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$\textrm{Li}_2\textrm{ZrO}_3$ 계를 이용한 $\textrm{CO}_2$ 가스 센서
박진성,김시욱,이은구,김재열,이현규,Park, Jin-Seong,Kim, Si-Uk,Lee, Eun-Gu,Kim, Jae-Yeol,Lee, Hyeon-Gyu 한국재료학회 1999 한국재료학회지 Vol.9 No.9
이산화탄소 기체센서를 Li$_2$ZrO$_2$계에서 온도와 $CO_2$농도의 함수로서 연구했다. Li$_2$ZrO$_3$를 열처리해서 합성했다. 시편은 직경 10mm, 두께 1mm의 벌크형과 알루미나 기판 위에 후막형으로 각각 제조했다. Li$_2$ZrO$_3$는 45$0^{\circ}C$에서 $650^{\circ}C$의 온도 범위에서 0.1%에서부터 100%까지 이산화탄소 농도 변화를 감지한다. 이산화탄소 감도는 측정온도와 연관성이 있다. Li$_2$ZrO$_3$는 45$0^{\circ}C$에서 $650^{\circ}C$의 온도 범위에서 $CO_2$와 반응해서 Li$_2$CO$_3$와 ZrO$_2$로 분해된다. $650^{\circ}C$ 이상에서 Li$_2$CO$_3$는 Li$_2$O와 $CO_2$로 재분해된다. Li$_2$ZrO$_3$센서의 재현성은 좋지 않았고, 동작온도는 55$0^{\circ}C$ 정도가 적당하였다. A carbon dioxide gas sensor was studied as a function of temperature and $CO_2$concentration in the Li$_2$ZrO$_3$ system. Lithium zirconate(Li$_2$ZrO$_3$) was synthesized by the heat-treatment of zirconia(ZrO$_2$)and Lithium carbonate(Li$_2$CO$_3$). The specimens were prepared both as bulk disk, 10mm in diameter and 1.0mm thickness, and thick films on an alumina substrate. Lithium zirconate readily responded to $CO_2$concentration from 0.1% to 100% in the range of 45$0^{\circ}C$ to $650^{\circ}C$. The sensitivity to $CO_2$ was dependent on the measuring temperature. Lithium zirconate(Li$_2$ZrO$_3$) decomposes into Li$_2$CO$_3$ and ZrO$_2$after the reaction with $CO_2$in the range of 45$0^{\circ}C$ to $650^{\circ}C$. Li$_2$CO$_3$ changes into Li$_2$O and $CO_2$ above $650^{\circ}C$. The material showed difficulty with reversibility and recovery. The optimum temperature for the highest sensitivity is around 55$0^{\circ}C$.
구조부재 인식을 위한 인공지능 학습데이터 생성방법 연구
윤정현 ( Yoon Jeong-hyun ),김시욱 ( Kim Si-uk ),김치경 ( Kim Chee-kyeong ) 한국건축시공학회 2022 한국건축시공학회 학술발표대회 논문집 Vol.22 No.1
With the development of digital technology, construction companies at home and abroad are in the process of computerizing work and site information for the purpose of improving work efficiency. To this end, various technologies such as BIM, digital twin, and AI-based safety management have been developed, but the accuracy and completeness of the related technologies are insufficient to be applied to the field. In this paper, the learning data that has undergone a pre-processing process optimized for recognition of construction information based on structural members is trained on an existing artificial intelligence model to improve recognition accuracy and evaluate its effectiveness. The artificial intelligence model optimized for the structural member created through this study will be used as a base technology for the technology that needs to confirm the safety of the structure in the future.
이승빈 ( Lee Seung-been ),박경규 ( Park Kyung Kyu ),서민조 ( Seo Min Jo ),김시욱 ( Choi Won Jun ),최원준 ( Kim Si Uk ),김치경 ( Kim Chee Kyung ) 한국건축시공학회 2023 한국건축시공학회 학술발표대회 논문집 Vol.23 No.2
The process of construction site supervision plays a crucial role in ensuring safety and quality assurance in construction projects. However, traditional methods of supervision largely depend on human vision and individual experience, posing limitations in quickly detecting and preventing all defects. In particular, the thorough supervision of expansive sites is time-consuming and makes it challenging to identify all defects. This study proposes a new construction supervision system that utilizes vision processing technology and Artificial Intelligence(AI) to automatically detect and analyze defects as a solution to these issues. The system we developed is provided in the form of an application that operates on portable devices, designed to a lower technical barrier so that even non-experts can easily aid construction site supervision. The developed system swiftly and accurately identifies various potential defects at the construction site. As such, the introduction of this system is expected to significantly enhance the speed and accuracy of the construction supervision process.