• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1311-1316
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• 2021

In this paper, we present a bifocal stereo camera system combining two cameras with different focal length cameras to generate stereoscopic image and their corresponding depth map. In order to obtain the depth data using the bifocal stereo camera system, we perform camera calibration to extract internal and external camera parameters for each camera. We calculate a common image plane and perform a image rectification for generating the depth map using camera parameters of bifocal stereo camera. Finally we use a SGM(Semi-global matching) algorithm to generate the depth map in this paper. The proposed bifocal stereo camera system can performs not only their own functions but also generates distance information about vehicles, pedestrians, and obstacles in the current driving environment. This made it possible to design safer autonomous vehicles.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.619-628
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• 2022

Although face recognition and face generation have been growing in popularity, the privacy issues of using facial images in the wild have been a concurrent topic. In this paper, we propose a face editing network that can reduce privacy issues by generating face images with various properties from a small number of real face images and facial mask information. Unlike the existing methods of learning face attributes using a lot of real face images, the proposed method generates new facial images using a facial segmentation mask and texture images from five parts as styles. The images are then trained with our network to learn the styles and locations of each reference image. Once the proposed framework is trained, we can generate various face images using only a small number of real face images and segmentation information. In our extensive experiments, we show that the proposed method can not only generate new faces, but also localize facial attribute editing, despite using very few real face images.

• KNOM Review
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• v.24 no.1
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• pp.20-28
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• 2021

Extensive use of social media applications and mobile devices continues to increase data traffic. Social media applications generate an endless and massive amount of multimedia traffic, specifically video traffic. Many social media platforms such as YouTube, Daily Motion, and Netflix generate endless video traffic. On these platforms, only a few popular videos are requested many times as compared to other videos. These popular videos should be cached in the user vicinity to meet continuous user demands. MEC has emerged as an essential paradigm for handling consistent user demand and caching videos in user proximity. The problem is to understand how user demand pattern varies with time. This paper analyzes three publicly available datasets, MovieLens 20M, MovieLens 100K, and The Movies Dataset, to find the user request pattern over time. We find hourly, daily, monthly, and yearly trends of all the datasets. Our resulted pattern could be used in other research while generating and analyzing the user request pattern in MEC-based video caching scenarios.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.103-111
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• 2023

Mountains area, especially walking in open space is important for special active field which is based on mountain terrain. Recent research on pedestrian-path includes elements about pedestrian and various environment by analyzing network, but it is mainly focusing on limited space except for data-poor terrain like a mountain terrain. This paper proposes an architecture to generate walking path considering the slope for mountain terrain open space through virtual network made of mesh. This architecture shows that it reflects real terrain more effective when measuring distance using slope and is possible to generate mountain walking path using open space unlike other existing services, and is verified through the test. The proposed architecture is expected to utilize for pedestrian-path generation way considering mountain terrain open space in case of distress, mountain rescue and tactical training and so on.

• Smart Structures and Systems
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• v.34 no.2
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• pp.129-143
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• 2024

In recent years, with the vigorous development of visual algorithms, a large amount of research has been conducted on blade surface defect detection methods represented by deep learning. Detection methods based on deep learning models must rely on a large and rich dataset. However, the geographical location and working environment of wind turbines makes it difficult to effectively capture images of blade surface defects, which inevitably hinders visual detection. In response to the challenge of collecting a dataset for surface defects that are difficult to obtain, a multi-class blade surface defect generation method based on the StyleGAN3 (Style Generative Adversarial Networks 3) deep learning model and PBGMs (Physics-Based Graphics Models) method has been proposed. Firstly, a small number of real blade surface defect datasets are trained using the adversarial neural network of the StyleGAN3 deep learning model to generate a large number of high-resolution blade surface defect images. Secondly, the generated images are processed through Matting and Resize operations to create defect foreground images. The blade background images produced using PBGM technology are randomly fused, resulting in a diverse and high-resolution blade surface defect dataset with multiple types of backgrounds. Finally, experimental validation has proven that the adoption of this method can generate images with defect characteristics and high resolution, achieving a proportion of over 98.5%. Additionally, utilizing the EISeg annotation method significantly reduces the annotation time to just 1/7 of the time required for traditional methods. These generated images and annotated data of blade surface defects provide robust support for the detection of blade surface defects.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.5
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• pp.803-812
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• 2002

