Drying Kinetics of a Porous Spherical Particle and the Inversion Temperature

A model is presented for drying of a single porous particle with superheated steam and humid air. Experimental data for spherical porous ceramic particle reported in the literature were used for the validation of the model. An inversion temperature at which the evaporation rates within superheated steam and humid air are equal was predicted. The effect of thermophysical properties of the particle (permeability 10^-14 - 10^-17 m², diameter 3 × 10^-3 - 10 × 10^-3 m) and operating variables (gas mass flux 0.26 - 0.78 kg m^-2 s^-1, drying agent temperature 120-200°C) is tested. The inversion temperature is shown to be affected by the thermophysical properties of the porous particle and of the drying agent.     

Feature extraction and ML techniques for static gesture recognition

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.     

Robustness design of a dynamic output-feedback decentralized controller using <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> synthesis and LMI paradigm

This paper proposes developing a H _∞ dynamic output-feedback decentralized control design method for nonlinear interconnected systems subject to time-varying parameters and external disturbances. The designed controller is formulated as an optimization problem subject to linear matrix inequalities (LMIs) for the concurrent computation of the decentralized observation and control gains, and for the external disturbance mitigation by means of a H _∞ performance criterion minimization. The propounded optimization problem, designed in LMI conditions, is expeditiously resolved by a one-step procedure to override the conservatism generated by using many step-based procedures often used in the analysis and synthesis of interconnected systems. The effectiveness of the developed control scheme is demonstrated through simulation results of multimachine power systems.     

Traps centers and deep defects contribution in current instabilities for AlGaN/GaN HEMT's on silicon and sapphire substrates

AlGaN/GaN high electron mobility transistors (HEMTs) with Si and Al₂O₃ substrates reveals anomalies on I_ds-V_ds-T and I_gs-V_gs-T characteristics (degradation in drain current, kink effect, barrier height fluctuations, etc.). Stress and random telegraph signal (RTS) measurements prove the presence of trap centers responsible for drain current degradation. An explanation of the trapping mechanism responsible for current instabilities is proposed. Deep defects analysis performed by capacitance transient spectroscopy (C-DLTS), frequency dispersion of the output conductance (G_ds(f)), respectively, on gate/source and drain/source contacts and RTS prove the presence of deep defects localized, respectively, in the gate and in the channel regions. Defects detected by C-DLTS and G_ds(f) are strongly correlated, respectively, to barrier height inhomogeneities and kink anomalies. Gate current analysis confirms the presence of (G-R) centers acting like traps at the interface GaN/AlGaN. Finally, the localization of these traps defects is proposed.     

Computing Travel Time Reliability in Transportation Networks with Multistates and Dependent Link Failures

The measurement of transportation network travel time reliability is imperative to provide drivers with accurate route guidance information and to generate the shortest path (or alternative paths) connecting origins and destinations, especially under conditions of varying demands and limited capacities. Previous studies assumed that link failures in a road network are statistically independent and that reliability probabilities are perfectly determinable. In real life, these assumptions are dubious, because the failure of a link in one particular area does not necessarily result in the complete failure of the neighboring link, but may lead to deterioration of its performance. This paper presents a new methodology to study the multistate system reliability analysis of transportation networks for which one cannot formulate an “all or nothing” type of failure criterion and in which dependent link failures are considered. The methodology is presented, using a numerical example, for computing the probability that travel time between an origin and a destination may exceed a threshold.     

Towards a unified visual framework in a binocular active robot vision system

This paper presents the results of an investigation and pilot study into an active binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognising objects in a cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the ‘searchlight metaphor’ of biological systems. We present results of an investigation that yield a maximum vergence error of ～6.5 pixels, while ～85% of known objects were recognised in five different cluttered scenes. Finally a ‘stepping-stone’ visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the field of view resulting from any individual saccade.     

A k‐means clustering‐based security framework for mobile data mining

Data mining is a process of digging data sets from large data‐bases, especially for use to improve the operation efficiency of a company, institution, or an organization. With the increasing cases of cyber and other computer‐related crimes, computer and mobile data security has become a matter of concern, secure, and non‐contaminated data is very important for data mining to improve the performance of a system. This paper proposes a system framework that is able to collect information and then be able to generate alerts in real time. The proposed scheme is then simulated using the K‐means clustering algorithm, which is one of the most popular clustering algorithms to determine the efficiency and the accuracy of the proposed scheme. The paper concludes by proposing further improvements to be undertaken on the proposed system to improve its efficiency and accuracy. Copyright © 2017 John Wiley & Sons, Ltd.