• 제목/요약/키워드: Artificial life

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생활 환경에서의 인공지능 시스템 성능 개선 및 평가를 위한 리빙랩 및 혼동 매트릭스 (Living Lab and Confusion Matrix for Performance Improvement and Evaluation of Artificial Intelligence System in Life Environment)

  • 하지원;서지석;이성수
    • 전기전자학회논문지
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    • 제24권4호
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    • pp.1180-1183
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    • 2020
  • 최근 들어 IoT와 스마트홈의 발전에 따라 낙상 사고 감지, 화상 위험 감지와 같이 일상 생활에서의 안전 감지 기능이 많이 보급되기 시작했다. 이러한 안전 감지 기능은 대부분 인공지능에 의해 수행된다. 그러나 실험실 환경에서 안전 감지의 정확도만 평가하는 경우에는 실제로 일상 생활 환경에서 체감하게 되는 성능과 꽤 큰 차이를 보이는 경우가 많다. 본 논문에서는 이러한 문제점을 보완하기 위해 사용하는 두 가지 기법인 리빙랩과 혼동 매트리스를 소개한다. 리빙랩은 단순히 일상 생활환경의 모사를 넘어서 사용자가 직접 기술 개발 및 제품 설계에 참여할 수 있는 통로가 된다. 또한 혼동 매트리스에서 도출되는 다양한 성능 척도는 사용 목적에 적합하게 인공지능 시스템의 성능을 평가하는데 큰 도움을 준다.

인공생명 알고리듬을 이용한 프로팅 링 저널 베어링 지지 축계의 최적설계 (Optimum design of rotor supported on floating ring journal bearing by the enhanced artificial life optimization algorithm)

  • 송진대;석호일;양보석
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2002년도 추계학술대회논문집
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    • pp.1034-1037
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    • 2002
  • This paper presents an optimum design of rotor-bearing system using a hybrid method to compute the solutions of optimization problem. The present hybrid algorithm namely Enhanced Artificial Life Algorithm(EALA), is a synthesis of an artificial life algorithm(ALA) and the random tabu search(R-tabu) method. We applied EALA to the optimum design of rotor-shaft system supported by the floating ring journal bearings. we will propose the optimum shape of rotor, position and shape of bearings. Through this study, we investigate the reliability and usefulness of EALA.

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인공생명 알고리듬을 이용한 프로팅 링 저널 베어링 지지 축계의 최적설계 (Optimum design of rotor supported on floating ring journal bearing by the enhanced artificial life optimization algorithm)

  • Song, Jin-Dea;Suk, Ho-Il;Yang, Bo-Suk
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2002년도 추계학술대회논문초록집
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    • pp.400.1-400
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    • 2002
  • This paper presents an optimum design of rotor-bearing system using a hybrid method to compute the solutions of optimization problem. The present hybrid algorithm, namely Enhanced Artificial Life Algorithm(EALA), is a synthesis of an artificial life algorithm(ALA) and the random tabu search(R-tabu) method. We applied EALA to the optimum design of rotor-shaft system supported by the floating ring journal bearings. (omitted)

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University Students' Thoughts on Artifical Abortion

  • Kim, Jungae
    • International Journal of Advanced Culture Technology
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    • 제10권2호
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    • pp.122-129
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    • 2022
  • This study is a phenomenological qualitative study that confirms the structure of college students' thoughts on artificial abortion. The data collection period was from 5 March to 10 April 2022. To this end, a total of three interviews were conducted on seven college students aged 20 to 25. Interview data were conducted through analysis and interpretation using the phenomenological research method, the Giorgi method, and as a result, 40 semantic units were derived, grouped into six sub-components, and divided into three categories. As a result of the analysis, college students' thoughts on artificial abortion consisted of fetal rights, respect for women's rights, and choices for a healthy life. Based on the above meaning, college students' thoughts on artificial abortion were, in conclusion, that considering the happiness of the baby and the quality of life of the woman, consideration for non-marriage mothers was more urgent than legal sanctions, and that abortion was not irresponsible. Accordingly, this study suggests that understanding and consideration for pregnant women should be prioritized over legal sanctions.

Investigation of random fatigue life prediction based on artificial neural network

  • Jie Xu;Chongyang Liu;Xingzhi Huang;Yaolei Zhang;Haibo Zhou;Hehuan Lian
    • Steel and Composite Structures
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    • 제46권3호
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    • pp.435-449
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    • 2023
  • Time domain method and frequency domain method are commonly used in the current fatigue life calculation theory. The time domain method has complicated procedures and needs a large amount of calculation, while the frequency domain method has poor applicability to different materials and different spectrum, and improper selection of spectrum model will lead to large errors. Considering that artificial neural network has strong ability of nonlinear mapping and generalization, this paper applied this technique to random fatigue life prediction, and the effect of average stress was taken into account, thereby achieving more accurate prediction result of random fatigue life.

효율적인 생물서식공간을 위한 인공부도 조성기법 개발 (Development of Artificial Floating Island for the Wild-Life Habitat)

  • 심우경;이광우;안창연;김민경
    • 한국환경복원기술학회지
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    • 제4권2호
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    • pp.84-91
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    • 2001
  • This study was carried out to develop the technology of artificial floating island for the wild-life habitat at the reservoir of Korea University farm near Seoul. After the execution of an artificial floating island with 6 cells(each $3{\times}3m$), each cell was planted with 5 different species and one mixed of them, to the reservoir in 1999 through 2000. The monitored results were as follows; 1. Typha orientalis, Zizania latifolia and Oenanthe japonica were died back, but Phragmites communis, Phragmites japonica and Juncus effusus var. decipiens were well growing. 2. The limits of sinking water depth of the planting foundation were different with the plant species, that is, 40cm to the Juncus effusus var. dicipiens and 50cm to Phragmites communis. Accordingly the water depth should be kept differently with each species. 3. 33 species of fauna were monitored in the first year(1999) and 43 species in the second (2000) increasingly. 4. For the more wild-lives inducing to the artificial floating island, establishing the eco-corridor from the surrounding environment was needed.

