Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Multi-Agent Systems

[EN] With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.Julian Inglada, VJ.; Botti V. (2019). Multi-Agent Systems. Applied Sciences. 9(7):1-7. https://doi.org/10.3390/app9071402S1797Kravari, K., & Bassiliades, N. (2015). A Survey of Agent Platforms. Journal of Artificial Societies and Social Simulation, 18(1). doi:10.18564/jasss.2661Baldoni, M., Baroglio, C., May, K., Micalizio, R., & Tedeschi, S. (2018). Computational Accountability in MAS Organizations with ADOPT. Applied Sciences, 8(4), 489. doi:10.3390/app8040489Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747-761. doi:10.1016/j.scico.2011.10.004Challenger, M., Tezel, B., Alaca, O., Tekinerdogan, B., & Kardas, G. (2018). Development of Semantic Web-Enabled BDI Multi-Agent Systems Using SEA_ML: An Electronic Bartering Case Study. Applied Sciences, 8(5), 688. doi:10.3390/app8050688Challenger, M., Demirkol, S., Getir, S., Mernik, M., Kardas, G., & Kosar, T. (2014). On the use of a domain-specific modeling language in the development of multiagent systems. Engineering Applications of Artificial Intelligence, 28, 111-141. doi:10.1016/j.engappai.2013.11.012Boztepe, İ., & Erdur, R. (2018). Linked Data Aware Agent Development Framework for Mobile Devices. Applied Sciences, 8(10), 1831. doi:10.3390/app8101831Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence, 171(7), 365-377. doi:10.1016/j.artint.2006.02.006Duan, K., Fong, S., Zhuang, Y., & Song, W. (2018). Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Applied Sciences, 8(6), 862. doi:10.3390/app8060862Zhang, Q., Yao, J., Yin, Q., & Zha, Y. (2018). Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution. Applied Sciences, 8(7), 1077. doi:10.3390/app8071077Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277-298. doi:10.1016/j.pmcj.2009.04.001Kranz, M., Holleis, P., & Schmidt, A. (2010). Embedded Interaction: Interacting with the Internet of Things. IEEE Internet Computing, 14(2), 46-53. doi:10.1109/mic.2009.141Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Costa, A., Novais, P., Corchado, J. M., & Neves, J. (2011). Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic Journal of IGPL, 20(4), 689-698. doi:10.1093/jigpal/jzr021Tapia, D. I., & Corchado, J. M. (2009). An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care. International Journal of Ambient Computing and Intelligence, 1(1), 15-26. doi:10.4018/jaci.2009010102Barriuso, A., De la Prieta, F., Villarrubia González, G., De La Iglesia, D., & Lozano, Á. (2018). MOVICLOUD: Agent-Based 3D Platform for the Labor Integration of Disabled People. Applied Sciences, 8(3), 337. doi:10.3390/app8030337Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico. Applied Sciences, 8(3), 446. doi:10.3390/app8030446Ramos, J., Oliveira, T., Satoh, K., Neves, J., & Novais, P. (2018). Cognitive Assistants—An Analysis and Future Trends Based on Speculative Default Reasoning. Applied Sciences, 8(5), 742. doi:10.3390/app8050742SATOH, K. (2005). Speculative Computation and Abduction for an Autonomous Agent. IEICE Transactions on Information and Systems, E88-D(9), 2031-2038. doi:10.1093/ietisy/e88-d.9.2031Miyashita, K. (2017). Incremental Design of Perishable Goods Markets through Multi-Agent Simulations. Applied Sciences, 7(12), 1300. doi:10.3390/app7121300Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Roscia, M., Longo, M., & Lazaroiu, G. C. (2013). Smart City by multi-agent systems. 2013 International Conference on Renewable Energy Research and Applications (ICRERA). doi:10.1109/icrera.2013.6749783Lozano, Á., De Paz, J., Villarrubia González, G., Iglesia, D., & Bajo, J. (2018). Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems. Applied Sciences, 8(1), 67. doi:10.3390/app8010067Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Billhardt, H., Fernández, A., Lujak, M., & Ossowski, S. (2018). Agreement Technologies for Coordination in Smart Cities. Applied Sciences, 8(5), 816. doi:10.3390/app805081

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

Is Ambient Intelligence a truly Human-Centric Paradigm in Industry? Current Research and Application Scenario

The use of pervasive networked devices is nowadays a reality in the service sector. It impacts almost all aspects of our daily lives, although most times we are not aware of its influence. This is a fundamental characteristic of the concept of Ambient Intelligence (AmI). Ambient Intelligence aims to change the form of human-computer interaction, focusing on the user needs so they can interact in a more seamless way, with emphasis on greater user-friendliness. The idea of recognizing people and their context situation is not new and has been successfully applied with limitations, for instance, in the health and military sectors. However its appearance in the manufacturing industry has been elusive. Could the concept of AmI turn the current shop floor into a truly human centric environment enabling comprehensive reaction to human presence and action? In this article an AmI scenario is presented and detailed with applications in human’s integrity and safety.Ambient Intelligence, networks, human-computer interaction