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67th Annual Conference and Expo of the Institute of industrial Engineers 2017

作者： Moura, Louisi Francis De Lima, Edson Pinheiro Van Aken, Eileen Deschamps, Fernando Treinta, Fernanda Tavares Da Costa, Sergio E. Gouvea Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Federal University of Technology Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg United States

ISBN: (纸本)9780983762461

Nonprofit organizations have specific and complex characteristics that influence their performance measurement and differentiate it from others business models. So, it is important to know what are the factors, as motivations, barriers, and drivers, that influence the design of a performance measurement system to develop a complete system with all organizational issues, as social aspects, accountability, and legitimacy. A systematic literature review was previously performed and a set of factors that influence the design of performance measurement system for nonprofit organizations were identified. This paper presents a case study conducted in a Fire Department in the United States with the purpose investigate the set of factors in the practice. The data were collected using four techniques: interviews, documents and archival records, and observation. The findings show that the influence of the factors can vary depending on the type of organization and the paper indicates some reasons for that. Therefore, understanding the organization and these factors is a challenge for the performance measurement area to provide tools and frameworks that contribute to the organizational management and research area advance.

关键词： Social aspects

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Critical success factors for digital manufacturing implementation in the context of industry 4.0 67

Critical success factors for digital manufacturing implement...

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67th Annual Conference and Expo of the Institute of industrial Engineers 2017

作者： Shinohara, Ana Carolina Da Silva, Elias Hans Dener Ribeiro De Lima, Edson Pinheiro Deschamps, Fernando Da Costa, Sergio Eduardo Gouvea Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Brazil Federal University of Technology Parana Brazil Federal University of Parana Curitiba Brazil

ISBN: (纸本)9780983762461

Considering Industry 4.0 context, Digital Manufacturing (DM) stands out by combining conventional manufacturing technologies with digital techniques for modeling and analyzing the production system. Basically, its main goal is to assist product lifecycle management and to enable the development, through improvements and maturity evolution, of all aspects related to factory and product design. It is noticed there is a significant amount of information related to DM concepts and technologies, however its implementation process is not widely framed. This paper aims to propose a set of critical success factors (CSF) for DM implementation. For mapping CSF it is applied a research strategy based on academic literature review and consulting reports. Therefore, interviews were conducted for assessing digital manufacturing CSF influences in automotive assembly factory. The main result is a conceptual framework to assist organizations in developing a strategy for DM implementation, and map for that purpose all the required resources and capabilities. This research contributes for updating digital manufacturing CSF discussion in the new context of Industry 4.0 and it provides a guide to check the organizational readiness for DM.

关键词： Industry 4.0

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A semantic reconciliation view to support the interoperable information relationships in product design and manufacturing

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IFAC-PapersOnLine 2017年第1期50卷 15896-15903页

作者： Szejka, Anderson L. Junior, Osiris Canciglieri Panetto, Hervé Aubry, Alexis Loures, Eduardo F. Rocha Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Centre de Recherche en Automatique de Nancy University of Lorraine Vandoeuvre-lès-Nancy Cedex France CNRS CRAN UMR 7039 France

Global competitiveness has challenged manufacturing industry to rationalize different ways of bringing to the market new products in a short lead-time with competitive prices, high quality, and customization. Modern Product Development Process (PDP) has been requiring simultaneously collaboration of multiple groups, exchanging information from multiple perspectives within and across enterprise boundaries. However, semantic interoperability issues (misinterpretation and mistakes) in view of the information heterogeneity from multiple perspectives and their relationships. This research proposes a semantic reconciliation view to support the interoperable information relationships in product design and manufacturing. This view is part of the conceptual framework of an Interoperable Product Design and Manufacturing System (IPDMS). The semantic reconciliation method uses three approaches (Adjustment Context, Ontology Intersection, and Semantic Alignment) to provide support for the semantic information relationships across the product design and manufacturing. The method is applied in a rotational thin-wall plastic injected design and manufacturing and evaluated through the development of semantic rules responsible for the information mapping of sharing, conversion and translation. This semantic rules were modeled in Semantic Web Rule Language (SWRL) supported by Web Ontology Languages (OWL). Sequentially, the potential benefits and limitation of the method was discussed, contributing to the semantic information interoperability during the development of complex products. © 2017

