• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.34-44
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• 2024

Recently, due to abnormal climate caused by climate change, natural disasters such as floods, landslides, and soil outflows are rapidly increasing. In Korea, more than 63% of the land is vulnerable to slope disasters due to the geographical characteristics of mountainous areas, and in particular, Quarry mines soil and rocks, so there is a high risk of landslides not only inside the workplace but also outside.Accordingly, this study built a DEM using UAV and aviation LiDAR for monitoring the quarry, conducted a time series change analysis, and proposed an optimal DEM construction method for monitoring the soil collection site. For DEM construction, UAV and LiDAR-based Point Cloud were built, and the ground was extracted using three algorithms: Aggressive Classification (AC), Conservative Classification (CC), and Standard Classification (SC). UAV and LiDAR-based DEM constructed according to the algorithm evaluated accuracy through comparison with digital map-based DEM.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.21-30
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• 2024

In this paper, we propose a method to build a sample dataset of the features of eight sensor-only facilities built as infrastructure for autonomous cooperative driving. The feature extracted from point cloud data acquired by LiDAR and build them into the sample dataset for recognizing the facilities. In order to build the dataset, eight sensor-only facilities with high-brightness reflector sheets and a sensor acquisition system were developed. To extract the features of facilities located within a certain measurement distance from the acquired point cloud data, a cylindrical projection method was applied to the extracted points after applying DBSCAN method for points and then a modified OTSU method for reflected intensity. Coordinates of 3D points, projected coordinates of 2D, and reflection intensity were set as the features of the facility, and the dataset was built along with labels. In order to check the effectiveness of the facility dataset built based on LiDAR data, a common CNN model was selected and tested after training, showing an accuracy of about 90% or more, confirming the possibility of facility recognition. Through continuous experiments, we will improve the feature extraction algorithm for building the proposed dataset and improve its performance, and develop a dedicated model for recognizing sensor-only facilities for autonomous cooperative driving.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.173-178
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• 2024

This study presents a technique for assessing the dimensional quality of assembly parts in Prefabricated Steel Structures (PSS) using a stereo vision sensor. The stereo vision system captures images and point cloud data of the assembly area, followed by applying image processing algorithms such as fuzzy-based edge detection and Hough transform-based circular bolt hole detection to identify bolt hole locations. The 3D center positions of each bolt hole are determined by correlating 3D real-world position information from depth images with the extracted bolt hole positions. Principal Component Analysis (PCA) is then employed to calculate coordinate axes for precise measurement of distances between bolt holes, even when the sensor and structure orientations differ. Bolt holes are sorted based on their 2D positions, and the distances between sorted bolt holes are calculated to assess the assembly part's dimensional quality. Comparison with actual drawing data confirms measurement accuracy with an absolute error of 1mm and a relative error within 4% based on median criteria.

• Applied Chemistry for Engineering
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• v.35 no.3
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• pp.214-221
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• 2024

Polyether ketone (PEEK) has been widely used in membranes because of its excellent thermal stability, chemical resistance, and significant mechanical strength. However, the melting temperature is very high, making it difficult to find suitable solvents. Therefore, in this study, PEEK and benzophenone (DPK) were used as diluents to prepare a membrane with excellent mechanical strength and chemical stability using the thermally induced phase separation (TIPS) method to compensate for the shortcomings of PEEK membrane preparation and achieving the highest performances. The optimal membrane manufacturing conditions were confirmed through the crystallization temperature and cloud point according to the polymer content through the phase diagram. Subsequently, the morphological changes of the membrane, influenced by the polymer and diluent content, were confirmed through scanning electron microscopy (SEM). Additionally, the membrane thickness tended to increase with higher polymer content. Tensile strength and DI-water permeability tests were conducted to confirm the mechanical strength and permeability of the membrane. Through the previous characteristic evaluation, it was confirmed that the membrane using PEEK had excellent mechanical strength and permeability.

• Applied Chemistry for Engineering
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• v.21 no.5
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• pp.542-547
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• 2010

n-Paraffin and saturated fatty acid methyl esters in the diesel and bio-diesel fuel crystallize at low temperature. Many articles have addressed various solutions for the low temperature crystallization problem and one of them is the use of methacrylate copolymers. In this work, we synthesized a series of copolymers in the reaction condition of 70 : 30 molar ratio of lauryl methacrylate (LMA) (or stearyl methacrylate (SMA)) and alkyl methacrylates. The structures of the copolymers were characterized by $^1H$-NMR and FT-IR spectroscopy, and the molecular weight of copolymers were obtained from Gel Permeation Chromatography (GPC) method. The concentrations of additives were 500~1000 ppm and 1000~10000 ppm in diesel fuels and bio-diesel fuel (BD5 and BD20), respectively. The addition of copolymers changes the many properties of fuel such as the pour point (PP), cloud point (CP) and cold filtering plugging point (CFPP). For example, the low temperature properties of the copolymers containing SMA ($PSMAmR_2n$) were excellently improved about 15, 7, and $10^{\circ}C$ for PP, CP and CFPP, respectively.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.354-367
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• 2020

This study is on the consideration of the scope of application of art therapy and fractal through the review of literature at home and abroad. The complex system is the opposite of the Euclidean system, a concept suitable for understanding the contemporaries with ambiguous boundaries and decentralized phenomena. The self-similarity and inventiveness of fractal, the geometry of nature, is used as fractal art in art as well as tree trunk, cloud and plant, especially in art therapy, fractal is considered to be available in the field of mandala and neuroscience. From brain-based research to mandala, exposure to natural patterns, clinical diagnosis through fractal analysis and software development, fractal has potential elements that can be developed in art therapy. Fractal, which is easy to link with computers due to its nature, is a necessary study at this point when non-face-to-face contact with the Corona virus is recommended. Currently, research on fractal art therapy is insufficient in Korea. Therefore, this research is intended to present as a basis for scientific and objective diagnostic tools and treatment at clinical sites using art therapy using fractal.

• Analytical Science and Technology
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• v.24 no.6
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• pp.467-476
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• 2011

Solubility data of carbon dioxide ($CO_2$) in the imidazolium-based ionic liquids with methylsulfate anion are presented at pressures up to about 45 MPa and at temperatures between 303.15 K and 343.15 K. The ionic liquids studied in this work were 1-ethyl-3-methylimidazolium methylsulfate ([emim][$mSO_4$]), 1-butyl-3-methylimidazolium methylsulfate ([bmim][$mSO_4$]). The solubilities of $CO_2$ were determined by measuring the bubble point or cloud point pressures of the binary mixtures using a high-pressure equilibrium apparatus equipped with a variable-volume view cell. The equilibrium pressure increased very steeply at high $CO_2$ compositions. The $CO_2$ solubility in ionic liquids increased with increase of the total length of alkyl chains attached to the imidazolium cation of the ionic liquids. The phase equilibrium data for the $CO_2$ + ionic liquid systems have been correlated using the Peng-Robinson equation of state.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.243-251
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• 2016

LiDAR data processing steps include noise removal, filtering, classification, segmentation, shape recognition, modeling, and quality assessment. This paper focuses on modeling and accuracy evaluation of 3D objects with curved surfaces. The appropriate modeling functions were determined by analyzing surface patch shape. Existing methods for modeling curved surface features require linearization, initial approximation, and iteration of the non-linear functions. However, proposed method could directly estimate the unknown parameters of the modeling functions. The results demonstrate feasibility of the proposed method. The proposed method was applied to the simulated and real building data of hemi-spherical and semi-cylindrical surfaces. The parameters and accuracy of the modeling functions were estimated. It is expected that the proposed method would contribute to automatic modeling of various objects.