Search

Testing Programmable Logic Controllers (PLCs) is challenging, partially due to the lack of tools for testing. Isolating a part of the PLC program, feeding it with test inputs and checking the test outputs often require manual work and physical hardware. The Siemens PLCSIM Advanced tool can simulate PLCs and provide a rich application programming interface (API). This paper presents a new CERN made tool based on PLCSIM Advanced and the TIA Portal Openness API. The tool takes a test case described in an intuitive, tabular format, which is then executed with the full PLC program or a selected part of it, effectively allowing unit testing. The inputs can be fed and the outputs can be captured via the PLCSIM API. This way the tests can be executed and evaluated automatically, without manual work or physical hardware. Therefore, it is possible to provide an automated and scalable continuous testing solution for PLC programs to reveal errors as early as possible., Proceedings of the 17th International Conference on Accelerator and Large Experimental Physics Control Systems, ICALEPCS2019, New York, NY, USA

The development of critical systems requires the application of verification techniques in order to guarantee that the requirements are met in the system. Standards like IEC 61508 provide guidelines and recommend the use of formal methods for that purpose. The ITER Interlock Control System has been designed to protect the tokamak and its auxiliary systems from failures of the components or incorrect machine operation. ITER has developed a method to assure that some critical operator commands have been correctly received and executed in the PLC (Programmable Logic Controller). The implementation of the method in a PLC program is a critical part of the interlock system. A methodology designed at CERN has been applied to verify this PLC program. The methodology is the result of 5 years of research in the applicability of model checking to PLC programs. A proof-of-concept tool called PLCverif implements this methodology. This paper presents the challenges and results of the ongoing collaboration between CERN and ITER on formal verification of critical PLC programs., Proceedings of the 16th Int. Conf. on Accelerator and Large Experimental Control Systems, ICALEPCS2017, Barcelona, Spain

This book describes the theoretical foundations of cognitive infocommunications (CogInfoCom), and provides a survey on state-of-the-art solutions and applications within the field. The book covers aspects of cognitive infocommunications in the research fields of affective computing, BCI, future internet, HCI, HRI, sensory substitution, and virtual/augmented interactions. The book focuses on describing the merging between humans and information and communications technology (ICT) at the level of cognitive capabilities with an approach towards developing future cognitive ICT. Provides a comprehensive overview of cognitive infocommunications Covers theoretical and practical aspects of cognitive infocommunications Discusses applications employing various aspects of infocommunication.

Network neutrality issue has been recently received a great attention from the research community, the industry and the network regulators alike. From the European perspectives, the net neutrality issue is increasingly important because it is also part of the Digital Agenda 2020 recently approved by the European Commission [1, 2]. Until now, usage-based pricing and network neutrality have been considered as two different research problems and very few network neutrality researchers examine usage-based pricing as a main consideration. In addition, the user behaviour such as user loyalty issue should be seriously taken into consideration because users will have an increasingly more important role in the operation and management of the network. In this paper, we address the net neutrality issue by investigating the usage-based pricing in presence of user loyalty from the game-theoretic perspective. The contributions of the paper are twofold. First, we provide game-theoretic models for the interaction among the stakeholders under usage-based pricing under the presence of customer loyalty. Based on the models, we compute the Nash equilibrium prices of the pricing games and discuss the consequences and the impact on net neutrality. Results show both positive and negative impacts of usage-based pricing on net neutrality. From the user perspective, the usage-based pricing does not show advantage over simple flat-rate pricing. On the positive side, our results also suggest that under certain circumstances cooperation between broadband providers can make the network be more profitable and yet sustainable. Based on the results, we discuss the practical feasibility of applying usage-based pricing for net neutrality under the presence of user loyalty.

