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    A framework for variable content document generation with multiple actors

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    โ€œNOTICE: this is the authorโ€™s version of a work that was accepted for publication in Information and Software Technology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information and Software Technology, Volume 56, Issue 9, September 2014, Pages 1101โ€“1121 DOI10.1016/j.infsof.2013.12.006Context - Advances in customization have highlighted the need for tools supporting variable content document management and generation in many domains. Current tools allow the generation of highly customized documents that are variable in both content and layout. However, most frameworks are technology-oriented, and their use requires advanced skills in implementation-related tools, which means their use by end users (i.e. document designers) is severely limited. Objective - Starting from past and current trends for customized document authoring, our goal is to provide a document generation alternative in which variants are specified at a high level of abstraction and content reuse can be maximized in high variability scenarios. Method Based on our experience in Document Engineering, we identified areas in the variable content document management and generation field open to further improvement. We first classified the primary sources of variability in document composition processes and then developed a methodology, which we called DPL based on Software Product Lines principles to support document generation in high variability scenarios. Results - In order to validate the applicability of our methodology we implemented a tool DPLfw to carry out DPL processes. After using this in different scenarios, we compared our proposal with other state-of-the-art tools for variable content document management and generation. Conclusion - The DPLfw showed a good capacity for the automatic generation of variable content documents equal to or in some cases surpassing other currently available approaches. To the best of our knowledge, DPLfw is the only framework that combines variable content and document workflow facilities, easing the generation of variable content documents in which multiple actors play different roles.This work has been partially funded by the Spanish Ministerio de Economia y Competitividad under Grant TIPEx (TIN2010-19859-C03-03).Gรณmez Llana, A.; Penadรฉs Gramage, MC.; Canos Cerda, JH.; Borges, MR.; Llavador Campos, M. (2014). A framework for variable content document generation with multiple actors. Information and Software Technology. 56(9):1101-1121. https://doi.org/10.1016/j.infsof.2013.12.006S1101112156

    Research Productivity of Library and Information Science Faculty of West Zone of India: A Bibliometric Study

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    This study conducts a Bibliometrics analysis with the aim of identifying significant contributions of LIS faculties. There has been rapid increase of academic literature by faculties so that it is an important aspect to analyse their intellectual work with Bibliometric parameters. Bibliometrics used as a research method in Library and Information Science field. This research work based on secondary data total 13 universities taken from the West Zone of India in which 37 permanent faculties presently working. Researchers collected data from University websites, Google Scholar and Research Gate. In this study examined the Prolific Authors, Authorship Pattern, degree of collaboration and top Preferred Journals. Purpose: The