Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.     

Decentralized enterprise systems: a multiplatform wireless sensor network approach

Massively deployed wireless sensor and actuator networks, co-existing with RFID technology, can bring clear benefits to large-scale enterprise systems, by delegating parts of the business functionality closer to the point of action. However, a major impediment in the integration process is represented by the variety of customized platforms and proprietary technologies. In this article, we present a three-layer, service-oriented architecture that accommodates different sensor platforms and exposes their functionality in a uniform way to the business application. Our work is motivated by real business cases from the oil and gas industry. In our implementation, we use three sensor platforms (particle, muNode, and Sindrion) integrated through the universal plug and play (UPnP) standard and incorporated into an enterprise software system. The practical tests and application trials confirm the feasibility of our solution but also reveal a number of challenges to be taken into account when deploying wireless sensor and actuator networks at industrial sites.     

A decoupled sliding mode control for a continuum arm

The paper treats the control problem of a class of robots constituted by a chain of continuum segments. The technological model basis is a central, long and thin, highly flexible and elastic backbone. The segment control system is a decoupled one. The main parameters of the arm control are determined by the curvature and curvature gradient. The dynamic model is inferred. The primary benefit of the proposed method is that the dynamic equations are represented by a set of ODE’s in time instead of PDE’s in time and space, and the new curvature gradient lumped parameter model is used. A sliding mode control system is used in order to achieve the desired shape of the arm. The stability of the closed-loop control system is proven. Numerical simulations and an experimental platform are also provided to verify the effectiveness of the presented approach.     

Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

Low quality sensor data limits WSN capabilities for providing reliable real-time situation-awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.     

A directional data dissemination protocol for vehicular environments

This paper presents a simple and robust dissemination protocol that efficiently deals with data dissemination in both dense and sparse vehicular networks. Our goal is to address highway scenarios where vehicles equipped with sensors detect an event, e.g., a hazard and broadcast an event message to a specific direction of interest. In order to deal with broadcast communication under diverse network densities, we design a dissemination protocol in such a way that: (i) it prevents the so-called broadcast storm problem in dense networks by employing an optimized broadcast suppression technique; and (ii) it efficiently deals with disconnected networks by relying on the store-carry-forward communication model. The novelty of the protocol lies in its simplicity and robustness. Simplicity is achieved by only considering two states (i.e., cluster tail and non-tail) for vehicles. Furthermore, vehicles in both directions help disseminating messages in a seamlessly manner, without resorting to different operation modes for each direction. Robustness is achieved by assigning message delivery responsibility to multiple vehicles in sparse networks. Our simulation results show that our protocol achieves higher delivery ratio and higher robustness when compared with DV-CAST under diverse road scenarios.