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Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementation

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arXiv 2021年

作者： Alonso, Carmen Amo Li, Jing Shuang Anderson, James Matni, Nikolai Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA United States Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA United States Department of Electrical Engineering the Data Science Institute Columbia University New YorkNY United States Department of Electrical and Systems Engineering the University of Pennsylvania PhiladelphiaPA United States

The increasing presence of large-scale distributed systems highlights the need for scalable control strategies where only local communication is required. Moreover, in safety-critical systems it is imperative that such control strategies handle constraints in the presence of disturbances. In response to this need, we present the Distributed and Localized Model Predictive Control (DLMPC) algorithm for large-scale linear systems. DLMPC is a distributed closed-loop model predictive control (MPC) scheme wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We use the System Level Synthesis (SLS) framework to reformulate the centralized MPC problem, and show that this allows us to naturally impose localized communication constraints between sub-controllers. The structure of the resulting problem can be exploited to develop an Alternating Direction Method of Multipliers (ADMM) based algorithm that allows for distributed and localized computation of closed-loop control policies. We demonstrate that computational complexity of the subproblems solved by each subsystem in DLMPC is independent of the size of the global system. DLMPC is the first MPC algorithm that allows for the scalable computation and implementation of distributed closed-loop control policies, and deals with additive disturbances. In our companion paper, we show that this approach enjoys recursive feasibility and asymptotic stability. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

RLAs for 2-Seat STV Elections: Revisited

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arXiv 2024年

作者： Blom, Michelle Stuckey, Peter J. Teague, Vanessa Vukcevic, Damjan School of Computing and Information Systems University of Melbourne Parkville Australia Department of Data Science and AI Monash University Clayton Australia Thinking Cybersecurity Pty. Ltd. Melbourne Australia Department of Econometrics and Business Statistics Monash University Clayton Australia

Single Transferable Vote (STV) elections are a principled approach to electing multiple candidates in a single election. Each ballot has a starting value of 1, and a candidate is elected if they gather a total vote value more than a defined quota. Votes over the quota have their value reduced by a transfer value so as to remove the quota, and are passed to the next candidate on the ballot. Risk-limiting audits (RLAs) are a statistically sound approach to election auditing which guarantees that failure to detect an error in the result is bounded by a limit. A first approach to RLAs for 2-seat STV elections has been defined. In this paper we show how we can improve this approach by reasoning about lower bounds on transfer values, and how we can extend the approach to partially audit an election, if the method does not support a full audit. Copyright © 2024, The Authors. All rights reserved.

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Explicit Distributed and Localized Model Predictive Control via System Level Synthesis

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arXiv 2020年

作者： Alonso, Carmen Amo Matni, Nikolai Anderson, James Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Department of Electrical Engineering Data Science Institute Columbia University New YorkNY United States

An explicit Model Predictive Control algorithm for large-scale structured linear systems is presented. We base our results on Distributed and Localized Model Predictive Control (DLMPC), a closed-loop model predictive control scheme based on the System Level Synthesis (SLS) framework wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We provide an explicit solution for each of the subproblems resulting from the distributed MPC scheme. We show that given the separability of the problem, the explicit solution is only divided into three regions per state and input instantiation, making the point location problem very efficient. Moreover, given the locality constraints, the subproblems are of much smaller dimension than the full problem, which significantly reduces the computational overhead of explicit solutions. We conclude with numerical simulations to demonstrate the computational advantages of our method, in which we show a large improvement in runtime per MPC iteration as compared with the results of computing the optimization with a solver online. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

Allotaxonometry and rank-turbulence divergence: A universal instrument for comparing complex systems

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Gray, Tyler J. Frank, Morgan R. Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States MassMutual Data Science AmherstMA01002 United States Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States

Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry. Copyright © 2020, The Authors. All rights reserved.

关键词： Turbulence

Robust distributed and localized model predictive control

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arXiv 2021年

作者： Alonso, Carmen Amo Li, Jing Shuang Matni, Nikolai Anderson, James Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Department of Electrical Engineering Data Science Institute Columbia University New YorkNY United States

We present a robust Distributed and Localized Model Predictive Control (rDLMPC) framework for large-scale structured linear systems. The proposed algorithm uses the System Level Synthesis to provide a distributed closed-loop model predictive control scheme that is robust to exogenous disturbances. The resulting controllers require only local information exchange for both synthesis and implementation. We exploit the fact that for polytopic disturbance constraints, SLS-based distributed control problems have been shown to have structure amenable for distributed optimization techniques. We show that similar to the disturbance-free DLMPC algorithm, the computational complexity of rDLMPC is independent of the size of the global system. To the best of our knowledge, robust DLMPC is the first MPC algorithm that allows for the scalable distributed computation of distributed closed-loop control policies in the presence of additive disturbances. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

Machine Learning Development in Solving Critical Medical Problems

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Machine Learning Development in Solving Critical Medical Pro...

