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PDG Pressure Estimation in Offshore Oil Well: Extended Kalman Filter vs. Artificial Neural Networks

IFAC-PapersOnLine 2019年第1期52卷 508-513页

作者： Andressa Apio Jônathan W.V. Dambros Fabio C. Diehl Marcelo Farenzena Jorge O. Trierweiler Group of Intensification Modeling Simulation Control and Optimization of Processes (GIMSCOP) Chemical Engineering Department Federal University of Rio Grande do Sul (UFRGS) R. Eng Luiz Englert s/n Campus Central Porto Alegre RS Brazil Department of Computer Science University of Kaiserslautern 67663 Kaiserslautern Deutschland Research and Development Center Petrobras 21941915 Rio de Janeiro Brazil

The Permanent Downhole Gauge (PDG) pressure measurement is of great importance for offshore oil well modeling and control since it is measured close to the bottom hole. The PDG is installed in a remote undersea environment, which makes expensive the maintenance in case of fault. For this reason, PDG measurements are frequently unavailable. To overcome this limitation, the PDG pressure can be estimated using other available measurements. The estimation is not a simple task since, depending on process operational conditions, the multiphase flow might present limit cycles. In this work, Artificial Neural Network (ANN) and Extended Kalman Filter (EKF) are proposed as potential techniques for the PDG pressure estimation. The comparison of the results shows that ANN returns precise estimation for a short-time window after the failure, but fails when a different process operating condition is applied, while EKF returns good estimation in all the cases.

关键词： Modelling System Identification Data Analytics Machine Learning Inferential sensing State Estimation Sensor Development

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Calibration of the integrating sphere for O4 in KAGRA gravitational wave telescope. 38

Calibration of the integrating sphere for O4 in KAGRA gravit...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Fujii, Shingo Abe, H. Akutsu, T. Ando, M. Aoumi, M. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bajpai, R. Cannon, K. Cao, Z. Chang, R.-J. Chen, A.H.-Y. Chen, D. Chen, H. Chen, Y. Chiba, A. Chiba, R. Chou, C. Eisenmann, M. Fujii, S. Fukunaga, I. Haba, D. Haino, S. Han, W.-B. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Hoshino, S. Hsieh, H.-F. Hsiung, C. Hsu, S.-C. Hui, D.C.Y. Inayoshi, K. Itoh, Y. Iwaya, M. Jin, H.B. Jung, K. Kajita, T. Kamiizumi, M. Kanda, N. Kato, J. Kato, T. Kim, S. Kimura, N. Kiyota, T. Kohri, K. Kokeyama, K. Komori, K. Kong, A.K.H. Koyama, N. Kume, J. Kuroyanagi, S. Kuwahara, S. Kwak, K. Lai, S. Lee, H.W. Lee, R. Lee, S. Leonardi, M. Li, K.L. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Liu, G.C. Ma, L.-T. Maeda, K. Matsuyama, M. Meyer-Conde, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, S. Miyoki, S. Morisaki, S. Moriwaki, Y. Murakoshi, M. Nakamura, K. Nakano, H. Narikawa, T. Nguyen Quynh, L. Nishino, Y. Nishizawa, A. Obayashi, K. Oh, J.J. Oh, K. Ohashi, M. Ohkawa, M. Oohara, K. Oshima, Y. Oshino, S. Page, M.A. Pan, K.-C. Park, J. Peña Arellano, F.E. Saha, S. Sakai, K. Sako, T. Sato, R. Sato, S. Sato, Y. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimode, K. Shinkai, H. Shiota, J. Somiya, K. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takatani, K. Takeda, H. Takeda, M. Tamaki, M. Tanaka, K. Tanaka, S.J. Tanaka, T. Taruya, A. Tomaru, T. Tomita, K. Tomura, T. Toriyama, A. Trani, A.A. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Wang, H. Washimi, T. Wu, C. Wu, H. Yamamoto, K. Yamamoto, M. Yamamoto, T. Yamamoto, T.S. Yamamura, S. Yamazaki, R. Yang, L.-C. Yang, Y. Yeh, S.-W. Yokoyama, J. Yokozawa, T. Yuzurihara, H. Zhao, Y. Zhu, Z.-H. Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan LIGO Hanford Observatory RichlandWA99352 United States 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Physics National Cheng Kung University No.1 University Road Tainan City701 Taiwan Institute of Physics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Department of Physics National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Science University of Toyama 3190 Gofuku Toyama City Toyama930-8555 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa City277-8582 Japan Department of Electrophysics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics Graduate School of Science Osaka Metropolitan University 3-3-138 Sugimoto-cho Sumiyoshi-ku Osaka City Osaka558-8585 Japan Institute of Physics Academia Sinica 128 Sec. 2 Academia Rd. Na

