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Artificial Neural Network Backpropagation with Particle Swarm Optimization for Crude Palm Oil Price Prediction

Journal of Physics: Conference Series 2018年第1期1114卷

作者： Nur Salman Armin Lawi Syafruddin Syarif Dept. Informatics STMIK Dipanegara Makassar Indonesia Dept. Computer Science Hasanuddin University Makassar Indonesia Dept. Electrical Engineering Hasanuddin University Makassar Indonesia

Crude Palm Oil (CPO) is one of the plantation commodities provide the greatest contribution to Indonesia's foreign exchange. Because this plantation is one of the vegetable oil-producing plants with a high economic value. Therefore, the accuracy of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. This study aims to design a method of forecasting the price level for CPO. Neural Network Backpropagation (NN-BP) has been seen as a successful model in many systems recently. In this paper, we will apply Neural Network Backpropagation with a powerful stochastic optimization technique called Particle Swarm Optimization (PSO) to optimize the weight on NN-BP of Crude Palm Oil commodity price. The proposed method is a prediction model using an algorithm which combining particle swarm optimization (PSO) with Neural Network back-propagation (NN-BP) namely PSO-BP. The experimental results show that the proposed PSO–BP algorithm is better than standard Artificial Neural Network Backpropagation for accurate prediction and error convergence by providing better RMSE values.

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Mathematical modelling of calcium signalling taking into account mechanical effects

arXiv

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arXiv 2017年

作者： Kaouri, K. Chapman, S.J. Maini, P.K. Wolfson Centre for Mathematical Biology Mathematical Institute University of Oxford United Kingdom Department of Electrical Engineering Computer Engineering Informatics Cyprus University of Technology Cyprus Oxford Centre for Industrial and Applied Mathematics Mathematical Institute University of Oxford United Kingdom

Most of the calcium in the body is stored in bone. The rest is stored elsewhere, and calcium signalling is one of the most important mechanisms of information propagation in the body. Yet, many questions remain open. In this work, we initially consider the mathematical model proposed in Atri et al. [1]. Omitting diffusion, the model is a system of two nonlinear ordinary differential equations (ODEs) for the calcium concentration, and the fraction of IP3 receptors that have not been inactivated by the calcium. We analyse in detail the system as the IP3 concentration, the bifurcation parameter, increases presenting some new insights. We analyse asymptotically the relaxation oscillations of the model by exploiting a separation of timescales. Furthermore, motivated by experimental evidence that cells release calcium when mechanically stimulated and that, in turn, calcium release affects the mechanical behaviour of cells, we propose an extension of the Atri model to a 3D nonlinear ODE mechanochemical model, where the additional equation, derived consistently from a full viscoelastic ansatz, models the evolution of cell/tissue dilatation. Furthermore, in the calcium dynamics equation we introduce a new "stretch-activation" source term that induces calcium release and which involves a new bifurcation parameter, the "strength" of the source. Varying the two bifurcation parameters, we analyse in detail the interplay of the mechanical and the chemical effects, and we find that as the strength ofthe mechanical stimulus is increased, the IP3 parameter range for which oscillations emerge decreases, until oscillations eventually vanish at a critical value. Finally, we analyse the model when the calcium dynamics are assumed faster than the dynamics of the other two *** 34E10 · 37G10 · 92B05 · 35B3234E10, 37G10, 92B05, 35B32 Copyright © 2017, The Authors. All rights reserved.

关键词： Calcium

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The TRAVEE neuromotor rehabilitation system: In-vivo testing

The TRAVEE neuromotor rehabilitation system: In-vivo testing

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Oana-Maria Ferche Alin Dragos Bogdan Moldoveanu Maria-Iuliana Dascalu Constanta Nicoleta Bodea Robert Gabriel Lupu Danut Irimia Florica Moldoveanu Computer Science and Engineering Department University POLITEHNICA of Bucharest Bucharest Romania Department of Engineering in Foreign Language University POLITEHNICA of Bucharest Bucharest Romania Department of Economic Informatics and Cybernetics Bucharest University of Economic Studies Bucharest Romania Department of Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania Faculty of Electrical Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

The TRAVEE system is a Virtual Reality (VR) based system that will assist patients disabled as a result of a cerebrovascular accident as well as doctors, physicians and physiotherapists working alongside the patients toward the rehabilitation of their upper limbs. The system has been developed throughout the past two years and it has reached the state of the first in-vivo tests with the patients. The initial testing sessions were directed towards discovering any potential improvements that can be brought to the system regarding ease of use, comfort as well as performance and accuracy. The feedback received from the patients showed the great potential TRAVEE has in the field of rehabilitation as well as the openness of the patients towards a VR rehabilitation solution.