International Conference on Inventive Computation Technologies (ICICT)

作者： Shaista Fatima G Sangeetha P Ponmurugan A. N. Arularasan A. Prabhu Chakkaravarthy R Denis A. Chinnasamy Department of Electronics and Communication Engineering MJ college Hyderabad Telangana India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Thiruvallur Tamil Nadu India Department of Research Meenakshi Academy of Higher Education and Research Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Networking and Communications School of Computing College of Engineering and Technology Kattankulathur Tamil Nadu India Department of Computer Science Mount Carmel College Autonomous Bengaluru Karnataka India Department of data science and business systems School of computing SRMIST Kattankulathur campus Tamil Nadu India

As a result of ML, the healthcare industry undergoes substantial innovation and improvement. As a result, data management, clinical operations, drug research, and surgery are all progressing more quickly. The healthcare sector is now required to use this cutting-edge technology because of the Covid-19 pandemic. More importantly, people stand to benefit the most from technology because it may improve their health outcomes by identifying the best treatment alternatives for them. Thanks to ML's enhanced early disease detection capabilities, the frequency of re-admissions to hospitals and clinics can be reduced. In this article, we'll look at the main applications of machine learning in healthcare as well as its exceptional benefits in changing the industry.

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Detection and Classification of Skin Cancer by Using a Parallel Deconvolutional Network Model

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Detection and Classification of Skin Cancer by Using a Paral...

Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： M. Sangeetha C. Karthikeyini S. Vasundhara D. Saravanan G. Arunkumar Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur C Department of ECE Excel Engineering College Komarapalayam Tamil Nadu Department of Humanities & Mathematics Affiliates JNTUH G.Narayanamma Institute of Technology &Science Hyderabad Department of CSE IFET College of Engineering Villupuram Tamil Nadu Department of CSE Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh

Skin Cancer is one of the most common cancer forms in many countries, it is considered to be one of the dangerous types in the sense that it is lethal and its occurrence over time has been dramatically high. It is one of the deadliest cancers among all diseases and has a large rate of mortality. The efficiency of the earlier approaches to assess one of the most hazardous melanoma diagnosis in dermoscopic criteria are not up to the mark. Therefore, in this research, the work has been carried out in three stages in order to detect melanoma in an efficient manner. In the first stage, prior to the implementation of the image segmentation technique, noise elimination and pre-processing steps are carried out to remove the noise and to achieve better execution results. This segmentation model focuses on the separation of the interesting portions from the background and collects the necessary information from the neighboring pixels of the same category. Gaussian analytical patterns are used to handle the heterogeneous regions/sections of dermoscopy images whose mean and variance can be dynamic. In addition, the third stage of work is based on the texture classification, which is proposed as the CSTC-Mel Identification Model.

关键词： Image segmentation Adaptation models Solid modeling Shape Computational modeling Melanoma Feature extraction

Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices

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arXiv 2024年

作者： Hemel, Md. Shahriar Khan Reaz, Mamun Bin Ibne Ali, Sawal Hamid Bin Md Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia Department of Electrical and Electronic Engineering Independent University Bangladesh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Arts Science and Technology Wrexham University United Kingdom Faculty of Computing Engineering and Science University of South Wales United Kingdom

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps. © 2024, CC BY.

关键词： Phase locked loops

A deep learning approach for ovarian cysts detection and classification (OCD-FCNN) using fuzzy convolutional neural network

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Measurement: Sensors 2023年 27卷

作者： Ravishankar, T. Nadana Makarand Jadhav, Hemlata Satheesh Kumar, N. Ambala, Srinivas Pillai N, Muthuvairavan Department of Data Science and Business Systems School of Computing SRM Institute of Science & Technology KTR Tamil Nadu Chennai India Department of E & T C Marathwada Mitra Mandal's College of Engineering Pune India Department of Electronics and Communication Engineering PBR Visvodaya Institute of Technology and Science Kavali Andhra Pradesh 524201 India Computer Engineering Department Pimpri Chinchwad College of Engineering Sector -26 Pradhikaran Nigdi Maharashtra Pune411 044 India Department of Computer Science and Business Systems R.M.D Engineering College Kavarapettai India

Most women generally have an ovarian cyst, causing the disorder. Pregnant cysts occur when many water-packed tumors appear in the womb. This is especially true for women who have a worthy cause for childbearing. Women are related to menstrual problems and cyst problems during pregnancy. Recently, ultrasound imaging and machine learning techniques have been used to detect ovarian cysts. Different domain experts provide their own decisions on finding out what kind of ovarian cyst it is from ultrasound images. However, a most accurate and uniform decision-making system is necessary for the early detection of cysts. To aid physicians, an automated detection system has been proposed in this paper to make it more effective for physicians to eliminate these problems. This system uses the extracted features from the image for cysts detection and classification. Automatic ovary cyst detection (OCD) and classification are implemented in this work using a fuzzy rule-based Convolutional Neural Network (FCNN). The proposed system (OCD-FCNN) has yielded 98.37% accurate results when tested with benchmark datasets. © 2023 The Authors

关键词： Fuzzy inference