• 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.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.664-669
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• 2014

As the prevalence of cloud computing increases, various cloud service types have emerged, such as IaaS, PaaS, and SaaS. The growth and diversification of these cloud services has also resulted in the development of technology for cloud service brokers (CSBs), which serve as intermediate cloud services that can assist cloud tenants (users) in deploying services that fit their requirements. In order to broker cloud services, CSBs require the specification of structural models in order to facilitate the analysis and search for cloud services. In this study, we propose a variability-based service analysis model (SAM) that can be used to describe various cloud services. This model is based on the concept of variability in the software product line and represents the commonality and variability of cloud services by binding variants to each variation point that exists in the specification, quality, and pricing of the services. We also propose a virtual cloud bank architecture as a CSB that serves as an intermediate to provides tenants with appropriate cloud services based on the SAM.

• Bulletin of the Korean Chemical Society
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• v.32 no.8
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• pp.2637-2642
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• 2011

A new ultrasonic-assisted micellar extraction and cloud-point pre-concentration method was developed for the determination of major saikosaponins, namely saikosaponins -A, -C and -D, in Radix Bupleuri by high performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD). The non-ionic surfactant Genapol X-080 (oligoethylene glycol monoalkyl ether) was chosen as the extraction additive and parameters affecting the extraction efficiency were optimized. The highest yield was obtained with 10% (w/v) Genapol X-080, a liquid/solid ratio of 200:1 (mL/g) and ultrasonic-assisted extraction for 40 min. In addition, the optimum cloud-point pre-concentration was reached with 10% sodium sulfate and equilibration at $60^{\circ}C$ for 30 min. Separation was achieved on an Ascentis Express C18 column (100 ${\times}$ 4.6 mm i.d., 2.7 ${\mu}M$) using a binary mobile phase composed of 0.1% acetic acid and acetonitrile. Saikosaponins were detected by ELSD, which was operated at a $50^{\circ}C$ drift tube temperature and 3.0 bar nebulizer gas ($N_2$) pressure. The water-based solvent modified with Genapol X-080 showed better extraction efficiency compared to that of the conventional solvent methanol. Recovery of saikosaponins ranged from 93.1 to 101.9%. An environmentally-friendly extraction method was successfully applied to extract and enrich major saikosaponins in Radix Bupleuri.

• Cartoon and Animation Studies
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• s.48
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• pp.199-215
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• 2017

Recently, a drone is used for the general purpose application although the drone was builtfor the military purpose. A drone is actively used for the creation of contents, and an image acquisition. In this paper, we develop a 3D library module platform using 3D mesh model data, which is generated by a drone image and its point cloud. First, a lot of 2D image data are taken by a drone, and a point cloud data is generated from 2D drone images. A 3D mesh data is acquired from point cloud data. Then, we develop a service library platform using a transformed 3D data for multi-purpose uses. Our platform with 3D data can minimize the cost and time of contents creation for special effects during the production of a movie, drama, or documentary. Our platform can contribute the creation of experts for the digital contents production in the field of a realistic media, a special image, and exhibitions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5695-5706
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• 2015

Recently adoption of BIM technology for building renovation and remodeling has been increased in construction industry. However most buildings have trouble in 2D drawing-based BIM modeling, because 2D drawings have not been updated real situations continually. Applying reverse engineering, this study analysed the point cloud data structure and the process for guideline of reverse engineering of architectural MEP, and deducted the relating considerations. To active usage of 3D scanning technique in domestic, the objective of this study is to analyze the point cloud data processing from real site with terrestrial LiDAR and the process from data gathering to data acquisition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2443-2450
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• 2014

In the motion recognition system using depth image, the depth image is converted to the real world formed 3D point cloud data for efficient algorithm apply. And then, output depth image is converted by the projective world after algorithm apply. However, when coordinate conversion, rounding error and data loss by applied algorithm are occurred. In this paper, when convert 3D point cloud data to depth image, we proposed efficient conversion method and its hardware implementation without rounding error and data loss according image size change. The proposed system make progress using the OpenCV and the window program, and we test a system using the Kinect in real time. In addition, designed using Verilog-HDL and verified through the Zynq-7000 FPGA Board of Xilinx.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.204-211
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• 2021

In this study, information on vegetation was collected using a point cloud through a 3-D Terrestrial Lidar Scanner, and the physical shape was analyzed by reconfiguring the object based on the refined data. Each filtering step of the raw data was optimized, and the reference volume and the estimated results using the Alpha Shape and Voxel techniques were compared. As a result of the analysis, when the volume was calculated by applying the Alpha Shape, it was overestimated than reference volume regardless of data filtering. In addition, the Voxel method to be the most similar to the reference volume after the 8th filtering, and as the filtering proceeded, it was underestimated. Therefore, when re-implementing an object using a point cloud, internal voids due to the complex shape of the target object must be considered, and it is necessary to pay attention to the filtering process for optimal data analyzed in the filtering process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.611-622
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• 2020

As the advancement of technologies on indoor positioning systems and measuring devices such as LiDAR (Light Detection And Ranging) and cameras, the demands on analyzing and searching indoor spaces and visualization services via virtual and augmented reality have rapidly increasing. To this end, it is necessary to model 3D objects from measured data from real-world structures. In addition, it is important to store these structured data in standardized formats to improve the applicability and interoperability. In this paper, we propose a method to construct IndoorGML data, which is an international standard for indoor modeling, from point cloud data acquired from LiDAR sensors. After examining considerations that should be addressed in IndoorGML data, we present a construction method, which consists of free space extraction and connectivity detection processes. With experimental results, we demonstrate that the proposed method can effectively reconstruct the 3D model from point cloud.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.483-493
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• 2022

It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands. In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role. However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming. Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly. The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping. The obtained results were then verified through manually acquired data from the sorghum samples. This study measured the plant height, plant crown diameter and the panicle height and diameter. The R² of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively. The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping. The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.84-88
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• 2023

This study proposes an automated tilt measurement method using point cloud for buildings. The proposed method consists of two main steps: 1) exterior wall plane extraction, and 2) edge estimation and angle calculation. To validate the performance of the proposed method, the algorithm is applied to a target building, of which the estimated tilt values are compared with those obtained from a total station, a commonly used tool for tilt measurement. The result shows that most estimated tilt values are within the maximum and minimum ranges of the total station measurement, suggesting that the proposed algorithm provides sufficient measurement accuracy. Furthermore, the proposed method is shown to be automated and reliable as well as free from human-induced errors compared to the total station.