Bibliographies thématiques / Automated networks

Littérature scientifique sur le sujet « Automated networks »

Auteur : Grafiati
Publié le 26 juillet 2024
Mis à jour le 27 juillet 2024

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Articles de revues sur le sujet "Automated networks"

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Pingale, Subhash, et Sanjay R. Sutar. « Automated network intrusion detection using multimodal networks ». International Journal of Computational Science and Engineering 1, no 1 (2022) : 1. http://dx.doi.org/10.1504/ijcse.2022.10044448.

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Hou, Tingting, et Zamira Madina. « Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks ». Mathematical Problems in Engineering 2022 (21 février 2022) : 1–7. http://dx.doi.org/10.1155/2022/7176111.

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Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.
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Gurskiy, A. A., A. E. Goncharenko et S. M. Dubna. « AUTOMATIC SYNTHESIS OF PETRI NETS AT TUNING UP OF THE COORDINATING AUTOMATIC CONTROL SYSTEMS ». ELECTRICAL AND COMPUTER SYSTEMS 33, no 108 (30 novembre 2020) : 34–44. http://dx.doi.org/10.15276/eltecs.32.108.2020.4.

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The process of automated tuning for the coordinating automatic control system is considered in this paper. This process of tuning for the coordinating control system is linked to the automatic synthesis of Petri nets based on functioning of the artificial neural network. Thereby, we can automate the process of tuning and synthesis of system models and also solve the urgent task linked to the minimization of tuning time for the multilevel control systems. The purposes of the scientific work are time reduction of the tuning and automatization of the tuning for the multilevel coordinating systems of the automatic control. In order to achieve this purpose in the MATLAB \ Simulink software environment it is necessary to devel- op the system for automated tuning of the regulators of various levels for the coordinating automatic control system. The application of artificial neural network with automatic synthesis of Petri nets allows to introduce intelligent technology in the automated tuning system. In this work we have presented the corresponding block diagrams of considered automated tuning system and the principles of its functioning. The certain principle of the formation of Petri nets is proposed. These Petri nets represent the algorithms of tuning in the systems for analysis the corresponding processes. The formation of the composition in the scheme from Petri net during the functioning of the artificial neural network is presented in the paper. The results of experiment are presented in the final part of this work. This time characteristics of the pro- cess of setting up for the coordinating automatic control system of foodstuffs cooling in tunnel chamber. The experiments were conducted in the Matlab 2012a environment. Based on the results of the experiment we have depicted the process of synthesis of the Petri net representing the system tuning algorithm. The performed experiments have showed the principal suitability of the automated search system for the settings of the regulators of various levels of the coordinating control system. The technique of automatic synthesis of Petri nets based on the functioning of artificial neural networks has obtained the further devel- opment while performing the approved task in the scientific paper.
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Gerber, Mia, et Nelishia Pillay. « Automated Design of the Deep Neural Network Pipeline ». Applied Sciences 12, no 23 (29 novembre 2022) : 12215. http://dx.doi.org/10.3390/app122312215.

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Deep neural networks have proven to be effective in various domains, especially in natural language processing and image processing. However, one of the challenges associated with using deep neural networks includes the long design time and expertise needed to apply these neural networks to a particular domain. The research presented in this paper investigates the automation of the design of the deep neural network pipeline to overcome this challenge. The deep learning pipeline includes identifying the preprocessing needed, the feature engineering technique, the neural network to use and the parameters for the neural network. A selection pertubative hyper-heuristic (SPHH) is used to automate the design pipeline. The study also examines the reusability of the generated pipeline. The effectiveness of transfer learning on the generated designs is also investigated. The proposed approach is evaluated for text processing—namely, sentiment analysis and spam detection—and image processing—namely, maize disease detection and oral lesion detection. The study revealed that the automated design of the deep neural network pipeline produces just as good, and in some cases better, performance compared to the manual design, with the automated design requiring less design time than the manual design. In the majority of instances, the design was not reusable; however, transfer learning achieved positive transfer of designs, with the performance being just as good or better than when transfer learning was not used.
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Al-Hazaimeh, Obaida M., et Ma'moun Al-Smadi. « Automated Pedestrian Recognition Based on Deep Convolutional Neural Networks ». International Journal of Machine Learning and Computing 9, no 5 (octobre 2019) : 662–67. http://dx.doi.org/10.18178/ijmlc.2019.9.5.855.