main purpose of this study is to examine the analytical patterns of the literature published in the International Journal of Information Dissemination and Technology on the basis of citation analysis and to analyse the articles published from the year 2017-2021. Results show that total 218 articles referred 1289 citations in which journals are the most favourable form of documents with the contribution of 54.46%. Methodology: For the present study International Journal of Information Dissemination and Technology has been selected as the source journal. (IJIDT) is a Quarterly Refereed/ Juried Journal for Information Scientist/ Professionals and Knowledge Managers. All the lead articles and research paper of five volumes (Vol 7-Vol 11) containing 20 issues published during 2017-2021 have been taken up for the study. Statistical tools, like tabular presentation of various data, line diagram, bar diagram and pie diagram are used to represent the study effectively. Findings: From this study the major findings are: (a) The number of papers in various volumes in IJIDT from 2017-2021,The maximum articles are published in 7 volume i.e. 56(25.68%) and the lowest number of contributions are 35 (16.05%) in the volume 11. (b) Maximum number of references is found in 96 articles cite 11-15 references with 44.03% followed by 6-10 references with 19.26% and 0-5 and 16-20 references with 12.84 % respectively both. (c) The distribution of bibliographical forms of cited documents preferred journals as the source of information.Journals contribute highest citations with 702 (54.46%) followed by web resources with 296 (22.96%) and books with 182(14.11%). Citations from theses/ dissertations are marginal. Here, others include seminar proceedings and report etc. (d) Two authors papers are the most with 73 articles (34.43%) followed by single authors with 64 articles (30.18%) and three authors with 42 articles (19.81%). There are small percentages of articles published by more than five authors with 4 articles (1.88%). Value: It is expected that citation methods are used in studies of properties and behaviour of recorded knowledge for analysis of the structures of scientific and research areas

    Sustainable Higher Education Development through Technology Enhanced Learning

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    [EN] Higher education is incorporating Information and Communication Technology (ICT) at a fast rate for different purposes. Scientific papers include within the concept of Technology Enhanced Learning (TEL) the myriad applications of information and communication technology, e-resources, and pedagogical approaches to the development of education. TELยฟs specific application to higher education is especially relevant for countries under rapid development for providing quick and sustainable access to quality education (UN sustainable development goal 4). This paper presents the research results of an online pedagogical experience in collaborative academic research for analyzing good practice in TEL-supported higher education development. The results are obtained through a pilot implementation providing curated data on TEL competencyยฟs development of faculty skills and analysis of developing sustainable higher education degrees through TEL cooperation, for capacity building. Given the increased volume and complexity of the knowledge to be delivered, and the exponential growth of the need for skilled workers in emerging economies, online training is the most effective way of delivering a sustainable higher education. The results of the PETRA Erasmus+ capacity-building project