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arXiv 2024年

作者： De Marchi, Alberto Themelis, Andreas Department of Aerospace Engineering Institute of Applied Mathematics and Scientific Computing Universität der Bundeswehr München Werner-Heisenberg-Weg 39 Neubiberg85577 Germany Kyushu University 744 Motooka Nishi-ku Fukuoka 819-0395 Japan

Focusing on minimization problems with structured objective function and smooth constraints, we present a flexible technique that combines the beneficial regularization effects of (exact) penalty and interior-point methods. Working in the fully nonconvex setting, a pure barrier approach requires careful steps when approaching the infeasible set, thus hindering convergence. We show how a tight integration with a penalty scheme overcomes such conservatism, does not require a strictly feasible starting point, and thus accommodates equality constraints. The crucial advancement that allows us to invoke generic (possibly accelerated) subsolvers is a marginalization step: closely related to a conjugacy operation, this step effectively merges (exact) penalty and barrier into a smooth, full domain functional object. When the penalty exactness takes effect, the generated subproblems do not suffer the ill-conditioning typical of barrier methods, nor do they exhibit the nonsmoothness of exact penalty terms. We provide a theoretical characterization of the algorithm and its asymptotic properties, deriving convergence results for fully nonconvex problems. Stronger conclusions are available for the convex setting, where optimality can be guaranteed. Illustrative examples and numerical simulations demonstrate the wide range of problems our theory and algorithm are able to *** Codes 49J52, 49J53, 65K05, 90C06, 90C30 Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Efficient Estimation of Sensor Biases for the 3-Dimensional Asynchronous Multi-Sensor System

arXiv

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arXiv 2021年

作者： Pu, Wenqiang Liu, Ya-Feng Luo, Zhi-Quan Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

An important preliminary procedure in multi-sensor data fusion is sensor registration, and the key step in this procedure is to estimate sensor biases from their noisy measurements. There are generally two difficulties in this bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, and the other is the highly nonlinear coordinate transformation between the local and global coordinate systems of the sensors. In this paper, we focus on the 3-dimensional asynchronous multi-sensor scenario and propose a weighted nonlinear least squares (NLS) formulation by assuming that there is a target moving with a nearly constant velocity. We propose two possible choices of the weighting matrix in the NLS formulation, which correspond to classical and weighted NLS estimation and maximum likelihood (ML) estimation, respectively. To address the intrinsic nonlinearity, we propose a block coordinate descent (BCD) algorithm for solving the formulated problem, which alternately updates different kinds of bias estimates. Specifically, the proposed BCD algorithm involves solving linear LS problems and nonconvex quadratically constrained quadratic program (QCQP) problems with special structures. Instead of adopting the semidefinite relaxation technique, we develop a much more computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the nonconvex QCQP subproblems. The convergence of the ADMM to the global solution of the QCQP subproblems is established under mild conditions. The effectiveness and efficiency of the proposed BCD algorithm are demonstrated via numerical simulations. Copyright © 2021, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Automation Intervention for Monitoring Agriculture Farming Scale: A Review 14

Automation Intervention for Monitoring Agriculture Farming S...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Kumar, Santosh Bhatla, Shruti Kaushik, Vishal Anandaram, Harishchander Kumar, Anil Uttaranchal University Department of Cse Uttaranchal Institute of Technology Dehradun India Sharda University Department of Mathematics Sharda School of Basic Sciences and Research UP Greater Noida India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

Agricultural data is only valuable when it has been managed successfully. As data has evolved into the fundamental ingredient in contemporary agriculture to assist farmers with vital decision, latest developments in managing data are causing Smart Agriculture to rise dramatically. With factual information obtained by sensors, significant advantages emerge with the goal of enhancing production and ecology. This type of data-driven farm management is built on information that can boost efficiency by preventing resource waste and contamination of the ecosystem. Data-driven farming, with the assistance of automated solutions using artificial intelligence techniques, lays the groundwork for potential sustainable *** article analyses the current state of sophisticated agricultural systems by rehashing each critical step, from data processing in field crops to variable interest rate implementations, so that farmers can make optimised decision making to save funds while safeguarding the natural environment and converting how meal would be generated to meet the upcoming population explosion. © 2023 IEEE.

关键词： Big data

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Optimal feedback law recovery by gradient-augmented sparse polynomial regression

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 2205-2236页

作者： Behzad Azmi Dante Kalise Karl Kunisch Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Linz Austria School of Mathematical Sciences University of Nottingham University Park Nottingham UK Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences and Institute of Mathematics and Scientific Computing University of Graz Graz Austria

A sparse regression approach for the computation of high-dimensional optimal feedback laws arising in deterministic nonlinear control is proposed. The approach exploits the control-theoretical link between Hamilton-Jacobi-Bellman PDEs characterizing the value function of the optimal control problems, and first-order optimality conditions via Pontryagin's Maximum Principle. The latter is used as a representation formula to recover the value function and its gradient at arbitrary points in the space-time domain through the solution of a two-point boundary value problem. After generating a dataset consisting of different state-value pairs, a hyperbolic cross polynomial model for the value function is fitted using a LASSO regression. An extended set of low and high-dimensional numerical tests in nonlinear optimal control reveal that enriching the dataset with gradient information reduces the number of training samples, and that the sparse polynomial regression consistently yields a feedback law of lower complexity.

