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Marine Predators Algorithm for Energy Scheduling Problem Using Renewable Energy

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Marine Predators Algorithm for Energy Scheduling Problem Usi...

Cyber Resilience (ICCR), International Conference on

作者： Sharif Naser Makhadmeh Ammar Kamal Abasi Mohammed Azmi Al-Betar Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The Energy Scheduling Problem (ESP) involves scheduling smart home appliances based on electricity pricing schemes. This entails adjusting the timing of operations for these appliances across different periods. The primary aim of this scheduling approach is to reduce cost and the Peak-to-Average Ratio (PAR), while enhancing user comfort. This study employs the Marine Predators Algorithm (MPA) to address ESP and derive an optimal schedule for smart home appliances. Additionally, a renewable energy source (RES) on the basis of a PV system is introduced to further optimize appliance schedules by providing a smart home with energy, particularly during peak periods. The obtained outcomes demonstrate the effectiveness of the introduced RES in reducing costs of electricity and enhancing user satisfaction. Furthermore, the performance of MPA is compared with that of the wind-driven optimization algorithm, with MPA proving superior in achieving ESP objectives.

关键词： Home appliances Optimization Pricing Heuristic algorithms Optimal scheduling Electricity Dynamic scheduling

The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks

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arXiv 2023年

作者： Cao, Yuan Zou, Difan Li, Yuanzhi Gu, Quanquan Department of Statistics and Actuarial Science Department of Mathematics The University of Hong Kong Hong Kong Department of Computer Science Institute of Data Science The University of Hong Kong Hong Kong Machine Learning Department Carnegie Mellon University PittsburghPA United States Department of Computer Science University of California Los AngelesCA United States

We study the implicit bias of batch normalization trained by gradient descent. We show that when learning a linear model with batch normalization for binary classification, gradient descent converges to a uniform margin classifier on the training data with an exp(−Ω(log2 t)) convergence rate. This distinguishes linear models with batch normalization from those without batch normalization in terms of both the type of implicit bias and the convergence rate. We further extend our result to a class of two-layer, single-filter linear convolutional neural networks, and show that batch normalization has an implicit bias towards a patch-wise uniform margin. Based on two examples, we demonstrate that patch-wise uniform margin classifiers can outperform the maximum margin classifiers in certain learning problems. Our results contribute to a better theoretical understanding of batch normalization. © 2023, CC BY-SA.

关键词： Convolution

Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis

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arXiv 2022年

作者： Herold, Maternus Veselovska, Anna Jehle, Jonas Krahmer, Felix Department of Mathematics Technical University of Munich Germany Bmw Group Germany Munich Data Science Institute Germany Munich Center for Machine Learning Germany

Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks. © 2022, CC BY-NC-ND.

关键词： Crashworthiness

Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond

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The Journal of Machine Learning Research

The Journal of machine learning Research 2023年第1期24卷 1339-1349页

作者： Anna Hedström Leander Weber Dilyara Bareeva Daniel Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne Understandable Machine Intelligence Lab TU Berlin Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Department of Computer Science University of Potsdam Potsdam Germany Department of Electrical Engineering and Computer Science TU Berlin and Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Understandable Machine Intelligence Lab TU Berlin Berlin Germany and BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus--a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/).

关键词： explainability responsible AI reproducibility open source Python

CONSTRAINED CONSENSUS-BASED OPTIMIZATION AND NUMERICAL HEURISTICS FOR THE FEW PARTICLE REGIME

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arXiv 2024年

作者： Beddrich, Jonas Chenchene, Enis Fornasier, Massimo Huang, Hui Wohlmuth, Barbara Faculty of Mathematics University of Vienna Austria Department of Mathematics Munich Data Science Institute Technical University of Munich Garching by Munich & Munich Center for Machine Learning Munich Germany Department of Mathematics and Scientific Computing University of Graz Austria Department of Mathematics Technical University of Munich Garching by Munich Germany

Consensus-based optimization (CBO) is a versatile multi-particle optimization method for performing nonconvex and nonsmooth global optimizations in high dimensions. Proofs of global convergence in probability have been achieved for a broad class of objective functions in unconstrained optimizations. In this work we adapt the algorithm for solving constrained optimizations on compact and unbounded domains with boundary by leveraging emerging reflective boundary conditions. In particular, we close a relevant gap in the literature by providing a global convergence proof for the many-particle regime comprehensive of convergence rates. On the one hand, for the sake of minimizing running cost, it is desirable to keep the number of particles small. On the other hand, reducing the number of particles implies a diminished capability of exploration of the algorithm. Hence numerical heuristics are needed to ensure convergence of CBO in the few-particle regime. In this work, we also significantly improve the convergence and complexity of CBO by utilizing an adaptive region control mechanism and by choosing geometry-specific random noise. In particular, by combining a hierarchical noise structure with a multigrid finite element method, we are able to compute global minimizers for a constrained p-Allen–Cahn problem with obstacles, a very challenging variational *** Codes 90C26, 90C56, 90C59, 65K10, 35Q90, 60H10 © 2024, CC BY.

