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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Silva, Bruno Lopes, Luiz Guerreiro University of Madeira Sre Regional Government of Madeira PhD Program in Computer Engineering Funchal Madeira Portugal University of Madeira Faculty of Exact Sciences and Engineering Department of Mathematics Funchal Madeira9020-105 Portugal

ISBN: (纸本)9798350306941

Population-based computational intelligence algorithms are natural candidates for parallelization and have been utilized to solve a variety of difficult and complex real-world problems. The gray wolf optimizer (GWO) is one of these algorithms. It is a metaheuristic that simulates the leadership hierarchy and hunting mechanism of gray wolves in the wild and has been used successfully to solve several hard optimization problems. However, the study of its applicability to the solution of nonlinear equation systems, which is arguably the most difficult class of numerical problems, is still quite incipient and needs to be better accessed and verified, particularly in the case of large-scale systems of nonlinear equations, which have not been considered until now and whose resolution difficulty increases with the number of equations they contain. This paper presents a new and efficient GPU-based massively parallel implementation of the gray wolf optimizer algorithm for solving large-scale optimization problems. The proposed parallelization of the GWO algorithm is illustrated by its application to solving large systems of nonlinear equations, a class of problems that appear and have great importance in different fields of pure and applied sciences and engineering. The GPU-accelerated version of GWO was implemented in Julia and tested on a GeForce RTX 3090 GPU with 24 GB GDDR6X VRAM and 10 496 CUDA cores using a set of hard, scalable systems of nonlinear equations with dimensions ranging from 500 to 2000. The obtained results, with average speedups between 69.91× and 241.54× , show the efficiency of the proposed GPU-based acceleration of the GWO algorithm. © 2023 IEEE.

关键词： Graphics processing unit

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Minimum Environmental Criteria and Climate Issue in the Metropolitan Urban Ecosystem 23rd

Minimum Environmental Criteria and Climate Issue in the Metr...

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23rd International Conference on Computational Science and Its Applications, ICCSA 2023

作者： Sinatra, Martina Balletto, Ginevra Borruso, Giuseppe DICAAR – Department of Civil and Environmental Engineering and Architecture University of Cagliari Via Marengo 2 Cagliari Italy DEAMS - Department of Economics Business Mathematics and Statistics Sciences "Bruno de Finetti" University of Trieste Trieste34127 Italy

ISBN: (纸本)9783031371196

In Italy, the adoption of the Minimum Environmental Criteria (MEC) is associated with Legislative Decree 50/2016, which regulates public tenders. The main objective of the MECs is to promote the purchase of sustainable goods and services thus contributing to the reduction of the environmental impact and the protection of natural resources. Furthermore, the adoption of MEC by public authorities represents a strategic choice to promote the circular economy, reduce waste and greenhouse gas emissions, thus preserving the quality of environmental matrices. MECs, although they are innovative tools, are however lacking in geographic information related to context analysis. In this synthetic framework, the objective of this work is to evaluate a set of contextual environmental indicators to support the MECs. In fact, the knowledge and representation of the context constitutes a fundamental action for recognizing local problems, which are also useful for understanding global issues. Furthermore, how can MECs contribute to adaptation to climate change within the relative territorial context? This question guided the manuscript and the metropolitan city of Cagliari represents the case study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Environmental impact

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Combining Active and Passive Acoustic Sensing in Teams of Mobile Robots

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IFAC-PapersOnLine 2024年第28期58卷 522-527页

作者： Aidan J. Bradley Nicole Abaid Engineering Mechanics Program Virginia Tech Blacksburg VA 24061 USA Mathematics Department Virginia Tech Blacksburg VA 24061 USA

Evidence suggests that bats are able to take advantage of both their own echolocation signals (active sensing) and the signals of conspecifics in their environment (passive sensing). This work follows a bioinspired approach to investigate whether we can enable robots to do the same. We have simulated a pair of vehicles that acoustically sense their environment both actively and passively. Our results show that, while the ability to fuse acoustic sensing techniques may not provide a significant improvement over active sensing alone, it is rarely worse and often allows for more information about the environment to be observed.

