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1. |
Record Nr. |
UNIPARTHENOPE000032406 |
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Titolo |
6: Da Augusto a Diocleziano / [a cura di Giusto Traina] |
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Pubbl/distr/stampa |
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Titolo uniforme |
6: Da Augusto a Diocleziano |
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ISBN |
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Descrizione fisica |
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791 p., [16] carte di tav. : ill. ; 25 cm |
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Disciplina |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Prima del titolo: Il mondo antico. Sezione 3, L'ecumene romana |
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Nota di contenuto |
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Introduzione. Imperium, romanizzazione, espansione / Giusto Traina <<Il >>secolo di Augusto / Andrea Raggi <<I >>Flavi / Domitilla Campanile Da Traiano agli Antonini / Tommaso Gnoli Dai Severi alla crisi del 3. secolo / Tommaso Gnoli <<Il >>mondo iranico dai Parti ai Sasanidi / Carlo G. Cereti <<I >>Romani e l'Africa / Antonio Ibba <<Il >>mondo greco e il principato / Carlo Franco <<L'>>Egitto romano / Carla Salvaterra <<Il >>mondo ebraico nella prima età imperiale / Giancarlo Lacerenza <<L'>>esercito romano nell'Alto impero: da Augusto alla Tetrarchia / Sylvain Janniard <<L'>>amministrazione del Principato / Giovanna Daniela Merola <<La >>cittadinanza romana nell'ecumene imperiale / Valerio Marotta <<L'>>identità romana: pubblico, privato, famiglia / Francesca Lamberti <<L'>>Urbe come spazio economico / Emanuele Papi Economia e territorio da Augusto a Diocleziano / Gianluca Soricelli <<La >>nascita del cristianesimo / Michel-Yves Perrin Scritture, simboli e lingue / Claudia A. Ciancaglini e Sara Kaczko |
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2. |
Record Nr. |
UNINA9910253965803321 |
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Titolo |
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2015 / / edited by Oliver Niggemann, Jürgen Beyerer |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (124 p.) |
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Collana |
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Technologien für die intelligente Automation, Technologies for Intelligent Automation, , 2522-8587 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Data mining |
Knowledge management |
Computational Intelligence |
Data Mining and Knowledge Discovery |
Knowledge Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with |
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Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems. |
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Sommario/riassunto |
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The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. |
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