Eco‐plastics: Morphological and mechanical properties of recycled poly(carbonate)‐crushed rubber (rPC‐CR) blends

Crushed tire rubber particles (CR) have been dispersed into a recycled poly(carbonate) matrix (rPC) to obtain an eco‐friendly plastic (EFP). A positive synergy was expected from the association of an elastomeric phase to a tough thermoplastic matrix, helping on the other hand to develop a plastic with low impact on the environment. Mechanical melt‐mixing alone cannot provide a suitable interface, and led to blends with poor mechanical properties. Consequently, we have investigated different strategies to improve the EFP properties: First, the rubber surface has been treated by flaming or washing with dichloromethane and second, two copolymers, poly(ethylene‐co‐ethyl acrylate‐tert‐hydroxyl methacrylate) (E‐EA‐MAH) and poly(ethylene‐co‐methyl acrylate‐ter‐glycidyl methacrylate) (E‐MA‐GMA), were used to compatibilize CR particles with rPC matrix by reactive melt‐mixing in an internal mixer. The resulting blends mechanical properties were studied through static tension experiments and interpreted to the light of electronic microscopy fractography analysis and nanoindentation experiments. Significant gain of mechanical properties can be obtained by decreasing CR size under 140 μm (especially for CR contents between 5 and 20% m/m). To reach similar properties with rubber particles of diameter over 140 μm (but under 350 μm), it is necessary to activate their surface by either dichloromethane washing or flaming. Additional use of a compatibilizer extends the plastic behaviour domain of the EFP. rPC‐20% w/w CR is the best alternative material of our study. POLYM. ENG. SCI., 47:1768–1776, 2007. © 2007 Society of Plastics Engineers     

A recursive state estimator in the presence of state inequality constraints

This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.     

Unsupervised Bayesian image segmentation using orthogonal series

This paper deals with the problem of unsupervised image segmentation which consists in first mixture identification phase and second a Bayesian decision phase. During the mixture identification phase, the conditional probability density function (pdf) and the a priori class probabilities must be estimated. The most difficult part is the estimation of the number of pixel classes or in other words the estimation of the number of density mixture components. To resolve this problem, we propose here a Stochastic and Nonparametric Expectation-Maximization (SNEM) algorithm. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The non-parametric aspect comes from the use of the orthogonal series estimator. Experimental results are promising, we have obtained accurate results on a variety of real images.     

Output feedback stabilization of systems with non-linear actuators

An output feedback controller is developed to provide stabilization for feedback-linearizable systems with asymptotically-stable non-linear actuators. The controller consists of a globally-bounded state feedback and a high-gain observer. It is established that if the actuator dynamics are sufficiently fast, then the developed output feedback controller will stabilize the origin of the closed-loop system regardless of how fast the observer is. A simulation example is included to illustrate the developed theory.     

Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization

In this paper, we propose a new hierarchical method for the flexible job-shop scheduling problem (FJSP). This approach is mainly adapted to a job-shop problem (JSP) with high flexibility and is based on the decomposition of the problem in an assignment subproblem and a sequencing subproblem. For the first subproblem, we propose two methods: the first one is based successively on a heuristic approach and a local search; the second one, however, is based on a branch-and-bound algorithm. The quality of the assignment is evaluated by a lower bound. For the second subproblem we apply a hybrid genetic algorithm to deal with the sequencing problem. Computational tests are finally presented.     

Extra-Virgin Olive Oil and Cheap Vegetable Oils: Distinction and Detection of Adulteration as Determined by GC and Chemometrics

Refined oils including corn, sunflower, soybean, and palm oils as well as low-quality olive oil such as refined lampante and pomace olive oils are commonly used for extra-virgin olive oil (EVOO) adulteration. Indeed, K ₂₇₀ could be used as a parameter for the detection of EVOO fraud for each type of the studied refined oils, 10 % olive, 4 % pomace olive, 10 % palm, 5 % corn, and 2 % soybean oils. Moreover, the adulteration could also be detected by the increase of the trans fatty acid contents with 10 % pomace olive, 3 % soybean, 3 % sunflower, 2 % corn, and 10 % palm oils. Actually, stigmasta-3,5-diene content is one of the most effective means of detecting refined oils in EVOO at low levels: 2 % olive, 0.4 % pomace olive, 1 % palm, 0.2 % soybean, 0.5 % sunflower, and 0.1 % corn oils. Finally, the application of linear discriminant analysis could represent an alternative and innovative tool for faster and cheaper evaluation of EVOO adulteration.     

