• International Journal of Precision Engineering and Manufacturing
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• v.8 no.2
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• pp.20-25
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• 2007

This paper describes a second-generation dual sine-grid surface encoder for 2-D position measurements. The surface encoder consisted of a 2-D grid with a 2-D sinusoidal pattern on its surface, and a 2-D angle sensor that detected the 2-D profile of the surface grid The 2-D angle sensor design of previously developed first-generation surface encoders was based on geometric optics. To improve the resolution of the surface encoder, we fabricated a 2-D sine-grid with a pitch of $10{\mu}m$. We also established a new optical model for the second-generation surface encoder that utilizes diffraction and interference to generate its measured values. The 2-D sine-grid was fabricated on a workpiece by an ultra precision lathe with the assistance of a fast tool servo. We then performed a UV-casting process to imprint the sine-grid on a transparent plastic film and constructed an experimental setup to realize the second-generation surface encoder. We conducted tests that demonstrated the feasibility of the proposed surface encoder model.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.3
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• pp.82-84
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• 2008

A surface encoder based on the Half-shaded square pattern (HSSP) is presented. The HSSP working as reference grid is composed of the straight lines which are easy to be fabricated and make measuring time short. Since the periodic cell is separated in ON/OFF by the $45^{\circ}$ straight line, the duration from the starting point of scanning to the first rising edge and the duty cycle of the pulse train vary with respect to the position of the starting point. And the relationship between X and Y position and the duration, and duty cycle is described in the simple linear equation. Therefore, it is possible to measure X and Y position with the measured duration and duty cycle without calculating load. Through the test set-up, the feasibility of the proposed surface encoder was verified. Also the future works for improvement of performance were suggested.

• Journal of Institute of Convergence Technology
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• v.2 no.1
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• pp.43-48
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• 2012

Generally, in-plane position of moving object is measured referring to the reference pattern attached to the object. From optical camera to magnetic reluctance probe, there are many ways detecting a variation of the periodical pattern. In this paper, the various operating principles developed for in-plane positioning are reviewed and compared each other. And, a novel method measuring large rotation as well as x, y linear displacements is suggested, including a detailed description of the overall system layout. It is a modified version of the surface encoder, which is a robust digital measuring method. From the surface encoder, the rotation of an object is measured indirectly through a compensated input of optical servo and independently of linear displacements. So, the operating range can be extended simply by enlarging the reference pattern, without magnifying the decoding units.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.193-200
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• 2019

The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.243-250
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• 2012

This paper proposes a 3D map-building algorithm using one LRF (Laser Range Finder) while a mobile robot is navigating on the slanted surface. There are several researches on 3D map buildings using the LRF. However most of them are performing the map building only on the flat surface. While a mobile robot is moving on the slanted surface, the view angle of LRF is dynamically changing, which makes it very difficult to build the 3D map using encoder data. To cope with this dynamic change of the view angle in build 3D map, IMU and balance filters are fused to correct the unstable encoder data in this research. Through the real navigation experiments, it is verified that the fusion of multiple sensors are properly performed to correct the slope angle of the slanted surface. The effectiveness of the balance filter are also checked through the hill climbing navigations.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.53-54
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• 2009

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.4
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• pp.51-56
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• 2006

This paper proposes a control algorithm, which is verified experimentally, for aspherical surface grinding and polishing. The algorithm provides simultaneous control of the position and interpolation of an aspheric curve. The nonlinear formula for the tool position was derived from the aspheric equation and the shape of the tool. The function was partitioned at specific intervals and the control parameters were calculated at each control section. The position, acceleration, and velocity at each interval were updated during the process. A position error feedback was introduced using a rotary encoder. The feedback algorithm corrected the position error by increasing or decreasing the feed speed. In the experimental verification, a two-axis machine was controlled to track an aspherical surface using the proposed algorithm. The effects of the control and process parameters were monitored. The results demonstrated that the maximum tracking error with tuned parameters was at the submicron level for concave and convex surfaces.