关键词： Constrained optimization

Cost-Effective Communication in UDN in Indoor and Outdoor Environment via machine learning

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Cost-Effective Communication in UDN in Indoor and Outdoor En...

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Karman, K. Nattar Velmurugan, V. Raju, Kommisetti Murthy Sajana, T. Vijayalakshmi, V. Dhanraj, JoshuvaArockia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala RandD Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh West Godavari India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350334357

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management. © 2023 IEEE.

关键词： Cost effectiveness

An iterative-based difference scheme for nonlinear fractional integro-differential equations of Volterra type

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Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Saini, Gaurav Ghosh, Bappa Chand, Sunita Mohapatra, Jugal Center for Data Science Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Center for Artificial Intelligence and Machine Learning Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics National Institute of Technology Rourkela India

This paper presents an iterative difference scheme for solving nonlinear fractional integro-differential equations of Volterra type, which are widely used in modeling memory-dependent phenomena in various scientific and engineering applications. The scheme employs Caputo's fractional derivative to handle the non-local nature of the fractional derivative and introduces an iterative approach to enhance accuracy and stability. By discretizing the domain of interest on a uniform mesh and the present non-local differential operator using a well-established L1 technique, we derive a nonlinear difference equation that captures the essential dynamics of the underlying continuous problem. The corresponding nonlinear equation is solved by employing the Daftardar-Gejji and Jafari (DGJ) method. The convergence and stability of the proposed scheme are rigorously analyzed under sufficiently smooth initial data, and the method produces reliable numerical solutions. Numerical experiments are conducted to validate the effectiveness of the scheme, demonstrating its efficiency. © 2025 The Authors

关键词： Composite trapezoidal formula Error estimation L1 technique Nonlinear integro-differential equations

Deep learning for Uneven data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network 5

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Deep Learning for Uneven Data in Industrial IoT Using a Dist...

5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Gupta, Raj Kumar Naveena, N. Rao, B. Srinivasa Rs, Rajasree Sarkar, Swagata Kumar, Kallakunta Ravi Sardar Vallabhbhai Patel College Veer Kunwar Singh University Physics Department Bihar Ara Bhabua India Kongu Engineering College Computer Technology-UG Perundurai India Gokaraju Rangaraju Institute of Engineering & Technology Department of Computer Science and Engineering Hyderabad500090 India New Horizon College of Engineering Artificial Intelligence and Machine Learning Bangalore India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Sai Leo Nagar Chennai44 India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

In minority class and noisy data situations, supervised learning performs more favorably for the majority class but cannot generalize testing data. Performance in the aforementioned use cases might be improved with the help of neural-based data augmentation approaches for generating new data and deep convolutional models for classification. GANs (generative adversarial networks) have recently demonstrated impressive advancements in picture generation. To address the restrictions imposed by the distribution bias problem between the produced data and the unique data, and to provide a more vigorous data augmentation, the presented distribution bias aware collaborative GAN (DGAN) model for unbalanced deep learning in industrial IoT. By including an auxiliary classifier in the foundational GAN model, a comprehensive data augmentation system may be built. In particular, a provisional source of energy with random labels is envisioned and trained combatively with the classification model to appropriately augment the amount of data specimens in minority classes, and a mass fraction system is newly envisioned between two distinct feature extraction, allowing the cooperative adversarial training among some of the power source, voltage divider, and classification algorithm. The next step is to develop an augmentation approach for smart anomaly detection in class imbalance, which, by adjusting for dispersion bias using the properly balanced data, might significantly improve classification precision. Experiment assessments using real-world unbalanced datasets show the superior recital of the suggested model in addressing the distribution biased issue for multi-class classification in class imbalance for commercial IoT applications, as compared to five baseline techniques. © 2023 IEEE.

关键词： Generative adversarial networks

Existence of Direct Density Ratio Estimators

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arXiv 2025年

作者： Banzato, Erika Drton, Mathias Saraf-Poor, Kian Shi, Hongjian Department of Statistical Sciences University of Padova Italy TUM School of Computation Information and Technology Munich Data Science Institute Technical University of Munich Munich Center for Machine Learning Germany Department of Statistics Columbia University United States TUM School of Computation Information and Technology Technical University of Munich Germany

Many two-sample problems call for a comparison of two distributions from an exponential family. Density ratio estimation methods provide ways to solve such problems through direct estimation of the differences in natural parameters. The term direct indicates that one avoids estimating both marginal distributions. In this context, we consider the Kullback–Leibler Importance Estimation Procedure (KLIEP), which has been the subject of recent work on differential networks. Our main result shows that the existence of the KLIEP estimator is characterized by whether the average sufficient statistic for one sample belongs to the convex hull of the set of all sufficient statistics for data points in the second sample. For high-dimensional problems it is customary to regularize the KLIEP loss by adding the product of a tuning parameter and a norm of the vector of parameter differences. We show that the existence of the regularized KLIEP estimator requires the tuning parameter to be no less than the dual norm-based distance between the average sufficient statistic and the convex hull. The implications of these existence issues are explored in applications to differential network analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Parameter estimation