• Journal of Digital Contents Society
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• v.13 no.2
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• pp.255-262
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• 2012

Human motion tracking algorithm is receiving attention from many research areas, such as human computer interaction, video conference, surveillance analysis, and game or entertainment applications. Over the last decade, various tracking technologies for each application have been demonstrated and refined among them such of real time computer vision and image processing, advanced man-machine interface, and so on. In this paper, we introduce cost-effective and real-time human motion tracking algorithms based on depth image 3D point matching with a given superellipsoid body representation. The body representative model is made by using parametric volume modeling method based on superellipsoid and consists of 18 articulated joints. For more accurate estimation, we exploit initial inverse kinematic solution with classified body parts' information, and then, the initial pose is modified to more accurate pose by using 3D point matching algorithm.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.4
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• pp.593-599
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• 2022

In this study, a crack monitoring system capable of detecting cracks based on image processing techniques was developed to effectively check cracks, which are the main damage of concrete structures, and a program capable of imaging and analyzing cracks was developed using machine vision. This system provides objective and quantitative data by replacing the appearance inspection that checks cracks with the naked eye. The verification of the development system was applied to the construction site of a self-healing concrete water tank to monitor the crack and the amount of change in the crack width according to age. In the case of crack width detected by image analysis, the difference from the measured value using a digital microscope was up to 0.036 mm, and the crack healing effect of self-healing concrete could be confirmed through the reduction of crack width.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.291-298
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• 2022

A generative adversarial network (GAN) is a network in which two internal neural networks (generative network and discriminant network) learn while competing with each other. The generator creates an image close to reality, and the delimiter is programmed to better discriminate the image of the constructor. This technology is being used in various ways to create, transform, and restore the entire image X into another image Y. This paper describes a method that can be forged into another object naturally, after extracting only a partial image from the original image. First, a new image is created through the previously trained DCGAN model, after extracting only a partial image from the original image. The original image goes through a process of naturally combining with, after re-styling it to match the texture and size of the original image using the overall style transfer technique. Through this study, the user can naturally add/transform the desired object image to a specific part of the original image, so it can be used as another field of application for creating fake images.

• The Journal of Art Theory & Practice
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• no.14
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• pp.87-124
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• 2012

This paper focuses on the social implication of new media art, which has evolved with the advance of technology. To understand the notion of human-computer interactivity in media art, it examines the meaning of "cybernetics" theory invented by Norbert Wiener just after WWII, who provided "control and communication" as central components of his theory of messages. It goes on to investigate the application of cybernetics theory onto art since the 1960s, to which Roy Ascott made a significant contribution by developing telematic art, utilizing the network of telecommunication. This paper underlines the significance of the relationship between human and machine, art and technology in transforming the work of art as a site of communication and experience. The interactivity in new media art transforms the viewer into the user of the work, who is now provided free will to make decisions on his or her action with the work. The artist is no longer a godlike figure who determines the meaning of the work, yet becomes another user of his or her own work, with which to interact. This paper believes that the interaction between man and machine, art and technology can lead to various ways of interaction between humans, thereby restoring a sense of community while liberating humans from conventional limitations on their creativity. This paper considers the development of new media art more than a mere invention of new aesthetic styles employing advanced technology. Rather, new media art provides a critical shift in subverting the modernist autonomy that advocates the medium specificity. New media art envisions a new art, which would embrace impurity into art, allowing the coexistence of autonomy and heteronomy, embracing a technological other, thereby expanding human relations. By enabling the birth of the user in experiencing the work, interactive new media art produces an open arena, in which the user can create the work while communicating with the work and other users. The user now has freedom to visit the work, to take a journey on his or her own, and to make decisions on what to choose and what to do with the work. This paper contends that there is a significant parallel between new media artists' interest in creating new experiences of the art and Jacques Ranci$\grave{e}$re's concept of the aesthetic regime of art. In his argument for eliminating hierarchy in art and for embracing impurity, Ranci$\grave{e}$re provides a vision for art, which is related to life and ultimately reshapes life. Ranci$\grave{e}$re's critique of both formalist modernism and Jean-Francois Lyotard's postmodern view underlines the social implication of new media art practices, which seek to form "the common of a community."