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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPERATURE PREDICTION IN VANADIUM REDOX FLOW BATTERIES

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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPE...

International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Zhang, Anxiu Juwono, Filbert H. Li, Hongfei Purwanto, Erick Wong, W.K. Liu, Linfeng Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781837242689

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction. © The Institution of Engineering & Technology 2024.

关键词： machine learning prediction Vanadium redox flow battery

PCOS Perception analysis prediction using Machine learning algorithms

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PCOS Perception analysis prediction using Machine learning a...

Recent Advances and Innovations in Engineering (ICRAIE)

作者： Sakthipriya Dhinakaran Chandrakumar Thangavel Shivayavashilaxmipriya S Harinee V S Department of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India

PCOS-Polycystic ovary syndrome is a prevalent disorder among women in their reproductive years, characterised by irregular periods, pelvic pain, and other symptoms like excessive hair growth on the face, stomach, thighs, and chest, excessive weight gain, and thickened skin patches. According to a large-scale national survey on PCOS, 65 percent of Indian women were unaware of PCOS signs and symptoms. This paper focuses on disseminating knowledge by developing a machine learning-based model for diagnosing whether individuals exhibit PCOS symptoms or not . As PCOS grows, a woman's ovaries and adrenal glands produce an imbalanced amount of hormones. Based on the visible changes in their bodies caused by a hormonal imbalance, this model predicts whether they should take a PCOS test or not. In this paper, we use machine learning algorithms such as logistic regression, random forest, decision tree, K nearest neighbour,naïve bayes and support vector machine (SVM) to diagnose PCOS based on network-obtained data. With the help of performance metrics like accuracy,F-statistics, recall, and precision, the performance of the algorithms was validated after an analysis of the results. The results of logistic regression with an accuracy of 90% have proven to be the most accurate prediction model out of all the possible approaches.

关键词： Support vector machines Measurement Analytical models Technological innovation Stomach Machine learning algorithms Thigh

Bi-endomorphism induces new types of derivations on BH-algebras

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Italian Journal of Pure and Applied Mathematics

Italian Journal of Pure and applied mathematics 2024年第52期 138-152页

作者： Muangkarn, Patchara Suanoom, Cholatis Saybounheaung, Thongsouk Sriyotha, Wilaiwan Iampan, Aiyared Program of Mathematics Faculty of Science and Technology Kamphaeng Phet Rajabhat University Kamphaeng Phet62000 Thailand Science and Applied Science Center Program of Mathematics Faculty of Science and Technology Kamphaeng Phet Rajabhat University Kamphaeng Phet62000 Thailand Department of Mathematics Faculty of Natural Sciences National University of Laos Xaythany District Vientiane Laos Department of Mathematics School of Science University of Phayao 19 Moo 2 Tambon Mae Ka Amphur Mueang Phayao56000 Thailand

In this paper, we study the concepts of an (l, r) and an (r, l)-τ-derivation on a BH-algebra, which is induced by a left and a right bi-endomorphism and we provide important properties. In addition, the relationship ... 详细信息

关键词： Algebra

Learning equivariant tensor functions with applications to sparse vector recovery

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arXiv 2024年

作者： Gregory, Wilson G. Tonelli-Cueto, Josué Marshall, Nicholas F. Lee, Andrew S. Villar, Soledad Department of Applied Mathematics and Statistics Mathematical Institute for Data Science Johns Hopkins University United States Department of Mathematics Oregon State University United States School of STEM St. Thomas Aquinas College United States Center for Computational Mathematics Flatiron Institute Simons Foundation United States

This work characterizes equivariant polynomial functions from tuples of tensor inputs to tensor outputs. Loosely motivated by physics, we focus on equivariant functions with respect to the diagonal action of the orthogonal group on tensors. We show how to extend this characterization to other linear algebraic groups, including the Lorentz and symplectic groups. Our goal behind these characterizations is to define equivariant machine learning models. In particular, we focus on the sparse vector estimation problem. This problem has been broadly studied in the theoretical computer science literature, and explicit spectral methods, derived by techniques from sum-of-squares, can be shown to recover sparse vectors under certain assumptions. Our numerical results show that the proposed equivariant machine learning models can learn spectral methods that outperform the best theoretically known spectral methods in some regimes. The experiments also suggest that learned spectral methods can solve the problem in settings that have not yet been theoretically analyzed. This is an example of a promising direction in which theory can inform machine learning models and machine learning models could inform theory. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

THE MANY-BODY GROUND STATE MANIFOLD OF FLAT BAND INTERACTING HAMILTONIAN FOR MAGIC ANGLE TWISTED BILAYER GRAPHENE

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arXiv 2025年

作者： Stubbs, Kevin D. Ragone, Michael Macdonald, Allan H. Lin, Lin Department of Mathematics University of California BerkeleyCA94720 United States Department of Physics University of Texas at Austin AustinTX78712 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

At a magic relative twist angle, magic angle twisted bilayer graphene (MATBG) has an octet of flat bands that can host strong correlation physics when partially filled. A key theoretical discovery in MATBG is the existence of ferromagnetic Slater determinants as exact ground states of the corresponding flat band interacting (FBI) Hamiltonian. The FBI Hamiltonian describes the behavior of electrons that interact with each other in a high-dimensional space, and is constructed from the band structure of the non-interacting Bistritzer–MacDonald model at the chiral limit. A key property of the FBI Hamiltonian for MATBG is that it is frustration free and can be written as a sum of non-commuting terms. In this work, we provide a complete characterization of the ground state manifold of the FBI Hamiltonian, proving that it is precisely the linear span of such ferromagnetic Slater determinants. © 2025, CC BY.

