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Disturbance observer-based finite-time attitude maneuver control for micro satellite under actuator deviation fault

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Aerospace science and Technology 2018年 82-83卷 262-271页

作者： Jianzhong Qiao Dafa Zhang Yukai Zhu Peixi Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Systems Engineering Research Institute China State Shipbuilding Corporation Beijing China The 9th Designing of China Aerospace Science and Industry Corporation Wuhan China

Precise and fast attitude maneuvers are a requirement in many future space missions. However, the actuator deviation fault of micro satellite is inevitable, which affects the precision of spacecraft pointing. To overcome this difficulty, this paper presents a novel attitude control scheme to compensate the actuator deviation fault and achieve the finite-time attitude maneuver. Moreover, the proposed control scheme is constituted by an outer attitude tracking loop and an inner angular velocity tracking loop according to dynamic features of micro satellite. In the outer loop, a novel virtual control law, namely a desired angular velocity command, is developed, under which the finite-time attitude tracking can be achieved. Furthermore, in the inner loop, an effective composite controller which consists of a terminal sliding mode controller and a finite-time disturbance observer (FTDO) is proposed. The aim of composite controller is to ensure that the angular velocity can track the virtual control law in finite time, where the FTDO is designed to estimate and compensate the actuator deviation fault in the feed-forward channel. Finally, simulation results are conducted to demonstrate the effectiveness of the proposed control scheme.

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2022 Roadmap on Neuromorphic Computing and engineering

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arXiv 2021年

作者： Christensen, Dennis Valbjørn Dittmann, Regina Linares-Barranco, Bernabé Sebastian, Abu Le Gallo, Manuel Redaelli, Andrea Slesazeck, Stefan Mikolajick, Thomas Spiga, Sabina Menzel, Stephan Valov, Ilia Milano, Gianluca Ricciardi, Carlo Liang, Shi-Jun Miao, Feng Lanza, Mario Quill, Tyler J. Keene, Scott T. Salleo, Alberto Grollier, Julie Marković, Danijela Mizrahi, Alice Yao, Peng Yang, J. Joshua Indiveri, Giacomo Strachan, John Paul Datta, Suman Vianello, Elisa Valentian, Alexandre Feldmann, Johannes Li, Xuan Pernice, Wolfram H.P. Bhaskaran, Harish Furber, Steve Neftci, Emre Scherr, Franz Maass, Wolfgang Ramaswamy, Srikanth Tapson, Jonathan Panda, Priyadarshini Kim, Youngeun Tanaka, Gouhei Thorpe, Simon Bartolozzi, Chiara Cleland, Thomas A. Posch, Christoph Liu, Shih-Chii Panuccio, Gabriella Mahmud, Mufti Mazumder, Arnab Neelim Hosseini, Morteza Mohsenin, Tinoosh Donati, Elisa Tolu, Silvia Galeazzi, Roberto Christensen, Martin Ejsing Holm, Sune Ielmini, Daniele Pryds, N. Department of Energy Conversion and Storage Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Peter Gruenberg Institute 7 Forschungszentrum Juelich GmbH Juelich52425 Germany JARA-FIT RWTH Aachen University Aachen52056 Germany CSIC Universidad de Sevilla Seville41092 Spain IBM Research Zurich Switzerland STMicroelectronics Agrate Italy Dresden01187 Germany Institute of Semiconductors and Microsystems TU Dresden Dresden Germany CNR-IMM Unit of Agrate Brianza via C. Olivetti 2 MB Agrate Brianza Italy Juelich Germany Torino Italy Department of Applied Science and Technology Politecnico di Torino Torino Italy National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Thuwal23955-6900 Saudi Arabia Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Unité Mixte de Physique CNRS Thales Université Paris-Saclay Palaiseau91767 France Electrical and Computer Engineering Department University of Southern California Los AngelesCA United States Institute of Neuroinformatics University of Zurich ETH Zurich Switzerland Hewlett Packard Laboratories Hewlett Packard Enterprise San JoseCA United States Department of Electrical Engineering University of Notre Dame Notre DameIN United States CEA LETI Université Grenoble Alpes Grenoble France CEA LIST Université Grenoble Alpes Grenoble France Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Institute of Physics University of Münster Heisenbergstr. 11 Heisenbergstr Münster48149 Germany Center for Soft Nanoscience University of Münster Münster48149 Germany The University of Manchester United Kingdom Department of Cognitive Sciences University of California Irvine IrvineCA United States Institute of Theoretica

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community. © 2021,

关键词： data transfer

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： Computerized tomography

Author Correction: CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

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Nature communications 2025年第1期16卷 1569页

