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arXiv 2024年

作者： Leon, Florin Department of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iași Romania

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence. © 2024, CC BY-NC-SA.

关键词： Decision making

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EU Battery Regulation and the Role of Blockchain in Creating Digital Battery Passports

EU Battery Regulation and the Role of Blockchain in Creating...

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Blockchain, Robotics and AI for Networking Security Conference (BRAINS)

作者： Alberto Butera Valentina Gatteschi Department of Computer and Control Engineering Politecnico di Torino Torino Italy

ISBN: (数字)9798350367843

ISBN: (纸本)9798350367850

In response to concerns about climate change and environmental sustainability, there is a global emphasis on innovative solutions to reduce emissions and optimize resource use. A key initiative is the introduction of digital passports for batteries, with the primary goal of improving the circular economy. The European Commission has taken a pioneering role by publishing regulations, laying the groundwork for digital passports and standardization. This article has a twofold aim: first, with the aim of supporting the adoption of batteries passports, it provides an overview of current initiatives and regulations in the context of electric vehicle batteries; secondly, it examines the potential of blockchain technology in implementing digital passports, in order to demonstrate its compatibility with recent regulations, such as the one proposed by the European Commission.

关键词： Protocols Publishing Green products Europe Prototypes Regulation Batteries Blockchains Proposals Standards

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Toward a Realistic Benchmark for Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Recalcati, Pietro Garcea, Fabio Piano, Luca Lamberti, Fabrizio Morra, Lia Department of Control and Computer Engineering Politecnico di Torino Torino Italy

Deep neural networks are increasingly used in a wide range of technologies and services, but remain highly susceptible to out-of-distribution (OOD) samples, that is, drawn from a different distribution than the original training set. A common approach to address this issue is to endow deep neural networks with the ability to detect OOD samples. Several benchmarks have been proposed to design and validate OOD detection techniques. However, many of them are based on far-OOD samples drawn from very different distributions, and thus lack the complexity needed to capture the nuances of real-world scenarios. In this work, we introduce a comprehensive benchmark for OOD detection, based on ImageNet and Places365, that assigns individual classes as in-distribution or out-of-distribution depending on the semantic similarity with the training set. Several techniques can be used to determine which classes should be considered in-distribution, yielding benchmarks with varying properties. Experimental results on different OOD detection techniques show how their measured efficacy depends on the selected benchmark and how confidence-based techniques may outperform classifier-based ones on near-OOD samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Research of interleaved hysteresis control based on soft-switching power amplifier

Research of interleaved hysteresis control based on soft-swi...

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China International Electrical and Energy Conference (CIEEC)

作者： Guangjuan Qiu Xinyue Zhang Jian Wei Jingxin Chen Peng E College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

This paper delves into the interleaved hysteresis control method based on soft-switching power amplifiers. Firstly, through modeling and analysis, the basic topology and mode transition of the soft-switching power amplifier are clearly defined, revealing the conditions for implementing soft-switching. Secondly, a strategy for interleaved soft-switching hysteresis control is proposed, detailing its working principle, and the frequency range of the interleaved hysteresis loop is obtained through analysis. In order to optimize the design of the filter, this paper also deeply analyzes the design methods and parameter selection of the filter inductance, providing a theoretical foundation for the implementation of the power amplifier. The research results show that the proposed interleaved soft-switching hysteresis control method can reduce switch losses and electromagnetic interference, thereby improving the efficiency and reliability of the power amplifier. This study provides beneficial theoretical support and practical guidance for the application of high-performance motor drive systems.

关键词： Motor drives Circuits Power amplifiers Switches Reliability theory Topology Magnetic losses

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A Hybrid Deep Learning Framework for Accurate Prediction of Significant Wave Height in the North Pacific Ocean

SSRN

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SSRN 2024年

作者： Wang, Jianhui Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai264000 China

Significant wave height (WVHT) is one of the important parameters applied in the field of ocean engineering. Accurate predictions of WVHT can help improve wave energy conversion efficiency, coastal facility management, and disaster prevention and mitigation. However, extracting features from complex ocean parameters to accurately predict wave heights remains a challenge. A hybrid deep learning framework is proposed for precise prediction of WVHT, which combining the complementary strengths of deep learning architectures and gradient boosting methods. Incorporating an attention mechanism (AM), a convolutional neural network (CNN), and a bidirectional long and short-term memory network (BiLSTM), the framework is designed to deeply learn the information contained in historical data. Furthermore, the extreme gradient boosting (XGBoost) algorithm is integrated using a multi-model fusion strategy based on weighted ensemble techniques. First, input features are selected using Pearson's correlation coefficient and XGBoost feature importance score. The feature set is then fed into AM-based CNN-BiLSTM (CBLA) and XGBoost for training to predict WVHT. Finally, the predictions of the above models are fused to produce the final predictions. We predict the WVHT at three stations in the North Pacific at 1, 2, 4, and 6 hours in advance. The results show that the prediction performance and robustness of hybrid framework outperforms that of CBLA and XGBoost, and significantly outperforms that of other benchmark models. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Sustainable business model in practice: a digital business game training for high school students 15

Sustainable business model in practice: a digital business g...

