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A Study on Performance Evaluation and Status-based decision for Cyber-Physical Production systems

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A Study on Performance Evaluation and Status-based Decision ...

IEEE International Conference on Automation Science and engineering

作者： Feifan Wang Feng Ju Yan Lu School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA National Institute of Standards and Technology Gaithersburg MD USA

In concert with advances in information and communication technology and their application to manufacturing environments, physical entities in factories are acquiring more intelligence via integration with cyber systems. This integration brings about Cyber-Physical Production systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high levels of agility, flexibility, and real-time control make it possible to keep the system running efficiently and self-organized. At the same time, however, it becomes difficult in a self-organized and decentralized system to capture the system's status, evaluate the system's performance, and predict the system's future events. In this article, we suggest improvements to smart manufacturing systems where the intelligence from smart entities could be fully utilized without losing system control. To achieve this goal, a solution for integrating schedule-driven production (push systems) and event-driven production (pull systems) is proposed to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems looselycoupled and predictable. A simulation case study based on Devices Profile for Web Services [1] is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing and that they can be part of an approach that balances the selforganized control with overall performance. Therefore, we can make full use of intelligent entities in lower levels of a factory while keeping the entire system under control.

关键词： Smart Manufacturing systems CyberPhysical Production systems Status-based decision Devices Profile of Web Services

A Novel Dictionary Learning and Selection Method for Image Fusion

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A Novel Dictionary Learning and Selection Method for Image F...

第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Zhiqin Zhu Ke Zhang Guanqiu Qi Yi Chai Jian Sun Kunpeng Wang College of Automation Chongqing University of Posts and Telecommunications College of Automation Chongqing University School of Computing Informaticsand Decision Systems EngineeringArizona State University

In recent years,sparse representation approaches have been integrated into multi-focus image fusion *** fused images of sparse-representation-based image fusion methods show great *** an informative dictionary is a key step for sparsity-based image fusion *** order to ensure sufficient number of useful bases for sparse representation in informative dictionary construction,image patches from all source images are classified into groups based on geometric *** key information of each image-patch group is extracted by K-SVD to build *** to the constructed dictionary,image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit(SOMP) algorithm for representing the source multi-focus *** last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused *** comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution.

关键词： Image Fusion Sparse Representation Dictionary Learning Geometric Classification

Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

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SSRN

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

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SSRN

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

RETRACTED ARTICLE: Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization

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Neural computing and Applications 2018年第9期32卷 4373-4386页

作者： Selvanambi, Ramani Natarajan, Jaisankar Karuppiah, Marimuthu Islam, SK Hafizul Hassan, Mohammad Mehedi Fortino, Giancarlo School of Computing Science and Engineering Vellore Institute of Technology (VIT) Vellore India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani Kalyani India Chair of Smart Cities Technology College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Informatics Modeling Electronics and Systems University of Calabria Rende Italy

Therapeutic issues are commonly found in every single person. Tumor is a standout among the most unsafe sicknesses a human can ever had. It is exceptionally hard to distinguish it in its beginning times as its side effects seem just in the progressed stages. Subsequently, the early forecast of lung growth is compulsory for the analysis procedure, and it gives the higher possibilities for fruitful treatment. It is the most difficult approach to upgrade a patient’s possibility for survival. Henceforth, a higher-order neural network system called recurrent neural network with Levenberg–Marquardt model with the help of glowworm swarm optimization algorithm is proposed for managing multimodal disease information. The execution of the proposed strategies is tried with information and the benchmark dataset, and the outcomes demonstrate that the higher-order recurrent neural systems with glowworm swarm optimization give better accuracy of 98% in comparison with customary optimized neural network.

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On Auxiliary Entity Allocation Problem in Multi-layered Interdependent Critical Infrastructures

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arXiv 2017年

作者： Banerjee, Joydeep Sen, Arunabha Zhou, Chenyang School of Computing Informatics and Decision System Engineering Arizona State University TempeAZ85287 United States

Operation of critical infrastructures are highly interdependent on each other. Such dependencies causes failure in these infrastructures to cascade on an initial failure event. Owing to this vulnerability it is imperative to incorporate efficient strategies for their protection. Modifying dependencies by adding additional dependency implications using entities (termed as auxiliary entities) is shown to mitigate this issue to a certain extent. With this finding, in this article we introduce the Auxiliary Entity Allocation problem. The objective is to maximize protection in Power and Communication infrastructures using a budget in number of dependency modifications using the auxiliary entities. The problem is proved to be NP-complete in general case. We provide an optimal solution using Integer Linear program and a heuristic for a restricted case. The efficacy of heuristic with respect to the optimal is judged through experimentation using real world data sets with heuristic deviating 6.75% from optimal on average. Copyright © 2017, The Authors. All rights reserved.

关键词： Integer programming

On robustness in multilayer interdependent network

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arXiv 2017年

作者： Banerjee, Joydeep Zhou, Chenyang Sen, Arunabha School of Computing Informatics and Decision System Engineering Arizona State University TempeAZ85287 United States

Critical Infrastructures like power and communication networks are highly interdependent on each other for their full functionality. Many significant research have been pursued to model the interdependency and failure analysis of these interdependent networks. However most of these models fail to capture the complex interdependencies that might actually exist between the infrastructures. The Implicative Interdependency Model that utilizes Boolean Logic to capture complex interdependencies was recently proposed which overcome the limitations of the existing models. A number of problems were studies based on this model. In this paper we study the Robustness problem in Interdependent Power and Communication Network. The robustness is defined with respect to two parameters K ∈ I+ ∪ {0} and ρ ∈ (0, 1]. We utilized the Implicative Interdependency Model model to capture the complex interdependency between the two networks. The model classifies the interdependency relations into four cases. Computational complexity of the problem is analyzed for each of these cases. A polynomial time algorithm is designed for the first case that outputs the optimal solution. All the other cases are proved to be NP-complete. An in-approximability bound is provided for the third case. For the general case we formulate an Integer Linear Program to get the optimal solution and a polynomial time heuristic. The applicability of the heuristic is evaluated using power and communication network data of Maricopa County, Arizona. The experimental results showed that the heuristic almost always produced near optimal value of parameter K for ρ Copyright © 2017, The Authors. All rights reserved.

关键词： Optimal systems

Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders

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arXiv 2017年

作者： Kim, Minje Indiana University Bloomington School of Informatics and Computing Department of Intelligent Systems Engineering

We show that a Modular Neural Network (MNN) can combine various speech enhancement modules, each of which is a Deep Neural Network (DNN) specialized on a particular enhancement job. Differently from an ordinary ensemble technique that averages variations in models, the propose MNN selects the best module for the unseen test signal to produce a greedy ensemble. We see this as Collaborative Deep Learning (CDL), because it can reuse various already-trained DNN models without any further refining. In the proposed MNN selecting the best module during run time is challenging. To this end, we employ a speech AutoEncoder (AE) as an arbitrator, whose input and output are trained to be as similar as possible if its input is clean speech. Therefore, the AE can gauge the quality of the module-specific denoised result by seeing its AE reconstruction error, e.g. low error means that the module output is similar to clean speech. We propose an MNN structure with various modules that are specialized on dealing with a specific noise type, gender, and input Signal-to-Noise Ratio (SNR) value, and empirically prove that it almost always works better than an arbitrarily chosen DNN module and sometimes as good as an oracle result. Copyright © 2017, The Authors. All rights reserved.

关键词： Speech enhancement

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration