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37th International Cosmic Ray Conference, ICRC 2021

作者： Omura, Yugo Tsuda, Ryosuke Tsunesada, Yoshiki Bergman, Douglas R. Krizmanic, John F. Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Osaka City University Graduate School of Science Osaka Japan The University of Utah Department of Physics and Astronomy Salt Lake CityUT United States University of Maryland Baltimore County BaltimoreMD United States Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Li

The Non-Imaging CHErenkov (NICHE) Experiment is a low energy extension to Telescope Array (TA) using an array of closely spaced (∼100 m) light collectors covering an area of ∼2 square km. It is being deployed in the field-of-view of the FD for the TA Low Energy Extension (TALE) and overlaps with the TALE FD in the energy range above 2 PeV. Cosmic ray air showers with energies 1–100 PeV will be reconstructed using the Lateral Distribution of Cherenkov light from the air showers. This method allows shower energy and the maximum of shower depth (Xmax) to be determined. A prototype of the array, j-NICHE, has been making routine observations with 14 detectors since May, 2019. We will present the latest results of NICHE including the energy spectrum and the shower maximum distribution around the cosmic ray knee. © Copyright owned by the author(s).

关键词： Cosmic rays

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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IEEE Symposium on Network Operations and Management

作者： Árpád Somogyi Tamás Tettamanti Pál Varga Zsolt Szalay Dániel Baranyai Tamás Lovas Dept. of Photogrammetry and Geoinformatics Fac. of Civil Engineering Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Loránd Research Network Budapest Hungary Dept. of Telecommunications and Media Informatics Fac. of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Dept. of Automotive Technologies Fac. of Transportation Eng. and Vehicle Eng. Budapest University of Technology and Economics Budapest Hungary

One major goal of digital twin technology applied in the Architecture, engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated.

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Diffusion Approximation for Multiserver Retrial Queue with Two-Way Communication 23rd

Diffusion Approximation for Multiserver Retrial Queue with T...

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23rd International Conference on Distributed computer and Communication Networks, DCCN 2020

作者： Nazarov, Anatoly Phung-Duc, Tuan Paul, Svetlana Lizyura, Olga Institute of Applied Mathematics and Computer Science National Research Tomsk State University 36 Lenina ave. Tomsk634050 Russia Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan

ISBN: (纸本)9783030664701

In this paper, we consider a multiserver retrial queue with two-way communication. Incoming calls arrive according to the stationary Poisson process and occupy the servers. Durations of incoming calls have an exponential distribution. If all the servers are busy upon arrival, the incoming call joins the orbit. The time spent by the call in the orbit is an exponential random variable. Idle servers also make outgoing calls whose durations follow an exponential distribution. We derive the diffusion limits of the number of calls in the orbit and the approximation of its stationary probability distribution. © 2020, Springer Nature Switzerland AG.

关键词： Diffusion

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Wasserstein distributionally robust motion control for collision avoidance using conditional value-at-risk

arXiv

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arXiv 2020年

作者： Hakobyan, Astghik Yang, Insoon Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of

In this paper, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknown. We propose a novel model predictive control (MPC) method for limiting the risk of unsafety even when the true distribution of the obstacles' movements deviates, within an ambiguity set, from the empirical distribution obtained using a limited amount of sample data. By choosing the ambiguity set as a statistical ball with its radius measured by the Wasserstein metric, we achieve a probabilistic guarantee of the out-of-sample risk, evaluated using new sample data generated independently of the training data. To resolve the infinite-dimensionality issue inherent in the distributionally robust MPC problem, we reformulate it as a finite-dimensional nonlinear program using modern distributionally robust optimization techniques based on the Kantorovich duality principle. To find a globally optimal solution in the case of affine dynamics and output equations, a spatial branch-and-bound algorithm is designed using McCormick relaxation. The performance of the proposed method is demonstrated and analyzed through simulation studies using a nonlinear car-like vehicle model and a linearized quadrotor model. Copyright © 2020, The Authors. All rights reserved.

关键词： Predictive control systems

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Graph Machine Learning for Design of High-Octane Fuels

arXiv

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arXiv 2022年

作者： Rittig, Jan G. Ritzert, Martin Schweidtmann, Artur M. Winkler, Stefanie Weber, Jana M. Morsch, Philipp Heufer, K. Alexander Grohe, Martin Mitsos, Alexander Dahmen, Manuel Aachen52074 Germany Aarhus University Department of Computer Science Aarhus Denmark Delft University of Technology Department of Chemical Engineering Delft2629 HZ Netherlands RWTH Aachen University Chair of Computer Science 7 Aachen52074 Germany Delft University of Technology Delft Bioinformatics Lab Intelligent Systems Delft2628 XE Netherlands RWTH Aachen University High Pressure Gas Dynamics Aachen52074 Germany Forschungszentrum Jülich GmbH Institute for Energy and Climate Research IEK-10: Energy Systems Engineering Jülich52425 Germany JARA-ENERGY Aachen52056 Germany

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data. © 2022, CC BY.

