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11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020

作者： Wang, Xianping Chen, Weiru Qiu, Hao Eldurssi, Awad Xie, Fei Shen, Jiayue Purdue University Northwest Dept of Computer Information Science and Graphics HammondIN46323 United States Old Dominion University Dept of Information Technology and Decision Sciences NorfolkVA23529 United States Fort Valley State University Dept of Engineering Technology Fort ValleyGA31030 United States Fort Valley State University Dept of Engineering Technology Fort ValleyGA31030 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAR86011 United States College of Engineering Suny Polytechnic Institute UticaNY13502 United States

ISBN: (纸本)9781728184166

E-learning is globalized in a short period of time due to the physical distancing requirement for curbing the spread of COVID-19. There are two major challenges imposed by the migration from face-To-face learning to E-learning: live lecturing and lab sessions. In this survey, we studied the E-learning platforms used in the COVID-19 pandemic: learning management systems or course management systems, tools for live-lecturing and tools for lab sessions. We found that this migration is quite smooth with contemporary technologies and the Internet. Prescriptive suggestions are also provided for this migration. For the future trend of education, it is expected that E-learning will become a preferable education and learning style in the future, especially for self-study and life-long learning. © 2020 IEEE.

关键词： COVID-19

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Editorial

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International Journal of Simulation and Process Modelling 2020年第4期15卷 309-310页

作者： Chen, Yinong Xia, Li Wu, Zhengtian School of Computing Informatics and Decision Systems Engineering Arizona State University P.O. Box 878809 TempeAZ85287-8809 United States Sun Yat-sen University 135 Xingang Xi Road Guangzhou Guangdong510275 China School of Electronic and Information Engineering Suzhou University of Science and Technology 99 Xuefu Road Huqiu District Suzhou Jiangsu215009 China

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Mining Associations in Large Graphs forÂ Dynamically Incremented Marked Nodes 14th

Mining Associations in Large Graphs forÂ Dynamically Incre...

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14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018

作者： Rai, Anshul Crosley, Zackary Pacham Sri Srinivasan, Srivignessh School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9783319961354

The edges between nodes of a graph describe some sort of relationship between the two nodes. In this paper, we would like to efficiently determine the relationship between specific nodes of importance, which we call marked nodes, in a large graph. These relationships obtained must be optimal, which requires us to segregate the marked nodes from the less important nodes and group them together using partitioning algorithms. We introduce an improved algorithm which allows for the efficient addition of new marked nodes to a partition without rerunning the algorithm on previously marked nodes. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Graph theory

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The dynamic nature of percolation on networks with triadic interactions

arXiv

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arXiv 2022年

作者： Sun, Hanlin Radicchi, Filippo Kurths, Jürgen Bianconi, Ginestra School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington47408 United States Potsdam Institute for Climate Impact Research Potsdam Germany Department of Physics Humboldt University of Berlin Berlin Germany The Alan Turing Institute The British Library LondonNW1 2DB United Kingdom

Percolation establishes the connectivity of complex networks and is one of the most fundamental critical phenomena for the study of complex systems. On simple networks, percolation displays a second-order phase transition;on multiplex networks, the percolation transition can become discontinuous. However, little is known about percolation in networks with higher-order interactions. Here, we show that percolation can be turned into a fully fledged dynamical process when higher-order interactions are taken into account. By introducing signed triadic interactions, in which a node can regulate the interactions between two other nodes, we define triadic percolation. We uncover that in this paradigmatic model the connectivity of the network changes in time and that the order parameter undergoes a period doubling and a route to chaos. We provide a general theory for triadic percolation which accurately predicts the full phase diagram on random graphs as confirmed by extensive numerical simulations. We find that triadic percolation on real network topologies reveals a similar phenomenology. These results radically change our understanding of percolation and may be used to study complex systems in which the functional connectivity is changing in time dynamically and in a non-trivial way, such as in neural and climate networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Phase diagrams

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Safe goal-directed autonomy and the need for sound abstractions

Safe goal-directed autonomy and the need for sound abstracti...

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2018 AAAI Spring Symposium

作者： Srivastava, Siddharth School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85282 United States

The field of sequential decision making (SDM) captures a range of mathematical frameworks geared towards the synthesis of goal-directed behaviors for autonomous systems. Abstract benchmark problems such as the blocks-world domain have facilitated immense progress in solution algorithms for SDM. there is some evidence that a direct application of SDM algorithms in real-world situations can produce unsafe behaviors. This is particularly apparent in task and motion planning in robotics. We believe that the reliability of today’s SDM algorithms is limited because SDM models, such as the blocks-world domain, are unsound abstractions (those that yield false inferences) of real world situations. This position paper presents the case for a focused research effort towards the study of sound abstractions of models for SDM and algorithms for efficiently computing safe goal-directed behavior using such abstractions. © 2018 AI Access Foundation. All rights reserved.

关键词： Motion planning

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Predicting extreme events from data using deep machine learning: when and where

arXiv

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arXiv 2022年

作者： Jiang, Junjie Huang, Zi-Gang Grebogi, Celso Lai, Ying-Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi’an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King’s College University of Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University TempeAZ85287 United States

We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex Ginzburg-Landau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world. © 2022, CC BY.

