• International conference on construction engineering and project management
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• 2017.10a
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.791-798
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• 2024

Construction industry records poor safety records annually due to a large number of injuries and accidents on construction jobsite. In order to improve existing safety performance, object detection approaches have been extensively studied using vision-sensing techniques and deep learning algorithms. Unfortunately, an insufficient number of datasets (e.g., images) and challenges that reside in manually collecting quality datasets constitute a significant hurdle in fully deploying object recognition approaches in real construction sites. Although advanced technologies (e.g., virtual reality) have attempted to address such challenges, they have achieved limited success because they still rely on labor-intensive work. A promising alternative is to adopt generative AI-based data augmentation methods attributed to their efficiency in creating realistic visual datasets and proven performance. However, there remain critical knowledge gaps on how such alternatives can be effectively employed by safety managers on real construction sites in terms of practicability and applications. In this context, this study establishes a framework that can identify effective strategies for improving object detection performance (e.g., accuracy) using generative AI technologies. The outcome of this study will contribute to providing guidelines and best practices for practitioners as well as researchers by exploring different generative AI-driven augmentation approaches and comparing the corresponding results in a quantitative manner.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.188-193
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• 1999

Technology of GIS evolved as a means of assembling and analyzing diverse spatial data. Many systems have been developed, and almost of systems are proprietary. There is a lots of lack of interoperability and reusability between them. This paper describes the development of Open GIS component software. The developing system have an end in view of GIS tool software which is interoperable and reusable. To increase the interoperability and reusability, the system is based on the OGC(Open GIS Consortium)'s Open GIS Simple Features Specification for OLE/COM. The OGC's specification is announced to increasing the full interoperability of various geospatial data and geoprocessing resources. With the Open specification, component based software ensures the reusability. We implement three kinds of component: Geometry component, Spatial Reference System Component, and MapBase Component. The first two components are compatible to the OGC's specification and the third one is designed to GIS tool software for variant GIS applications. The Open GIS component software system is developed on object-oriented computing environment, ATL/COM and Visual C++. As we made application programs using Visual Basic, the advantages of component based Open GIS software was proved.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.2
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• pp.219-228
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• 1996

The list of the titles of surveying-related papers and books is updated by adding newly polished paper/book titles to the existing one. A special title-search program is developed for managing and searching the surveying-re-lated paper/book titles, and the database is established from the list of the papers. The total number of papers/books in surveying-related field published in Korea upto now is 971. The number of papers/books published in the specific fields are 206 in the conventional surveying field, 218 in Photogrammetry, 183 in Remote Sensing, 127 in Geodesy and GPS, and 237 in Digital Mapping and GIS. The special title-search program is developed using the Visual Basic 3.0 for Windows 3.1 to be operated in the Window environment. The SQL(Structured Query Language) is used for the searching commands.

• Special Issue of the Society of Naval Architects of Korea
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• 2013.12a
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• pp.73-80
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• 2013

From broad point of view, seam tracking has been one of main issues with respect to welding automation. Several attempts have been successful for seam tracking of fixed weaving width. As a solution of the seam tracking methods for varying groove width, the visual sensors such as CCD cameras have been adopted. Although the vision sensing techniques can achieve high accuracy, the weak point is that well-prepared vision sensor environment should be required to obtain high-quality visual measurements which can be easily affected by significant noises in industrial areas. This paper proposed an alternative seam tracking algorithm for narrow groove. A special measurement device for arc voltage, in this study, is developed to enhance the reliability of the measured welding signals. Based on the developed arc sensor algorithm, an automatic weld-width tracking algorithm is also proposed, which is able to predict the weld-position more accurately. The usefulness of the automatic weld-width tracking algorithm was well verified by applying it to gas tungsten arc welding (GTAW).

• Journal of the Korean Geographical Society
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• v.33 no.3
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• pp.435-446
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• 1998

This Paper reports that a new remote sensing techlique focused on a narrow and long strip target (e.g. 15m wide and 100km long) has been specifically developed for pipeline ROW (Right-Of-Way) recovery monitoring. With video it was possible to isolate the maior vegetation communities of the narrow pipeline ROW with acceptable spatial precision by visual or quantitative methods. It was particuarly useful when used to assess a variety of spatial patch dynamics for ROW recovery through digital change-detection techniques in a GIS environment. The main conclusion of this paper is that VSM is a realistic operational technique for a pipeline ROW application. The results also indicate that VSM could be extensively used for other examples of linear thematic mapping.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.801-803
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• 2003

In this paper, we present a method for generating and providing spatially encoded video data that can be effectively used by GIS applications. We collect the video data by a mobile mapping system called 4S-Van that is equipped by GPS, INS, CCD camera, and DVR system. The information about spatial object appearing in video, such as occupied region in each frame, attribute value, and geo-coordinate, are generated and encoded. We suggest methods that can generate such data for each frame in semi-automatic manner. We adopt standard MPEG-7 metadata format for representation of the spatially encoded video data to be generally used by GIS application. The spatial and attribute information encoded to each video frame can make visual browsing between map and video possible. The generated video data can be provided and applied to various GIS applications where location and visual data are both important.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.626-629
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• 2004

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program for GLCM algorithm is newly implemented in the MS Visual IDE environment. While, additional texture imaging modules based on GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV texture variables, it composed of six types of second order texture function in the several quantization levels of 2(binary image), 8, and 16: Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality, four directions are provided as $E-W(0^{\circ}),\;N-E(45^{\circ}),\;S-W(135^{\circ}),\;and\;N-S(90^{\circ}),$ and W-E direction is also considered in the negative direction of E- W direction. While, two direction modes are provided in this program: Omni-mode and Circular mode. Omni-mode is to compute all direction to avoid directionality problem, and circular direction is to compute texture variables by circular direction surrounding target pixel. At the second phase of this study, some examples with artificial image and actual satellite imagery are carried out to demonstrate effectiveness of texture imaging or to help texture image interpretation. As the reference, most previous studies related to texture image analysis have been used for the classification purpose, but this study aims at the creation and general uses of texture image for urban remote sensing.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.162-170
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• 2008

The promotion of new product is one of most important object for exhibition design. The majority of conventional exhibition technique has been implemented through one-way communication. However, using digital technology, development of new exhibition techniques is necessary for promotion of high-tech products and consideration of the phases of the times. This paper propose the various possibilities of exhibition design model which is using camera sensing and video input/output. Interactivity is efficient method to communicate between viewers and displays. Viewers can be participate actively and exhibition goal will be accomplish through this. Using camera as visual senses, video input/output technique is offered not only introducing new product but also entertaining viewers through various ways of representation. Physical experience and learning process can be expected from the exhibition space and the object. The classification of the artist who are using video input/output technique and exhibition examples are described. The collaboration on a work with the new media artist and the exhibition design will be complement each other. It will be solved the problems of realization and corporation of idea and technology.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1363-1372
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• 2021

Red tide, defined as the major reproduction of harmful birds, has the characteristics of being generated and diffused in a wide area. This has limitations in detection only with the existing investigation method. Therefore, in this study, red tide was detected using a remote sensing technique. In addition, it was intended to increase the accuracy of detection by using optical characteristics, not just the concentration of chlorophyll. Red tide mainly occurs on the southern coast where sea signals are complex, and the main red tide control species on the southern coast is Cochlodinium polykirkoides. Therefore, it was intended to secure objectivity by reflecting features that could not be found depending on the researcher's observation and experience, not limited to visual judgment using machine learning techniques. In this study, support background machines and random forest were used among machine learning models, and as a result of calculating accuracy as performance evaluation indicators of the two models, the accuracy was 85.7% and 80.2%, respectively.