关键词： Machine learning

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Triangle counting accelerations: From algorithm to in-memory computing architecture

arXiv

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arXiv 2021年

作者： Wang, Xueyan Yang, Jianlei Zhao, Yinglin Jia, Xiaotao Yin, Rong Chen, Xuhang Qu, Gang Zhao, Weisheng MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang University Beijing100191 China The Institute of Information Engineering Chinese Academy of Sciences Beijing China The Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States

Triangles are the basic substructure of networks and triangle counting (TC) has been a fundamental graph computing problem in numerous fields such as social network analysis. Nevertheless, like other graph computing problems, due to the high memory-computation ratio and random memory access pattern, TC involves a large amount of data transfers thus suffers from the bandwidth bottleneck in the traditional Von-Neumann architecture. To overcome this challenge, in this paper, we propose to accelerate TC with the emerging processing-in-memory (PIM) architecture through an algorithm-architecture co-optimization manner. To enable the efficient in-memory implementations, we come up to reformulate TC with bitwise logic operations (such as AND), and develop customized graph compression and mapping techniques for efficient data flow management. With the emerging computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) array, which is one of the most promising PIM enabling techniques, the device-to-architecture co-simulation results demonstrate that the proposed TC in-memory accelerator outperforms the state-of-the-art GPU and FPGA accelerations by 12.2× and 31.8×, respectively, and achieves a 34× energy efficiency improvement over the FPGA accelerator. Copyright © 2021, The Authors. All rights reserved.

关键词： Data transfer

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Reduced Firing of Nucleus Accumbens Parvalbumin Interneurons Impairs Risk Avoidance in DISC1 Transgenic Mice

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Neuroscience Bulletin 2021年第9期37卷 1325-1338页

作者： Xinyi Zhou Bifeng Wu Wenhao Liu Qian Xiao Wei He Ying Zhou Pengfei Wei Xu Zhang Yue Liu Jie Wang Jufang He Zhigang Zhang Weidong Li Liping Wang Jie Tu Guangdong Provincial Key Laboratory of Brain Connectome and Behavior CAS Key Laboratory of Brain Connectome and Manipulationthe Brain Cognition and Brain Disease InstituteShenzhen-Hong Kong Institute of Brain ScienceShenzhen Fundamental Research InstitutionsShenzhen Institute of Advanced TechnologyChinese Academy of Sciences518055 ShenzhenChina University of Chinese of Academy of Sciences Beijing 100049China Department of Information Technology and Electrical Engineering Zurich Swiss Federal Institute of Technology Zurich8092 ZurichSwitzerland Department of Biomedical Sciences City University of Hong KongKowloon TongHong Kong Virginia Polytechnic Institute and State University BlacksburgVirginia 24061USA Department of Psychiatry and Behavioral Sciences Emory University School of MedicineAtlanta 30322USA Bio-X Institutes Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders(Ministry of Education)Shanghai Key Laboratory of Psychotic Disordersand Brain Science and Technology Research CenterShanghai Jiao Tong UniversityShanghai 200240China Center of Brain Science State Key Laboratory of Magnetic Resonance and Atomic and Molecular PhysicsInnovation Academy for Precision Measurement Science and TechnologyNational Center for Magnetic Resonance in WuhanKey Laboratory of Magnetic Resonance in Biological SystemsWuhan Institute of Physics and MathematicsChinese Academy of Sciences430071 WuhanChina Department of Neuroscience City University of Hong KongKowloon TongHong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen 518057China

A strong animal survival instinct is to approach objects and situations that are of benefit and to avoid *** humans,a large proportion of mental disorders are accompanied by impairments in risk *** of the most important genes involved in mental disorders is disrupted-in-schizophrenia-1(DISC1),and animal models in which this gene has some level of dysfunction show emotion-related ***,it is not known whether DISC1 mouse models have an impairment in avoiding potential *** the present study,we used DISC1-N terminal truncation(DISC1-N^(TM))mice to investigate risk avoidance and found that these mice were impaired in risk avoidance on the elevated plus maze(EPM)and showed reduced social preference in a three-chamber social interaction *** EPM tests,c-Fos expression levels indicated that the nucleus accumbens(NAc)was associated with risk-avoidance behavior in DISC1-N^(TM)*** addition,in vivo electrophysiological recordings following tamoxifen administration showed that the firing rates of fast-spiking neurons(FS)in the NAc were significantly lower in DISC1-N^(TM)mice than in wild-type(WT)*** addition,in vitro patch clamp recording revealed that the frequency of action potentials stimulated by current injection was lower in parvalbumin(PV)neurons in the NAc of DISC1-N^(TM)mice than in WT *** impairment of risk avoidance in DISC1-N^(TM)mice was rescued using optogenetic tools that activated NAcPV ***,inhibition of the activity of NAcPV neurons in PV-Cre mice mimicked the risk-avoidance impairment found in DISC1-N^(TM)mice during tests on the elevated zero *** together,our findings confirm an impairment in risk avoidance in DISC1-N^(TM)mice and suggest that reduced excitability of NAc^(PV) neurons is responsible.