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Khanafer, R. M., B. Solana, J. Triola, R. Barco, L. Moltsen, Z. Altman et P. Lazaro. « Automated Diagnosis for UMTS Networks Using Bayesian Network Approach ». IEEE Transactions on Vehicular Technology 57, no 4 (juillet 2008) : 2451–61. http://dx.doi.org/10.1109/tvt.2007.912610.

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Mapayi, Temitope, Pius A. Owolawi et Adedayo O. Adio. « Automated Detection and Tortuosity Characterization of Retinal Vascular Networks ». Journal of Biomimetics, Biomaterials and Biomedical Engineering 50 (avril 2021) : 89–102. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.50.89.

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Automated retinal vascular network detection and analysis using digital retinal images continue to play a major role in the field of biomedicine for the diagnosis and management of various forms of human ailments like hypertension, diabetic retinopathy, retinopathy of prematurity, glaucoma and cardiovascular diseases. Although several literature have implemented different automatic approaches of detecting blood vessels in the retinal and also determining their tortuous states, the results obtained show that there are needs for further investigation on more efficient ways to detect and characterize the blood vessel network tortuosity states. This paper implements the use of an adaptive thresholding method based on local spatial relational variance (LSRV) for the detection of the retinal vascular networks. The suitability of a multi-layer perceptron artificial neural network (MLP-ANN) technique for the tortuosity characterization of retinal blood vascular networks is also presented in this paper. Some vessel geometric features of detected vessels are fed into ANN classifier for the automatic classification of the retinal vascular networks as being tortuous vessels or normal vessels. Experimental studies conducted on DRIVE and STARE databases show that the vascular network detection results obtained from the method implemented in this paper detects large and thin vascular networks in the retina. In comparison to preious methods in the literature, the proposed method for vascular network segmentation achieved better performance than several methods in the literature with a mean accuracy value of 95.04% and mean sensitivity value of 75.16% on DRIVE and mean accuracy value of 94.02% and average sensitivity value of 76.55% on STARE with computational processing time of 4.5 seconds and 9.4 seconds on DRIVE and STARE respectively. The MLP-ANN method proposed for the vascular network tortuosity characterization achieves promising accuracy rates of 77.5%, 80%, 83.33%, 85%, 86.67% and 100% for varying training sample sizes.
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Pavlov, A. A., I. O. Datyev et M. G. Shishaev. « MULTI-HOP WIRELESS NETWORKS AUTOMATED SIMULATION ». Informacionno-technologicheskij vestnik 13, no 3 (30 septembre 2017) : 68–78. http://dx.doi.org/10.21499/2409-1650-2017-3-68-78.

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Simulation is the main way for testing technologies in the field of multi-hop wireless networks (MWN). Creating a simulation model MWN - a time-consuming task associated with the use of specialized software tools, called network simulators. In this paper, the modern experience of modeling MWN and the main problems are formulated. One of the main problem is the comparative analysis' impossibility of the experiments results conducted by various researchers. This is due to the reasons associated with the models used for testing, the planning an imitation experiment and the principal differences in the network simulators. To solve this problem, authors propose a generalized conceptual model of MWN simulation and a specialized software package that automates the execution of experiment series in a heterogeneous modeling environment.
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Lurgi, Miguel, et David Robertson. « Automated experimentation in ecological networks ». Automated Experimentation 3, no 1 (2011) : 1. http://dx.doi.org/10.1186/1759-4499-3-1.

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Triantafyllis, N., E. Sokos et A. Ilias. « Automatic moment tensor determination for the Hellenic Unified Seismic Network. » Bulletin of the Geological Society of Greece 47, no 3 (21 décembre 2016) : 1308. http://dx.doi.org/10.12681/bgsg.10912.

