noise in the Kalman recursion in each iteration to reduce the effect of the detection error propagation. Moreover, the employed Kalman filter is from the canonical state space model, which does not include the parameters of the autoregressive (AR) model, so the proposed method does not need to estimate the parameters of AR model, whose accuracy affects the convergence speed. Simulation results show that the proposed method is robust to the fast time-varying channel, and it can obtain more gains compared with the available methods.