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15th International Conference on Society and Information Technologies, ICSIT 2024

作者： Fontanella, Sabrina Fraccascia, Luca Nonino, Fabio Scarnicchia, Alessandra Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Roma Lazio Italy Department of High-Tech Business and Entrepreneurship University of Twente Enschede Overijssel Netherlands

ISBN: (纸本)9781950492770

This paper explores the nexus between sustainable business models, education and technology, addressing pressing challenges in economic, social, and environmental spheres. On one hand, education is identified as a key tool for fostering sustainability principles and essential skills for future managers;on the other, businesses, particularly through sustainable business models (SBMs) and evolving digital platforms, play a pivotal role in advancing sustainability goals. The research answer to the need for sustainable development examining the potential of educational business games, blending entertainment and education to engage the 'gamer generation' actively. Considering the growing literature upon sustainable entrepreneurship and business models, the objective of the paper is to implement a digital business game for sustainability education designed to teach high school students how to implement an entrepreneurial activity through a sustainable business model. This study contributes to understanding sustainable business practices and innovative educational approaches, aligning with the global imperative for a sustainable future. In the end of the manuscript some prelaminar result of the game testing phase are presented. © Proceedings ICSIT, International Conference on Society and Information Technologies. All Rights Reserved.

关键词： Blending

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Reinforcement Learning-Based Output Stabilization control for Nonlinear Systems With Generalized Disturbances 25

Reinforcement Learning-Based Output Stabilization Control fo...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Tang, Zezhi Rossiter, J Anthony Dong, Yi Panoutsos, George University Of Sheffield Department Of Automatic Control And Systems Engineering SheffieldS13JD United Kingdom University Of Southampton Department Of Electronics And Computer Science Southampton United Kingdom

ISBN: (纸本)9798350340266

This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals;so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Nonlinear systems

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BONES: a Benchmark fOr Neural Estimation of Shapley values

arXiv

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arXiv 2024年

作者： Napolitano, Davide Cagliero, Luca Department of Control and Computer Engineering Politecnico di Torino Torino Italy

Shapley Values are concepts established for eXplainable AI. They are used to explain black-box predictive models by quantifying the features’ contributions to the model’s outcomes. Since computing the exact Shapley Values is known to be computationally intractable on real-world datasets, neural estimators have emerged as alternative, more scalable approaches to get approximated Shapley Values estimates. However, experiments with neural estimators are currently hard to replicate as algorithm implementations, explainer evaluators and results visualizations are neither standardized nor promptly usable. To bridge this gap, we present BONES, a new benchmark focused on neural estimation of Shapley Value. It provides researchers with a suite of state-of-the-art neural and traditional estimators, a set of commonly used benchmark datasets, ad hoc modules for training black-box models, as well as specific functions to easily compute the most popular evaluation metrics and visualize results. The purpose is to simplify XAI model usage, evaluation, and comparison. In this paper, we showcase BONES results and visualizations for XAI model benchmarking on both tabular and image data. The opensource library is available at the following link: https://***/DavideNapolitano/BONES. © 2024, CC BY.

关键词： Benchmarking

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BONES: a benchmark for neural estimation of shapley values

BONES: a benchmark for neural estimation of shapley values

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Davide Napolitano Luca Cagliero Department of Control and Computer Engineering Politecnico di Torino Torino Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Shapley Values are concepts established for eXplainable AI. They are used to explain black-box predictive models by quantifying the features’ contributions to the model’s outcomes. Since computing the exact Shapley Values is known to be computationally intractable on real-world datasets, neural estimators have emerged as alternative, more scalable approaches to get approximated Shapley Values estimates. However, experiments with neural estimators are currently hard to replicate as algorithm implementations, explainer evaluators and results visualizations are neither standardized nor promptly usable. To bridge this gap, we present BONES, a new benchmark focused on neural estimation of Shapley Value. It provides researchers with a suite of state-of-the-art neural and traditional estimators, a set of commonly used benchmark datasets, ad hoc modules for training black-box models, as well as specific functions to easily compute the most popular evaluation metrics and visualize results. The purpose is to simplify XAI model usage, evaluation, and comparison. In this paper, we showcase BONES results and visualizations for XAI model benchmarking on both tabular and image data. The open-source library is available at the following link: https://***/DavideNapolitano/BONES.

关键词： Training Measurement Computational modeling Estimation Data visualization Closed box Benchmark testing Predictive models Bones Libraries

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Fault Detection Method of Maintenance Equipment Based on ECAT 5

Fault Detection Method of Maintenance Equipment Based on ECA...

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5th International Conference on Smart Grid and Energy engineering, SGEE 2024

作者： Qin, Shuo Zhang, Yimei Zhang, Yinghao Wang, Hongchao Chang, Bo Feng, Hao State Grid Beijing Electric Power Company Changping Power Supply Company Beijing China North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331530686

To improve the fault detection performance of power system operation and maintenance equipment, this paper studies the ECAT model through the method of integrating Empirical Mode Decomposition (EMD), Convolutional Neural Networks (CNN), and Attention mechanisms. The results show that: the ECAT model significantly outperforms traditional methods in fault detection accuracy and robustness on multiple public datasets, with an average accuracy improvement of about 10%;the model parameters are globally optimized using the Zebra Optimization Algorithm (ZOA), further enhancing detection performance;and the model effectively captures critical information and long-range dependencies. It can be seen that ECAT provides an efficient and accurate solution for intelligent fault detection of power system operation and maintenance equipment, demonstrating good application prospects. © 2024 IEEE.

关键词： Empirical mode decomposition

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