• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1577-1585
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• 2022

As the interest in the 4th industrial revolution and metaverse increases, metaverse with multi edge structure is proposed and noted. Metaverse is a structure that can create digital doctor-like system through a large amount of image processing and data transmission in a multi edge system. Since metaverse application requires calculating performance, which can reconstruct 3-D space, edge hardware's insufficient calculating performance has been a problem. To provide streamable AI software in runtime, image processing, and data transmission, which is edge's loads, needs to be lightweight. Also lightweight at the edge leads to power consumption reduction of the entire metaverse application system. In this paper, we propose collaborative edge-cloud image processing with remote image processing method and Region of Interest (ROI) to overcome edge's power performance and build streamable and runtime executable AI software. The proposed structure was implemented using a PC and an embedded board, and the reduction of time, power, and network communications were verified.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.335-341
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• 2023

In this study, four algorithms (Standard, Bone, Dual volume, and Stent Follow up) were applied to the image of the aneurysm in cerebral angiography to reconstruct the image in 3D, and quantitatively evaluate Noise, SNR, and CNR based on the reconstructed image to find out the optimal algorithm. As an analysis method, Image J program, which can analyze images and calculate area and pixel values, was used for images reconstructed with four algorithms. In order to obtain Noise, SNR, and CNR, the region of interest (ROI) is measured by designating the point where the abnormal artery (aneurysm) is located and the surrounding normal artery in the image are measured, and the mean value and SD value are obtained. Background noise was set to two surrounding normal artery to increase reliability. The values of SNR and CNR were calculated based on the given formula. As a result, the noise was the lowest in the stent follow-up algorithm, and the SNR and CNR were the highest. Therefore, the stent follow-up algorithm is judged to be the most appropriate algorithm. The data of this study are expected to be useful as basic data for 3D image evaluation of the vascular and aneurysm in cerebral angiography, and it is believed that appropriate algorithm changes will serve as an opportunity to further improve image quality.

• Korean Journal of Culture and Social Issue
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• v.17 no.2
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• pp.175-197
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• 2011

This study aimed to expand and reconstruct the Driving Behavior Determinants' factors in order to confirm the relationship between Driving Behavior Determinants(DBD) and drivers' reckless driving behavior level. To expand the structure of DBD, drivers anger, introversion and type A characteristics were added, which were never considered as related factors in existing DBD studies before. The correlations between the new factors of DBD and reckless driving behavior(includes driver's personal records of driving experiences for the last three years) were verified. A factor analysis result showed us that new DBD questionnaire consists of five factors such as, 'Problem Evading', 'Benefits/Sensation Seeking', 'Anti-personal Anxiety', 'Anti-personal Anger', and 'Aggression'. Also, reckless driving behavior consists of 'Speeding', 'Inexperienced Coping', 'Wild Driving', 'Drunken Driving', and 'Distraction'. The result of correlation between the DBD and reckless driving behavior indicates that inappropriate level of DBD is highly correlated with dangerous driving behavior and strong possibilities of traffic accidents. Based on these results, we might be able to discriminate drivers according to DBD level and predict their reckless driving behavior through a standardization procedure. Futhermore, this will make us to provide drivers differentiated safety education service.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.23-28
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• 2023

Windows operating system generates various logs with timestamps. Timestamp tampering is an act of anti-forensics in which a suspect manipulates the timestamps of data related to a crime to conceal traces, making it difficult for analysts to reconstruct the situation of the incident. This can delay investigations or lead to the failure of obtaining crucial digital evidence. Therefore, various techniques have been developed to detect timestamp tampering. However, there is a limitation in detection if a suspect is aware of timestamp patterns and manipulates timestamps skillfully or alters system artifacts used in timestamp tampering detection. In this paper, a method is designed to detect changes in timestamps, even if a suspect alters the timestamp of a file on a storage device, it is challenging to do so with precision beyond millisecond order. In the proposed detection method, the first step involves verifying the timestamp of a file suspected of tampering to determine its write time. Subsequently, the confirmed time is compared with the file size recorded within that time, taking into consideration the performance of the storage device. Finally, the total capacity of files written at a specific time is calculated, and this is compared with the maximum input and output performance of the storage device to detect any potential file tampering.

