检索结果-内蒙古大学图书馆

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

学校读者我要写书评

暂无评论

computers, Materials & Continua 2022年第4期71卷 683-695页

作者： K.S.Bhuvaneshwari J.Uma K.Venkatachalam Mehedi Masud Mohamed Abouhawwash T.Logeswaran Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Electrical and Electronics Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Electrical and Electronics Engineering Kongu Engineering CollegeErode638060India

Air pollution is one of the major concerns considering detriments to human *** type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye *** pollution also poses serious issues to the *** from the vehicle industry is the cause of greenhouse effect and CO2 ***,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary *** monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor *** is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not *** study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart *** systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding *** dataset is divided into training and testing parts based on 10 *** evaluation on predicting the air pollutant for localization is performed with the training *** error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

关键词： Air pollution monitoring air pollutant SVM Gaussian EHO fast correlation WSN localization

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2023年第9期37卷 3159-3177页

作者： Nitin Mittal Rohit Salgotra Abhishek Sharma Sandeep Kaur SSAskar Mohamed Abouhawwash University Center for Research and Development Chandigarh UniversityMohaliPunjab140413India Faculty of Physics and Applied Computer Science AGH University of Science&TechnologyKrakowPoland Faculty of Information Technology Middle East UniversityAmman11813Jordan Department of Computer Engineering and Applications GLA UniversityMathura281406India Department of Computer Engineering&Technology Guru Nanak Dev UniversityAmritsarPunjab143005India Department of Statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Computational Mathematics Science and EngineeringCollege of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this *** Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of *** has already proved its competence in various optimization prob-lems,but it suffers from slow convergence *** improve the convergence performance of FA,a new variant named EFA is *** effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and *** an application of this algorithm to real-world problems,EFA is also applied to optimize the CR *** is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity ***,it requires optimization to meet specific performance *** results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic *** results further prove that the proposed algorithm is highly efficient and provides superior results.

关键词： Firefly algorithm cognitive radio bit error rate genetic algorithm simulated annealing biogeography-based optimization

Choquet-Based Fuzzy Rough Sets

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Theerens, Adnan Lenz, Oliver Urs Cornelis, Chris Computational Web Intelligence Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium

Fuzzy rough set theory can be used as a tool for dealing with inconsistent data when there is a gradual notion of indiscernibility between objects. It does this by providing lower and upper approximations of concepts. In classical fuzzy rough sets, the lower and upper approximations are determined using the minimum and maximum operators, respectively. This is undesirable for machine learning applications, since it makes these approximations sensitive to outlying samples. To mitigate this problem, ordered weighted average (OWA) based fuzzy rough sets were introduced. In this paper, we show how the OWA-based approach can be interpreted intuitively in terms of vague quantification, and then generalize it to Choquet-based fuzzy rough sets (CFRS). This generalization maintains desirable theoretical properties, such as duality and monotonicity. Furthermore, it provides more flexibility for machine learning applications. In particular, we show that it enables the seamless integration of outlier detection algorithms, to enhance the robustness of machine learning algorithms based on fuzzy rough sets. Copyright © 2022, The Authors. All rights reserved.

关键词： Integral equations

Robust Scheduling of a Charging Station Considering Uncertain Charging Demand and Demand Response

学校读者我要写书评

暂无评论

Robust Scheduling of a Charging Station Considering Uncertai...

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yuling Ren Mao Tan Zibin Li Zhuocen Dai Wei Tan School of Mathematics and Computational Science Xiangtan University Xiangtan China Hunan National Center for Applied Mathematics Xiangtan University Xiangtan China School of Computer Science & School of Cyberspace Science Xiangtan University Xiangtan China Saiwei Avionics Technology Co. Ltd Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

With advancements in increasing adoption of electric vehicles (EVs), the potential for Vehicle-to-Grid (V2G) to contribute to a more sustainable energy ecosystem is substantial. However, fluctuations in real-time electricity prices on the grid may cause the overall cost of EVs during continuous charging and discharging processes to exceed that of the pure charging mode. At the same time, the random arrivals of EVs will exacerbate the uncertainty of charging demand at EV stations. Therefore, the paper first introduces a demand response (DR) mechanism that combines the pricing factors of EVs and charging stations, ensuring that neither party incurs increased cost due to participating in the V2G. Additionally, to address the uncertainty of charging demands, this paper develops a distributed robust optimization scheduling scheme (DRO) based on moment information. Experimental results demonstrate the effectiveness of our proposed method.

关键词： Vehicle-to-grid Uncertainty Costs Fluctuations System integration Pricing Charging stations Demand response Real-time systems Optimization

On Scheduling Mechanisms Beyond the Worst Case

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Gao, Yansong Zhang, Jie Applied Mathematics and Computational Science University of Pennsylvania United States Electronics and Computer Science University of Southampton United Kingdom

The problem of scheduling unrelated machines has been studied since the inception of algorithmic mechanism design [Nisan and Ronen, 1999]. It is a resource allocation problem that entails assigning m tasks to n machines for execution. Machines are regarded as strategic agents who may lie about their execution costs so as to minimize their allocated workload. To address the situation when monetary payment is not an option to compensate the machines’ costs, Koutsoupias [2014] devised two truthful mechanisms, K and P respectively, that achieve an approximation ratio of n+12 and n, for social cost minimization. In addition, no truthful mechanism can achieve an approximation ratio better than n+12 . Hence, mechanism K is optimal. While approximation ratio provides a strong worst-case guarantee, it also limits us to a comprehensive understanding of mechanism performance on various inputs. This paper investigates these two scheduling mechanisms beyond the worst case. We first show that mechanism K achieves a smaller social cost than mechanism P on every input. That is, mechanism K is pointwise better than mechanism P. Next, for each task j, when machines’ execution costs tji are independent and identically drawn from a task-specific distribution F j (t), we show that the average-case approximation ratio of mechanism K converges to a constant. This bound is tight for mechanism K. For a better understanding of this distribution dependent constant, on the one hand, we estimate its value by plugging in a few common distributions;on the other, we show that this converging bound improves a known bound [Zhang, 2018] which only captures the single-task setting. Last, we find that the average-case approximation ratio of mechanism P converges to the same constant. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine design

ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Parkinson, Suzanna Ongie, Greg Willett, Rebecca Committee on Computational and Applied Mathematics University of Chicago ChicagoIL United States Department of Mathematical and Statistical Sciences Marquette University MilwaukeeWI United States Department of Statistics Department of Computer Science and Committee on Computational and Applied Mathematics University of Chicago ChicagoIL United States

Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is, training the network effectively learns an interpolating function, and properties of the interpolant affect predictions the network will make on new samples. This manuscript explores how properties of such functions learned by neural networks of depth greater than two layers. Our framework considers a family of networks of varying depths that all have the same capacity but different representation costs. The representation cost of a function induced by a neural network architecture is the minimum sum of squared weights needed for the network to represent the function;it reflects the function space bias associated with the architecture. Our results show that adding additional linear layers to the input side of a shallow ReLU network yields a representation cost favoring functions with low mixed variation – that is, it has limited variation in directions orthogonal to a low-dimensional subspace and can be well approximated by a single- or multi-index model. Such functions may be represented by the composition of a function with low two-layer representation cost and a low-rank linear operator. Our experiments confirm this behavior in standard network training regimes. They additionally show that linear layers can improve generalization and the learned network is well-aligned with the true latent low-dimensional linear subspace when data is generated using a multi-index model. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Boukanjime, Brahim Maama, Mohamed Laboratory of Mathematical Modeling and Economic Calculations Hassan First University of Settat Morocco Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young’s inequality and Chebyshev’s inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describes scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system’s state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model’s behavior under different parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models

Relevance of the Basset history term for Lagrangian particle dynamics

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Urizarna-Carasa, Julio Ruprecht, Daniel von Kameke, Alexandra Padberg-Gehle, Kathrin Lehrstuhl Computational Mathematics Institut für Mathematik Technische Universität Hamburg Hamburg Germany Heinrich-Blasius-Institute Faculty of Engineering and Computer Science Hamburg University of Applied Sciences Hamburg Germany Applied Mathematics Institute for Mathematics and its Didactics Leuphana University Lüneburg Lüneburg Germany

The movement of small but finite spherical particles in a fluid can be described by the Maxey-Riley equation (MRE) if they are too large to be considered passive tracers. The MRE contains an integral "history term" modeling wake effects, which causes the force acting on a particle at some given time to depend on its full past trajectory. The history term causes complications in the numerical solution of the MRE and is therefore often neglected, despite both numerical and experimental evidence that its effects are generally not negligible. By numerically computing trajectories with and without the history term of a large number of particles in different flow fields, we investigate its impact on the large-scale Lagrangian dynamics of simulated particles. We show that for moderate to large Stokes numbers, ignoring the history term leads to significant differences in clustering patterns. Furthermore, we compute finite-time Lyapunov exponents and show that, even for small particles, the differences in the resulting scalar field from ignoring the BHT can be significant, in particular if the underlying flow is turbulent. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

An Entropy-Stable Discontinuous Galerkin Discretization of the Ideal Multi-Ion Magnetohydrodynamics System

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Rueda-Ramírez, Andrés M. Sikstel, Aleksey Gassner, Gregor J. Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Cologne50931 Germany Applied and Computational Mathematics RWTH Aachen University Schinkelstraße 2 Aachen52062 Germany Center for Data and Simulation Science University of Cologne Cologne50931 Germany

In this paper, we present an entropy-stable (ES) discretization using a nodal discontinuous Galerkin (DG) method for the ideal multi-ion magneto-hydrodynamics (MHD) equations. We start by performing a continuous entropy analysis of the ideal multi-ion MHD system, described by, e.g., [Toth (2010) Multi-Ion Magnetohydrodynamics] [1], which describes the motion of multi-ion plasmas with independent momentum and energy equations for each ion species. Following the continuous entropy analysis, we propose an algebraic manipulation to the multi-ion MHD system, such that entropy consistency can be transferred from the continuous analysis to its discrete approximation. Moreover, we augment the system of equations with a generalized Lagrange multiplier (GLM) technique to have an additional cleaning mechanism of the magnetic field divergence error. We first derive robust entropy-conservative (EC) fluxes for the alternative formulation of the multi-ion GLM-MHD system that satisfy a Tadmor-type condition and are consistent with existing EC fluxes for single-fluid GLM-MHD equations. Using these numerical two-point fluxes, we construct high-order EC and ES DG discretizations of the ideal multi-ion MHD system using collocated Legendre-Gauss-Lobatto summation-by-parts (SBP) operators. The resulting nodal DG schemes satisfy the second-law of thermodynamics at the semi-discrete level, while maintaining high-order convergence and local node-wise conservation properties. We demonstrate the high-order convergence, and the EC and ES properties of our scheme with numerical validation experiments. Moreover, we demonstrate the importance of the GLM divergence technique and the ES discretization to improve the robustness properties of a DG discretization of the multi-ion MHD system by solving a challenging magnetized Kelvin-Helmholtz instability problem that exhibits MHD turbulence. Copyright © 2024, The Authors. All rights reserved.

关键词： Galerkin methods