The rate of deployment and adoption issues of new network technologies, IPv6 in particular, have recently been hotly debated in the research community. However, the question of how protocols migrate, especially the dynamics of migration, to new paradigms is still largely open. In this paper, we address the issue from a game theoretic point of view. We model and analyze the profit maximizing strategies of Autonomous Systems (ASes); both the properties of ASes and the topology of the Internet is considered. The contribution our work is threefold. First, we propose an economic model of the ASes and their relations from the IPv4-IPv6 migration viewpoint. Second, we show - after analysing the strategies of ASes using gametheoretic tools - that under the proposed economic model, the all IPv6 AS topology is a Nash equilibrium on any network topology. Third, we apply the findings of evolutionary dynamics on the problem of migration by incorporating Internet-specific properties to the evolutionary model, namely the size of the ASes and the cost of migration. The analyses show that even if IPv6 has higher payoff than IPv4, the whole migration does not happen always fast. Finally, extensive simulations are carried out based on the proposed models to illustrate the impacts of different parameters on the IPv6 migration dynamics in realistic scenarios.

The ANP has been applied to a large variety of decisions: marketing, medical, political, military, social, prediction and many others. ANP is able to take analysis of benefits, opportunities, costs, and risks (BOCR) (Wijnmalen, 2007). Bayesian Networks are applied in query languages in scientific area of information retrieval (Cheng & Yang, 1999)0, in environmental modelling (Uusitalo, 2007). There are some improvements or combined versions of BN, e.g. combination with fuzzy (Li & Kao, 2005), and many authors deal with further development. There are some complex (decisional and engineering) problems, where neither ANP nor BN could help to solve alone. Some of these problems are usually such sophisticated, which involve human opinions with uncertainty, causal relationships, and uncertainties in the occurrence of events. These tasks can be solved by the proposed method constructed by combination of ANP and extended version of Bayesian Networks, i.e. by joining these two methods in cascade. Vector Bayesian Networks (VBN) is a generalized BN, which able to handle not only the probabilities, but any numerical value attached to nodes. This extension is able to calculate spreading of effects in any network. The combined method is particularly useful to investigate fault spreading problem in infocommunication networks.