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CSR규정에 따른 수정 인공생명 알고리즘을 이용한 75.5k DWT 산적화물선의 최적설계 (The optimum design for 75.5k DWT bulk carrier using the multi-object modified artificial life algorithm by CSR rule)

  • 배동명;김학수
    • 수산해양기술연구
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    • 제48권2호
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    • pp.155-164
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    • 2012
  • The CSR rule was defined by IACS as the unified rule for a commercial ship like a bulk carrier and a tanker. It have been required more strict conditions for various parts like loading conditions, the local and girder strength, fatigue strength, FEM for the ship rule. It was changed in many parts of the ship rules. In this paper, the mid-parts of 17.5K DWT bulk carrier were optimized by the CSR rule. On the other hand, the modified artificial life algorithms with multi-object functions were developed for optimizing the scantling. It is possible to find multi-global optimum solutions in the multi-object functions. And it is faster and efficient than the artificial life algorithm. First, to be optimizing the scantling and the weight by CSR rule, that is calculated by the CSR rule. The next, the result is re-calculated by the modified artificial life algorithm with multi-object functions. The optimized results which are satisfied with the CSR rule like the minimum size and the thickness of stiffener and the minimum cost have been searched by the optimizing algorithm. And the results have been compared with the non-optimizing results.

인공 신경망을 이용한 크리프-피로 상호작용시 수명예측기법에 관한 연구 (A Study on the Life Prediction Method using Artificial Neural Network under Creep-Fatigue Interaction)

  • 권영일;김범준;임병수
    • 한국자동차공학회논문집
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    • 제9권6호
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    • pp.135-142
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    • 2001
  • The effect of tensile hold time on the creep-fatigue interaction in AISI 316 stainless steel was investigated. To study the fatigue characteristics of the material, strain controlled low cycle fatigue(LCF) tests were carried out under the continuous triangular waveshape with three different total strain ranges of 1.0%, 1.5% and 2.0%. To study the creep-fatigue interaction, 5min., 10min., and 30min. of tensile hold times were applied to the continuous triangular waveshape with the same three total strain ranges. The creep-fatigue life was found to be the longest when the 5min. tensile hold time was applied and was the shortest when the 30min. tensile hold time was applied. The cause fur the shortest creep-fatigue life under the 30min. tensile hold time is believed to be the effect of the increased creep damage per cycle as the hold time increases. The creep-fatigue life prediction using artificial neural network(ANN) showed closer prediction values to the experimental values than by the modified Coffin-Manson method.

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초등 생물분류 학습에서 인공지능 융합교육의 적용 사례 연구 (A Case Study on Application of Artificial Intelligence Convergence Education in Elementary Biological Classification Learning)

  • 신원섭
    • 한국초등과학교육학회지:초등과학교육
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    • 제39권2호
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    • pp.284-295
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    • 2020
  • The purpose of this study is to explore the possibility of artificial intelligence convergence education (AICE) in elementary biological classification learning. First, the possibility of AICE was analyzed in the field of 2015 revised elementary life science curriculum. The artificial intelligence biological classification (AIBC) education program targeted plant life. The possibility of AICE in the elementary life science curriculum was suggested through the consultation process of three elementary science education experts. The AIBC education program was developed through the review process of elementary education experts. The results of this study are as follows. First, 8(32%) achievement standards were available for AICE in elementary life science. Second, 18(86%) of the 21 items reviewed by the experts for the AIBC education program developed in this study were positively evaluated. Third, in this study, through the analysis of the possibility of AIBC in the elementary life field and the review of the experts, the AIBC education program including teaching and learning models, strategies, and guidance was developed. The results of this study were based on the review of the experts, and as a follow-up study, applied research to elementary students is needed. It is also hoped that various studies on AICE will be conducted not only in the life field but also in science and other fields. Finally, we expect that the results of this study will be applied to bio-classification learning to help students improve classification capabilities and generate classification knowledge.

Design of Artificial Intelligence Course for Humanities and Social Sciences Majors

  • KyungHee Lee
    • 한국컴퓨터정보학회논문지
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    • 제28권4호
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    • pp.187-195
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    • 2023
  • 본 연구는 엔트리 인공지능 모델을 활용하여 인문사회계열 대학생을 위한 인공지능 교양 교과목을 개발하는 데 목적이 있다. 컴퓨터, 인공지능, 교육학 전문가 집단을 구성하고 선행연구 분석, 델파이 기법을 활용하여 최종 인공지능 교양 교과목을 개발하였다. 연구결과 교육 주제는 크게 이미지 분류, 영상인식, 텍스트 분류, 소리 분류 총 4가지로 구성하였다. 교육 내용은 주제별로 1) 인공지능 원리 이해, 2) 엔트리 인공지능 모델 활용 실습, 3) 윤리적 영향성 확인, 4) 배운 내용을 기반으로 실생활 문제 해결을 위한 팀별 아이디어 회의 단계로 구성하였다. 본 교과목을 통해 인문사회계열 대학생은 인공지능 핵심기술의 원리 이해를 바탕으로 엔트리 인공지능 모델을 통해 직접 구현할 수 있고 더 나아가 실생활의 다양한 문제를 인공지능으로 해결해보는 경험을 기저로 기술을 이해하고 인공지능 시대 필요한 윤리를 모색해보며 책임감 있게 사용하는데 긍적적인 기여를 기대해볼 수 있을 것이다.