• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.352-362
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• 2017

This paper proposes an algorithm for automatically converting and displaying rainfall radar data on a 3D GIS platform. The weather information displayed like rainfall radar data is updated frequently and large-scale. Thus, in order to efficiently display the data, an algorithm to convert and output the data automatically, rather than manually, is required. In addition, since rainfall data is extracted from the space, the use of the display image fused with the 3D GIS data representing the space enhances the visibility of the user. To meet these requirements, this study developed the Auto Data Converter application that analyzes the raw data of the rainfall radar and convert them into a universal format. In addition, Unity 3D, which has good development accessibility, was used for dynamic 3D implementation of the converted rainfall radar data. The software applications developed in this study could automatically convert a large volume of rainfall data into a universal format in a short time and perform 3D modeling effectively according to the data conversion on the 3D platform. Furthermore, the rainfall radar data could be merged with other GIS data for effective visualization.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.170-176
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• 2016

Purpose: This study aimed to improve the method for determining the position of the center of gravity for agricultural tractors. Methods: The proposed method uses trigonometric functions and coordinate transformation. Data were measured according to the ISO 789-6 test procedures for the center of gravity of agricultural tractors. The height calculated using the proposed method was compared with that determined from an AutoCAD drawing. To find the center of gravity of the tractor, the algorithm for finding the intersection of the two lines was used. Results: The vertical height from the ground to the center of gravity is 682.06 mm. The vertical coordinates obtained from the calculation and the drawing were the same. Conclusions: The developed method uses trigonometric and polar coordinate transformation. The method was compared and verified with the AutoCAD drawing results. The results indicate that users can apply this developed method instead of the plotting method which is an inconvenient and time-consuming. Further, users can program Microsoft Excel to easily determine the vertical coordinate. In addition, researchers will propose this method to the ISO as a standard method for determining the center of gravity in accordance with ISO 789-6.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.157-163
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• 2022

Recently, with the development of automation in the industrial field, research on anomaly detection is being actively conducted. An application for anomaly detection used in factory automation is camera-based defect inspection. Vision camera inspection shows high performance and efficiency in factory automation, but it is difficult to overcome the instability of lighting and environmental conditions. Although camera inspection using deep learning can solve the problem of vision camera inspection with much higher performance, it is difficult to apply to actual industrial fields because it requires a huge amount of normal and abnormal data for learning. Therefore, in this study, we propose a network that overcomes the problem of collecting abnormal data with 72 geometric transformation deep learning methods using only normal data and adds an outlier exposure method for performance improvement. By applying and verifying this to the MVTec data set, which is a database for auto-mobile parts data and outlier detection, it is shown that it can be applied in actual industrial sites.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.52.1-52.1
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• 2015

We have recently installed SQUEAN on the 82 inch telescope at the McDonald Observatory, USA. This instrument consists of an ANDOR CCD camera, a focal reducer, an electronic box, an auto guiding system and a new filter wheel which holds up to 20 filters. Currently the filter wheel is equipped with Johnson-Cousins BVRI filters, SDSS rizY and isiz filters, and 50nm medium band pass filters (M625(625nm), M675(675nm), M725(725nm), M775(775nm), M825(825nm), M875(875nm), M925s(925nm), M975(975nm), and M1025(1025nm)). Our medium band pass filter system is suitable with SED fitting. Filter transformation methods are essential for time-domain observations including transient objects, e.g., supernovae, variable stars, and solar system bodies. In this work, we develop a series of equations to convert the open clusters photometry data within these filter systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.713-725
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• 2022

Mostly, artificial intelligence does not show any definite change in emotions. For this reason, it is hard to demonstrate empathy in communication with humans. If frequency modification is applied to neutral emotions, or if a different emotional frequency is added to them, it is possible to develop artificial intelligence with emotions. This study proposes the emotion conversion using the Generative Adversarial Network (GAN) based voice frequency synthesis. The proposed method extracts a frequency from speech data of twenty-four actors and actresses. In other words, it extracts voice features of their different emotions, preserves linguistic features, and converts emotions only. After that, it generates a frequency in variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) in order to make prosody and preserve linguistic information. That makes it possible to learn speech features in parallel. Finally, it corrects a frequency by employing Amplitude Scaling. With the use of the spectral conversion of logarithmic scale, it is converted into a frequency in consideration of human hearing features. Accordingly, the proposed technique provides the emotion conversion of speeches in order to express emotions in line with artificially generated voices or speeches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.561-570
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• 2009

Ultra-wideband communication systems based on impulse radio have merits that are possible for the high data rate transmission, high resolution ranging are positioning system. Conventionally, in order to accomplish these features, the high-speed ADC (Analog to Digital Convertor) is necessary to apply radio determination system operating in time domain. However, considering low rate - wireless personal area network (LR-WPAN) aims to low-cost hardware implementation, the expensive ADC converting GHz sampling per second is not appropriate. So, this paper introduces a low complex AR (Auto Regressive) model based non-coherent ranging scheme operating in frequency domain with using low-speed ADC utilizing analog Voltage Control Oscillator (VCO) mode for the frequency domain transformation. To verify the superiority of the proposed ranging and location algorithm working in frequency domain, the suggested IEEE 802.15.4a TG channel model is used to exploit affirmative features of the proposed algorithm with conducting the simulation results.

• Journal of Korea Multimedia Society
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• v.9 no.6
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• pp.709-719
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• 2006

To protect against unlawful reproductions and distribution, the current paper proposes a digital watermarking technique for architectural drawings produced using a CAD system. First, the POLYLINEs are extracted from the drawing; then, an adaptive algorithm is used to embed a watermark in the characteristics of each POLYLINE. Next, the CIRCLEs are embedded using an adaptive watermarking algorithm related to the radius of circle from drawing. The proposed watermarking scheme is robust to various attacks, such as the geometrical transformation. Additionally, the proposed method satisfies the requirement of transparency for CAD program. It used AutoCAD 2002, which is commonly used as a CAD program for experiments. Experimental results confirmed the robustness and invisibility of the embedded watermarks in several conversions of an architectural drawing.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.2
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• pp.117-131
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• 2007

Representation of building internal space is an active research area as the need for more geometrically accurate and visually realistic increases. 3 dimensional representation is common ground of research for disciplines such as computer graphics, architectural design and engineering and Geographic Information System (GIS). In many cases CAD plans are the starting point of reconstruction of 3D building models. The main objectives of building reconstruction in GIS applications are visualization and spatial analysis. Hence, CAD plans need to be preprocessed and edited to adapt to the data models of GIS SW and then georeferenced to enable spatial analysis. This study automated the preprocessing of CAD data using AutoCAD VBA (Visual Basic Application), and the processed data was topologically restructured for further analysis in GIS environment. Accuracy of georeferencing CAD data was also examined by comparing the results of coordinate transformation by using digital maps and GPS measurements as the sources of ground control points. The reconstructed buildings were then applied to visualization and network modeling.