Predictive adaptive control of multiple robots in cooperative motion

In this paper we address the problem of controlling multiple non redundant robots manipulating a rigid object cooperatively when the parameters of the robots and the parameters of the load are uncertain. We propose a controller that takes into account the dynamics of both the load and the manipulators. The linearity of the dynamics of the robots and the load, with respect to the unknown parameters, is exploited during the derivation of the parameter's adaptation scheme. In order to design control and update laws that do not require the measurements of the joint accelerations or the load acceleration, the dynamics of both the robots and the load are filtered through a stable first order filter. Two prediction error vectors are then defined as the difference between the measured filtered dynamics and the predicted filtered dynamics of both the robots and the load. The least-squares estimation method or gradient method can be used to estimate the parameters of the multi-robot system from the prediction errors. We then develop a controller that is based on the cancellation of the nonlinearities. The proposed controller guarantees asymptotic tracking of the load trajectories and also guarantees asymptotic tracking of the internal forces trajectories.     

A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, based on an aperture synthesis L-band radiometer was successfully launched in November 2009. In the context of a validation campaign for the SMOS mission, intensive airborne and in situ observations were performed in southwestern France for the SMOS CAL/VAL, from April to May 2009 and from April to July 2010. The CAROLS (Cooperative Airborne Radiometer for Ocean and Land Studies) bi-angular (34°-0°) and dual-polarized (V and H) L-band radiometer was designed, built and installed on board the French ATR-42 research aircraft. During springs of 2009 and 2010, soil moisture observations from the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France were complemented by airborne observations of the CAROLS L-band radiometer, following an Atlantic-Mediterranean transect in southwestern France. Additionally to the 12 stations of the SMOSMANIA soil moisture network, in situ measurements were collected in three specific sites within an area representative of a SMOS pixel. Microwave radiometer observations, acquired over southwestern France by the CAROLS instrument were analyzed in order to assess their sensitivity to surface soil moisture (w_g). A combination of microwave brightness temperature (T_b) at either two polarizations or two contrasting incidence angles was used to retrieve w_g through regressed empirical logarithmic equations with good results, depending on the chosen configuration. The regressions derived from the CAROLS measurements were applied to the SMOS T_b and their retrieval performance was evaluated. The retrievals of w_g showed significant correlation (p-value < 0.05) with surface measurements for most of the SMOSMANIA stations (8 of 12 stations) and with additional field measurements at two specific sites, also. Root mean square errors varied from 0.03 to 0.09 m³ m^− 3 (0.06 m³ m^− 3 on average).     

Multiple model reduction approach using gap metric and stability margin for control nonlinear systems

This paper deals with the control of nonlinear systems using multimodel approach. The main idea of this work consists on the association of the gap metric and the stability margin tools to reduce the number of models constituting the multimodel bank. In fact, the self-organisation map (SOM) algorithm is used, firstly, to develop a preliminary multimodel bank. Then, the gap metric and the stability margin are computed to determine the redundancy of the initial multimodel bank. So, the multimodel controller is elaborated based on the reduced model bank. Simulations confirm the method for selecting the appropriate number of local models which should be used in the controller design.

     

Experimental evaluation of a variable structure controller for constrained robots

In this paper, a variable structure control scheme for constrained robots is developed. This control law ensures the asymptotic convergence of the position errors to zero and also the boundedness of the force tracking error. The control scheme requires a few online computations and thus can be easily implemented. The proposed control law does not require measurements of the joint accelerations or the estimation of the derivative of the force. Experimental results are presented to verify the theoretical developments.