关键词： Ferromagnetism

Ant Colony Optimization based approach to filter IP Address from TOR network

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Ant Colony Optimization based approach to filter IP Address ...

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： N.K Sreelaja Department of Applied Mathematics and Computational Sciences PSG College of Technology Coimbatore India

Tor network is used to power the connections through Tor. The Tor network has several proxy servers through which the online requests are encrypted and routed. Thus it is difficult to identify the users in a tor network. Tor network is used to host hidden websites which are utilized by the cybercriminals to hide their identity and engage in malicious behavior. To prevent the hackers from malicious behavior, the connections from tor IP addresses must be blocked. An Ant Colony Optimization based approach to filter tor IP address (ACOFT) algorithm is proposed in this paper which filters the incoming tor IP address based on the IP addresses of the tor exit nodes in a database. The advantage of ACOFT algorithm is that the search space is categorized and the incoming IP address is searched in the reduced search space. This approach of searching reduces the search time compared to the existing search techniques. The time complexity of ACOFT is O(log 2 s) where ‘ $\mathbf{s}$ ‘ denotes the count of tor IP addresses in the search space and $\mathrm{s} < \mathrm{m}$ , where ‘ $\mathbf{m}$ ‘ denotes the count of tor IP addresses in the database.

关键词： Ant colony optimization Technological innovation Databases Search methods Filtering algorithms Information filters Behavioral sciences

Dynamical Analysis of a Cocaine-Heroin Epidemiological Model with Spatial Distributions

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arXiv 2024年

作者： Zinihi, Achraf Sidi Ammi, Moulay Rchid Ehrhardt, Matthias Bachir, Ahmed Department of Mathematics MAIS Laboratory AMNEA Group Faculty of Sciences and Technics Moulay Ismail University of Meknes Errachidia52000 Morocco University of Wuppertal Chair of Applied and Computational Mathematics Gaußstrasse 20 Wuppertal42119 Germany Department of Mathematics College of Science King Khalid University Abha Saudi Arabia

This article conducts an in-depth investigation of a new spatio-temporal model for the cocaine-heroin epidemiological model with vital dynamics, incorporating the Laplacian operator. The study rigorously establishes the existence, uniqueness, non-negativity, and boundedness of solutions for the proposed model. In addition, the local stability of both a drug-free equilibrium and a drug-addiction equilibrium are analyzed by studying the corresponding characteristic equations. The research provides conclusive evidence that when the basic reproductive number R0 exceeds 1, the drug-addiction equilibrium is globally asymptotically stable. Conversely, using comparative arguments, it is shown that if R0 is less than 1, the drug-free equilibrium is globally asymptotically stable. Furthermore, the article includes a series of numerical simulations to visually convey and support the analytical *** Codes 92D30, 93D05, 35K40, 65D15 © 2024, CC BY.

关键词： Mathematical operators

An Efficient Quantum Circuit for Block Encoding a Pairing Hamiltonian

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arXiv 2024年

作者： Liu, Diyi Du, Weijie Lin, Lin Vary, James P. Yang, Chao Department of Mathematics University of Minnesota Twin CitiesMN United States Department of Physics Iowa State University of Minnesota AmesIA United States Department of Mathematics University of California BerkeleyCA United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States

We present an efficient quantum circuit for block encoding pairing Hamiltonians often studied in nuclear physics. Our block encoding scheme does not require mapping the creation and annihilation operators to the Pauli operators and representing the Hamiltonian as a linear combination of unitaries. Instead, we show how to encode the Hamiltonian directly using controlled swap operations. We analyze the gate complexity of the block encoding circuit and show that it scales polynomially with respect to the number of qubits required to represent a quantum state associated with the pairing Hamiltonian. We also show how the block encoding circuit can be combined with the quantum singular value transformation to construct an efficient quantum circuit for approximating the density of states of a pairing Hamiltonian. The techniques presented can be extended to encode more general second-quantized *** Codes 68Q12, 81P68 © 2024, CC BY.

关键词： Hamiltonians

EXISTENCE OF MAXIMAL SOLUTIONS FOR THE FINANCIAL STOCHASTIC STEFAN PROBLEM OF A VOLATILE ASSET WITH SPREAD

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arXiv 2023年

作者： Antonopoulou, D.C. Farazakis, D. Karali, G. Department of Mathematics and Applied Mathematics University of Crete HeraklionGR-714 09 Greece Department of Physical and Mathematical Sciences Engineering University of Chester CH2 4NU United Kingdom Institute of Applied and Computational Mathematics FORTH HeraklionGR-711 10 Greece

In this work, we consider the outer Stefan problem for the short-time prediction of the spread of a volatile asset traded in a financial market. The stochastic equation for the evolution of the density of sell and buy orders is the Heat Equation with a non-smooth noise in the sense of Walsh, posed in a moving boundary domain with velocity given by the Stefan condition. This condition determines the dynamics of the spread, and the solid phase [s−(t), s+(t)] defines the bid-ask spread area wherein the transactions vanish. We introduce a reflection measure and prove existence and uniqueness of maximal solutions up to stopping times in which the spread s+(t)−s−(t) stays a.s. non-negative and bounded. For this, we use a Picard approximation scheme and some of the estimates of [19] for the Green's function and the associated to the reflection measure obstacle problem. Analogous results are obtained for the equation without reflection corresponding to a signed density. Additionally, we apply some formal asymptotics when the noise depends only on time to derive that the spread is given by the integral of the solution of a linear diffusion stochastic *** Codes 35K55, 35K40, 60H30, 60H15, 91G80, 91B70 © 2023, CC BY.

关键词： Stochastic systems