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Integration, management and communication of heterogeneous design resources with WWW technologies
Recently, advanced information technologies have opened new pos-sibilities for collaborative designs. In this paper, a Web-based collaborative de-sign environment is proposed, where heterogeneous design applications can be integrated with a common interface, managed dynamically for publishing and searching, and communicated with each other for integrated multi-objective de-sign. The CORBA (Common Object Request Broker Architecture) is employed as an implementation tool to enable integration and communication of design application programs; and the XML (eXtensible Markup Language) is used as a common data descriptive language for data exchange between heterogeneous applications and for resource description and recording. This paper also intro-duces the implementation of the system and the encapsulating issues of existing legacy applications. At last, an example of gear design based on the system is il-lustrated to identify the methods and procedure developed by this research
CAN Fieldbus Communication in the CSP-based CT Library
In closed-loop control systems several realworld entities are simultaneously communicated to through a multitude of spatially distributed sensors and actuators. This intrinsic parallelism and complexity motivates implementing control software in the form of concurrent processes deployed on distributed hardware architectures. A CSP based occam-like architecture seems to be the most convenient for such a purpose. Many, often conflicting, requirements make design and implementation of distributed real-time control systems an extremely difficult task. The scope of this paper is limited to achieving safe and real-time communication over a CAN fieldbus for an\ud
existing CSP-based framework
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges
With the rapid development of marine activities, there has been an increasing
number of maritime mobile terminals, as well as a growing demand for high-speed
and ultra-reliable maritime communications to keep them connected.
Traditionally, the maritime Internet of Things (IoT) is enabled by maritime
satellites. However, satellites are seriously restricted by their high latency
and relatively low data rate. As an alternative, shore & island-based base
stations (BSs) can be built to extend the coverage of terrestrial networks
using fourth-generation (4G), fifth-generation (5G), and beyond 5G services.
Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs.
Despite of all these approaches, there are still open issues for an efficient
maritime communication network (MCN). For example, due to the complicated
electromagnetic propagation environment, the limited geometrically available BS
sites, and rigorous service demands from mission-critical applications,
conventional communication and networking theories and methods should be
tailored for maritime scenarios. Towards this end, we provide a survey on the
demand for maritime communications, the state-of-the-art MCNs, and key
technologies for enhancing transmission efficiency, extending network coverage,
and provisioning maritime-specific services. Future challenges in developing an
environment-aware, service-driven, and integrated satellite-air-ground MCN to
be smart enough to utilize external auxiliary information, e.g., sea state and
atmosphere conditions, are also discussed
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
We investigate a method to deal with congestion of sectors and delays in the
tactical phase of air traffic flow and capacity management. It relies on
temporal objectives given for every point of the flight plans and shared among
the controllers in order to create a collaborative environment. This would
enhance the transition from the network view of the flow management to the
local view of air traffic control. Uncertainty is modeled at the trajectory
level with temporal information on the boundary points of the crossed sectors
and then, we infer the probabilistic occupancy count. Therefore, we can model
the accuracy of the trajectory prediction in the optimization process in order
to fix some safety margins. On the one hand, more accurate is our prediction;
more efficient will be the proposed solutions, because of the tighter safety
margins. On the other hand, when uncertainty is not negligible, the proposed
solutions will be more robust to disruptions. Furthermore, a multiobjective
algorithm is used to find the tradeoff between the delays and congestion, which
are antagonist in airspace with high traffic density. The flow management
position can choose manually, or automatically with a preference-based
algorithm, the adequate solution. This method is tested against two instances,
one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.Comment: IEEE Congress on Evolutionary Computation (2013). arXiv admin note:
substantial text overlap with arXiv:1309.391
Echo State Queueing Network: a new reservoir computing learning tool
In the last decade, a new computational paradigm was introduced in the field
of Machine Learning, under the name of Reservoir Computing (RC). RC models are
neural networks which a recurrent part (the reservoir) that does not
participate in the learning process, and the rest of the system where no
recurrence (no neural circuit) occurs. This approach has grown rapidly due to
its success in solving learning tasks and other computational applications.
Some success was also observed with another recently proposed neural network
designed using Queueing Theory, the Random Neural Network (RandNN). Both
approaches have good properties and identified drawbacks. In this paper, we
propose a new RC model called Echo State Queueing Network (ESQN), where we use
ideas coming from RandNNs for the design of the reservoir. ESQNs consist in
ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The
paper positions ESQNs in the global Machine Learning area, and provides
examples of their use and performances. We show on largely used benchmarks that
ESQNs are very accurate tools, and we illustrate how they compare with standard
ESNs.Comment: Proceedings of the 10th IEEE Consumer Communications and Networking
Conference (CCNC), Las Vegas, USA, 201
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