关键词： Product design Competition industrial research Interoperability Manufacture Ontology Product development Semantics Formal Rules Information heterogeneity Manufacturing industries Multiple domains Product development process Semantic information Semantic interoperability Semantic Web Rule Language(SWRL)

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Developing a big data/analytics project: A case study in the auto industry 67

Developing a big data/analytics project: A case study in the...

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67th Annual Conference and Expo of the Institute of industrial Engineers 2017

作者： De Oliveira Cordeiro, Gabriel Deschamps, Fernando De Lima, Edson Pinheiro Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil Federal University of Technology Parana Curitiba Brazil

ISBN: (纸本)9780983762461

For an organization to develop a sustainable position in today's competitive and volatile market, efforts in increasing its maturity of Industry 4.0 thinking and implementation need to be performed. Inside the context of Industry 4.0, the Internet of Things (IoT) and Big Data/Analytics are concepts that are receiving an increased attention of both academics and industry practitioners. However, their implementation is challenging and requires a high level of competences, both in skilled labor and financial resources related to technology selection and implementation. There is a diversity of scenarios in which those two concepts can be applied, especially in large manufacturing enterprises, where large amounts of data are generated, and need to be interpreted, helping managers make better decisions. Hence, it is necessary to organize and better structure projects that conceive, manage, develop and implement IoT and Big Data/Analytics technologies. The objective of this study is to develop a case study presenting the architecture of a small IoT and Big Data/Analytics project in an automotive company. Results suggest that to promote such projects in a larger scale, organizations need to greatly enhance their infrastructure to develop the necessary integration between IoT components and Big Data/Analytics tools.

关键词： Big data

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Electromagnetic Optimization Based on Gaussian Crow Search Approach

Electromagnetic Optimization Based on Gaussian Crow Search A...

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2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2018

作者： Dos Santos Coelho, Leandro Klein, Carlos Eduardo Mariani, Viviana Cocco Do Nascimento, Carlos Augusto Richter Askarzadeh, Alireza PUCPR Industrial and Systems Eng. Graduate Program PPGEPS Federal University of Parana UFPR Department of Electrical Engineering DEE/PPGEE Pontifical Catholic University of Parana Curitiba PR Brazil PUCPR Mechanical Engineering Graduate Program PPGEM Curitiba Pontifical Catholic University of Parana PR Brazil Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran

ISBN: (纸本)9781538649411

Metaheuristic algorithms have provided efficient tools to designers by which it became possible to determine the optimum solutions of optimization problems encountered in several fields such as engineering, finance and computation. Generally metaheuristics are based on metaphors that are taken from nature or some other processes. Recently, an optimization metaheuristic paradigm based on the social behavior of the crows, called crow search algorithm (CSA), has been proposed. The idea of CSA is motivated from the storing process of the excess food in hiding places then restoring it in the necessary time. The CSA has many favorable characteristics, such as ease of implementation, the need to adjust only a few parameters, a favorable balance between search diversification and intensification, and fast convergence and high sensitivity. However, to enhance the performance of the standard CSA approach, this paper proposes to tune the control parameters using population diversity information and normal (Gaussian) probability distribution function. Loney's solenoid benchmark and circular antenna array design problems are used to evaluate the effectiveness of the conventional CSA and the proposed variant. Simulation results and comparisons with the CSA demonstrated that the performance of the proposed MCSA approach is promising in electromagnetics optimization. © 2018 IEEE.