关键词： optimal feedback control optimality conditions Hamilton-Jacobi-Bellman PDE polynomial approximation sparse optimization

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Incorporating AI and IoT for Smart Poultry Monitoring and Health Detection 2

Incorporating AI and IoT for Smart Poultry Monitoring and He...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Joshi, Kapil Kumar, Arun Anandaram, Harishchander Kumar, Anil Aggarwal, Ambika Chhabra, Gunjan Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Sharda University Sharda School of Basic Sciences and Research Department of Mathematics Greater Noida India Amrita School of Artificial Intelligence Centre for Computational Engineering and Networking Coimbatore India Dit University School of Computing Dehradun India Upes Department of Cse Dehradun India Graphic Era Hill University Department of Cse Dehradun India

ISBN: (纸本)9798350325799

One vital sector of animal husbandry, poultry farming, is moving towards scale, intelligence, and automated operations. The emergence of digital technology has significantly improved a number of fields. An extensive analysis of the literature is presented in this paper, with a focus on AI-enabled IoT applications for managing poultry health and well-being. This study primarily centred on chicken welfare, considering modern poultry husbandry is challenged with problems pertaining to standardised criteria for welfare evaluation and reliable monitoring methods, notably for broiler wellness and disease outbreak prevention. There is a compelling rationale for using contemporary technology to continually monitor big farms with millions of birds and increase overall production. This is because there are now increasingly affordable processing resources, IoT sensors, and standard algorithms available. The most important sources of protein in the diet are eggs and poultry, thus poultry farm oversight should prioritize the use of cutting-edge technology solutions. As a result, this study presents a systematic review of the most cutting-edge IoT systems with AI capabilities as well as their most recent developments in the creation of smart systems in this field. The study also explores the benefits and problems that AI and IoT present for chicken farming. © 2023 IEEE.

关键词： Internet of things

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Sensing With Communication Signals: From Information Theory to Signal Processing

arXiv

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arXiv 2025年

作者： Liu, Fan Liu, Ya-Feng Cui, Yuanhao Masouros, Christos Xu, Jie Han, Tony Xiao Buzzi, Stefano Eldar, Yonina C. Jin, Shi National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Guangdong518172 China Huawei Technologies Co. Ltd China Department of Electrical and Information Engineering University of Cassino and Southern Lazio CassinoI-03043 Italy Department of Electronics Information and Bioengineering Politecnico di Milano MilanoI-20133 Italy ParmaI-43124 Italy Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

The Integrated Sensing and Communications (ISAC) paradigm is anticipated to be a cornerstone of the upcoming 6G networks. In order to optimize the use of wireless resources, 6G ISAC systems need to harness the communication data payload signals, which are inherently random, for both sensing and communication (S&C) purposes. This tutorial paper provides a comprehensive technical overview of the fundamental theory and signal processing methodologies for ISAC transmission with random communication signals. We begin by introducing the deterministic-random tradeoff (DRT) between S&C from an information-theoretic perspective, emphasizing the need for specialized signal processing techniques tailored to random ISAC signals. Building on this foundation, we review the core signal models and processing pipelines for communication-centric ISAC systems, and analyze the average squared auto-correlation function (ACF) of random ISAC signals, which serves as a fundamental performance metric for multi-target ranging tasks. Drawing insights from these theoretical results, we outline the design principles for the three key components of communication-centric ISAC systems: modulation schemes, constellation design, and pulse shaping filters. The goal is to either enhance sensing performance without compromising communication efficiency or to establish a scalable tradeoff between the two. We then extend our analysis from a single-antenna ISAC system to its multi-antenna counterpart, discussing recent advancements in multiinput multi-output (MIMO) precoding techniques specifically designed for random ISAC signals. We conclude by highlighting several open challenges and future research directions in the field of sensing with communication signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal modulation

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CONVERGENCE RATE OF GRADIENT DESCENT METHOD FOR MULTI-OBJECTIVE OPTIMIZATION

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Journal of computational mathematics 2019年第5期37卷 689-703页

作者： Liaoyuan Zeng Yuhong Dai Yakui Huang Institute of Computational Mathematics and Scientific/Engineering Computing State Key Labomtory of Scientific and Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Science Hebei University of TechnologyTianjin 300401 China

The convergence rate of the gradient descent method is considered for unconstrained multi-objective optimization problems (MOP). Under standard assumptions, we prove that the gradient descent method with constant stepsizes converges sublinearly when the objective functions are convex and the convergence rate can be strengthened to be linear if the objective functions are strongly convex. The results are also extended to the gradient descent method with the Armijo line search. Hence, we see that the gradient descent method for MOP enjoys the same convergence properties as those for scalar optimization.

关键词： Multi-objective optimization Gradient descent Convergence rate.