provides evidence of a successful implementation of a TEL-supported methodology for collaborative faculty development focused on future online degrees built collaboratively and applied locally.This research was co-funded by the European Commission through the Erasmus+ KA2 project "Promoting Excellence in Teaching and Learning in Azerbaijani Universities (PETRA)" project number 573630-EPP-1-2016-1-ES-EPPKA2-CBHE-JP.Orozco-Messana, J.; Martรญnez-Rubio, J.; Gonzรกlvez-Pons, AM. (2020). Sustainable Higher Education Development through Technology Enhanced Learning. Sustainability. 12(9):1-13. https://doi.org/10.3390/su12093600S113129Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. doi:10.1016/j.chb.2015.11.036Becker, H. J., & Ravitz, J. (1999). The Influence of Computer and Internet Use on Teachersโ€™ Pedagogical Practices and Perceptions. Journal of Research on Computing in Education, 31(4), 356-384. doi:10.1080/08886504.1999.10782260Mumford, S., & DikilitaลŸ, K. (2020). Pre-service language teachers reflection development through online interaction in a hybrid learning course. Computers & Education, 144, 103706. doi:10.1016/j.compedu.2019.103706Lee, D., Watson, S. L., & Watson, W. R. (2020). The Relationships Between Self-Efficacy, Task Value, and Self-Regulated Learning Strategies in Massive Open Online Courses. The International Review of Research in Open and Distributed Learning, 21(1), 23-39. doi:10.19173/irrodl.v20i5.4389Passey, D. (2019). Technologyโ€enhanced learning: Rethinking the term, the concept and its theoretical background. British Journal of Educational Technology, 50(3), 972-986. doi:10.1111/bjet.12783Lai, Y.-C., & Peng, L.-H. (2019). Effective Teaching and Activities of Excellent Teachers for the Sustainable Development of Higher Design Education. Sustainability, 12(1), 28. doi:10.3390/su12010028Lee, S., Lee, H., & Kim, T. (2018). A Study on the Instructor Role in Dealing with Mixed Contents: How It Affects Learner Satisfaction and Retention in e-Learning. Sustainability, 10(3), 850. doi:10.3390/su10030850โ€œContinuous Improvement in Teaching Strategies through Lean Principlesโ€. Teaching & Learning Symposium, University of Southern Indiana http://hdl.handle.net/20.500.12419/455The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. (2003). Journal of Management Information Systems, 19(4), 9-30. doi:10.1080/07421222.2003.11045748Goodman, J., Melkers, J., & Pallais, A. (2019). Can Online Delivery Increase Access to Education? Journal of Labor Economics, 37(1), 1-34. doi:10.1086/698895Alexander, J., Barcellona, M., McLachlan, S., & Sackley, C. (2019). Technology-enhanced learning in physiotherapy education: Student satisfaction and knowledge acquisition of entry-level students in the United Kingdom. Research in Learning Technology, 27(0). doi:10.25304/rlt.v27.2073How Can Adaptive Platforms Improve Student Learning Outcomes? A Case Study of Open Educational Resources and Adaptive Learning Platforms https://ssrn.com/abstract=3478134Sun, A., & Chen, X. (2016). Online Education and Its Effective Practice: A Research Review. Journal of Information Technology Education: Research, 15, 157-190. doi:10.28945/3502EU Commission https://ec.europa.eu/education/education-in-the-eu/digital-education-action-plan_enEssence Project https://husite.nl/essence/Orozco-Messana, J., de la Poza-Plaza, E., & Calabuig-Moreno, R. (2020). Experiences in Transdisciplinary Education for the Sustainable Development of the Built Environment, the ISAlab Workshop. Sustainability, 12(3), 1143. doi:10.3390/su12031143Kurilovas, E., & Kubilinskiene, S. (2020). Lithuanian case study on evaluating suitability, acceptance and use of IT tools by students โ€“ An example of applying Technology Enhanced Learning Research methods in Higher Education. Computers in Human Behavior, 107, 106274. doi:10.1016/j.chb.2020.10627