关键词： Estimation filtering Multi-agent systems Modeling Mobile robots Perception sensing

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Real-Time Distributed Model Predictive Control with Limited Communication Data Rates

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IEEE Transactions on Automatic Control 2025年

作者： Yang, Yujia Wang, Ye Manzie, Chris Pu, Ye University of Melbourne Department of Electrical and Electronic Engineering ParkvilleVIC3010 Australia University of Melbourne School of Mathematics and Statistics ParkvilleVIC3010 Australia

The application of distributed model predictive controllers (DMPC) for multiagent systems (MASs) necessitates communication between agents, yet the consequence of communication data rates is typically overlooked. This work focuses on developing stability-guaranteed control methods for MASs with limited data rates. Initially, a distributed optimization algorithm with dynamic quantization is considered for solving the DMPC problem. Due to the limited data rate, the optimization process suffers from inexact iterations caused by quantization noise and premature termination, leading to suboptimal solutions. In response, we propose a novel real-time DMPC framework with a quantization refinement scheme that updates the quantization parameters on-line so that both the quantization noise and the optimization suboptimality decrease asymptotically. To facilitate the stability analysis, we treat the suboptimally controlled MAS, the quantization refinement scheme, and the optimization process as three interconnected subsystems. The cyclic-small-gain theorem is used to derive sufficient conditions on the quantization parameters for guaranteeing the stability of the system under a limited data rate. Finally, the proposed algorithm and theoretical findings are demonstrated in a multi-AUV formation control example. © 1963-2012 IEEE.

关键词： Predictive control systems

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Identifiability and Observability of Nonsmooth Systems via Taylor-like Approximations

arXiv

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arXiv 2024年

作者： Stechlinski, Peter Eisa, Sameh Abdelfattah, Hesham The department of Mathematics and Statistics University of Maine Maine United States The department of Aerospace Engineering and Engineering Mechanics University of Cincinnati OH United States

New sensitivity-based methods are developed for determining identifiability and observability of nonsmooth input-output systems. More specifically, lexicographic calculus is used to construct nonsmooth sensitivity rank condition (SERC) tests, which we call lexicographic SERC (L-SERC) tests. The introduced L-SERC tests are: (i) practically implementable and amenable to large-scale problems;(ii) accurate since they directly treat the nonsmoothness while avoiding, e.g., smoothing approximations;and (iii) analogous to (and indeed recover) their smooth counterparts. To accomplish this, a first-order Taylor-like approximation theory is developed using lexicographic differentiation to directly treat nonsmooth functions. A practically implementable algorithm is proposed that determines partial structural identifiability or observability, a useful characterization in the nonsmooth setting. Lastly, the theory is illustrated through an application in climate modeling. Copyright © 2024, The Authors. All rights reserved.

关键词： Observability

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Stabilization of nonlinear safety-critical systems by relaxed converse Lyapunov-barrier approach and its applications in robotic systems

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Autonomous Intelligent Systems 2024年第1期4卷 73-80页

作者： Haoqi Li Jiangping Hu Xiaoming Hu Bijoy K.Ghosh School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313000China Optimization and Systems Theory Royal Institute of TechnologyStockholmSE-10044Sweden Department of Mathematics and Statistics Texas Tech UniversityLubbock79409-1042TXUSA

Combining safety objectives with stability objectives is crucial for safety-critical *** studies generally unified these two objectives by constructing Lyapunov-type barrier ***,insufficient analysis of key set relationships within the system may render the proposed safety and stability conditions conservative,and these studies also did not provide how to use such conditions to design safety-stability control *** paper proposed a feasible and constructive design to achieve stabilization of safety-critical systems by a relaxed converse Lyapunov-barrier *** analyzing the relationships between a series of sets associated with the safety-critical system,the stability and safety conditions can be appropriately ***,with the help of relaxed converse control Lyapunov-barrier functions(RCCLBFs),a theoretical result was obtained for the stability of affine nonlinear systems with safety ***,a constructive method was developed for a second-order strict-feedback system to transform the process of solving RCCLBFs into a Lyapunov-like stabilization ***,the proposed safety-stability control method is exerted on a robotic system and demonstrated by simulations.

关键词： Lyapunov functions Barrier functions Safety Stability

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A Machine Learning and Ratemaking Evaluation of Four Auto Insurance Pure Premium Modeling Algorithms 28

A Machine Learning and Ratemaking Evaluation of Four Auto In...

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28th International Computer Science and engineering Conference, ICSEC 2024

作者： Kumar, Rakesh Rakhra, Manik Prashar, Deepak Upadhyay, Saurabh Mrsic, Leo Khan, Arafat Ahmad Lovely Professional University Department of Mathematics and Statistics Punjab Phagwara India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Algebra University Vice Rector for Science and Research Zagreb10000 Croatia Khon Kaen Thailand

