• Journal of KIBIM
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• v.10 no.4
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.345-346
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• 2023

As the shape and size of detectable objects diversifying recognition and segmentation algorithms have been developed to acquire accurate shape information. Although a high density of data captured by the repetition of scanning improves the accuracy of algorithms the high dense data decreases the efficiency due to its large size. This paper proposes standardization algorithms using the feature of structural members on indoor point cloud data to improve the process. First of all we determine the reduction rate of the density based on the features of the target objects then the data reduction algorithm compresses the data based on the reduction rate. Second the data arrangement algorithm rotates the data until the normal vector of data is aligned along the coordinate axis to allow the following algorithms to operate properly. Final the data arrangement algorithm separates the rotated data into their leaning axis. This allows reverse engineering of indoor point clouds to obtain the efficiency and accuracy of refinement processes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.435-438
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• 2001

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore, it becomes a major issue to handle the huge amount and various types of point data. To generate a CAD model from scanned point data efficiently, these point data should be well arranged through point data handling processes such as data reduction and segmentation. This paper proposes a new point data handling method using 3D grids. The geometric information of a part is extracted from point cloud data by estimating normal values of the points. The non-uniform 3D grids for data reduction and segmentation are generated based on the geometric information. Through these data reduction and segmentation processes, it is possible to create CAD models autmatically and efficiently. The proposed method is applied to two quardric medels and the results are discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1283-1287
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• 2003

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore. it becomes a important to handle the huge amount and various types of point data to generate a surface model efficiently. This paper proposes a new triangular mesh generation method using 3D grids. The geometric information of a part can be obtained from point cloud data by estimating normal values of the points. In our research, the non-uniform 3D grids are generated first for feature based data reduction based on the geometric information. Then, triangulation is performed with the reduced point data. The grid structure is efficiently used not only for neighbor point search that can speed up the mesh generation process but also for getting surface connectivity information to result in same topology surface with the point data. Through this integrated approach, it is possible to create surface models from scanned point data efficiently.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.105-106
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• 2020

Recently, a method of deriving an efficient 2D floor plan has been attracting attention for remodeling of old buildings with inaccurate 2D floor plans, and thus, studies on reverse engineering of indoor Point Cloud Date(PCD) have been actively conducted. However, in the case of a indoor PCD, due to interference of indoor objects, available equipment is limited to Mobile Laser Scanner(MLS), which causes a efficiency reduction of data processing. Therefore, this study proposes an automatic derivation algorithm for 2D floor plan of indoor PCD based on pixelation. First, the scanned indoor PCD is projected on the XY coordinate plane. Second, a point distribution of each pixel in the projected PCD is derived using a pixelation. Lastly, 2 floor plan derivation based on the algorithm is performed.

• Elastomers and Composites
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• v.41 no.2
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• pp.116-124
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• 2006

Cloud-point data to $160^{\circ}C$ and 1,000 bar are presented with poly(ethylene-co-15.3 mole% octene) copolymers ($PEO_{15}$) in pure 1-octene and mixtures of ethylene - 1-octene. The cloud-point curves for $PEO_{15}$ - ethylene - 1-octene mixture dramatically increase in pressure to as high as 1,000 bar with an increasing ethylene concentration. At ethylene concentrations less than 18 wt%, the ternary mixture has bubble- and cloud-point curves. As the ethylene concentration of the ternary mixture increases, the bubble-point curve and the single-phase region reduce. The reduction in the single phase region with increasing ethylene concentrations is the result of reduced dispersion interactions between $PEO_{15}$ and the mixed solvent. The single-phase region decreases with increasing temperatures when ethylene concentrations are lower than 36 wt%, whereas the single-phase region increases with temperatures at ethylene concentrations greater than 50 wt%. At ethylene concentrations greater than 50 wt% the effect of the polar interactions of the mixed solvent, which is unfavorable to dissolve PEO, is greater than the effect of the density of the mixed solvent. Therefore, the cloud-point pressures increase with a decreasing temperature. However, at ethylene concentrations less than 50 wt%, the cloud-point pressures decrease with temperature, because the effect of the polar interactions is less than the density effect.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.

• 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.

• 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.