Partial syndrome-based system-level fault diagnosis using game theory

This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identification problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identifies the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identification algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we first show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoffs of all players (nodes). We then demonstrate, using results from a thorough simulation, the effectiveness of this approach in solving the fault identification problem using partial syndromes from randomly generated diagnosable systems of different sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.     

Enhanced EDCA with Deterministic Transmission Collision Resolution for Real-Time Communication in Vehicular Ad Hoc Networks

The collision prevention system is one of most important research issues on vehicle safety technology. Sending worming messages within the right time and reliable transmission will get prevention of a possible vehicle accident. The communication standards of vehicular networks (VANET) are unable to guarantee the delivery of critical messages within tight deadlines. Indeed, the transmission collisions are handled with probabilistic manner that can reduce the transmission latency; however, it is inept to predict an upper bound value of this delay to verify the deadline. In this paper, we propose a medium access protocol that ensures the delivery of critical messages within a deadline. It is a hard real-time system with delay constant guarantee. We are focusing on improving the EDCA medium access protocol to prioritize critical messages and to get access to the transmission channel within a predictable communication delay. We create a new enhanced access protocol that is compatible with the IEEE 802.11p VANET standards and adapted to real-time communication requirements related to the vehicle collision avoidance problem.     

Influence of machining parameters and new nano-coated tool on drilling performance of CFRP/Aluminium sandwich

Drilling and fastening of hybrid materials in one-shot operation reduces cycle time of assembly of aerospace structures. One of the most common problems encountered in automatic drilling and riveting of multimaterial is that the continuous chips curl up on the body of the tool. Drilling of carbon fiber reinforced plastic (CFRP) is manageable, but when the minute drill hits the aluminium (Al) or titanium (Ti), the hot and continuous chips produced during machining considerably damage the CFRP hole. This study aims to solve this problem by employing nano-coated drills on multimaterial made of CFRP and aluminium alloy. The influence of cutting parameters on the quality of the holes, chip formation and tool wear were also analyzed. Two types of tungsten carbide drills were used for the present study, one with nano-coating and the other, without nano coating. The experimental results indicated that the shape and the size of the chips are strongly influenced by feed rate. The thrust force generated during drilling of the composite plate with coated drills was 10–15% lesser when compared to that generated during drilling with uncoated drills; similarly, the thrust force in the aluminium alloy was 50% lesser with coated drills when compared to thrust force generated without coated drills. Thus, the use of nano-coated drills significantly reduced the surface roughness and thrust force when compared with uncoated tools.     

Optimization of the mechanical properties of virtual porous solids using a hybrid approach

A hybrid strategy based on artificial neural network/genetic algorithms is suggested to optimize the mechanical properties of cellular solids. Three-dimensional void structures are generated using a random sequential addition algorithm in which spherical void particles are positioned in a solid phase matrix with a control of their size distribution and overlapping. This allows us to create an open-cell structure with relative densities in the range 0.1–0.3. Finite element calculation is used to assess the effective Young’s modulus as a function of generation parameters. Relative Young’s moduli in the three main directions of the solid are isotropic with respect to the generation algorithm constraints and adhere qualitatively to the common theory on effective properties of open-cell structures. Moreover, the effective response is found to be correlated to the randomness of the void structure, which exhibits, in some cases, an ordered cell configuration. In order to quantitatively describe these correlations and to check the sensitivity of the mechanical response to the structural features in addition to sampling and discretization levels, the hybrid strategy described above is considered. The main motivation was to decrease the finite element simulation time by predicting, where possible, the correlations instead of calculating them. In addition, the use of the hybrid strategy informs about the optimal mesh with respect to the generation variables. This strategy proved to be an efficient technique in enhancing the predictions and complementary to the finite element methodology.     

Neural Computation to Estimate Heat Flux in an Atmospheric Plasma Spray Process

Temperature is a key parameter in the thermal spray process and is a consequence of the heat flux experienced by the workpiece. This paper deals with the estimation of the heat flux transmitted to a workpiece from an atmospheric plasma spray torch during the preheating process often implemented in thermal spraying. An inverse heat conduction problem solution using a conjugate gradient method was considered to determine the heat flux starting from a known temperature distribution. Results from the later method were used to train an artificial neural network to discover correlations between selected processing parameters and heat flux.     