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Modern seismic networks with broadband sensors and real time digital telemetry made Moment Tensor (MT) determination a routine procedure. Automatic MT’s are now provided by global networks and a few very dense regional networks, within minutes after a significant event. An automatic MT determination wasn’t possible for the broader Hellenic area since seismic station density wasn’t sufficient. The creation of the Hellenic Unified Seismic Network (HUSN) provided the opportunity to apply an automated MT procedure using the available broad band data from almost one hundred stations. Thus the ISOLA code was extended towards the automatic operation based on Linux OS shell scripts, stand alone Fortran codes and SAC2000. Software supports both manual and automatic mode; at the first case, the user manually runs the program with the desired input parameters while at the latter, the system monitors a mailbox or RSS feed and if it receives an appropriate notification triggers the MT inversion procedure based on certain conditions. As it is setup now it calculates automatically the moment tensor of earthquakes larger than 3.5M w using data from HUSN. Application of an automated MT inversion procedure for HUSN will provide important real time information for studies like ground motion evaluation, tsunami warning etc.
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Thèses sur le sujet "Automated networks"

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Lu, Ching-sung. « Automated validation of communication protocols / ». The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu148726702499786.

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Apaydin, Oncu. « Automated Calibration Of Water Distribution Networks ». Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615692/index.pdf.

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Water distribution network models are widely used for various purposes such as long-range planning, design, operation and water quality management. Before these models are used for a specific study, they should be calibrated by adjusting model parameters such as pipe roughness values and nodal demands so that models can yield compatible results with site observations (basically, pressure readings). Many methods have been developed to calibrate water distribution networks. In this study, Darwin Calibrator, a computer software that uses genetic algorithm, is used to calibrate N8.3 pressure zone model of Ankara water distribution network
in this case study the network is calibrated on the basis of roughness parameter, Hazen Williams coefficient for the sake of simplicity. It is understood that there are various parameters that contribute to the uncertainties in water distribution network modelling and the calibration process. Besides, computer software&rsquo
s are valuable tools to solve water distribution network problems and to calibrate network models in an accurate and fast way using automated calibration technique. Furthermore, there are many important aspects that should be considered during automated calibration such as pipe roughness grouping. In this study, influence of flow velocity on pipe roughness grouping is examined. Roughness coefficients of pipes have been estimated in the range of 70-140.
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English, Philip J. « Automated discovery of chemical reaction networks ». Thesis, University of Newcastle Upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500929.

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The identification of models of chemical reaction networks is of importance in the safe, economic and environmentally sensitive development of chemical products. Qualitative models of a network of interactions are used in the design of drugs and other therapies. Quantitative models of the behaviour of reaction networks are the foundation of the science of reaction engineering (e.g. see Levenspiel, 1999); allowing the use of simulation software in the rapid development of commercial scale production processes. This work extends the existing methods reported by Burnham et al. (2006); adopting the global basis fonction method first applied to this problem by Crampin et al. (2004a).
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Singh, Shailendra. « Automated fault injection and analysis for wired/wireless networks ». Diss., Online access via UMI:, 2009.

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Wallace, Brian T. « Automated system for load-balancing EBGP peers ». [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0008800.

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Darr, Timothy, Ronald Fernandes, Michael Graul, John Hamilton et Charles H. Jones. « Automated Configuration and Validation of Instrumentation Networks ». International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606234.

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ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California
This paper describes the design and implementation of a test instrumentation network configuration and verification system. Given a multivendor instrument part catalog that contains sensor, actuator, transducer and other instrument data; user requirements (including desired measurement functions) and technical specifications; the instrumentation network configurator will select and connect instruments from the catalog that meet the requirements and technical specifications. The instrumentation network configurator will enable the goal of mixing and matching hardware from multiple vendors to develop robust solutions and to reduce the total cost of ownership for creating and maintaining test instrumentation networks.
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Nilsson, Henrik, et Anders Svensson. « Automated Mobile Cranes ». Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-29479.

