• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.95-107
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• 1999

The design models of a new product in general are created using clay models or wooden mock-ups. The reverse engineering(RE) technology enables us to quickly create the CAD model of the new product by capturing the surface of the model using laser digitizers or coordinate measuring machines. Rapid prototyping (RP) is another technology that can reduce the product development time by fabricating the physical prototype of a part using a layered manufacturing technique. In reverse engineering process, however, the digitizer generates an enormous amount of point data, and it is time consuming and also inefficient to create surfaces out of these data. In addition, the surfacing operation takes a great deal of time and skill and becomes a bottleneck. In rapid prototyping, a faceted model called STL file has been the industry standard for providing the CAD input to RP machines. It approximates the CAD model of a part using many planar triangular patches and has drawbacks. A novel procedure that overcomes these problems and integrates RE with RP is proposed. Algorithms that drastically reduce the point clouds data have been developed. These methods will facilitate the use of reverse engineered geometric data for rapid prototyping, and thereby will contribute in reducing the product development time.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.29-34
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• 2019

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.234-252
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• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.11-21
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• 2015

An Ortho-image is the production of removed geometrical displacement, which is generated the aerial image distortion and the relief displacement, etc., using the DSM (Digital Surface Model). Accordingly, the resolution of raw image and the accuracy of DSM will has significant impacts on the ortho-image accuracy. Since the latest DMCII250 aerial camera delivers the high resolution images with five centimeters Ground Sampling Distance(GSD), it expects to generate the high density point clouds and the high quality ortho-images. Therefore, this research has planned for reviewing the potentiality and accuracy of high quality ortho-image production. Following to proceed the research, DSM has been produced through the high density point cloud extracted from DMCII250 aerial image to supply of high density DSM by creation of ortho-image. The research results has been identified that images with the DSM brought out higher degrees in positional accuracy and quality of ortho-image, compared with the ortho-image, produced from the existing digital terrain map or DSM data.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.19-27
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• 2020

Recently, rapid and accurate 3D models creation is required in various applications using virtual reality and augmented reality technology. In this paper, we propose an on-site learning based shape deformation method which transforms the clothed 3D human model into the shape of an input point cloud. The proposed algorithm consists of two main parts: one is pre-learning and the other is on-site learning. Each learning consists of encoder, template transformation and decoder network. The proposed network is learned by unsupervised method, which uses the Chamfer distance between the input point cloud form and the template vertices as the loss function. By performing on-site learning on the input point clouds during the inference process, the high accuracy of the inference results can be obtained and presented through experiments.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• Information Systems Review
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• v.22 no.2
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• pp.1-24
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• 2020

Cloud computing technology is expanding its services to individual consumers through storage and applications. This study aims to compare the predisposing factors that affect the perceived value and the intention to use between users who have used or experienced services and those who have never experienced services from the perspective of benefit and sacrifice based on the value-based acceptance model. The results showed that the sacrifice factor (perceived cost) had a significant effect on perceived value and perceived value had a significant effect on intention to use, but showed a difference in perceived benefit. Perceived usefulness, ubiquity, and network effects had significant impact for experienced users' perceived value, but for inexperienced users, ubiquity did not have significant impact. In addition, usefulness was the most significant factor for experienced users while network effect was the same for inexperienced users. The results of this study suggest that consumers' intention to use personal cloud service is evaluated as a benefit and sacrifice point and a new attempt to re-examine the role of previous experience.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.451-454
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• 2007

There is an inseparable relation between human race and engineering work. As world developed into highly industrialized society, a diversity of large structures is being built up correspondently to limited topographical circumstance. Though large structures are national establishments which provide us with convenience of life, there are some disastrous possibilities which were never predicted such as ground subsidence and degradation. It is very difficult to analyze the volume of total metamorphosis with the relative displacement measurement system which is now used and it is impossible to know whether there is structural metamorphosis within a permissible range of design or not. In this research with an object of 13-year-old earthen dam, through generating point-cloud which has 3D spatial coordinates(x, y, z) of this dam by means of 3D Laser Scanning, we can get real configuration data of slanting surface of this dam with this method of getting a number of 3D spatial coordinates(x, y, z). It gives 3D spatial model to us and we can get various information of this dam such as the distance of slanting surface of dam, dimensions and cubic volume. It can be made full use of as important source material of reinforcement and maintenance works to detect previously the bulging of the dam through this research.