关键词： Forecasting

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Morphology-based visible-infrared image fusion framework for smart city

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International Journal of Simulation and Process Modelling 2018年第6期13卷 523-536页

作者： Qi, Guanqiu Zhu, Zhiqin Chen, Yinong Wang, Jinchuan Zhang, Qiong Zeng, Fancheng School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287 United States College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China

Sparse representation-based approaches are often applied to image fusion. Owing to the difficulties of obtaining a complete and non-redundant dictionary, this paper proposes a hierarchical image fusion framework that applies layer-by-layer deep learning techniques to explore the detailed information of images and extract key information of images for dictionary learning. According to morphological similarities, this paper clusters source image patches into smooth, stochastic, and dominant orientation patch group. High-frequency and low-frequency components of three clustered image-patch groups are fused by max-L1 and L2-norm based weighted average fusion rule respectively. The fused low-frequency and high-frequency components are combined to obtain the final fusion results. The comparison experimentations confirm the feasibility and effectiveness of the proposed image fusion solution. © 2018 Inderscience Enterprises Ltd.

关键词： Image fusion

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Investigation of the hyperchaos and control in the fractional order financial system with profit margin

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Partial Differential Equations in Applied Mathematics 2024年 9卷

作者： Johansyah, Muhamad Deni Sambas, Aceng Qureshi, Sania Zheng, Song Abed-Elhameed, Tarek M. Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Department of Mathematics Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Department of Mathematics Near East University Mersin 99138 Turkey Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro 72060 Pakistan School of Data Science Zhejiang University of Finance and Economics Hangzhou 310018 China Institute of Mathematics and Interdisciplinary Sciences Zhejiang University of Finance and Economics 310018 China Department of Mathematics Faculty of Science Assiut University Assiut 71516 Egypt Centre for Control Systems Vel Tech University Avadi Tamil Nadu Chennai 600062 India Institute of Strategic Industrial Decision Modelling School of Quantitative Sciences Universiti Utara Malaysia Sintok Bukit Kayu Hitam Kedah 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman

This research study proposes a novel hyperchaotic finance system with profit margin and then utilizes the Adomian Decomposition Method (ADM) to tackle the solution of the associated Caputo-derivative based fractional-order hyperchaotic finance system with profit margin. Numerical simulations and analyses, such as the evaluation of Lyapunov exponents (LE), the creation of bifurcation diagrams (BD), the complexity analysis (CA), and the 0-1 test, are used to get a full picture of the system. The results of our study demonstrate the occurrence of periodic, chaotic, and hyperchaotic dynamics in the system. Furthermore, the overall criteria for the existence and the uniqueness of the exact solutions for Caputo fractional-order models are presented. In addition, we present a control methodology for the fractional-order hyperchaotic financial system utilizing the Laplace transform and linear feedback control. Significantly, our research showcases a noteworthy correlation between the analytical results and numerical simulations, emphasizing the soundness and effectiveness of our methodology. © 2024 The Author(s)

关键词： Chaotic system Financial system Fractional order Hyperchaos

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A novel visual attention method for target detection from SAR images

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Chinese Journal of Aeronautics 2019年第8期32卷 1946-1958页

作者： Fei GAO Aidong LIU Kai LIU Erfu YANG Amir HUSSAIN School of Electronic and Information Engineering Beihang UniversityBeijing 100083China Space Mechatronic Systems Technology Laboratory Department of DesignManufacture and Engineering ManagementUniversity of StrathclydeGlasgow G1 1XJUK Cognitive Big Data and Cyber-Informatics(CogBID)Laboratory School of ComputingEdinburgh Napier UniversityEdinburgh EH10 5DTScotlandUK

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down *** the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.

关键词： Learning strategy Synthetic Aperture Radar(SAR) images Target detection Top-down Visual attention mechanism

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Diagnosis for Sucker Rod Pumps Using Bayesian Networks and Dynamometer Card

Diagnosis for Sucker Rod Pumps Using Bayesian Networks and D...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Boyuan Zheng Xianwen Gao Rong Pan College of Information Science and Engineering Northeastern University Shengyang China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe USA

The automatic diagnosis for sucker rod pump (SRP) is an essential measurement to ensure the oil fields' interests in the oil recovery. As the important information resource on monitoring and diagnosis, the dynamometer card (DC) plays an irreplaceable role in oil engineering. In the application, how to use DC to fulfill the diagnosis is always the key to this problem. Thus, a novel method based on load analysis and Bayesian network is proposed in this paper. At first off, DC's coordinate is transformed to cater to the load analysis, which provides an instinctive way for analyzing. After that, five statistical features and Shannon entropy are extracted from the DC, which are employed as the input of the Bayesian network (BN) presented in the particular framework. At last, a set of field dynamometer card is employed as the experimental data and the experimental results demonstrate the feasibility and superiority of the proposed method for diagnosing the working states of SRPs.

关键词： Feature extraction Dynamometers Oils Entropy Shape Bayes methods Probabilistic logic

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