• Title/Summary/Keyword: Generative Tools

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A study on a machining cycle and optimal cutting conditions on multi-satations (금속 절삭가공 공정의 최적 절삭 조건 및 가공주기 결정 방안 연구)

  • 황홍석;황규완
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1996.04a
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    • pp.104-107
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    • 1996
  • This paper focuses on a automation selection of optimal cutting conditions and cycle time for multi-spindle metal cutting machines based on machining parameters and tool change schemes which are the two most common terms used in the metal cutting. In this research we used two step generative approach, step 1 is mathematical modeling for the selection fo optimal cutting conditions and the other is GMDH-Type modeling to estimate the system performance evaluation. We developed computer programs for these models and the fitting manufacturing examples are applied to this model and it was shown that the proposed approach has a good potential and offers a valuable tools to analyse the metal cutting system.

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A Study on Performance Evaluation in Metal Cuttin System (금속 절삭가공 시스템의 성능평가에 관한 연구)

  • 황규완;김순경;황흥석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1996.04a
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    • pp.689-693
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    • 1996
  • This paper was performed on the automatic selection of cutting condition on multispindle machine. the several mathematical relationships were formulated for simulataneous selection of machining parameters and tool changing scheme. In this research we used two step generative approach; step 1 is mathematical modeling for the selection of optimal cutting conditions and the other is GMDH-TYPE modeling to find prediction equation of system performance. thus in this paper, mathematical machining models combined with a heuristic GMDH-TYPE modeling to estimate the system performance, these models are developed computer programs for practical application and it was shown that the proposed approach has a good potential and offers a valuable tools to performance evaluation for metal cutting system.

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The Utilization and Impact of ChatGPT in Engineering Education: A Learner-Centered Approach (공학교육에서 ChatGPT 활용의 실태 및 영향: 학습자 중심의 접근)

  • Wang, Bi;Bae, So-hyeon;Buh, Gyoung-ho
    • Journal of Engineering Education Research
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    • v.27 no.3
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    • pp.3-13
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    • 2024
  • Since the launch of ChatGPT, many college students used it extensively in various ways in their curricular learning activities. This study investigates the utilization of ChatGPT in the curriculum of first and second-year engineering students, aiming to examine its influence from a learner perspective. We explored how ChatGPT is used in each subject and learning activity to understand how learners perceive the use of ChatGPT. From the survey data on engineering college students at E university, we examined students' perception on 'shortening time to perform tasks' through ChatGPT, 'dependence on ChatGPT', 'their contribution to individual capacity building', and 'their influence on academic grade'. The majority of students reported extensive use of ChatGPT for learning activities, particularly showing high dependency in liberal arts subjects and coding-related activities. While the use of ChatGPT in liberal arts was seen as not contributing to the enhancement of individual capacity, its use in coding was positively evaluated. Furthermore, the contribution of ChatGPT to the creativity in report writing tasks was highly rated. These findings offer several important implications for the use of AI tools like ChatGPT in engineering education. Firstly, the positive impact of ChatGPT's high usability and individual-capacity enhancement in coding should be expanded to other areas of learning. Secondly, as AI technology progresses, the contribution of AI tools compared to learners is expected to increase, suggesting that students should be encouraged to effectively use AI tools to achieve their learning objectives while maintaining a balanced approach to avoid overreliance on AI.

GAN-based Automated Generation of Web Page Metadata for Search Engine Optimization (검색엔진 최적화를 위한 GAN 기반 웹사이트 메타데이터 자동 생성)

  • An, Sojung;Lee, O-jun;Lee, Jung-Hyeon;Jung, Jason J.;Yong, Hwan-Sung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.79-82
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    • 2019
  • This study aims to design and implement automated SEO tools that has applied the artificial intelligence techniques for search engine optimization (SEO; Search Engine Optimization). Traditional Search Engine Optimization (SEO) on-page optimization show limitations that rely only on knowledge of webpage administrators. Thereby, this paper proposes the metadata generation system. It introduces three approaches for recommending metadata; i) Downloading the metadata which is the top of webpage ii) Generating terms which is high relevance by using bi-directional Long Short Term Memory (LSTM) based on attention; iii) Learning through the Generative Adversarial Network (GAN) to enhance overall performance. It is expected to be useful as an optimizing tool that can be evaluated and improve the online marketing processes.