The goal of this paper is to find Wardrop equilibrium in transport networks at case of uncertainty situations, where the uncertainty comes from lack of information. We use simulation tool to find the equilibrium, which gives only approximate solution, but this is sufficient for large networks as well. In order to take the uncertainty into account we have developed an interval-based procedure for finding the paths with minimal cost using the Dempster-Shafer theory. Furthermore we have investigated the users- behaviors using game theory approach, because their path choices influence the costs of the other users- paths., {"references":["A. Nagurney, \"Spatial Equilibrium in Transport Networks\", in\nHandbook of Transport Geography and Spatial Systems, Handbooks in\nTransport Vol. 5, ed. David Hensher, et al., Elsevier, 2004, pp. 583-608.","D. E. Kaufman, J. Nonis, and R. L. Smith, \"A mixed integer linear\nprogramming model for dynamic route guidance\", Transportation\nResearch Part B: Methodological, Volume 32, Issue 6, August 1998, pp.\n431-440.","D. Boyce, D. H. Lee and B. Ran: Analytical Models of the Dynamic\nTraffic Assignment Problem, Networks and Spatial Economics, Vol. 1,\nNumbers 3-4, September, 2001, pp. 377-390.","I. Chabini, H. Jiang, P. Macneille, and R. Miller, \"Parallel\nImplementation of Dynamic Traffic Assignment Models\", Presented at\nthe 2003 IEEE International Conference on Systems, Man &\nCybernetics, Washington DC, October 2003.","Q. Yang: A Simulation Laboratory for Evaluation of Dynamic Traffic\nManagement Systems, PhD thesis, Massachusetts Institute of\nTechnology, Cambridge, Massachusetts, June 1997.","H. Miller, Y. Wu, M. Hung, \"Gis-Based Dynamic Traffic Congestion\nModeling To Support Time-Critical Logistics\", Published in the\nProceedings of the Hawai-i International IEEE Conference on System\nScience, January 5-8, Maui, Hawaii, 1999.","Y. Wu, H. Miller, M. Hung, \"A GIS-based decision support system for\nanalysis of route choice in congested urban road networks\", Journal of\nGeographical Systems, 2001. No. 3 pp. 3-24.","A. Rapoport, T. Kugler, S. Dugar, and E. J. Gisches, \"Choice of routes in\ncongested traffic networks: Experimental tests of the Braess Paradox\",\nGames and Economic Behavior, Volume 65, Issue 2, March 2009, pp.\n538-571.","S. J. Li, G. Y. Chen, \"Multiclass, Two-criteria Traffic Network\nEquilibrium Models and Vector Variational Inequalities\", Systems\nEngineering - Theory & Practice, Volume 28, Issue 1, January 2008, pp.\n141-145.\n[10] L.C. Davis, \"Realizing Wardrop equilibria with real-time traffic\ninformation\", Physica A: Statistical Mechanics and its Applications,\nVolume 388, Issue 20, 15 October 2009, pp. 4459-4474.\n[11] V. Henn, M. Ottomanelli, \"Handling uncertainty in route choice models:\nFrom probabilistic to possibilistic approaches\", European Journal of\nOperational Research, Volume 175, Issue 3, 16 December 2006, pp.\n1526-1538.\n[12] Y. M. Nie, \"Equilibrium analysis of macroscopic traffic oscillations\",\nTransportation Research Part B: Methodological, Volume 44, Issue 1,\nJanuary 2010, pp. 62-72.\n[13] B. Raney, K. Nagel, \"Iterative route planning for large-scale modular\ntransportation simulations\", Future Generation Computer Systems,\nVolume 20, Issue 7, 1 October 2004, pp. 1101-1118.\n[14] A. L.C. Bazzan, F. Kl├╝gl, \"Case studies on the Braess Paradox:\nSimulating route recommendation and learning in abstract and\nmicroscopic models\", Transportation Research Part C: Emerging\nTechnologies, Volume 13, Issue 4, August 2005, pp. 299-319.\n[15] S. Rosswog, C. Gawron, S. Hasselberg, R. Böning, P. Wagner,\n\"Computational aspects in traffic simulation problems\", Future\nGeneration Computer Systems, Volume 17, Issue 5, March 2001, pp.\n659-665.\n[16] D. Braess, \"Uber ein Paradoxon der Verkehrsplanung,\"\nUnternehmenforschung, 1968, 12, pp. 258-268.\n[17] J. G. Wardrop, \"Some Theoretical Aspects of Road Traffic Research,\"\nProceedings of the Institute of Civil Engineers, Part II, 1952, pp. 325-\n378.\n[18] G. Szűcs, Gy. Sallai, \"Route Planning with Uncertain Information using\nDempster-Shafer theory\", 3rd International Conference Engineering\nManagement and Service Sciences (EMS 2009), Session of City\nOperation and Development, in Beijing, China, September 20-22, 2009.\n[19] M. Florian, M. Mahut, N. Tremblay, \"Application of a simulation-based\ndynamic traffic assignment model\", European Journal of Operational\nResearch 189, 2008, pp. 1381-1392.\n[20] R. Axelrod, \"Effective choice in the prisoner-s dilemma\" J. Conflict\nResolution, 24, 1980, pp. 3-25.\n[21] R. Axelrod, \"The Evolution of Cooperation\" (Revised edition) Perseus\nBooks Group, 2006.\n[22] J. Baron, \"Thinking and Deciding\", (Third Edition), Cambridge\nUniversity Press, 2000.\n[23] Gy. Szab├│, G. F├íth, \"Evolutionary games on graphs\", Physics Reports\n446, 2007, pp. 97-216.\n[24] S. Azhar, A. McLennan, J.H. Reif, \"Computation of equilibriain\nnoncooperative games\", Computers & Mathematics with Applications,\nVolume 50, Issues 5-6, September 2005, Pages 823-854.\n[25] G. Szűcs, Gy. Sallai: \"Finding Equilibrium in Transport Networks by\nSimulation in case of Lack of Information\", \"International Conference\non Innovation, Engineering Management and Technology\", WASET\nconference 65, Tokyo, 2010 May 26-28. pp. 172-176."]}