Reverse engineering has been widely used for the shape reconstruction of an object without CAD data and the measurement of clay or wood models for the development of new products. To generate a surface from measured points by a laser scanner, typical steps include the scanning of a clay or wood model and the generation of manufacturing data like STL file. A laser scanner has a great potential to get geometrical data of a model for its fast measuring speed and higher precision. The data from a laser scanner are composed of many line stripes of points. A new approach to remove point data with Delaunay triangulation is introduced to deal with problems during reverse engineering process. The selection of group of triangles to be triangulated based on the angle between triangles is used for robust and reliable implementation of Delaunay triangulation as preliminary steps. Developed software enables the user to specify the criteria for the selection of group of triangles either by the angle between triangles or the percentage of triangles reduced. The time and error for handling point data during modelling process can be reduced and thus RP models with accuracy will be helpful to automated process.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.4
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• pp.21-28
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• 2001

In this study, GNLP(GIS linked non-linear network analysis program) for pipeline system analysis has been developed. This GNLP gets the input data for pipeline analysis from existing GIS(geographic information system) data automatically, and has GUI(graphic user interface) for user. Non-Linear Method was used for hydraulic analysis of pipe network based on Hazen-Williams equation, and Microsoft Access of relational database management system(RDBMS) was used for the framework of database applied program. GNLP system environment program was improved so that a pipe network designer can input information data for hydraulic analysis of pipeline system more easily than that of existing models. Furthermore this model generate output such as pressure and water quantities in the form of a table and a chart, and also produces output data in Excel file. This model is also able to display data effectively for analysed data confirmation and query function which is the core of GIS program.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.382-389
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• 2018

Recently cyber attacks can cause serious damage on various information systems. Log data analysis would be able to resolve this problem. Security log analysis system allows to cope with security risk properly by collecting, storing, and analyzing log data information. In this paper, a security log analysis system is designed and implemented in order to analyze security log data using the Logstash in the Elasticsearch, a distributed search engine which enables to collect and process various types of log data. The Kibana, an open source data visualization plugin for Elasticsearch, is used to generate log statistics and search report, and visualize the results. The performance of Elasticsearch-based security log analysis system is compared to the existing log analysis system which uses the Flume log collector, Flume HDFS sink and HBase. The experimental results show that the proposed system tremendously reduces both database query processing time and log data analysis time compared to the existing Hadoop-based log analysis system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5039-5061
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• 2017

Cloud storage becomes a new trend that more and more users move their data to cloud storage servers (CSSs). To ensure the security of cloud storage, many cloud auditing schemes are proposed to check the integrity of users' cloud data. However, most of them are based on public key infrastructure, which leads to complex certificates management and verification. Besides, most existing auditing schemes are inefficient when user uploads a large amount of data or a third party auditor (TPA) performs auditing for multiple users' data on different CSSs. To overcome these problems, in this paper, we propose an efficient and secure auditing scheme based on identity-based cryptography. To relieve user's computation burden, we introduce a proxy, which is delegated to generate and upload homomorphic verifiable tags for user. We extend our auditing scheme to support auditing for dynamic data operations. We further extend it to support batch auditing in multiple users and multiple CSSs setting, which is practical and efficient in large scale cloud storage system. Extensive security analysis shows that our scheme is provably secure in random oracle model. Performance analysis demonstrates that our scheme is highly efficient, especially reducing the computation cost of proxy and TPA.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.29-34
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• 2005

Aerial photogrammetry, which is one of the most frequent used technology in mapping and surveying, has been appreciated for its work flow and accuracy to generate 2D and 3D geospatial data. In aerial photograrnrnetry, more than two photographs are taken over the same target area in different position with overlap. Using these photographs and minimum number of ground control points, 3D stereo model is so formed that the ground surface in reality is reconstructed through analogue/analytical plotter or digital photogrammetry system. In case of digital photogrammetry system, 3D geospatial data could be automatically extracted in partial. Recently, in the advent of aerial digital camera such as ADS40 and DMC, digital photogrammetry system will be in the frist place for generating 3D geospatial data. In this paper, we experimentally generated 3D geospatial data using digital photograrnrnetry system in the aspect of work flow.