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Conner, Steven. « Automated distribution network planning with active network management ». Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28818.

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Renewable energy generation is becoming a major part of energy supply, often in the form of distributed generation (DG) connected to distribution networks. While growth has been rapid, there is awareness that limitations on spare capacity within distribution (and transmission) networks is holding back development. Developments are being shelved until new network reinforcements can be built, which may make some projects non-viable. Reinforcements are costly and often underutilised, typically only loaded to their limits for a few occasions during the year. In order to accommodate new DG without the high costs or delays, active network management (ANM) is being promoted in which generation and other network assets are controlled within the limits of the existing network. There is a great deal of complexity and uncertainty associated with developing ANM and devising coherent plans to accommodate new DG is challenging for Distribution Network Operators (DNOs). As such, there is a need for robust network planning tools that can explicitly handle ANM and which can be trusted and implemented easily. This thesis describes the need for and the development of a new distribution expansion planning framework that provides DNOs with a better understanding of the impacts created by renewable DG and the value of ANM. This revolves around a heuristic planning framework which schedules necessary upgrades in power lines and transformers associated with changes in demand as well as those driven by the connection of DG. Within this framework a form of decentralised, adaptive control of DG output has been introduced to allow estimation of the impact of managing voltage and power flow constraints on the timing and need for network upgrades. The framework is initially deployed using simple scenarios but a further advance is the explicit use of time series to provide substantially improved estimates of the levels of curtailment implied by ANM. In addition, a simplified approach to incorporating demand side management has been deployed to facilitate understanding of the scope and role this may play in facilitating DG connections.
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Burnham, Samantha Claire. « Towards the automated determination of chemical reaction networks ». Thesis, University of Newcastle Upon Tyne, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445585.

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Lomas, David. « Improving automated postal address recognition using neural networks ». Thesis, University of York, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341602.

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Livres sur le sujet "Automated networks"

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Fingscheidt, Tim, Hanno Gottschalk et Sebastian Houben, dir. Deep Neural Networks and Data for Automated Driving. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4.

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R, Wagner, Rivers W, U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Engineering Technology. et Multimax Inc, dir. Automated seismic event monitoring system. Washington, DC : Division of Engineering Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 2000.

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IFIP Workshop on "Performance Modelling and Evaluation of ATM Networks" (2nd 1994 University of Bradford). Performance modelling and evaluation of ATM networks. London : Chapman & Hall, 1995.

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Illinois Library Automation Committee. Plan for funding automated resource sharing in Illinois libraries. [Springfield, Ill.] : The Committee, 1988.

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Trautman, William B. Regulating communication technology : The case of automated teller machine networks. Santa Monica, CA : Rand, 1989.

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Asante, N. D. K. Automated and interactive retrofit design of practical heat exchanger networks. Manchester : UMIST, 1996.

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Kouvatsos, Demetres D. Performance Modelling and Evaluation of ATM Networks. Boston, MA : Springer US, 1995.

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Livingston, David L. Investigation of automated task learning, decomposition and scheduling. Norfolk, Va : Old Dominion University Research Foundation, 1990.

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Directory of automated library systems. New York : Neal-Schuman Publishers, 1985.

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United States. Department of Homeland Security. Office of Inspector General. Office of Information Technology. DHS' management of automated procurement systems needs improvement. Washington, DC : U.S. Dept. of Homeland Security, Office of Inspector General, Office of Information Technology, 2004.

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Chapitres de livres sur le sujet "Automated networks"

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Mendoza, Hector, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer et Frank Hutter. « Towards Automatically-Tuned Deep Neural Networks ». Dans Automated Machine Learning, 135–49. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5_7.

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Bozga, Marius, Lucas Bueri et Radu Iosif. « Decision Problems in a Logic for Reasoning About Reconfigurable Distributed Systems ». Dans Automated Reasoning, 691–711. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_40.

