• The Transactions of the Korean Institute of Power Electronics
• /
• v.7 no.6
• /
• pp.579-586
• /
• 2002

With increase of servo motor in industrial and home application, a number of papers related to PMSM control have been researched. Among them, sensorless control schemes are especially concerned in a view point of their cost reduction. In a conventional approach, a rotor position is generally estimated by the integration of estimated rotor speed. In this method, because of their tight relationship between the amplitude of back-emf and rotor position, it is somewhat difficult to find two parameters at the same time. To solve this problem, a novel sensorless control scheme is proposed. It utilizes a back-emf normalization, so that it does not require the variables related with the amplitude of back-emf. The validity of the proposed control scheme is verified through experimental results.

• Journal of Advanced Marine Engineering and Technology
• /
• v.37 no.7
• /
• pp.784-791
• /
• 2013

Recent advanced IT made layout design fast and accurate by using algorithms. Layout design should be determined by considering the position of equipment with satisfying various space constraints and its component works with optimum performance. Especially, engine room layout design is performed with mother ship data, theoretical optimal solution, design requirements and several design constraints in initial design stage. Piping design is affected by position of equipment seriously. Piping design depends on experience of designer. And also piping designer should consider correlation of equipment and efficiency of space. In this study, space evaluation method has been used to evaluate efficiency of space. And also this study suggested object function for optimal piping route, Average Reservation Index(ARI), Estimated Piping Productivity(EPP) and with modified space evaluation method. In this study, optimum pipe routing system has been developed to reflect automated piping route with space efficiency and experience of piping designer. Engine room is applied to the design of the piping in order to confirm validity of the developed system.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.11
• /
• pp.1098-1102
• /
• 2014

Localization of aerial vehicles and map building of flight environments are key technologies for the autonomous flight of small UAVs. In outdoor environments, an unmanned aircraft can easily use a GPS (Global Positioning System) for its localization with acceptable accuracy. However, as the GPS is not available for use in indoor environments, the development of a SLAM (Simultaneous Localization and Mapping) system that is suitable for small UAVs is therefore needed. In this paper, we suggest a vision-based SLAM system that uses vision sensors and an AHRS (Attitude Heading Reference System) sensor. Feature points in images captured from the vision sensor are obtained by using GPU (Graphics Process Unit) based SIFT (Scale-invariant Feature Transform) algorithm. Those feature points are then combined with attitude information obtained from the AHRS to estimate the position of the small UAV. Based on the location information and color distribution, a Gaussian process model is generated, which could be a map. The experimental results show that the position of a small unmanned aircraft is estimated properly and the map of the environment is constructed by using the proposed method. Finally, the reliability of the proposed method is verified by comparing the difference between the estimated values and the actual values.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.2
• /
• pp.177-184
• /
• 2013

This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

• Journal of the Korean Society of Industry Convergence
• /
• v.18 no.3
• /
• pp.181-190
• /
• 2015

This paper presents a new approach to control the position of robot arm in workspace a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme was applied. Since parameters of the robot arm such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters was considered as a external disturbance force. To identify the known parameters, an improved robust control algorithm is directly derived from the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing forces coming from the difference between the actual and the estimated system parameters. Numerical examples are shown using SCARA arm with four joints.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.7
• /
• pp.40-47
• /
• 2014

This paper proposes a matching method of GIS lane information with a vehicle position using camera image to reduce DGPS error. Images of straight road are taken using a camera that is installed on the front center of the vehicle, and the distance between the vehicle and the lane are estimated using the images. The current GIS lane data is matched by comparing the estimated distance and the measured distance using a DGPS. Inverse perspective mapping is used to minimize the error of image processing from the heading angle, and single buffering method is applied to decide the exact moment of GIS match. Through practical test on the highway, feasibility of the GIS matching using camera image is confirmed.

• Applied Science and Convergence Technology
• /
• v.26 no.6
• /
• pp.201-207
• /
• 2017

An electron-excited temperature ($T_{ex}$) is not determined by the Boltzmann plots only with the spectral data of $4p{\rightarrow}4s$ in an Ar-plasma jet operated with a low frequency of several tens of kHz and the low voltage of a few kV, while $T_{ex}$ can be obtained at least with the presence of a high energy-level transition ($5p{\rightarrow}4s$) in the high-voltage operation of 8 kV. The optical intensities of most spectra that are measured according to the voltage and the measuring position of the plasma column increase or decay exponentially at the same rate as that of the intensity variation; therefore, the excitation temperature is estimated by comparing the relative optical-intensity to that of a high voltage. In the low-voltage range of an Ar-jet operation, the electron-excitation temperature is estimated as being from 0.61 eV to 0.67 eV, and the corresponding radical density of the Ar-4p state is in the order of $10^{10}{\sim}10^{11}cm^{-3}$. The variation of the excitation temperature is almost linear in relation to the operation voltage and the position of the plasma plume, meaning that the variation rates of the electron-excitation temperature are 0.03 eV/kV for the voltage and 0.075 eV/cm along the plasma plume.

• Current Optics and Photonics
• /
• v.5 no.5
• /
• pp.514-523
• /
• 2021

Methods for absolute depth estimation have received lots of interest, and most algorithms are concerned about how to minimize the difference between an input defocused image and an estimated defocused image. These approaches may increase the complexity of the algorithms to calculate the defocused image from the estimation of the focused image. In this paper, we present a new method to recover depth of scene based on a sharpness-assessment algorithm. The proposed algorithm estimates the depth of scene by calculating the sharpness of deconvolved images with a specific point-spread function (PSF). While most depth estimation studies evaluate depth of the scene only behind a focal plane, the proposed method evaluates a broad depth range both nearer and farther than the focal plane. This is accomplished using an asymmetric aperture, so the PSF at a position nearer than the focal plane is different from that at a position farther than the focal plane. From the image taken with a focal plane of 160 cm, the depth of object over the broad range from 60 to 350 cm is estimated at 10 cm resolution. With an asymmetric aperture, we demonstrate the feasibility of the sharpness-assessment algorithm to recover absolute depth of scene from a single defocused image.

• Communications for Statistical Applications and Methods
• /
• v.30 no.4
• /
• pp.369-388
• /
• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.444-450
• /
• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.