KAGRA, the gravitational wave telescope located in Japan, has started the 4th international observing run called O4 with LIGO and Virgo. The accuracy of parameter estimation for gravitational wave source objects depends on the accuracy of calibration. LIGO, Virgo, and KAGRA use the same calibration method called the photon calibrator (Pcal) method. In the Pcal system, we will obtain the precise displacement of a mirror induced by a well-calibrated radiation pressure of a laser beam which is injected into the mirror. As the uncertainty of the Pcal system is primarily limited by the uncertainty of the laser power measurement, we use integrating sphere (IS) power meters, which are considered to have good accuracy and precision, for the laser power measurement. However, during our calibration measurements in O3GK, we found that there were unidentified systematic errors in the laser power measurement. Therefore, we investigated the incident direction dependence and polarization dependence of the IS power meter. According to our study, the angular dependence differs between two types of ISs, namely GPS (General Purpose Spheres) and LPM (Laser Power Measurement Spheres), which are utilized in our calibration system. The angular dependence of the GPS type was greater than that of the LPM type. The GPS measurement showed an approximate 8.4% increase from the value obtained with perpendicular incidence at maximum. In contrast, the LPM measurement showed a 0.2% increase from the value obtained with perpendicular incidence at maximum. As to polarization dependence, the effect of that on the LPM type was within the range of -0.06% to 0.06%. From this study, we found a possibility that the angle dependence was able to explain the uncertainty observed in O3GK. In this proceeding, we report the angular and polarization dependence of ISs and consider the effect on the uncertainty of the KAGRA Pcal system. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Spheres

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Software Intrusion Detection Evaluation System: A Cost-Based Evaluation of Intrusion Detection Capability

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Communications and Network 2018年第4期10卷 211-229页

作者： Agbotiname L. Imoize Taiwo Oyedare Michael E. Otuokere Sachin Shetty Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg USA Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Modeling Analysis and Simulation Center Old Dominion University Norfolk USA

In this paper, we consider a cost-based extension of intrusion detection capability (CID). An objective metric motivated by information theory is presented and based on this formulation;a package for computing the intrusion detection capability of intrusion detection system (IDS), given certain input parameters is developed using Java. In order to determine the expected cost at each IDS operating point, the decision tree method of analysis is employed, and plots of expected cost and intrusion detection capability against false positive rate were generated. The point of intersection between the maximum intrusion detection capability and the expected cost is selected as the optimal operating point. Considering an IDS in the context of its intrinsic ability to detect intrusions at the least expected cost, findings revealed that the optimal operating point is the most suitable for the given IDS. The cost-based extension is used to select optimal operating point, calculate expected cost, and compare two actual intrusion detectors. The proposed cost-based extension of intrusion detection capability will be very useful to information technology (IT), telecommunication firms, and financial institutions, for making proper decisions in evaluating the suitability of an IDS for a specific operational environment.

