Sensorless fault tolerant control for induction motors

In this paper, a sensorless fault tolerant controller for induction motors is developed. In the proposed approach, a robust controller based on backstepping strategy is designed in order to compensate for both the load torque disturbance and the rotor resistance variation caused by the broken rotor bars faults. The proposed approach needs neither fault detection and isolation schemes nor controller re-design. Moreover, to avoid the use of speed and flux sensors, a second order sliding mode observer is introduced to estimate the flux and the speed. The observer converges in a finite time and leads to good estimates of the flux and the speed even in the presence of the rotor resistance variation and the load torque disturbance. Since the observer converges in the finite time, the stability of the closed-loop system (controller with observer) is shown in two steps. First, the boundedness of the closed-loop system trajectories before the convergence of the observer is proved. Second, the convergence of the closed-loop system trajectories is proved after the convergence of the observer. To highlight the efficiency and applicability of the proposed control scheme, simulation and experimental results are conducted for a 1.5 kW induction motor.     

Modelling of the filling of micro-cavities of finite geometry by amorphous polymers using hot-embossing

The possibility to fill cavities of finite geometry could be described using an analytical model of the hot-embossing process of viscoelastic polymers. This model is based on the volume conservation during the forming process which allows to predict data concerning the geometrical evolution of the material on one hand, and on the other hand the filling time of cavities in the mould. A particular attention was drawn on the necessary time to fill the cavities depending on their shape or a scale factor for a given cavity shape.     

Relay-Assisted Wireless Energy Transfer for Efficient Spectrum Sharing in Harsh Environments

International Journal of Wireless Information Networks - With exponential growth in the number of wireless devices and limited available spectrum, the problem of spectrum sharing remains in...     

Microblogs recommendations based on implicit similarity in content social networks

With the development of online social networking applications, microblogs have become a necessary online communication network in daily life. Users are interested in obtaining personalized recommendations related to their tastes and needs. In some microblog systems, tags are not available, or the use of tags is rare. In addition, user-specified social relations are extremely rare. Hence, sparsity is a problem in microblog systems. To address this problem, we propose a new framework called Pblog to alleviate sparsity. Pblog identifies users’ interests via their microblogs and social relations and computes implicit similarity among users using a new algorithm. The experimental results indicated that the use of this algorithm can improve the results. In online social networks, such as Twitter, the number of microblogs in the system is high, and it is constantly increasing. Therefore, providing personalized recommendations to target users requires considerable time. To address this problem, the Pblog framework groups similar users using the analytic hierarchy process (AHP) method. Then, Pblog prunes microblogs of the target user group and recommends microblogs with higher ratings to the target user. In the experimental results section, the Pblog framework was compared with several other frameworks. All of these frameworks were run on two datasets: Twitter and Tumblr. Based on the results of these comparisons, the Pblog framework provides more appropriate recommendations to the target user than previous frameworks.

     

Estimation of nonlinear systems via a Chebyshev approximation approach

This paper proposes to decompose the nonlinear dynamic of a chaotic system with Chebyshev polynomials to improve performances of its estimator. More widely than synchronization of chaotic systems, this algorithm is compared to other nonlinear stochastic estimator such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Chebyshev polynomials orthogonality properties is used to fit a polynomial to a nonlinear function. This polynomial is then used in an Exact Polynomial Kalman Filter (ExPKF) to run real time state estimation. The ExPKF offers mean square error optimality because it can estimate exact statistics of transformed variables through the polynomial function. Analytical expressions of those statistics are derived so as to lower ExPKF algorithm computation complexity and allow real time applications. Simulations under the Additive White Gaussian Noise (AWGN) hypothesis, show relevant performances of this algorithm compared to classical nonlinear estimators.     

Algorithms for a realistic variant of flowshop scheduling

This paper deals with a realistic variant of flowshop scheduling, namely the hybrid flexible flowshop. A hybrid flowshop mixes the characteristics of regular flowshops and parallel machine problems by considering stages with parallel machines instead of having one single machine per stage. We also investigate the flexible version where stage skipping might occur, i.e., not all stages must be visited by all jobs. Lastly, we also consider job sequence dependent setup times per stage. The optimization criterion considered is makespan minimization. While many approaches for hybrid flowshops have been proposed, hybrid flexible flowshops have been rarely studied. The situation is even worse with the addition of sequence dependent setups. In this study, we propose two advanced algorithms that specifically deal with the flexible and setup characteristics of this problem. The first algorithm is a dynamic dispatching rule heuristic, and the second is an iterated local search metaheuristic. The proposed algorithms are evaluated by comparison against seven other high performing existing algorithms. The statistically sound results support the idea that the proposed algorithms are very competitive for the studied problem.     