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AbstractWe consider a logic used to describe sets of configurations of distributed systems, whose network topologies can be changed at runtime, by reconfiguration programs. The logic uses inductive definitions to describe networks with an unbounded number of components and interactions, written using a multiplicative conjunction, reminiscent of Bunched Implications [37] and Separation Logic [39]. We study the complexity of the satisfiability and entailment problems for the configuration logic under consideration. Additionally, we consider the robustness property of degree boundedness (is every component involved in a bounded number of interactions?), an ingredient for decidability of entailments.
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Sakr, Ahmed Hamdi, Gaurav Bansal, Vladimeros Vladimerou, Kris Kusano et Miles Johnson. « Applications of Connectivity in Automated Driving ». Dans Wireless Networks, 245–71. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94785-3_10.

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Dagli, Cihan H., et Mahesh Kumar Vellanki. « Automated assembly systems ». Dans Artificial Neural Networks for Intelligent Manufacturing, 195–228. Dordrecht : Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0713-6_9.

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Nanda, U., A. Biswas, K. L. G. Prathyusha, S. Gaurav, V. S. L. Samhita, S. S. Mane, S. Chatterjee et J. Kumar. « Automated Plant Robot ». Dans Lecture Notes in Networks and Systems, 107–12. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4218-3_11.

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Rutagemwa, Humphrey, et François Patenaude. « Automated Data-Driven System for Compliance Monitoring ». Dans Wireless Networks, 291–312. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98064-1_13.

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Sahai, Akhil, Calton Pu, Gueyoung Jung, Qinyi Wu, Wenchang Yan et Galen S. Swint. « Towards Automated Deployment of Built-to-Order Systems ». Dans Ambient Networks, 109–20. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11568285_10.

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Hasan et Burkhard Stiller. « A Generic Model and Architecture for Automated Auditing ». Dans Ambient Networks, 121–32. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11568285_11.

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Reed, Joy, Dave Jackson, Borislav Deinov et Mike Reed. « Automated Formal Analysis of Networks ». Dans Dependable Network Computing, 143–66. Boston, MA : Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4549-1_7.

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Fossa, Fabio, Luca Paparusso et Francesco Braghin. « Interaction Attacks as Deceitful Connected and Automated Vehicle Behavior ». Dans Wireless Networks, 147–62. Cham : Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55044-7_10.

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Actes de conférences sur le sujet "Automated networks"

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Chen, Qian, Xiaojuan Zhang, Wei Lih Lim, Yuen Sam Kwok et Sumei Sun. « Automated Network Planning for Industrial Wireless Mesh Networks ». Dans GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8647561.

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Hamouda, Ossama, Mohamed Kaaniche et Karama Kanoun. « Safety modeling and evaluation of Automated Highway Systems ». Dans Networks (DSN). IEEE, 2009. http://dx.doi.org/10.1109/dsn.2009.5270352.

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Patterson, William R., et Matthew I. Campbell. « PipeSynth : An Algorithm for Automated Topological and Parametric Design and Optimization of Pipe Networks ». Dans ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47906.

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This paper describes a design automation approach that combines various optimization research and artificial intelligence methods for synthesizing fluid networks. Unlike traditional software tools available today, this approach does not rely on having any predefined network topology to design and optimize its networks. PipeSynth generates its designs by using only desired port locations, and the desired fluid properties at each of those ports. An ideal network is found by optimizing the number and connectivity of pipes and pipe fittings, the size and length of each pipe, and the size and orientation of each fitting. A Uniform-Cost-Search is used for topology optimization along with a combination of non-gradient based optimization methods for parametric optimization. PipeSynth demonstrates how advances in automated design can enable engineers to manage much more complex fluid network problems. PipeSynth uses a unique representation of fluid networks that synthesizes and optimizes networks one pipe at a time, in three-dimensional space. PipeSynth has successfully solved several problems containing multiple interlaced networks concurrently with multiple inputs and outputs. PipeSynth shows the power of automated design and optimization in producing solutions more effectively and efficiently than traditional design approaches.
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Makaya, Christian, et Douglas Freimuth. « Automated virtual network functions onboarding ». Dans 2016 IEEE Conference on Network Function Virtualization and Software-Defined Networks (NFV-SDN). IEEE, 2016. http://dx.doi.org/10.1109/nfv-sdn.2016.7919499.

