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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources 41

CaPS: Collaborative and Private Synthetic Data Generation fr...

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41st International Conference on Machine Learning, ICML 2024

作者： Pentyala, Sikha Pereira, Mayana De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Electrical Engineering Universidade de Brasilia Brazil Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken important steps towards a regulated data world, drastically impacting data sharing and data usability and resulting in massive amounts of data confined within the walls of organizations. While synthetic data generation (SDG) is an appealing solution to break down these walls and enable data sharing, the main drawback of existing solutions is the assumption of a trusted aggregator for generative model training. Given that many data holders may not want to, or be legally allowed to, entrust a central entity with their raw data, we propose a framework for collaborative and private generation of synthetic tabular data from distributed data holders. Our solution is general, applicable to any marginal-based SDG, and provides input privacy by replacing the trusted aggregator with secure multi-party computation (MPC) protocols and output privacy via differential privacy (DP). We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate SDG algorithms MWEM+PGM and AIM. Copyright 2024 by the author(s)

关键词： Data assimilation

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Decentralized Multi-Armed Bandit Can Outperform Classic Upper Confidence Bound: A Homogeneous Case over Strongly Connected Graphs 63

Decentralized Multi-Armed Bandit Can Outperform Classic Uppe...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhu, Jingxuan Liu, Ji Zhejiang Lab China Stony Brook University Department of Applied Mathematics and Statistics United States Stony Brook University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

This paper studies a homogeneous decentralized multi-armed bandit problem, in which a network of multiple agents faces the same set of arms, and each agent aims to minimize its own regret. A fully decentralized upper confidence bound (UCB) algorithm is proposed for a multi-agent network whose neighbor relations are described by a directed graph. It is shown that the decentralized algorithm guarantees each agent to achieve a lower logarithmic asymptotic regret compared to the classic UCB algorithm, provided the neighbor graph is strongly connected. The improved asymptotic regret upper bound is reciprocally related to the maximal size of a local neighborhood within the network. The roles of graph connectivity, maximum local degree, and network size are analytically elucidated in the expression of regret. © 2024 IEEE.

关键词： Decentralized systems

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Statistical mechanics and pressure of composite optical systems

Statistical mechanics and pressure of composite optical syst...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Efremidis, Nikolaos K. Christodoulides, Demetrios N. Department of Applied Mathematics University of Crete Heraklion71409 Greece Institute of Applied and Computational Mathematics FORTH Crete Heraklion70013 Greece Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

We develop a thermodynamic theory that takes into account the synergistic action of multiple components. We compute the optomechanical pressure and find that the type of the nonlinearity involved can lead to different... 详细信息

ISBN: (纸本)9781957171395

关键词： Electro-optical waveguides Laser theory Lasers and electrooptics Thermodynamics

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Integrating Multiscale Mapper Features with Machine Learning Techniques to Improve Breast Cancer Classification Accuracy 11

Integrating Multiscale Mapper Features with Machine Learning...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Netshifhire, Rabelani Visaya, Maria Vivien University of Johannesburg Department of Electrical and Electronic Engineering Science Johannesburg South Africa University of Johannesburg Department of Mathematics and Applied Mathematics Johannesburg South Africa DSI-NRF CoE-MaSS Johannesburg South Africa

ISBN: (纸本)9798350367300

Breast cancer classification is critical for early detection and treatment planning. The complexity of breast cancer data poses feature engineering challenges for conventional machine learning (ML) methods, which often fail to capture these *** employ the Mapper algorithm, a topological data analysis (TDA) tool, for feature engineering. Two variants of Mapper algorithm are utilized, namely Singlescale Mapper (SM) and Multiscale Mapper (MM). SM is constrained by scale sensitivity, dimensionality limitations, and parameter tuning challenges. MM enhance SM by capturing topological features at multiple resolutions, offering a robust hierarchical view of *** the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, three feature sets namely conventional, SM-derived, and MM-derived features are evaluated across various selected ML models to assess their potential to enhance classification *** demonstrated that MM-derived features integrated with ML models improved breast cancer classification accuracy, outperforming both conventional and SM-derived features. This findings highlight the potential of TDA's MM as a feature engineering tool to improve breast cancer classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Unveiling the Power of Sparse Neural Networks for Feature Selection 27

Unveiling the Power of Sparse Neural Networks for Feature Se...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Atashgahi, Zahra Liu, Tennison Pechenizkiy, Mykola Veldhuis, Raymond Mocanu, Decebal Constantin van der Schaar, Mihaela Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Luxembourg Luxembourg

ISBN: (纸本)9781643685489

Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than 50% memory and 55% FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. © 2024 The Authors.

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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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Algorithmic optimization of quantum optical storage in solids

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Physical Review Research 2024年第3期6卷 033153-033153页

作者： Yisheng Lei Haechan An Zongfeng Li Mahdi Hosseini Department of Electrical and Computer Engineering and Applied Physics Program Elmore Family School of Electrical and Computer Engineering

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth.

关键词： Quantum communication

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Our Role in Solving Global Challenges: An Opinion

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JOURNAL OF THE AMERICAN CHEMICAL SOCIETY 2024年第31期146卷 21193-21195页

作者： Carter, Emily A. Department of Mechanical and Aerospace Engineering Program in Applied and Computational Mathematics and the Andlinger Center for Energy and the Environment Princeton University Princeton New Jersey 08544-5263 United States Princeton Plasma Physics Laboratory Princeton New Jersey 08540-6655 United States

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Fuzzy Intelligence Inventory Decision Optimization Model of Sustainability and Green Technologies for Mixed Uncertainties of Carbon Emission 9

Fuzzy Intelligence Inventory Decision Optimization Model of ...

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9th International Conference on Information Management, ICIM 2023

作者： Anand, M. Clement Joe Moorthy, C. Balakrishna Sivamani, S. Indrakumar, S. Kalaiarasi, K. Barhoi, Amir Department of Mathematics Karnataka Bengaluru India University of Technology & Applied Sciences Electrical Engineering Section Department of Engineering Salalah211 Oman Kongu Engineering College Department of Mathematics Tamil Nadu Erode India Department of Mathematics Tamil Nadu Tiruchirappalli India Duliajan College Department of Mathematics Assam India

ISBN: (纸本)9798350336382

In many areas, carbon cap-and-trade and carbon offsets are frequent and significant mechanisms for reducing carbon emissions. Furthermore, particular capital investments in green technologies can efficiently cut carbon emissions from corporate activities. However, such capital investments are expensive, and not all businesses/company can afford them. As a result, if all members of a supply chain agree to share facility investments, the supply chain can cut carbon emissions while also increasing profit. This study utilized crisp and fuzzy models to fix perishable products in the production process. The suggested model, for study is solved using python in machine learning. To cut carbon emissions and increase the total value of the supply chain system, we planned to integrate manufacturing, distribution, replenishment, and technology. Several cases are simulated, and the key parameters are subjected to sensitivity analysis. Under various carbon emission rules, the optimal solutions and joint total profit are also compared. Companies should be able to share risks by co-investing and building sustainable supply chains as a part of the future carbon emission control trend. © 2023 IEEE.

关键词： Python

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