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Oil Spot Generative Formation of Oil Spot Denmoku (유적 천목의 유적 발생 구조)

  • Jung, Jong-Heuk;Lee, Byung-Ha
    • Journal of the Korean Ceramic Society
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    • v.43 no.10 s.293
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    • pp.619-625
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    • 2006
  • The study was intended to investigate production tools and conditions of oil spot following calculating optimal composition of oil spot tenmoku glaze which can be produced at 1250$\sim$l260$^{\circ}C$. Since oil spot is influenced by the viscosity of glaze, viscosity of various glazes fit for oil spot production was determined by an SciGlass 6.0-based calculating method. Applied amount and calcinating conditions of the resulting substance of oil spot, $Fe_2O_3$, were analyzed. As a result, the viscosity of the glaze durable at 1260$^{\circ}C$ was found to range from 4.2 to 4.4, natural cooling was used after oxidizing calcinations at 1260$^{\circ}C$ for an hour, and the best oil spot tenmoku was produced by the natural cooling process after 1 h calcinations at 1150$^{\circ}C$ in the middle of natural cooling. Also, the study showed that thickness of glaze was found to have an effect on the production of oil spot and resulting oil spot was filled mostly with $Fe_2O_3$.

Case Studies of Precast Facade Digital Design and Fabrication Strategies (사례 분석을 통한 프리캐스트 입면 디지털 설계 및 패브리케이션 전략)

  • Kim, Jin-Ho
    • Journal of KIBIM
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    • v.9 no.3
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    • pp.8-18
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    • 2019
  • Precast concrete manufacturing has proved economies of scale through the repetitive production by means of standardization, automation, and prefabrication. Advanced digital design and fabrication technologies can empower its benefits by enabling mass customization in the building design and construction. This study analyzed five case studies in terms of 1) design intent and background, 2) module development and facade construction, 3) integrated process among project stakeholder. This article has attempted to establish the following three points in conclusion: 1) Form generating digital design tools such as Rhino, CATIA, Generative Component, and Digital Project were implemented to produce parametric surface pattern and rationalization to maximize existing precast manufacturing benefits. Also, BIM program has been used to promote coordination and communication among engineering consultants and contractors, 2) In addition to traditional precast concrete materials, GFRC, RFP, brick cladding precast and 3D printed mould have been introduced to reduce the weight and cost and to comply the code from the zoning, seismic, and fireproof requirements, 3) Design-assist contract, design-assist financial support, and co-location measures have been introduced to facilitate collaboration between architect, fabricator, and contractor from the beginning of the project.

Marine life Image Recognition using Deep Learning

  • Jiyun Hong;Jiwon Lee;Somin Lee;Eun Ko;Gyubin Kim;Jungwoon Kang;Mincheol Kim
    • Journal of information and communication convergence engineering
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    • v.22 no.3
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    • pp.221-230
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    • 2024
  • The aim of this study is to investigate the automatic recognition and analysis of Jeju marine-life images using artificial intelligence (AI) technology. The dataset of marine-life images was prepared using tools such as Python, TensorFlow, and Google Colab (Google Colaboratory). We also developed models by training deep learning AI in image recognition to automatically recognize the species found in these images and extract their associated information, such as taxonomy, characteristics, and distribution. This study is innovative in that it uses deep learning technology combined with imagerecognition technology for marine biodiversity research. In addition, these results will lead to the development of the marine-life industry in Jeju by supporting marine environment monitoring and marine resource conservation. Furthermore, this study is anticipated to contribute to academic advancement, specifically in the study of marine species diversity.