The distributed permutation flowshop scheduling problem

This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPFSP. Under this generalization, we assume that there are a total of F identical factories or shops, each one with m machines disposed in series. A set of n available jobs have to be distributed among the F factories and then a processing sequence has to be derived for the jobs assigned to each factory. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. This production setting is necessary in today's decentralized and globalized economy where several production centers might be available for a firm. We characterize the DPFSP and propose six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance. We also propose two simple factory assignment rules together with 14 heuristics based on dispatching rules, effective constructive heuristics and variable neighborhood descent methods. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed methods.     

A high performing metaheuristic for job shop scheduling with sequence-dependent setup times

This paper investigates scheduling job shop problems with sequence-dependent setup times under minimization of makespan. We develop an effective metaheuristic, simulated annealing with novel operators, to potentially solve the problem. Simulated annealing is a well-recognized algorithm and historically classified as a local-search-based metaheuristic. The performance of simulated annealing critically depends on its operators and parameters, in particular, its neighborhood search structure. In this paper, we propose an effective neighborhood search structure based on insertion neighborhoods as well as analyzing the behavior of simulated annealing with different types of operators and parameters by the means of Taguchi method. An experiment based on Taillard benchmark is conducted to evaluate the proposed algorithm against some effective algorithms existing in the literature. The results show that the proposed algorithm outperforms the other algorithms.     

Fabrication of ultraviolet photoconductive sensor using a novel aluminium-doped zinc oxide nanorod-nanoflake network thin film prepared via ultrasonic-assisted sol-gel and immersion methods

Unique and novel thin films with aluminium (Al)-doped zinc oxide (ZnO) nanostructures consisting of nanorod-nanoflake networks were prepared for metal-semiconductor-metal (MSM)-type ultraviolet (UV) photoconductive sensor applications. These nanostructures were grown on a glass substrate coated with a seed layer using a combination of ultrasonic-assisted sol-gel and immersion methods. The synthesised ZnO nanorods had diameters varying from 10 to 40 nm. Very thin nanoflake structures were grown vertically and horizontally on top of the nanorod array. The thin film had good ZnO crystallinity with a root mean square roughness of approximately 13.59 nm. The photocurrent properties for the Al-doped ZnO nanorod-nanoflake thin films were more than 1.5 times greater than those of the seed layer when the sensor was illuminated with 365 nm UV light at a density of 5 mA/cm². The responsivity of the device was found to be dependent on the bias voltage. We found that similar photocurrent curves were produced over eight cycles, which indicated that the UV sensing capability of the fabricated sensor was highly reproducible. Our results provide a new approach for utilising the novel structure of Al-doped ZnO thin films with a nanorod-nanoflake network for UV sensor applications. To the best of our knowledge, UV photoconductive sensors using Al-doped ZnO thin films with a nanorod-nanoflake network have not yet been reported.     

Experimental analysis and numerical modelling of the forming process of polypropylene replicas of micro-cavities using hot embossing

Numerical modelling of the deformation of a polymer using the finite elements method in axisymetrical mode was performed using the LsDyna^® software to describe the filling of micro-cavities during the forming process of the material using the hot embossing. These simulations firstly allow verifying whether the chosen forming process conditions promote or not an optimized filling of the superficial cavities in order to achieve precise replicas which best reproduce the superficial topography of the mould. The simulations were carried out to evaluate the filling of the cavities taking into account the mechanical behaviour of the selected polymer into the model. Moreover, these models were developed to verify the effect of the distribution of the mould cavities on their filling. The influence of the mobility of non deformable rigid plates on the filling of the cavities represents an auxiliary variable. In the approach presented, the compression plates are assumed to be parallel and non deformable, whereas the polymer disk follows a rubbery behaviour around a temperature equal to 140°C. Globally the modelling results are satisfactory for they are rather close to the experimental observations conducted. In summary, the effect of the normal stress as also the distribution of micro-cavities at the mould surface seem to prevail in the case of the forming process by hot embossing.