作者： Chunge Zhong Wen-Jie Jiang Yingjia Yao Zexu Li You Li Shengnan Wang Xiaofeng Wang Wenjuan Zhu Siqi Wu Jing Wang Shuangshuang Fan Shixin Ma Yeshu Liu Han Zhang Wenchang Zhao Lu Zhao Yi Feng Zihan Li Ruifang Guo Li Yu Fengyun Pei Jun Hu Xingzhi Feng Zihuan Yang Zhengjia Yang Xueying Yang Yue Hou Danni Zhang Dake Xu Ren Sheng Yihao Li Lijun Liu Hua-Jun Wu Jun Huang Teng Fei Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health Sciences Northeastern University Shenyang 110819 China. National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110819 China. Foshan Graduate School of Innovation Northeastern University Foshan 528311 China. Peking University Third Hospital Beijing 100191 China. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Clinical Research Center the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Institute of Gastroenterology Guangzhou China. Department of Cardiothoracic Surgery Jinqiu Hospital of Liaoning Province Shenyang China. Department of Thoracic Surgery The Fourth Affiliated Hospital of China Medical University Shenyang China. Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education) School of Materials Science and Engineering Northeastern University Shenyang China. BeiGene Institute BeiGene (Shanghai) Research & Development Co. Ltd 200131 Shanghai China. Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Peking University Cancer Hospital & Institute Beijing 100142 China. hjwu@***. Department of Biomedical Informatics School of Basic Medical Sciences Peking University Health Science Center Beijing 100191 China. hjwu@***. Center for Precision Medicine Multi-Omics Research Institute of Advanced Clinical Medicine Peking University Beijing 100191 China. hjwu@***. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. huan

A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification 1st

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A Novel Intrusion Detection System Based on Advanced Naive B...

1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Wang, Yunpeng Li, Yuzhou Tian, Daxin Wang, Congyu Wang, Wenyang Hui, Rong Guo, Peng Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Intrusion detection

Reply to Response to Vacuous standards - Subversion of the OSAC standards-development process

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Forensic science international. Synergy

Forensic science international. Synergy 2021年 3卷 100149页

作者： Geoffrey Stewart Morrison Cedric Neumann Patrick Henry Geoghegan Gary Edmond Tim Grant R Brent Ostrum Paul Roberts Michael Saks Denise Syndercombe Court William C Thompson Sandy Zabell Forensic Data Science Laboratory & Forensic Speech Science Laboratory Computer Science Department & Aston Institute for Forensic Linguistics Aston University Birmingham UK. Forensic Evaluation Ltd Birmingham UK. Two N's Forensics Inc Brookings SD USA. Department of Biomedical Engineering Aston University Birmingham UK. Forensic Data Science Laboratory Computer Science Department & Aston Institute for Forensic Linguistics Aston University Birmingham UK. Faculty of Law and Justice University of New South Wales Sydney NSW Australia. Aston Institute for Forensic Linguistics Aston University Birmingham UK. Forensic Document Examination Section Science and Engineering Directorate Canada Border Services Agency Ottawa ON Canada. School of Law University of Nottingham Nottingham UK. China University of Political Science and Law Beijing China. Sandra Day O'Connor College of Law Department of Psychology And Center for Law Science & Innovation Arizona State University Phoenix AZ USA. King's Forensics King's College London London UK. Departments of Criminology and Psychology and School of Law University of California Irvine CA USA. Departments of Mathematics and Statistics Northwestern University Evanston IL USA.

This Letter to the Editor is a reply to Mohammed et al. (2021) https://***/10.1016/***.2021.100145, which in turn is a response to Morrison et al. (2020) "Vacuous standards - subversion of the OSAC standards-deve... 详细信息

关键词： Forensic science Method validation Quality management Standard Validation

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

Reliable Shortest Path Guidance in Stochastic Road Networks Using Convolution-Based Path Finding Algorithm 18

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Reliable Shortest Path Guidance in Stochastic Road Networks ...

18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Chen, Peng Tong, Rui Lu, Guangquan Wang, Yunpeng Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Xue Yuan Rd. No. 37 Hai Dian District Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Xue Yuan Rd. No. 37 Hai Dian District Beijing100191 China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

Most existing research on routing guidance only pays attention to the average value of path travel time, which fails to consider travel time variability (TTV) and travel time reliability preferences by different travelers. In this study, a convolution-based modified genetic algorithm (CMGA) is proposed to find the reliable shortest path in stochastic road networks. By accounting for traveler risk tolerance, the algorithm enables the provision of personalized routing guidance for individual travelers. To support online applications in a large-scale network, reasonable heuristic constraints are imposed to help reduce the computational workload and accelerate the convergence speed of the search process. Numerical case studies based on a grid network with random offsets are provided, and the results help verify that the algorithm has the potential to solve reliable shortest path searching problems in a large-scale network with desirable efficiency. © 2018 American Society of Civil Engineers.

关键词： Convolution

Adaptive diffusion processes of time-varying local information on networks

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arXiv 2018年

作者： Niu, Ruiwu Wu, Xiaoqun Lu, Jun-an Lü, Jinhu School of Mathematics and Statistics Wuhan University Hubei430072 China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing100083 China

—This paper mainly discusses the diffusion on complex networks with time-varying couplings. We propose a model to describe the adaptive diffusion process of local topological and dynamical information, and find that the Barabasi-Albert scale-free network (BA network) is beneficial to the diffusion and leads nodes to arrive at a larger state value than other networks do. The ability of diffusion for a node is related to its own degree. Specifically, nodes with smaller degrees are more likely to change their states and reach larger values, while those with larger degrees tend to stick to their original states. We introduce state entropy to analyze the thermodynamic mechanism of the diffusion process, and interestingly find that this kind of diffusion process is a minimization process of state entropy. We use the inequality constrained optimization method to reveal the restriction function of the minimization and find that it has the same form as the Gibbs free energy. The thermodynamical concept allows us to understand dynamical processes on complex networks from a brand-new perspective. The result provides a convenient means of optimizing relevant dynamical processes on practical circuits as well as related complex systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Entropy