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Ariza, Maurício, Antonio João Gonçalves de Azambuja, Jéferson Campos Nobre et Lisandro Zambenedetti Granville. « Automated Social Engineering Attacks using ChatBots on Professional Social Networks ». Dans Workshop de Gerência e Operação de Redes e Serviços. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/wgrs.2023.747.

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The growth of the internet and social networks has intensified human interactions, raising the risk of cyberattacks. Social Engineering targets those human relationships in the cyber environment, using technology as a support to exploit natural human failures. Research has shown the capacity of Social Engineering attacks, however, there are few papers focusing on the evolution and trust of ChatBots and automation as a support for those attacks. This paper presents an analysis of the capacity of professional social networks to detect and block automated Social Engineering threats to their users. The approach developed allowed us to identify the characteristics of the trust relationship between the user, the social network, and the ChatBot resulting from the established interaction, and failures on the part of social networks to identify and block this kind of behavior. To this end, an automated Social Engineering bot was developed. The analysis and discussion of the results allow demonstration of the security vulnerabilities present in professional networks and in building the user’s trust relationship with the ChatBot.
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Wang, Tong. « Automated Drafting of Complex Networks ». Dans 2011 Third Pacific-Asia Conference on Circuits, Communications and System (PACCS). IEEE, 2011. http://dx.doi.org/10.1109/paccs.2011.5990246.

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Schulz. « Automated networks/electronics teaching laboratories ». Dans Proceedings Frontiers in Education Conference. IEEE, 1989. http://dx.doi.org/10.1109/fie.1989.69377.

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Guerrero, Javier O., Bernard Chang, Dany Hachem, Maša Prodanović et D. Nicolas Espinoza. « Automated Reconstruction of Fracture Networks ». Dans Unconventional Resources Technology Conference. Tulsa, OK, USA : American Association of Petroleum Geologists, 2021. http://dx.doi.org/10.15530/urtec-2021-5600.

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Samhat, A., R. Skehill et Z. Altman. « Automated Troubleshooting in WLAN Networks ». Dans 2007 16th IST Mobile and Wireless Communications Summit. IEEE, 2007. http://dx.doi.org/10.1109/istmwc.2007.4299084.

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Repetto, M., A. Carrega, J. Yusupov, F. Valenza, F. Risso et G. Lamanna. « Automated Security Management for Virtual Services ». Dans 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2019. http://dx.doi.org/10.1109/nfv-sdn47374.2019.9040069.

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Rapports d'organisations sur le sujet "Automated networks"

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Easley, Matthew, et Elizabeth Bradley. Generalized Physical Networks for Automated Model Building. Fort Belvoir, VA : Defense Technical Information Center, janvier 1999. http://dx.doi.org/10.21236/ada454690.

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Upadhyaya, B. R., et W. Yan. Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods. Office of Scientific and Technical Information (OSTI), novembre 1993. http://dx.doi.org/10.2172/10108326.

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Andersson, Göran. Thematic synthesis “Energy Networks” of the NRP “Energy”. Swiss National Science Foundation (SNSF), décembre 2019. http://dx.doi.org/10.46446/publication_nrp70_nrp71.2019.2.en.

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Switzerland’s energy grids are reliable and stable – but they are facing new challenges. These include the fact that the new renewable energy sources, namely photovoltaic systems and wind farms, only produce electricity on an irregular basis. Greater flexibility is therefore required in the energy grid: with new storage solutions for electricity and heat on the supply side and automated load management on the demand side. The potential synergies between the various energy sources must also be exploited.
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Coyner, Kelley, et Jason Bittner. Infrastructure Enablers and Automated Vehicles : Trucking. SAE International, juillet 2022. http://dx.doi.org/10.4271/epr2022017.

