• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.184-198
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• 2020

Decisions of the design speed, MCR, and engine capacity have been empirically made by assuming the value termed the sea margin. Due to ambiguity regarding the effect of some factors on the sea margin, the value has been commonly decided based on experience. To evaluate the value from a new viewpoint, it is necessary to construct an approach to estimate the sea margin through an objective procedure based on a physical and mathematical model. In this paper, a framework to estimate the actual sea margin of an LNGC based on the maneuvering equations of motion is suggested by considering the hull, propeller, rudder, and given sea route under wind and waves. The fouling effect is additionally quantified as the increase of total resistance by considering the re-docking period. The operation data is reviewed to amend the increase of the total resistance considering the speed loss of a ship. Finally, the factor of how much the resistance increases due to fouling is newly obtained for the vessel. Based on the comparison of the estimated sea margin with the empirical range of the sea margin, the constructed framework is regarded as feasible.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.829-833
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• 2001

In a conventional speed sensorless stator flux­oriented (SFO) induction machine drive system, the estimated speed is delayed in transients by the use of a low pass filter. This paper investigates the problem of a conventional speed sensorless SFO system due to the delay of the estimated speed in the field weakening region. In addition, this paper proposes a method to estimate exactly speed by using Kalman filter. The proposed method is verified by simulation and experiment with a 5-hp induction motor drive.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.1
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• pp.169-175
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• 2006

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.289-293
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• 2004

The sensorless speed control technique for BLDCM using digital IP control is proposed in this paper for advanced speed characteristic which is robust for loads. The sensorless drive of BLDCM using terminal voltages is affected by load or speed because it uses analog filters to estimate the rotor position. For this reason, the robust speed controller with the accurate rotor position estimator is needed for sensorless control which is robust to load and insensitive to motor parameters. The constant speeds robust to load variation and the stable sensorless control of BLDCM robust to the increase or decrease of speed with constant load are implemented using digital IP control in this paper. The validity to these is established with experimentation.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2487-2489
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• 1999

This paper proposes a wide speed range sensorless vector control strategy. At low speed region, the difference of high frequency impedances is used in order to estimate the rotor flux angle. At high speed region this algorithm is combined with the adaptive observer. It enables the stable operation even at zero speed under the rated load condition This is verified by experimental results.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.47-48
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• 2005

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.330-333
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• 2009

This paper proposes a method to estimate the flow speed of pedestrians in surveillance videos. In the proposed method, the average moving speed of pedestrians is measured by estimating the size of real-world motion from the observed motion vectors. For this purpose, pixel-to-meter conversion factors are calculated from camera geometry. Also, the height information, which is missing because of camera projection, is predicted statistically from simulation experiments. Compared to the previous works for flow speed estimation, our method can be applied to various camera views because it separates scene parameters explicitly. Experiments are performed on both simulation image sequences and real video. In the experiments on simulation videos, the proposed method estimated the flow speed with average error of about 0.1m/s. The proposed method also showed a promising result for the real video.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.768-773
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• 1998

The speed control of SRM(Switched Reluctance Motor) needs the accurate position and speed data of rotor. This information is generally provided by a shaft encoder or resolver. In some cases, the environment is which the motor operates may cause difficulties in maintaining the satisfactory position detection performance. Therefore, the elimination of the position and speed sensor has gained wide attention. In this paper, a new algorithm for estimation of rotor position and speed is described for the SRM drives. This method uses is nonlinear adaptive observer using the MRAS(Model Reference Adaptive System). The observer is proved by Lyapunov Stability Theory. This algorithm was implemented with a TMS320C31 DSP. Experiment results prove that the observer is able to estimate the speed and position with a little errors.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.18-23
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• 2002

In this paper, The torque of CNC spindle motor during machining is estimated without speed measuring sensor. The CNC spindle system is divided into two parts, the induction spindle motor part and mechanical part. In mechanical part the variation of the frictional force due to the increment of the cutting torque and the effect of damping coefficient is investigated. Damping coefficient is found to be a function of spindle speed and not influenced by the weight of the load, while frictional force is a function of both the cutting torque and spindle speed. Experimental formulars are drawn for damping coefficient as a function of spindle speed and frictional force as a function of both cutting torque and spindle speed respectively, to estimate the cutting torque accurately. Graphical programming is used to implement the suggested algorithm, to monitor the torque of an induction motor in real time. Torque of the spindle induction motor is well monitored with 3% error range under various cutting conditions.