关键词： Intrusion Detection System Intrusion Detection Capability (CID) Information Theory Software Intrusion Detection Evaluation System (SIDES)

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

引用

Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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Singularity structure simplification of hexahedral mesh via weighted ranking

arXiv

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arXiv 2019年

作者： Xua, Gang Ling, Ran Zhang, Yongjie Jessica Xiao, Zhoufang Ji, Zhongping Rabczuk, Timon School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou310018 China Department of Mechanical Engineering Carnegie Mellon University United States Institute of Structural Mechanics Bauhaus-Universitat Weimar Germany

In this paper, we propose an improved singularity structure simplification method for hexahedral (hex) meshes using a weighted ranking approach. In previous work, the selection of to-be-collapsed base complex sheets/chords is only based on their thickness, which will introduce a few closed-loops and cause an early termination of simplification and a slow convergence rate. In this paper, a new weighted ranking function is proposed by combining the valence prediction function of local singularity structure, shape quality metric of elements and the width of base complex sheets/chords together. Adaptive refinement and local optimization are also introduced to improve the uniformity and aspect ratio of mesh elements. Compared to thickness ranking methods, our weighted ranking approach can yield a simpler singularity structure with fewer base-complex components, while achieving comparable Hausdorff distance ratio and better mesh quality. Comparisons on a hex-mesh dataset are performed to demonstrate the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Aspect ratio

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Attitude Synchronization for Multiple Quadrotors using Reinforcement Learning

Attitude Synchronization for Multiple Quadrotors using Reinf...

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第三十八届中国控制会议

作者： Hao Liu Wanbing Zhao Frank L.Lewis Zhong-Ping Jiang Hamidreza Modares School of Astronautics Beihang University Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies of Ministry of Education Beihang University the University of Texas at Arlington Research Institute University of Texas at Arlington the Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University the University of Texas at Arlington Research Institute

In this paper, a reinforcement learning based control law is proposed to solve the attitude synchronization problem of the leader-following multi-quadrotor systems. The overall system is composed of a team of quadrotors, modeled with highly nonlinear and coupled dynamics. An optimal control solution is obtained by solving an augmented HamiltonJacobi-Bellman equation. A reinforcement learning approach is used to learn the optimal control law. simulation results are provided to verify the effectiveness of the proposed controller.

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The status of KAGRA underground cryogenic gravitational wave telescope 15

The status of KAGRA underground cryogenic gravitational wave...

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15th International Conference on Topics in Astroparticle and Underground Physics, TAUP 2017

作者： Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Barton, M.A. Cannon, K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Furuhata, T. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, A.H. Inoue, Y. Ioka, K. Itoh, Y. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kambara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kim, C. Kim, H. Kim, J. Kim, Y. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kumar, R. Lee, H. Lee, H. Liu, Y. Luca, N. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Miyakawa, O. Miyamoto, T. Miyamoto, A. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Narikawa, T. Quynh, L.Nguyen Ni, W.-T. Ochi, T. Oh, J. Oh, S. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Alleano, F.E.P. Pinto, I. Sago, N. Saijo, M. Saito, Y. Sakai, K. Sakai, Y. Sasaki, Y. Sasaki, M. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shoda, A. Somiya, K. Son, E. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueki, S. Ueno, K. Ushiba, T. Van Putten, M.H.P.M. Vocca, H. Wada, S. Wakamatsu, T. Yamada, T. Yamamoto, S. Yamamoto, T. Yamamoto, K. Yamamoto, A. Yokoyama, J. Yokozawa, T. Yoon, T.H. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo113-0032 Japan Department of Advanced Physics Hirosaki University Aomori Hirosaki036-8561 Japan National Astronomical Observatory of Japan Hida Gifu506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama Toyama930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka558-8585 Japan Department of Physics Osaka University Osaka Toyonaka560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon 34141 Korea Republic of Yuseong Daejeon34055 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Computer Simulation Inje University Gimhae Gyeongsangnam50834 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of High Energy Accelerator Research Organization Ibaraki Tsukuba305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka814-0180 Japan Department of Advanced Materials Science University of Tokyo Chiba Kashiwa277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma00185 Italy Department o

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described. © Published under licence by IOP Publishing Ltd.