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While automated trucking developers have established regular commercial shipments, operations and testing remain limited largely to limited-access highways like interstates. This infrastructure provides a platform or operating environment that is highly structured, with generally good road conditions and visible lane markings. To date, these deployments have not included routine movements from hub to hub, whether on or off these limited-access facilities. Benefits such as safety, fuel efficiency, staffing for long-haul trips, and a strengthened supply chain turn enable broader deployment which can enable movement from one transportation system to another. Infrastructure Enablers and Automated Vehicles: Trucking focuses on unresolved issues between the automated vehicle industry and infrastructure owners and operators that stand in the way of using infrastructure—both physical and digital—to extend use cases for automated trucking to more operational design domains (ODDs). The report also examines opportunities and recommendations related the integration of automated trucking across transportation networks and the supply chain. The topics include road conditions and lane marking visibility, work zone navigation, transfer hubs, and facility design, as well as connected and electric charging infrastructure.
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Hsueh, Gary, David Czerwinski, Cristian Poliziani, Terris Becker, Alexandre Hughes, Peter Chen et Melissa Benn. Using BEAM Software to Simulate the Introduction of On-Demand, Automated, and Electric Shuttles for Last Mile Connectivity in Santa Clara County. Mineta Transportation Institute, janvier 2021. http://dx.doi.org/10.31979/mti.2021.1822.

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Despite growing interest in low-speed automated shuttles, pilot deployments have only just begun in a few places in the U.S., and there is a lack of studies that estimate the impacts of a widespread deployment of automated shuttles designed to supplement existing transit networks. This project estimated the potential impacts of automated shuttles based on a deployment scenario generated for a sample geographic area: Santa Clara County, California. The project identified sample deployment markets within Santa Clara County using a GIS screening exercise; tested the mode share changes of an automated shuttle deployment scenario using BEAM, an open-source beta software developed at the Lawrence Berkeley National Laboratory to run traffic simulations with MATSim; elaborated the model outputs within the R environment; and then estimated the related impacts. The main findings have been that the BEAM software, despite still being in its beta version, was able to model a scenario with the automated shuttle service: this report illustrates the potential of the software and the lessons learned. Regarding transportation aspects, the model estimated automated shuttle use throughout the county, with a higher rate of use in the downtown San José area. The shuttles would be preferred mainly by people who had been using gasoline-powered ride hail vehicles for A-to-B trips or going to the bus stop, as well as walking trips and a few car trips directed to public transport stops. As a result, the shuttles contributed to a small decrease in emissions of air pollutants, provided a competitive solution for short trips, and increased the overall use of the public transport system. The shuttles also presented a solution for short night trips—mainly between midnight and 2 am—when there are not many options for moving between points A and B. The conclusion is that the automated shuttle service is a good solution in certain contexts and can increase public transit ridership overall.
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Griffin, Andrew, Sean Griffin, Kristofer Lasko, Megan Maloney, S. Blundell, Michael Collins et Nicole Wayant. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), avril 2021. http://dx.doi.org/10.21079/11681/40182.

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Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak et Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, juillet 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale et Samuel Thomas. Exploring the potential of deep learning for classifying camera trap data : A case study from Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), septembre 2023. http://dx.doi.org/10.53055/icimod.1016.

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Data from camera trap networks provide crucial information on various important aspects of wildlife presence, movement, and behaviour. However, manual processing of large volumes of images captured is time and resource intensive. This study explores three different approaches of deep learning methods to detect and classify images of key animal species collected from the ICIMOD Knowledge Park at Godavari, Nepal. It shows that transfer learning with ImageNet pretrained models (A1) can be used to detect animal species with minimal model training and testing. These methods when scaled up offer tremendous scope for quicker and informed conflict management actions, including automated response, which can help minimise human wildlife conflict management costs across countries in the region.
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Baras, J. S., M. Ball, S. Gupta, P. Viswanathan et P. Shah. Automated Network Fault Management. Fort Belvoir, VA : Defense Technical Information Center, janvier 1997. http://dx.doi.org/10.21236/ada441009.

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Kaiser, Edward. Addressing Automated Adversaries of Network Applications. Portland State University Library, janvier 2000. http://dx.doi.org/10.15760/etd.4.

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