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Stochastic Higher-Order Three-Scale Strength Prediction Model for Composite Structures with Micromechanical Analysis

SSRN

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SSRN 2022年

作者： Dong, Hao Yang, Zihao Guan, Xiaofei Cui, Junzhi School of Mathematics and Statistics Xidian University Xi’an710071 China School of Mathematics and Statistics Northwestern Polytechnical University Xi’an710129 China School of Mathematical Sciences Tongji University Shanghai China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Stochastic multiscale modeling and analysis for the strength prediction of composite structures with complex multiscale random configurations remains a challenging problem. This is mainly due to the facts of high-dimensional physical properties, non-linear and non-Gaussian features, which many repeated evaluations of the corresponding stochastic multiscale model are often required. In this paper, we develop a stochastic higher-order three-scale strength prediction (SHTSP) model for composite structures, which is designed to overcome limitations of prohibitive computation involving the microscale, the mesoscale and the macroscale. By virtue of asymptotic homogenization theory and micromechanical analysis, the SHTSP model is derived from the detailed stochastic higher-order three-scale homogenization analysis implemented with analytic solutions of composite structures subjected to tension, bending and twist loads. The SHTSP model represents strength anisotropy through different strength criterions for evaluating the yield state of the different component materials of composite structures, which are induced by aggregate-matrix interface deboning or matrix cracking in multiple scales. Moreover, these are constructed and calibrated from the high-accuracy mechanical analysis with help of two classes of mesoscopic and microscopic auxiliary cell functions, respectively. The corresponding numerical algorithm of SHTSP model and a preprocessing multilevel strategy is designed to improve the computational efficiency. Finally, the numerical experiments in 3D cases illustrate the outstanding performance of the proposed SHTSP model, and the proposed method can significantly reduce the computational time. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Physics-constrained coupled neural differential equations for one dimensional blood flow modeling

arXiv

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arXiv 2024年

作者： Csala, Hunor Mohan, Arvind Livescu, Daniel Arzani, Amirhossein Department of Mechanical Engineering University of Utah Salt Lake CityUT United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM United States

Background: computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, especially with fluid-structure interaction (FSI) simulations. 1D models offer a computationally efficient alternative, by simplifying the 3D Navier-Stokes equations through axisymmetric flow assumption and cross-sectional averaging. However, traditional 1D models based on finite element methods (FEM) often lack accuracy compared to 3D averaged solutions. Methods: This study introduces a novel physics-constrained machine learning technique that enhances the accuracy of 1D cardiovascular flow models while maintaining computational efficiency. Our approach, utilizing a physics-constrained coupled neural differential equation (PCNDE) framework, demonstrates superior performance compared to conventional FEM-based 1D models across a wide range of inlet boundary condition waveforms and stenosis blockage ratios. A key innovation lies in the spatial formulation of the momentum conservation equation, departing from the traditional temporal approach and capitalizing on the inherent temporal periodicity of blood flow. Results: This spatial neural differential equation formulation switches space and time and overcomes issues related to coupling stability and smoothness, while simplifying boundary condition implementation. The model accurately captures flow rate, area, and pressure variations for unseen waveforms and geometries, having 3-5 times smaller error than 1D FEM, and less than 1.2% relative error compared to 3D averaged training data. We evaluate the model’s robustness to input noise and explore the loss landscapes associated with the inclusion of different physics terms. Conclusion: This advanced 1D modeling technique offers promising potential for rapid cardiovascular simulations, achieving computational efficiency and accuracy. By combining the strengths of physics-based and data-d

关键词： Navier Stokes equations

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A Secure ECG based Smart Authentication Scheme for IoT Devices 1

A Secure ECG based Smart Authentication Scheme for IoT Devic...

引用

1st IEEE International Conference on Emerging Trends in Industry 4.0, ETI 4.0 2021

作者： Chandrakar, Preeti Kumar, Aashish Ali, Rifaqat Patidar, R.D. National Institute of Technology Department of Computer Science and Engineering Raipur492010 India National Institute of Technology Department of Mathematics and Scientific Computing Hamirpur177005 India O P Jindal University Department of Electrical Engineering Chhattisgarh Raigarh496109 India

ISBN: (纸本)9781665422376

In this super-evolving world, there exist numerous hidden vulnerabilities and any kind of security breach in recent times would have implications in ways which are even difficult to imagine. To counter such probable scenarios the demand evolved to design security protocols which are sound enough to counter most of such situations in near future. In this scheme, an ECG based user authentication protocol for smart IoT devices is proposed. ECG is one of the unique characteristic traits of any individual which is experimented to remain similar throughout, provided any biological changes have not occured. The scheme uses the captured ECG signal from a smart IoT device taken as raw input by the computation model. The signal is then used to extract the user characteristics such as the QRS complex, P peak, T peak, etc. A feature vector is derived after pre-processing of the signal, which is then used by the computation model to check for the authenticity of the individual. The final authentication of the user depends upon the similarity index of the feature vector generated by the existing user's record captured during registration. The data used here is from the ECG ID database and the obtained accuracy is measured close to 94.2%. A comprehensive study of the performance of the protocol is presented taking communication cost, computation cost, storage cost, and even the security features as the parameters for comparison. © 2021 IEEE.

关键词： Electrocardiograms

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