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Evaluation of the Fine Structure Constant

Journal of Modern Physics 2020年第12期11卷 1918-1925页

作者： adriano alippi Dept. of Basic and Applied Sciences for Engineering Sapienza University of Rome Rome Italy

An equation is given for analytically defining the value of the fine structure constant, whose derivation follows two main steps, relative to the generation of electric charges and to the polarizability of vacuum due to virtual dipoles. The obtained value matches the experimental one by a factor lower than the relative standard uncertainty produced by the National Institute of Standards and Technology (NIST).

关键词： Fine Structure Constant Virtual Particles Vacuum Permittivity

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Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 954-964页

作者： Haowei Lin Bo Zhao Derong Liu Cesare alippi the School of Automation Guangdong University of TechnologyGuangzhou 510006China the School of Systems Science Beijing Normal UniversityBeijing 100875China the Dipartimento di Elettronica e Informazione Politecnico di MilanoMilano 20133Italy the Universita’Della Svizzera Italiana LuganoSwitzerland

In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more ***, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

关键词： Adaptive dynamic programming(ADP) critic neural network data-based fault tolerant control(FTC) particle swarm optimization(PSO)

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Deep learning for time series forecasting:The electric load case

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CAAI Transactions on Intelligence Technology 2022年第1期7卷 1-25页

作者： Alberto Gasparin Slobodan Lukovic Cesare alippi Faculty of Informatics Universitàdella Svizzera ItalianaLuganoSwitzerland Department of Electronics Informationand BioengineeringPolitecnico di MilanoMilanItaly

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging ***,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine *** of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the *** work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.

关键词： deep learning electric load forecasting multi-step ahead forecasting smart grid time-series prediction

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Precise Agriculture:Effective Deep Learning Strategies to Detect Pest Insects

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 246-258页

作者： Luca Butera Alberto Ferrante Mauro Jermini Mauro Prevostini Cesare alippi Universitàdella Svizzera italiana Faculty of InformaticsLugano 6900Switzerland Agroscope Research Centre CadenazzoCadenazzo 6594Switzerland IEEE

Pest insect monitoring and control is crucial to ensure a safe and profitable crop growth in all plantation types,as well as guarantee food quality and limited use of *** aim at extending traditional monitoring by means of traps,by involving the general public in reporting the presence of insects by using *** includes the largely unexplored problem of detecting insects in images that are taken in noncontrolled ***,pest insects are,in many cases,extremely similar to other species that are ***,computer vision algorithms must not be fooled by these similar insects,not to raise unmotivated *** this work,we study the capabilities of state-of-the-art(SoA)object detection models based on convolutional neural networks(CNN)for the task of detecting beetle-like pest insects on nonhomogeneous images taken outdoors by different ***,we focus on disambiguating a pest insect from similar harmless *** consider not only detection performance of different models,but also required computational *** study aims at providing a baseline model for this kind of *** results show the suitability of current SoA models for this application,highlighting how FasterRCNN with a MobileNetV3 backbone is a particularly good starting point for accuracy and inference execution *** combination provided a mean average precision score of 92.66%that can be considered qualitatively at least as good as the score obtained by other authors that adopted more specific models.

关键词： Computer vision machine learning neural network pest insect pest monitoring Popillia japonica precise agriculture

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A unique timely moment for embedding intelligence in applications

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CAAI Transactions on Intelligence Technology 2016年第1期1卷 1-3页

作者： Cesare alippi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Italy

Current IT society is populating the planet with a plethora of applications at an unprecedented rate pushed by advances in fabrication, mechatronics and communication technologies on one hand and availability of sophisticated sensor and actuator devices on the other[1] . Applications, that both target everyday life, industrial and mission critical needs, are either confined at a single target device or distributed within a net- work of units, by also taking advantage of seamless commu-nication capabilities and address different application scenarios. Examples of this trend are the Internet-of-Things, smart-whatsoever （home, grid, building, city, planet） and cyber-physical systems[2].

关键词：制造技术通信技术人工智能发展现状

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Classifying and Filtering Rats’ Hippocampal Spikes with Back-Propagation Neural Networks

Classifying and Filtering Rats’ Hippocampal Spikes with Bac...

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IJCNN International Joint Conference on Neural Networks

作者： Cesare alippi Michael Recce Dipartimento di Elettronica Politecnico di Milano Piazza L.da Vinci 32 20133 Milano ITALY Department of Computer Science University College London Gower street WClE 6BT London U.K

＜正＞The optimal partitioning of a set of data in clusters is an NP-complete *** fact, although the number of classes is fixed,the number of possible partitions to be analysed exponentially increases with the size N of the *** we explain how a Supervised Neural Network algorithm,such as Back-Propagation,provides good classifying and filtering *** data to be partitioned consist of biological spikes from rats’ hippocampi obtained at the department of Anatomy-University College London. The recorded spikes are firstly clustered with a three layered Back-Propagation Network which groups neural signals according to the emitting *** signals are affected by noise,generated by the instrumentation and the rat’s *** techniques are used to eliminate spurious *** spikes are finally filtered by a four-layered Back- propagation networks which behaves as a noise reduction *** and filtering performance outline,once more,the efficient utilisation of Back propagation algorithms to solve real problems.

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非稳态环境下的学习

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重庆理工大学学报（自然科学） 2016年第8期30卷 2-2页

作者： Cesare alippi 意大利米兰理工大学

大多数机器学习应用假设其生成数据的过程是稳定的。这种顺从的假设非常的广泛，被隐式地接受，有时我们甚至忘记这种假设并不会因为概念漂移而在实践中广泛地成立（例如已获取数据流生成过程中的结构变化）。因此，察觉概念漂移并做出... 详细信息

大多数机器学习应用假设其生成数据的过程是稳定的。这种顺从的假设非常的广泛，被隐式地接受，有时我们甚至忘记这种假设并不会因为概念漂移而在实践中广泛地成立（例如已获取数据流生成过程中的结构变化）。因此，察觉概念漂移并做出相对应的反应的能力是智能学习机器一项主要的成就，也是嵌入式系统最热门的研究课题。这种能力可让机器主动地调整应用，使应用维持高性能，联机改变运行策略，检测和隔离可能出现的故障。

关键词：机器学习非稳态环境概念漂移嵌入式系统结构变化生成过程学习机器运行策略

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