关键词： Optimization

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Green Logistics Approach in Bioethanol Conversion from Potato Starch in Central Java

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IOP Conference Series: Materials Science and engineering 2019年第1期598卷

作者： R Yusianto Marimin Suprihatin H Hardjomidjojo Graduate Program of Agro-Industrial Technology Bogor Agricultural University (IPB) Bogor Indonesia Department of Agro-Industrial Technology Faculty of Agricultural Technology Bogor Agricultural University (IPB) Bogor Indonesia Division of Industrial and Systems Engineering (TSI) Bogor Agricultural University (IPB) Bogor Indonesia

The demand for alternative sources of fossil fuel energy in Indonesia is increasing. One alternative to fossil fuels is bioethanol. Potato skin starch can be a raw material for making bioethanol. Potato skin contains starch, cellulose, hemicellulose, lignin and sugar. Bioethanol can be used must go through several stages, including the logistics system. Logistics activities require transportation, material handling equipment, and warehouses. In Indonesia, transportation sector which is the biggest user of fuel actually receives fuel subsidies. Bioethanol is the most appropriate alternative solution to replace gasoline. In processing, bioethanol through five stages, namely, potato skin starch, raw material logistics, processing bioethanol, delivery process and customer. This study presents green logistics model of potato transportation with a cost model and GWP. The results of the study show that the green logistics model by looking at the total costs and environmental impacts can be used as a reference in developing environmentally friendly bioethanol production in the future.

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Automated 3D bone modelling based on geometric features from images 24

Automated 3D bone modelling based on geometric features from...

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24th International Conference on Production Research, ICPR 2017

作者： Rudek, M. Gumiel, Y.B. Canciglieri, O. Carvalho, D.R. Trajanovic, M. Industrial and Systems Engineering Post-Graduate Program PUCPR Imaculada Conceicao Street 1155 zip code CuritibaParana80215-901 Brazil Graduate Program on Health Technology in Pontifical Catholic University of Parana PUCPR Imaculada Conceicao Street 1155 zip code CuritibaParana80215-901 Brazil Faculty of Mechanical Engineering University of Nis Aleksandra Medvedeva 14 Niš18000 Serbia

ISBN: (纸本)9781605955070

Currently the computational modelling tools, expert algorithms for image segmentation and three-dimensional printing devices have improved the process of manufacture of customized pieces in the context of automation of prosthesis modelling. Among several strategies to obtain the correct shape of a missing bone part, we explore a modelling method based on geometric features defined by Bezier cubic curves. The data is obtained from sets of slices of tomographic scans as a reference. From the images, we know about data from the edges in the image but we do not have any information about a missing area in a specific bone region. Thus, the objective is to search patterns for features whose values are known from similar tomographic image which matches to fill a hole in a bone. Due the free form of a bone there are a lot of parameters to be evaluated. Thus, a Data Mining approach is applied for classification and to discover the best features as shape descriptors. In this way, the prosthesis manufacture can be automated in all stages among image scanning and printing. © 2017 DEStech Publications. All rights reserved.

关键词： Image enhancement

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Heuristic Kalman Algorithm for Multiobjective Optimization.

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IFAC-PapersOnLine 2017年第1期50卷 4460-4465页

作者： Hultmann Ayala H.V. dos Santos Coelho L. Reynoso-Meza G. Industrial and Systems Engineering Graduate Program PPGEPS Polytechnic School Pontifical Catholic University of Paraná PUCPR Rua Imaculada Conceição 1155 Curitiba PR code 80215-901 Brazil Department of Electrical Engineering (PPGEE) Federal University of Parana (UFPR) Polytechnic Center Curitiba Parana 81531-970 Brazil

This papers presents a new heuristic multiobjective optimization procedure based on Kalman filtering. The novel approach is compared to the well-known Nondominated Sorting Genetic Algorithm version II with the Zitzler, Deb and Thiele test suite showing favorable results for the Kalman based solution. Further improvement on the proposed algorithm is made with respect to the learning factor, which is set to be time-dependent, improving the algorithm in most cases and diminishing the number of control parameters. The proposed multiobjective optimization algorithm is interesting due to its convergence and coverage quality, its computational complexity and few parameters to be adjusted. © 2017

关键词： loop pairing Multi-input/multi-output systems Multi-objective optimization Multiloop control

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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