关键词： Tracking Optical devices Avatars Medical services Robot sensing systems Haptic interfaces

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Digital Image Security Application With Arnold Cat Map (ACM)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： A M H Pardede Dahlan Abdullah Budhi Santri Kusuma Citra Kurniawan Suwarni Akbar Iskandar Lusi Dwi Putri Cut Ita Erliana Defi Irwansyah A A Saleh S Sriadhi H Hartono College of Informatics Management and Computers Kaputama Stmik Kaputama Department of Informatics Universitas Malikussaleh Lhokseumawe Indonesia Department of Industrial Engineering Universitas Medan Area Medan Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Malang Indonesia Departement of Management Universitas Dehasen Bengkulu Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Department of Civil Engineering Faculty of engineering Universitas Lancang Kuning Pekanbaru Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Computer Science STMIK IBBI Medan Indonesia

The development of science and technology is advancing. Therefore, the data security needs to be applied. Cryptography is one of the sciences that learn mathematical techniques related to aspects of information security such as secrecy. One of them is known as Arnold Cat Map (ACM) algorithm on encryption and decryption by iterating digital images to secure an image. Arnold Cat Map (ACM) algorithm is one of the common cryptographic algorithms used to encrypt images. Application designed to secure an image by performing the process of randomizing the image pixel, the randomized image will change the pixel position based on the Arnold Cat Map method, then encryption and then the image encryption results become unknown. The result of the Arnold Cat Map method is that the random pixel position will return to its original position after going through several iterations with reference to the image capacity, where if the image size is different, the iteration will return to the original with different iteration point at time of encryption or decryption system by Arnold Cat Map method.

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Retraction: Digital Image Security Application With Arnold Cat Map (ACM) (J. Phys.: Conf. Ser. 1114 012059)

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Journal of Physics: Conference Series 2018年第1期1114卷

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s = 5.02 TeV

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Journal of High Energy Physics 2025年第1期2025卷 1-49页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Putte, S. Vannerom, D. Clerbaux, B. Dansana, S. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, J. Zhang, H. Aga Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Venezuela Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The inclusive jet cross section is measured as a function of jet transverse momentum pT and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment a... 详细信息

The inclusive jet cross section is measured as a function of jet transverse momentum p_T and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb⁻¹. The jets are reconstructed with the anti-k_T algorithm using a distance parameter of R = 0.4, within the rapidity interval |y| < 2, and across the kinematic range 0.06 < p_T< 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling α_S. © The Author(s) 2025.

关键词： Hadron-Hadron Scattering Jet Physics QCD

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On the Generalization of Stochastic Gradient Descent with Momentum

arXiv

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arXiv 2018年

作者： Ramezani-Kebrya, Ali Antonakopoulos, Kimon Cevher, Volkan Khisti, Ashish Liang, Ben Department of Informatics University of Oslo and Visual Intelligence Centre Integreat Norwegian Centre for Knowledge-driven Machine Learning Gaustadalléen 23B Ole-Johan Dahls hus Oslo0373 Norway EPFL EPFL STI IEL LIONS Station 11 LausanneCH-1015 Switzerland Department of Electrical and Computer Engineering University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that there exists a convex loss function for which the stability gap for multiple epochs of SGD with standard heavy-ball momentum (SGDM) becomes unbounded. Then, for smooth Lipschitz loss functions, we analyze a modified momentum-based update rule, i.e., SGD with early momentum (SGDEM) under a broad range of step-sizes, and show that it can train machine learning models for multiple epochs with a guarantee for generalization. Finally, for the special case of strongly convex loss functions, we find a range of momentum such that multiple epochs of standard SGDM, as a special form of SGDEM, also generalizes. Extending our results on generalization, we also develop an upper bound on the expected true risk, in terms of the number of training steps, sample size, and momentum. Our experimental evaluations verify the consistency between the numerical results and our theoretical bounds. SGDEM improves the generalization error of SGDM when training ResNet-18 on ImageNet in practical distributed settings. Copyright © 2018, The Authors. All rights reserved.

关键词： Momentum

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Toward opportunistic services for the industrial Internet of Things

Toward opportunistic services for the industrial Internet of...

引用

IEEE International Conference on Automation Science and engineering (CASE)

作者： Giancarlo Fortino Claudio Savaglio Mengchu Zhou Dept. of Informatics Modelling Electronics and Systems (DIMES) University of Calabria Rende CS Italy Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Services are expected to represent the real drivers for all Internet of Things (IoT) application scenarios, including Industrial IoT (IIoT). Indeed, rather than focusing only on products, enterprises are now investing on product-service hybrids to improve operational efficiency, boost productivity and, most importantly, build new markets, hence diversifying their revenues. In this direction, IoT service modeling is a crucial activity requiring a multi-facet in-depth examination. In this paper, we first discuss the importance of IoT services by eliciting related benefits and challenges. Then, we tackle service modeling from both a developer-oriented (comprising highlevel descriptive metamodels and ontologies) and an enterprise-oriented (comprising operational models subject to formal verification and execution, such as extensions of the conventional Business Process Models, Petri Nets and WorkFlows) perspective, providing a conceptual mapping between these two complementary approaches. The proposed new paradigm of “Opportunistic IoT Service” extends the available models by explicitly considering essential features for service provision, i.e., dynamicity, context-awareness, co-location and transience.

关键词： Business Computational modeling Context modeling Analytical models Biological system modeling Ontologies Sensors

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Unsupervised odometry and depth learning for endoscopic capsule robots

arXiv

引用

arXiv 2018年

作者： Turan, Mehmet Ornek, Evin Pinar Nail, Ibrahimli Giracoglu, Can Almalioglu, Yasin Yanik, Mehmet Fatih Sitti, Metin Mehmet Turan Is with Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Can Giracoglu Is with Informatics Faculty Evin Pinar Ornek Technical University of Muenich Nail Ibrahimli Germany Yasin Almalioglu Is with Computer Science Department University of Oxford Oxford United Kingdom M. Fatih Yanik Is with Department of Information Technology and Electrical Engineering Zurich Switzerland Metin Sitti Is with Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, realtime odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualit ENGLative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery. Copyright © 2018, The Authors. All rights reserved.

关键词： Motion estimation

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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