An Analysis of Motivation in the Earth Science part of the 7th Grade Textbooks (2007 개정 7학년 과학 교과서에 나타난 지구과학의 동기유발 요소 분석)

  • Kim, Ju-Hyun;Han, Shin;Jeong, Jinwoo
    • Journal of Science Education
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    • v.37 no.1
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    • pp.11-22
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    • 2013
  • Motivation is a generative power of making learning interesting and sustaining learning for students. Textbooks are important tools in carrying out lessons. And it is meaningful to analyze how textbooks motivate learning. This study is to analyze components of motivation in learning of the 7th grade middle school science textbooks. Keller's ARCS model was used for the analysis. The result of the study is as follows. First, the eight textbooks had various components from A1 to R3. Second, analyzing textbooks by parts of the textbooks, the body had the most motivation strategies and the next was the introduction, lastly the finishing part. Third, the most frequently used strategy on the attention factor is A1. And the most frequently used strategy in the relevance factor is R3.

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O.P.E.N Triad: The Future Success for Individuals, Institutes, and Industries

  • Kim, Hae-Jung;Forney, Judith;Crowley, Ruth
    • Journal of the Korean Society of Clothing and Textiles
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    • v.34 no.12
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    • pp.1980-1991
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    • 2010
  • This study proposes the O P E N Triad framework as a future set of tools and perspectives for individual members and institutes to further their professional and academic potential as well as prospect and vitalize the future of the Korean Clothing and Textiles discipline through a global perspective. The millennial generation desires On-demand, Personal, Engaging, and Networked (O P E N) experiences effecting cultural change for creative and influential interaction in transactions, communication, and education. O P E N Individuals offers a WebSphere model as a holistic learning system that has a synergizing value of education across academic courses, industries, and cultures. Through a digitalized and virtualized class, it complements relevant technologies already familiar to the student population. By employing environmental scanning approaches, the most influential and viable future global issues related to the clothing and textiles discipline are identified and dialogued within O P E N Institutes. For future clothing and textiles institutes, this scanning allows them to be open to new ideas, to focus on inter-engagements, to collaborate among individuals, to associate as a part of web of people, organizations, and ideas, to personalize an institutes curricula, and to dialogue generative knowledge. O P E N Industries reveals three dominant future issues that cross academia and industry, sustainability, supply chain management, and social networking. In-depth interviews with U.S. industry experts identified interdependent gaps in global consumer experience practices and suggested the following gaps as future research areas: a standardized business model to the entrepreneurial model, strategic management to a sustainable competitive advantage, standardized to differentiated products, services and operations, market segmentation to global consumer clusters, business-driven marketplaces to consumer-engaged marketspaces, and excellent services to optimal experience. This O P E N Triad framework empowers millennial students, universities, and industries to anticipate and prepare for a radically changing world.

Evaluating ChatGPT's Competency in BIM Related Knowledge via the Korean BIM Expertise Exam (BIM 운용 전문가 시험을 통한 ChatGPT의 BIM 분야 전문 지식 수준 평가)

  • Choi, Jiwon;Koo, Bonsang;Yu, Youngsu;Jeong, Yujeong;Ham, Namhyuk
    • Journal of KIBIM
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    • v.13 no.3
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    • pp.21-29
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    • 2023
  • ChatGPT, a chatbot based on GPT large language models, has gained immense popularity among the general public as well as domain professionals. To assess its proficiency in specialized fields, ChatGPT was tested on mainstream exams like the bar exam and medical licensing tests. This study evaluated ChatGPT's ability to answer questions related to Building Information Modeling (BIM) by testing it on Korea's BIM expertise exam, focusing primarily on multiple-choice problems. Both GPT-3.5 and GPT-4 were tested by prompting them to provide the correct answers to three years' worth of exams, totaling 150 questions. The results showed that both versions passed the test with average scores of 68 and 85, respectively. GPT-4 performed particularly well in categories related to 'BIM software' and 'Smart Construction technology'. However, it did not fare well in 'BIM applications'. Both versions were more proficient with short-answer choices than with sentence-length answers. Additionally, GPT-4 struggled with questions related to BIM policies and regulations specific to the Korean industry. Such limitations might be addressed by using tools like LangChain, which allow for feeding domain-specific documents to customize ChatGPT's responses. These advancements are anticipated to enhance ChatGPT's utility as a virtual assistant for BIM education and modeling automation.