Revisiting Johnson and Jackson boundary conditions for granular flows

In this article, we revisit Johnson and Jackson boundary conditions for granular flows. The oblique collision between a particle and a flat wall is analyzed by adopting the classic rigid‐body theory and a more realistic semianalytical model. Based on the kinetic granular theory, the input parameter for the partial‐slip boundary conditions, specularity coefficient, which is not measurable in experiments, is then interpreted as a function of the particle‐wall restitution coefficient, the frictional coefficient, and the normalized slip velocity at the wall. An analytical expression for the specularity coefficient is suggested for a flat, frictional surface with a low frictional coefficient. The procedure for determining the specularity coefficient for a more general problem is outlined, and a working approximation is provided. Published 2011 American Institute of Chemical Engineers, 2012     

Perovskite Formation and Dielectric Responses in PZN Modified PMF-PZT Ceramics

PMF-PZN-PZT （0.01Pb（Mol/3Fe2/3）O3-xPb（Znl/3Nb2/3）O3-（O.99-x）P（Zro53Tio 47）03 piezoelectric ceramics）, where x = 0.00 0.01, 0.03, 0.05 and 0.07 were prepared by a conventional mixed-oxide method. The results show that the pure peroveskit phase forms in these ceramics. X-ray diffraction analysis indicated that the phase of the material is a MPB （morphotropic phase boundary） structure. The effects of PZN content on the crystal structure and electrical properties were investigated, optimal dielectric properties were achieved at composition x = 0.07 ceramics by calcination at 800 ℃ and sintering at 1,180 ℃, with a curie temperature of approximately 430 ℃. These results clearly show the significance of PZN in controlling the electrical responses of the PMF-PZN-PZT system.     

Cooperative Association/Re-association Approaches to Optimize Energy Consumption for Real-Time IEEE 802.15.4/ZigBee Wireless Sensor Networks

The major challenge of real-time Wireless Sensor Networks stills the optimization of both constraints: energy consumption, to get long network lifetime and the communication delay, to meet real-time requirements. In the context of IEEE 802.15.4/ZigBee networks, the association procedure has a direct effect on building paths optimizing those constraints. In this paper, we are interested on the definition of an ideal approach of load balancing to fairly distribute energy consumption among nodes in IEEE 802.15.4/ZigBee WSNs. This approach leads to conserve energy of each node in order to extend the network lifetime. To be closer to this ideal, we propose new dynamic association/re-association approaches allowing path alternation relative to association criteria and their threshold parameters. The implementation of those approaches in NS2 simulator highlights the efficiency of cooperative and dynamic association criteria particularly the one based on the sum of the inverses of remaining energy. Indeed, this approach gives better results with regard to energy distribution according to ideal approach which leads to a longer lifetime. It also performs lower latency for real time communication.     

A linear 60 GHz 65 nm-CMOS power amplifier realization and characterization for OFDM signal

A millimeter-wave Power Amplifier (PA) based on a 65nm CMOS technology from STMicroelectronics has been designed. The targeted feature is the unlicensed band around 60 GHz suitable for wireless personal area network application (WPAN). To optimize the linearity, the PA is designed under class A biasing to have an output compression point (OCP₁) close to its saturated Power (P _sat). S-parameters and large signal measurement results are demonstrated and compared with electromagnetic simulations. The PA offers a P _sat of 8.3 dBm, an OCP₁ of 6 dBm and a gain of 6.7 dB. The die area is 0.29 mm² with pads. Considering those results, one-tone simulations are not sufficient to characterize the linearity performances of the PA in its real conditions of use. Consequently, two-tone simulations are firstly performed. After, linearity figures of merit (FoM) are discussed applying an orthogonal frequency-division multiplexing (OFDM) modulated signal. The PA offers an adjacent channel power ratio (ACPR) of 15 dB and an error vector magnitude (EVM) of 20% at PA compression operating mode.     

Pansharpening multispectral remote sensing data by multiplicative joint nonnegative matrix factorization

Pansharpening aims at combining observable panchromatic and multispectral images to generate an unobservable image with the high spatial resolution of the former and the spectral diversity of the latter. In this paper a new fusion method is proposed. This method, related to linear spectral unmixing (LSU) techniques and based on non-negative matrix factorization (NMF), optimizes, by new iterative–multiplicative update rules, a joint criterion that exploits a spatial degradation model between the two images. The proposed Multiplicative Joint Non-negative Matrix Factorization (MJNMF) approach is applied to synthetic and real data, and its effectiveness in spatial and spectral domains is evaluated with commonly used performance criteria. Experimental results show that the proposed method yields good spectral and spatial fidelities of the pansharpened data. Also, it outperforms those tested from the literature.