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1027-1036
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• 2023

Coastal topographic information is crucial in coastal management, but point measurment based approeaches, which are labor intensive, are generally applied to land and underwater, separately. This study introduces an efficient method enabling land and undetwater surveys using an unmanned aerial vehicle (UAV). This method involves applying two different algorithms to measure the topography on land and water depth, respectively, using UAV imagery and merge them to reconstruct whole coastal digital elevation model. Acquisition of the landside terrain is achieved using the Structure-from-Motion Multi-View Stereo technique with spatial scan imagery. Independently, underwater bathymetry is retrieved by employing a depth inversion technique with a drone-acquired wave field video. After merging the two digital elevation models into a local coordinate, interpolation is performed for areas where terrain measurement is not feasible, ultimately obtaining a continuous nearshore terrain. We applied the proposed survey technique to Jangsa Beach, South Korea, and verified that detailed terrain characteristics, such as berm, can be measured. The proposed UAV-based survey method has significant efficiency in terms of time, cost, and safety compared to existing methods.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.34-40
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• 2024

Secret sharing is a cryptographic technique that involves dividing a secret or a piece of sensitive information into multiple shares or parts, which can significantly increase the confidentiality of a secret. There has been a lot of research on secret sharing for different contexts or situations. Tassa's conjunctive secret sharing method employs polynomial derivatives to facilitate hierarchical secret sharing. However, the use of derivatives introduces several limitations in hierarchical secret sharing. Firstly, only a single group of participants can be created at each level due to the shares being generated from a sole derivative. Secondly, the method can only reconstruct a secret through conjunction, thereby restricting the specification of arbitrary secret reconstruction conditions. Thirdly, Birkhoff interpolation is required, adding complexity compared to the more accessible Lagrange interpolation used in polynomial-based secret sharing. This paper introduces the multi-compartment secret sharing method as a generalization of the conjunctive hierarchical secret sharing. Our proposed method first encrypts a secret using external groups' shares and then generates internal shares for each group by embedding the encrypted secret value in a polynomial. While the polynomial can be reconstructed with the internal shares, the polynomial just provides the encrypted secret, requiring external shares for decryption. This approach enables the creation of multiple participant groups at a single level. It supports the implementation of arbitrary secret reconstruction conditions, as well as conjunction. Furthermore, the use of polynomials allows the application of Lagrange interpolation.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.326-334
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• 2024

Extra-oral imaging techniques such as Panoramic X-rays (PXs) and Cone Beam Computed Tomography (CBCT) are the most preferred imaging modalities in dental clinics owing to its patient convenience during imaging as well as their ability to visualize entire teeth information. PXs are preferred for routine clinical treatments and CBCTs for complex surgeries and implant treatments. However, PXs are limited by the lack of third dimensional spatial information whereas CBCTs inflict high radiation exposure to patient. When a PX is already available, it is beneficial to reconstruct the 3D oral structure from the PX to avoid further expenses and radiation dose. In this paper, we propose 3DentAI - an U-Net based deep learning framework for 3D reconstruction of oral structure from a PX image. Our framework consists of three module - a reconstruction module based on attention U-Net for estimating depth from a PX image, a realignment module for aligning the predicted flattened volume to the shape of jaw using a predefined focal trough and ray data, and lastly a refinement module based on 3D U-Net for interpolating the missing information to obtain a smooth representation of oral cavity. Synthetic PXs obtained from CBCT by ray tracing and rendering were used to train the networks without the need of paired PX and CBCT datasets. Our method, trained and tested on a diverse datasets of 600 patients, achieved superior performance to GAN-based models even with low computational complexity.

• Journal of Christian Education in Korea
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• v.76
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• pp.73-93
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• 2023

Purpose of study: The purpose of this study is to propose an autobiographical writing as an alternative Christian education for the elderly. Research Contents and Methods : First, after analyzing the previous studies on Christian education for the elderly, it was found that most of the studies suggest the need for integrated Christian elderly education and church elderly ministry, and there is a need for a study that suggests a curriculum that can be implemented in the field. Second, there are two educational objectives derived from Fowler's study of faith. One is to describe, analyze, and reconstruct the three elements that make up the content of faith: centers of value, image of powers, and central stories. Second, to explore vocation through a life of pilgrimage in response to the call to partnership with God. Third, autobiographical writing involves an approach based on the tradition of qualitative research and should be oriented toward teaching and learning principles based on descriptive, native, holistic, lived-experience, pathic, interpretive, and open-ended principles. Conclusions and Recommendations: Autobiographical writing will contribute to helping the elderly experiencing crises of despair and anxiety to integrate the meaning of their lives through the holistic expression of their thoughts and feelings, to helping socially isolated older adults to feel connected to society, and to helping them to envision and imagine the future through the present act of revealing their voice. It is hoped that autobiographical writing will increase the number of conversations through retrospection and confession of faith of the elderly.