• Journal of Information Processing Systems
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• v.16 no.4
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• pp.750-759
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• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.783-786
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• 2021

In recent years, the use of LiDAR sensors is increasing as autonomous driving, robot control, and drones are considered more. Contrary to ordinary cameras, LiDAR sensors make it possible to handle challenging problems by calculating the distance between objects. This crucial characteristic makes more active research on deep learning models dealing with point clouds which are data of LiDAR. In this paper, among the schemes of using the point cloud, the Point-based approach is mainly discussed. Furthermore, future streams and insights can be considered by looking at solving methods and the limitations.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.573-589
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• 2017

Cloud computing is an attractive solution that can provide low cost storage and powerful processing capabilities for government agencies or enterprises of small and medium size. Yet the confidentiality of information should be considered by any organization migrating to cloud, which makes the research on relational database system based on encryption schemes to preserve the integrity and confidentiality of data in cloud be an interesting subject. So far there have been various solutions for realizing SQL queries on encrypted data in cloud without decryption in advance, where generally homomorphic encryption algorithm is applied to support queries with aggregate functions or numerical computation. But the existing homomorphic encryption algorithms cannot encrypt floating-point numbers. So in this paper, we present a mechanism to enable the trusted party to encrypt the floating-points by homomorphic encryption algorithm and partial trusty server to perform summation on their ciphertexts without revealing the data itself. In the first step, we encode floating-point numbers to hide the decimal points and the positive or negative signs. Then, the codes of floating-point numbers are encrypted by homomorphic encryption algorithm and stored as sequences in cloud. Finally, we use the data structure of DoubleListTree to implement the aggregate function of SUM and later do some extra processes to accomplish the summation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.291-295
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• 2006

As GPS equipments improve, one can acquire GPS data easily in the field. However, to obtain precise and accurate coordinates, post processing is additionally required and the processing needs high degree of skills. Besides, it is very common that we can't operate processing software in the field because required system environment is usually not prepared. The aim of this study is the development of internet-based GPS data processing service. For post processing, we use GIPSY developed by JPL. It has many advantages such as precise point positioning, which enables a rapid determination of receiver positions. The developed service in this study proceeds as following orders by interlocking GIPSY and internet service on a Linux platform: Users upload raw data files on the internet, then GIPSY runs automatically and then the user get the result in the field. We use an Apache Web Server as a hosting program and PHP is used in coding web pages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.637-640
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• 2022

To maximize performance in a high-performance multicore processor system. it is essential to enable effective data communication between processing cores. Data communication between processor nodes can be broadly classified into collective and point-to-point communications. Collective communication comprises scattering and gathering. This paper presents a efficient message scattering and gathering based on processing node status. In the proposed algorithms, the transmission order is changed according to the data size of the pre-existing communication, to reduce the waiting time required until the collective communications begin. From the simulation, the performances of the proposed message scattering and gathering algorithms were improved by approximately 71.41% and 69.84%.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.787-800
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• 2021

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.387-388
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• 2010

There is no generalized and systematic method yet to data pre-processing for point cloud data classification even if there have been lots of previous studies such as local maxima filter, morphology filter, slope based filter and so on. Main focus of this study is to present classification method for bare ground information from LiDAR data for the mountainous area.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.355-356
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• 2011

LiDAR data is very large, which contains an amount of redundant information. The information not only takes up a lot of storage space but also brings much inconvenience to the LIDAR data transmission and application. Therefore, a simplified method was proposed for LiDAR data based on quad-tree structure in this paper. The boundary contour lines of the buildings are displayed as building extraction. Experimental results show that the method is efficient for point's simplification according to the rule of mapping.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.55-63
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• 2020

Earthwork automation has emerged as a promising technology in the construction industry, and the application of earthwork automation technology is starting from the acquisition and processing of point cloud data of the site. Point cloud data has more than a million data due to vast extent of the construction site, and the processing time of the original point cloud data is critical because it takes tens or hundreds of hours to generate a Digital Terrain Model (DTM), and enhancement of the processing time can largely impact on the efficiency of the modeling. Currently, a benchmark program (BP) is actively used for the purpose of both point cloud data processing and BIM design as an integrated program in Korea, however, there are some aspects to be modified and refined. This study modified the BP, and developed an updated program by adopting a compile-based development environment, newly designed UI/UX, and OpenGL while maintaining existing PCD processing functions, and expended compatibility of the PCD file formats. We conducted a comparative test in terms of loading speed with different number of point cloud data, and the results showed that 92 to 99% performance increase was found in the developed program. This program can be used as a foundation for the development of a program that reduces the gap between design and construction by integrating PCD and earthwork BIM functions in the future.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.705-711
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• 2000

This paper presents a fast 3D mesh generation method using a surface based method with a stitching algorithm. This method uses the surface based method since the volume based method that uses 3D Delaunay triangulation can hardly deal with a large scale of scanned points. To reduce the processing time, this method also uses a stitching algorithm: after dividing the whole point data into several sections and performing mesh generation on individual sections, the meshes from several sections are stitched into one mesh. Stitching method prevents the surface based method from increasing the processing time exponentially as the number of the points increases. This method works well with different types of scanned points: a scattered type points from a conventional 3D scanner and a cross-sectional type from CT or MRI.