    ๋Œ€ํ˜• ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ •์„ ์œ„ํ•œ UAS์™€ TLS ๊ธฐ๋ฐ˜ ๊ณต๊ฐ„์ •๋ณด ๊ตฌ์ถ•๊ธฐ๋ฒ• ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ˜‘๋™๊ณผ์ • ์กฐ๊ฒฝํ•™,2019. 8. ์ด๋™๊ทผ.๋Œ€ํ˜•์žฌ๋‚œ ๋ฐœ์ƒ์— ๋Œ€ํ•œ ์‚ฌ์ „์˜ˆ๋ฐฉ๋ถ€ํ„ฐ ๋Œ€์‘๋‹จ๊ณ„๊นŒ์ง€ ์ „๊ณผ์ •์˜ ์ฒด๊ณ„์ ์ด๊ณ  ํšจ์œจ์ ์ธ ๋Œ€์ฒ˜๋ฅผ ํ†ตํ•ด ์ธ๋ช…, ์žฌ์‚ฐ, ํ™˜๊ฒฝ ๋“ฑ์˜ ํ”ผํ•ด๋ฅผ ์ตœ์†Œํ™”ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€ํ˜•์žฌ๋‚œ ๋ฐœ์ƒ ์‹œ ๋Œ€์‘ ๊ณผ์ • ์ค‘ ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ •์— ์ง‘์ค‘ํ•˜์—ฌ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋Œ€ํ˜•ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ •์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๊ณผ๊ฑฐ๋ถ€ํ„ฐ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ์ง€๋งŒ ์‹ค์งˆ์ ์ธ ์ธก์ •์ด ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ๋ฐœ์ƒ ์ด์ „์˜ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ชจ๋ธ๋ง, ์›๊ฒฉํƒ์‚ฌ ๋“ฑ์˜ ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ํ๊ธฐ๋ฌผ๋Ÿ‰์„ ์˜ˆ์ธกํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋‹ค์ˆ˜ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ตœ๊ทผ ํ™œ๋ฐœํ•˜๊ฒŒ ์ด์šฉ๋˜๊ณ  ์žˆ๋Š” UAS (Unmanned Aerial System)๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ๊ธฐ๋ฌผ๋Ÿ‰์„ ์‚ฐ์ •ํ•˜๊ณ  ์ •ํ™•๋„๋ฅผ ํ‰๊ฐ€ํ•˜๋ฉฐ ๊ธฐ์กด ๊ธฐ์ˆ ๊ณผ์˜ ๋น„๊ต์™€ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. UAS๋Š” UAV (Unmanned Aerial Vehicle)๋ฅผ ์ด์šฉํ•˜์—ฌ ์˜์ƒ์„ ์ทจ๋“ํ•˜๊ณ  ๋ถ„์„ํ•˜๋Š” ์ „๋ฐ˜์ ์ธ ๊ณผ์ •์ด๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. UAS๋ฅผ ์ด์šฉํ•˜์—ฌ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ  ์ •ํ™•๋„๋ฅผ ํ‰๊ฐ€ํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ๊ณผ๊ฑฐ๋ถ€ํ„ฐ ์ฃผ๋กœ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ์ ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ์œ ์‚ฌํ•˜๊ฒŒ TLS (Terrestrial Laser Scanning)๋ฅผ ์ด์šฉํ•˜์—ฌ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๋ฅผ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ ์ธก๋Ÿ‰ ๋ถ„์•ผ์—์„œ ์ฃผ๋กœ ์ด์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ ๊ทธ ์ •ํ™•์„ฑ ๋˜ํ•œ ์šฐ์ˆ˜ํ•˜์—ฌ ์‹์ƒ, ๊ฑด์ถ•, ํ† ๋ชฉ, ๋ฌธํ™”์žฌ, ์ง€ํ˜•์ธก๋Ÿ‰ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ๋„๋ฆฌ ์ด์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋Œ€ํ˜•ํ๊ธฐ๋ฌผ๋Ÿ‰ ๋˜ํ•œ TLS๋ฅผ ์ด์šฉํ•˜์—ฌ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด ๊ตฌ์ถ• ํ›„ ์‚ฐ์ •ํ•  ์ˆ˜ ์žˆ์ง€๋งŒ ๋น„์šฉ, ์‹œ๊ฐ„ ๋“ฑ์˜ ์ œ์•ฝ์‚ฌํ•ญ์œผ๋กœ ์ธํ•ด ํ™œ์šฉ์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํฌ๊ฒŒ 3๊ฐ€์ง€ ๋ถ€๋ถ„์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” UAS๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด ๊ตฌ์ถ•๊ณผ ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ • ๊ฐ€๋Šฅ์„ฑ ๋ชจ์ƒ‰์ด๋‹ค. UAS๋ฅผ ์ด์šฉํ•˜์—ฌ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด ๊ตฌ์ถ•๊นŒ์ง€์˜ ๊ณผ์ •์„ ์ •๋ฐ€ ๋ถ„์„ํ•˜์—ฌ ์ตœ์ ์˜ ๋น„ํ–‰๋ณ€์ˆ˜์™€ ๊ธฐํƒ€ ๋ณ€์ˆ˜๋ฅผ ๋„์ถœํ•˜์—ฌ ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ •์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” TLS ๊ธฐ์ˆ ๊ณผ UAS ๊ธฐ์ˆ  ๊ธฐ๋ฐ˜์˜ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด์˜ ๋น„๊ต์™€ ๋ถ„์„์ด๋‹ค. ๊ฐ๊ฐ์˜ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๋ฅผ M3C2์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ๋น„๊ตํ•˜๊ณ  ๋ถ„์„ํ•˜์—ฌ ์ตœ์ ์˜ ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ • ๊ธฐ๋ฒ•์„ ๋„์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์„ธ ๋ฒˆ์งธ๋Š” 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด์˜ ์œตํ•ฉ๊ณผ ํšจ์œจ์„ฑ ๋ถ„์„์ด๋‹ค. ๋‘ ๊ฐ€์ง€ ๊ธฐ์ˆ ์„ ์œตํ•ฉํ•˜์—ฌ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ  ํšจ์œจ์„ฑ์„ ๋ถ„์„ํ•˜์—ฌ UAS, TLS, ์œตํ•ฉ๊ธฐ๋ฒ• ์„ธ๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋ก ๊ฐ„์˜ ์ฐจ์ด์™€ ์ตœ์ ์˜ ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ • ๊ธฐ๋ฒ•์„ ๋„์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ฃผ์š” ๋น„ํ–‰๋ณ€์ˆ˜๋Š” ๋น„ํ–‰๊ณ ๋„์™€ ์˜์ƒ์˜ ์ค‘๋ณต๋„์ด๋ฉฐ ์ด์™ธ ๋ณ€์ˆ˜๋Š” ์ง€์ƒ๊ธฐ์ค€์  ๊ฐœ์ˆ˜์ด๋‹ค. ์ด ์™ธ์—๋„ ์นด๋ฉ”๋ผ ๋‚ด๋ถ€ํ‘œ์ •, ์ง๋ฒŒ์˜ ํ”๋“ค๋ฆผ ์ •๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด 56๊ฐœ์˜ ์ผ€์ด์Šค ์ค‘ ์ตœ์ ์˜ ๋ณ€์ˆ˜๋ฅผ ๋„์ถœํ•˜์˜€์œผ๋ฉฐ ๊ณผ๊ฑฐ ์—ฐ๊ตฌ์™€๋Š” ๋‹ค๋ฅด๊ฒŒ ๊ณ ๋„์ฐจ์ด๊ฐ€ ๋งŽ์ด ๋‚˜๋Š” ํ๊ธฐ๋ฌผ ์ง€์—ญ์—์„œ๋Š” DW (Distance covered on the ground by on image in Width direction)์— ์˜ํ•ด ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋˜์—ˆ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๊ณ ๋„๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก ๋†’์€ ์ •ํ™•๋„๋ฅผ ๊ฐ€์ง€๋Š” 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๋ฅผ ๊ตฌ์ถ•ํ•˜์ง€๋งŒ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ ๋„๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก ์ •ํ™•๋„๊ฐ€ ๋‚ฎ์•„์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. 56๊ฐœ์˜ ์ผ€์ด์Šค ๋ชจ๋‘ ์ •ํ™•๋„ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ ์ •ํ™•๋„์™€ ํ๊ธฐ๋ฌผ๋Ÿ‰๊ฐ„์˜ ์ƒ๊ด€์„ฑ์ด ์žˆ์Œ์„ ๋„์ถœํ•˜์˜€๋‹ค. 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด์˜ ์ •ํ™•๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ์‚ฐ์ •ํ•œ ํ๊ธฐ๋ฌผ๋Ÿ‰์ด ์œ ์‚ฌํ–ˆ์œผ๋ฉฐ ์ด์™€ ๋ฐ˜๋Œ€๋กœ ์ •ํ™•๋„๊ฐ€ ๋‚ฎ์€ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๋“ค์—์„œ๋Š” ํ๊ธฐ๋ฌผ๋Ÿ‰์ด ์ œ๊ฐ๊ฐ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ผ๋ จ์˜ ๊ณผ์ •์„ ํ†ตํ•ด ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ •์„ ์œ„ํ•œ UAS ์ตœ์  ๋ณ€์ˆ˜๋ฅผ ๋„์ถœํ•˜์˜€์œผ๋ฉฐ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด ๊ธฐ๋ฐ˜์˜ ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ • ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. M3C2์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ UAS์™€ TLS ๊ธฐ๋ฐ˜์˜ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๋ฅผ ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด, ๊ฐ๊ฐ์˜ ๊ณต๊ฐ„์ •๋ณด๊ฐ€ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์žฅ๋‹จ์ ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ •ํ™•๋„์˜ ๊ฒฝ์šฐ, UAS๊ธฐ๋ฐ˜ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด์˜ RMSE๋Š” 0.032m, TLS์˜ RMSE๋Š” 0.202m๋กœ UAS์˜ ์ •ํ™•๋„๊ฐ€ ๋” ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘ ๊ฐ€์ง€ ๊ธฐ์ˆ ์„ ์œตํ•ฉํ•œ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด์˜ RMSE๋Š” 0.030m๋กœ์จ ์„ธ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋ก  ์ค‘์—์„œ ๊ฐ€์žฅ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€๋‹ค. ํ•˜์ง€๋งŒ ํšจ์œจ์„ฑ ๊ด€์ ์—์„œ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, UAS ๊ธฐ๋ฐ˜์˜ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด๊ฐ€ ๋‹จ์‹œ๊ฐ„์— ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์ด๋Š” ๊ฒฐ๊ณผ๋กœ ๋„์ถœ๋จ์œผ๋กœ์จ ๋Œ€ํ˜•ํ๊ธฐ๋ฌผ๋Ÿ‰ ์‚ฐ์ •์— ์ตœ์ ํ™”๋œ ๊ธฐ์ˆ ๊ณผ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ์™ธ์—๋„ ๋น„์šฉ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, UAS ๊ธฐ๋ฐ˜์˜ 3์ฐจ์› ๋ชจํ˜• ๊ตฌ์ถ•๊นŒ์ง€ ์†Œ๋น„๋œ ๋น„์šฉ์ด TLS์— ๋น„ํ•ด ์ ์€ ๋น„์šฉ์ด ์†Œ๋น„๋œ ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋Œ€ํ˜•์žฌ๋‚œ ์‹œ ๋น„๊ต์  ๋‹จ์‹œ๊ฐ„์— ๋Œ€์‘ํ•˜์—ฌ ํ”ผํ•ด๋ฅผ ์ตœ์†Œํ™” ํ•˜๊ณ  ๋‹ค์–‘ํ•œ ์˜์‚ฌ๊ฒฐ์ •์„ ์ง„ํ–‰ํ•ด์•ผ ํ•˜๋Š”๋ฐ, ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋„์ถœํ•œ UAS ๊ธฐ๋ฐ˜์˜ 3์ฐจ์› ๊ณต๊ฐ„์ •๋ณด ๊ตฌ์ถ• ๊ธฐ๋ฒ•์€ ๋Œ€ํ˜• ํ๊ธฐ๋ฌผ๋Ÿ‰์‚ฐ์ •๊ณผ ๊ณต๊ฐ„์  ์˜์‚ฌ๊ฒฐ์ •์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ธฐ๋Œ€ํ•œ๋‹ค.Damage to people, property, and the environment must be minimized through systematic and efficient handling of large-scale disasters throughout the entire process from prevention to the response stage. This study focused on the waste quantity calculations that are part of the response process during large-scale disasters. Studies on large-scale waste quantity calculations have been performed in the past, but actual measurements are difficult. Therefore, many studies are being performed on using information from previous instances to perform modeling and using technologies such as remote sensing to estimate waste quantities. This study calculated waste quantities based on UAS (unmanned aerial system), which is a technology that is often used these days. It evaluated the accuracy of this technology, and it analyzed and compared the technology with existing technologies. UAS can be seen as an overall process of using UAVs (Unmanned Aerial Vehicle) to capture images and analyzing them. Studies have been conducted in the past on using UAS to build 3D spatial information and evaluate accuracy, and they are being used integrally in a variety of fields. Similarly, 3D spatial information can be built using TLS (Terrestrial Laser Scanning), and these are chiefly used in the surveying field. This methods accuracy is excellent, and it is widely used in a variety of fields such as vegetation, construction, civil engineering, cultural assets, and topographical surveys. Large-scale waste can also be calculated by using TLS to build a 3D spatial information, but it is seen as unfeasible to use due to cost and time limitations. This study is broadly divided into 3 parts. The first part is examining the feasibility of using UAS to build a 3D spatial information and calculate waste quantity. The process up to the point of using UAS to build a 3D spatial information was analyzed in detail, and optimal flight variables and other variables were found in order to examine the feasibility of calculating waste quantity. The second part is comparing and analyzing 3D spatial information based on TLS and UAS technology. The 3D spatial information were compared and analyzed using the M3C2 algorithm, and the optimal waste quantity calculation methods were found. Finally, the third part is analyzing a combination of the 3D spatial information and the 3D spatial information efficiency. The two technologies were combined to build a 3D spatial information, and their efficiency was analyzed to find the differences between the three methodologies (UAS, TLS, and the combined method), as well as find the optimal waste quantity calculation method. The major flight variables are the flight altitude and image overlap. Another variable is the number of ground control points. In addition to this, the camera interior orientation and degree of gimbal shaking were analyzed. Through this study, the optimal variables among 56 cases were found. Unlike past studies, it was discovered that the results were contrary to previous studies due to the DW (Distance covered on the ground by on image in Width direction) in waste regions with a lot of altitude differences. Normally, as the altitude becomes lower, the accuracy of the 3D spatial information becomes higher, but in this study it was found that the accuracy became lower as the altitude became lower. The accuracy of all 56 cases was analyzed, and it was found that there is a correlation between accuracy and the amount of waste. As the accuracy of the 3D spatial information increased, the calculated waste