关键词： DISC1 Risk avoidance Parvalbumin Nucleus accumbens

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Cosmic Ray Composition in the Second Knee Region as Measured by the TALE Hybrid Detector 37

Cosmic Ray Composition in the Second Knee Region as Measured...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Fujita, Keitaro Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Graduate School of Science Osaka City University 3-3-138 Sugimoto Sumiyoshi-ku Osaka558-8585 Japan Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Cent

The Telescope Array Low-energy Extension (TALE) experiment is a hybrid air shower detector for the observation of air showers induced by cosmic rays with energy above 1016 eV. The TALE detector consists of a Fluorescence Detector (FD) station with 10 FD telescopes located at the TA Middle Drum FD Station (itself made up of 14 FD telescopes), and a Surface Detector (SD) array made up of 80 scintillation counters, including 40 with 400 m spacing and 40 with 600 m spacing. A triggering system for the TALE-SD using an external trigger from the TALE-FD, a so-called hybrid trigger, allows for a lower energy threshold. The TALE hybrid trigger system has been working since 2018. Here we present an estimate of the performance of hybrid detection using a Monte Carlo simulation, and a first measurement of the cosmic ray composition and spectrum using the TALE-Hybrid detector. © Copyright owned by the author(s).

关键词： Cosmic rays

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Queueing subject to action-dependent server performance: Utilization rate reduction

arXiv

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arXiv 2020年

作者： Lin, Michael Martins, Nuno C. La, Richard J. Department of Electrical & Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States

We consider a discrete-time system comprising a first-come-first-served queue, a non-preemptive server, and a stationary non-work-conserving scheduler. New tasks arrive at the queue according to a Bernoulli process. At each instant, the server is either busy working on a task or is available, in which case the scheduler either assigns a new task to the server or allows it to remain available (to rest). In addition to the aforementioned availability state, we assume that the server has an integer-valued activity state. The activity state is non-decreasing during work periods, and is non-increasing otherwise. In a typical application of our framework, the server performance (understood as task completion probability) worsens as the activity state increases. In this article, we expand on stabilizability results recently obtained for the same framework to establish methods to design scheduling policies that not only stabilize the queue but also reduce the utilization rate -understood as the infinite-horizon timeaveraged expected portion of time the server is working. This article has a main theorem leading to two main results: (i) Given an arrival rate, we describe a tractable method, using a finitedimensional linear program (LP), to compute the infimum of all utilization rates achievable by stabilizing scheduling policies. (ii) We propose a tractable method, also based on finitedimensional LPs, to obtain stabilizing scheduling policies that are arbitrarily close to the aforementioned infimum. We also establish structural and distributional convergence properties, which are used throughout the article, and are significant in their own right. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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Using Machine Learning for Industry 5.0 Efficiency Prediction Based on Security and Proposing Models to Enhance Efficiency

Using Machine Learning for Industry 5.0 Efficiency Predictio...

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International Conference on System Modeling & Advancement in research Trends (SMART)

作者： Piyush Pant Anand Singh Rajawat S.B. Goyal Deepmala Singh Neagu Bogdan Constantin Maria Simona Raboaca Chaman Verma School of Computer Sciences and Engineering Sandip University Nashik India School of Computer Science and engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Symbiosis Centre of Management Studies (SCMS) Nagpur Symbiosis International (Deemed University)(SIU) India Department of Power Engineering “Gheorghe Asachi” Technical University of Iasi Iasi ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Informatics University of Eötvös Loránd Budapest Hungary

ISBN: (纸本)9781665487351

Machine learning, with its huge untapped potential, is being researched all over the world to develop truly intelligent systems. Its applications are not enclosed in just one domain but in almost everything, from prediction models, recommender systems, and anomaly detection to automation and teaching a computer how to fly a helicopter. In this research, Multivariate Linear regression of supervised machine learning is studied to predict the efficiency of Industry 5.0, however, the efficiency of the model would be dependent on many factors and components such as security protocols and models, Industrial IoT - performance, connectivity, reachability, availability and many more. These factors and components would be categorized as the features of the algorithm which would be assigned weight ‘w’ and bias ‘b’. To improve the efficiency of the model, these components could be changed and updated in order to enhance the overall model. Previous research papers discussed the integration of “hot” technologies like 5G, Blockchain, AI, and IIoT in the industry 5.0 model, but this research is presented as their future work as it proposes to determine the efficiency of the model based on the features provided so that ultimate and optimal model could be determined. Later it proposes security and IIoT models that could improve the overall Industry 5.0. Quorum blockchain is proposed by the research in order to implement the ultimate security in the Industry 5.0.

关键词： Industries Protocols Machine learning algorithms Computational modeling Machine learning Predictive models Prediction algorithms

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