关键词： Interferometers

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Computational representation for a simulation scenario definition language

Computational representation for a simulation scenario defin...

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AIAA Modeling and simulation Technologies Conference, 2018

作者： Durak, Umut Jafer, Shafagh Wittman, Rob Mittal, Saurabh Hartmann, Sven Zeigler, Bernard P. Institute of Flight Systems Braunschweig38108 Germany Embry-Riddle Aeronautical University Daytona BeachFL32114-3900 United States The MITRE Corporation McLean VA22102 United States Clausthal University of Technology Clausthal-Zellerfeld38678 Germany RTSync Corp. and Arizona Center for Integrated M&S TucsonAZ85621 United States Flight Dynamics and Simulation Department of Electrical Computer Software and Systems Engineering Department Head Simulation Experimentation and Gaming Architecture Analytics Department Department of Informatics Department of Electrical and Computing Engineering

ISBN: (数字)9781624105289

ISBN: (纸本)9781624105289

The modeling and simulation community in aviation is in search of standardization in simulation scenario definition. While the System Entity Structure (SES) ontology promises an effective formal means of domain modeling, computational representation is still a key challenge to tackle for fostering wide-acceptance and standardization. This paper presents an eXtended Markup Language (XML) based approach. It first gives the relational specification and the schema definition of the SES ontology. Following a layered modeling workflow, it then presents a schema definition for the simulation scenario definition language and exemplifies its utilization. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Standardization

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Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis

arXiv

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arXiv 2020年

作者： Guo, Hongwei Zhuang, Xiaoying Chen, Pengwan Alajlan, Naif Rabczuk, Timon Department of Geotechnical Engineering Tongji University 1239 Siping Road Shanghai200092 China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji University 1239 Siping Road Shanghai200092 China Alisr Laboratory College of Computer and Information Sciences King Saud University P. O. Box 51178 Riyadh11543 Saudi Arabia Computational Science and Simulation Technology Institute of Photonics Leibniz Universität Hannover Appelstr. 11 Hannover30167 Germany State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology No. 5 South Street Zhongguancun Haidian District Beijing100081 China

In this work, we present a deep collocation method (DCM) for three dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations. Copyright © 2020, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Metrics and Mechanisms: Measuring the Unmeasurable in the Science of Science

arXiv

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arXiv 2021年

作者： Wu, Lingfei Kittur, Aniket Youn, Hyejin Milojević, Staša Leahey, Erin Fiore, Stephen M. Ahn, Yong Yeol School of Computing and Information The University of Pittsburgh 135 N Bellefield Ave PittsburghPA15213 United States Human-Computer Interaction Institute Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Kellogg School of Management Northwestern University 2211 Campus Dr EvanstonIL60208 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States Luddy School of Informatics Computing and Engineering Indiana University Bloomington 919 E 10th St. BloomingtonIN47408 United States School of Sociology University of Arizona 1145 E. South Campus Drive TucsonAZ85721 United States Department of Philosophy Institute for Simulation and Training University of Central Florida 3100 Technology Parkway OrlandoFL32826 United States

What science does, what science could do, and how to make science work? If we want to know the answers to these questions, we need to be able to uncover the mechanisms of science, going beyond metrics that are easily collectible and quantifiable. In this perspective piece, we link metrics to mechanisms by demonstrating how emerging metrics of science not only offer complementaries to existing ones, but also shed light on the hidden structure and mechanisms of science. Based on fundamental properties of science, we classify existing theories and findings into: hot and cold science referring to attention shift between scientific fields, fast and slow science reflecting productivity of scientists and teams, soft and hard science revealing reproducibility of scientific research. We suggest that interest about mechanisms of science since Derek J. de Solla Price, Robert K. Merton, Eugene Garfield, and many others complement the zeitgeist in pursuing new, complex metrics without understanding the underlying processes. We propose that understanding and modeling the mechanisms of science condition effective development and application of metrics. © 2021, CC BY-NC-ND.

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