ISBN: (纸本)9798350366860

Actuarial science is increasingly using machine learning. Machine learning has improved actuarial pricing prediction by forecasting future claims. Insurers are testing these algorithms, but model explainability and implementation cost are issues. The current literature barely touches upon this broad field of inquiry. Fujita et al. published a 2020 study on predictive frequency models using AGLM, GLM, GAM, and GBM. König and Loser (2020) compared GLMs, neural networks, and XGBoost for frequency prediction. We want to add to existing research by forecasting pure premium rather than frequency or severity, comparing four methodologies, and analyzing and presenting a wide range of quantitative and qualitative performance evaluation metrics. This paper compares four approaches for predicting pure premium: GLM, AGLM, XGBoost, and neural networks. On a test set, the study compares each model's pros and cons in P&C insurance pricing, as well as its quantitative and qualitative performance. Despite these obstacles, machine learning in actuarial pricing has clear benefits. By may improve risk profiles, pricing accuracy, profitability, and market competitiveness by using massive volumes of data and advanced analytics. However, getting these results needs a holistic approach that balances machine learning's technical sophistication with insurance's regulatory and operational limits. Further research should construct interpretable machine learning models customized to insurance pricing needs and limits while addressing the ethical, regulatory, and implementation obstacles to their adoption. © 2024 IEEE.

关键词： Adversarial machine learning

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Analyzing The Impact of Different Meteorological Variables on Large-Scale Solar generation: A Case Study of Spain

Analyzing The Impact of Different Meteorological Variables o...

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2022 Advances in Science and engineering Technology International Conferences, ASET 2022

作者： Kandil, Sarah Marzbani, Fatemeh Alzaatreh, Ayman American University of Sharjah Department of Industrial Engineering Sharjah United Arab Emirates American University of Sharjah Department of Mathematics and Statistics Sharjah United Arab Emirates

ISBN: (纸本)9781665418010

Scarcity and adverse environmental impacts of fossil fuels for electricity generation along with international emission reduction goals necessitate large-scale integration of renewable energies into power grids. These resources have intermittent nature which leads to a tradeoff between the inclusion of large shares of such sources of energy into the electricity network and their economical and environmental advantages. Therefore, it is important to study the impact of different meteorological variables on solar generation. To that end, correlation and interdependence among different meteorological variables were analyzed, and multiple linear regression was utilized to determine the significant variables that impact solar generation. Four years of hourly cumulative total solar generation of Spain has been used as a case study along with its meteorological variables. Twelve different multiple linear regression models were developed for each month and the significant variables were reported. The dependent variable was the cumulative solar generation in MW and the independent variables were the temperature, humidity, wind speed, pressure, and cloudiness. The variables with the highest influence were recorded for different months. © 2022 IEEE.

关键词： Multiple linear regression

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Assessing Readmission Rates in a Sharjah Healthcare Facility

Assessing Readmission Rates in a Sharjah Healthcare Facility

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2022 Advances in Science and engineering Technology International Conferences, ASET 2022

作者： Alnajar, Mohamad Aljabi, Yara Alzaatreh, Ayman American University of Sharjah Department of Industrial Engineering Sharjah United Arab Emirates American University of Sharjah Department of Mathematics and Statistics Sharjah United Arab Emirates

ISBN: (纸本)9781665418010

The healthcare industry is one of the most sensitive industries as it deals with patients' health. Machine Learning techniques have been implemented to assess the performance of such industries and further improve the allocation of their resources. Many measures of performance exist to infer how a healthcare facility uses its resources. Readmission rate is a very popular rate in analyzing the performance of a healthcare facility. In this paper, we assess the readmission rate of a Sharjah healthcare facility in the first ten months of 2021. We have used classification techniques such as Logistic Regression, Random Forests, Neural Networks, and Gradient Boosting to find the best prediction model. We then used logistic regression to infer the relationships between the most important variables and the readmission rate. Results showed that the readmission rate was most influenced by the hospital departments, insurance type, marital status, age, and diastolic blood pressure. Relationships of such variables are outlined in the paper and can be further investigated to reduce readmission rates for cost reduction. © 2022 IEEE.

关键词： Neural networks

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Fractional Order Networked Control System using Stochastic Iterative Learning Control

Fractional Order Networked Control System using Stochastic I...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Shakeri, Ehsan Abharian, Amir Esmaeili University of Maryland Baltimore County Department of Mathematics and Statistics Baltimore United States Islamic Azad University Garmsar Branch Department of Electronic Engineering Garmsar Iran

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Networked control systems (NCSs) are one of the main areas in control and computer engineering. Despite the many advantages that NCSs have, they impose restrictions such as random data dropout on control systems. We can remove the impact of data dropout by employing the iterative learning control (ILC) method for repetitive systems. In this paper, an ILC for stochastic fractional order NCSs has been presented to deal with the random data dropout imposed by the network. Using the Kalman filter approach, the optimal learning gain is determined so that the system output tracks to the desired output in the presence of random data dropout. The simulation results show that the mean of tracking error becomes close to zero after 500 iterations when a significant number of data packets have been lost in the network. © 2022 IEEE.

关键词： Networked control systems

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