amounts became similar. Conversely, in 3D spatial information with low accuracy, it was found that the waste amounts were different. Through this sequential process, the optimal UAS variables for calculating waste amounts were found, and it was possible to confirm the feasibility of calculating waste amounts based on 3D spatial iformation. The M3C2 algorithm was used to compare the UAS and TLS-based 3D spatial information, and by doing so, it was possible to confirm the advantages and disadvantages of each model. As for accuracy, the RMSE of the UAS-based 3D spatial information was 0.032 m, and the RMSE of the TLS model was 0.202, making the UAS models accuracy higher. The RMSE of the 3D spatial information which combined the two technologies was 0.030 m, and it showed the highest accuracy of the three methodologies. However, in terms of efficiency, the analyzed results were able to confirm that the UAS-based 3D spatial information had the optimal technology and methodology for large-scale waste amount calculations by creating a model which shows high accuracy in a short time. In addition, cost analysis results were able to confirm that the cost of building the UAS-based 3D spatial information was lower than that of TLS. During large-scale disasters, it is necessary to respond in a relatively short time to minimize damage and perform a variety of decision-making. The UAS-based 3D spatial information building method found in this study can be used for large-scale waste amount calculations and spatial decision-making.I. Introduction 1 II. Literature Review 7 1. Studies on Applying the UAS to Disaster Management 7 2. Accuracy of UAS-based 3D Model Construction 14 3. Disaster Waste Quantity 26 III. Materials and Methods 34 1. Optimal Flight Parameters for UAV Generating 3D Spatial Information 36 1.1. Design of UAV Flight 36 1.2. Photogrammetric Processing for the Acquisition of 3D Spatial Information 41 1.3. Assessment of the 3D Spatial Information Accuracy 43 1.4. Computation of the Amount of Waste 45 2. Comparison and Analysis of TLS and UAS Methodology for Optimal Volume Computation 47 2.1. TLS and UAS-based 3D Spatial Information Generation and Volume Computation 49 2.2. Comparison and Analysis of 3D Spatial Information 55 3. Multispace Fusion Methodology-based 3D Spatial Information Generating and Efficiency Analysis 57 3.1. Multispace Fusion Methodology-based 3D Spatial Information 57 3.2. Efficiency Analysis of 3D Spatial Information for Responding to Large-scale Disasters 58 III. Result and Discussion 59 1. Optimal Flight Parameters for UAV Generating 3D Spatial Information and Investigation of Feasibility 59 1.1. Generation of 3D Spatial Information using UAS 59 1.2. Assessment of the 3D Spatial Information Accuracy 64 1.3. Computation of the Amount of Waste and Optimal flights parameters 76 2. Comparison and Analysis of TLS and UAS-based 3D Spatial Information 84 2.1. Generation of 3D Spatial Information and Volume Computation using UAS 84 2.2. Spatial Comparison and Analysis 88 3. Multispace Fusion Methodology-based 3D Spatial Information Generating and Efficiency Analysis 93 3.1. Multispace Fusion Methodology-based 3D Spatial Information 93 3.2. 3D Spatial information Efficiency Analysis for Responding to Large-scale Disasters 96 IV. Conclusion 100 V. Bibliography 103Docto

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    Fighting Fire with Fire: Technology in Child Sex Trafficking

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    Mary G. Leary, Columbus School of Law at The Catholic University of Americ

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