Decentralized Fault-tolerant Resilient Control for Fractional-order Interconnected Systems with Input Saturation

International Journal of Control, Automation and Systems - This paper investigates the problem of robust decentralized fault-tolerant resilient control for fractional-order large-scale...     

Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

Neural Computing and Applications - This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding...     

Measuring height of high-voltage transmission poles using unmanned aerial vehicle (UAV) imagery

Aerial imagery is important in remote sensing applications. Unmanned aerial vehicle (UAV) has a wide range of applications in remote sensing and presents a substantial cost-effective solution when monitoring objects on the earth’s surface. Moreover, object detection and classification are important aspects of global information system, especially for remote sensing applications and power line monitoring, which are essential for the proper distribution of electricity to consumers. Manual inspection consumes much time and involves risk, especially in remote areas that host dangerous wildlife; hence, UAV-based approaches are more feasible for such monitoring. The authors propose an UAV approach that utilises a digital surface model and incorporates a stereo matching algorithm based on UAV stereo images. The proposed algorithm was based on a graph-cut (GC) algorithm that measured the disparity map. Results were compared with well-known algorithms; including, for example, global and local stereo matching algorithms. The proposed solution introduces and integrates ordering constraints along with a submodular energy minimisation function to/with the GC algorithm to enhance performance. The authors measured sensitivity and recall for all parameters against ground truth data for differently cropped images of 16 power poles. Results showed that the proposed model performed more accurately compared to extant methods.     

Fault detection via nonlinear profile monitoring using artificial neural networks

Fault detection is the characterization of a normal behavior of a system using a response function or profile of interest and the identification of any deviation from such normal behavior. As system complexity grows, predicting the underlying structure or form of response function becomes challenging if not impossible. This article presents a data‐driven approach for fault detection of complex systems using multivariate statistical process control based on artificial neural network (ANN) characterization. In this approach, the quality of a system is characterized where one explanatory variable is adequately explained as a function of the other variables using an ANN model. The vector of weights and biases of the ANN model is monitored by using Hotelling T ² through control charts. The proposed method is tested and compared with existing methods such as polynomial and sum of sine function regression for 3 cases from the literature. Moreover, it is applied to a 4‐story reinforced concrete building that uses continuous monitoring to avoid potentially catastrophic failures. The proposed ANN approach outperforms the existing methods for small shifts (deviations) from healthy states. For large and medium shifts, it provides comparable results that are on the conservative side.     

Comprehensive evaluation and sensitivity analysis of regeneration energy for acid gas removal plant using single and activated-methyl diethanolamine solvents

The absorption of acid gas using reactive amines is among the most widely used types of capturing technologies. However, the absorption process requires intensive energy expenditure majorly in the solvent regeneration process. This study simultaneously evaluated the regeneration energy of MDEA and PZ/MDEA solvents in terms of heat of absorption, sensible heat, and vaporization heat. Aspen Hysys version 8.8 simulation tool is applied to model the full acid gas removal plant for the chemical absorption process. The new energy balance technique presents around the absorption and desorption columns to bring a new perspective of energy distribution in the capturing of acid gas plants. Sensitivity analysis of regeneration energy and its three contributors is performed at several operation parameters such as absorber and stripper pressures, lean amine circulation rate, solvent concentration, reflux ratio, and CO₂ and H₂S concentrations. The results show that the heat of absorption of PZ/MDEA system is higher than that for MDEA system for the same operating conditions. The sensible heat is the main contributor in the required regeneration energy of MDEA solvent system. The simulation results have been validated against data taken from real plant and literature. The product specifications of our simulation corroborate with real plant data in an excellent approach; additionally, the profile temperature of the absorber and the stripper columns are in good agreement with literature. The overall results highlight the direction of the effects of each parameter on the heat of absorption, sensible heat, and vaporization heat.     

P(N) approximation for frequency-domain measurements in scattering media

Presented here are expressions for the P(N) approximation for light propagation in scattering media in the frequency domain. To elucidate parametric dependencies, the derivation uses normalization of the resulting expressions to either the total interaction coefficient or the reduced total interaction coefficient. For the latter case, a set of reduced phase function coefficients are introduced. Expression of the P(N) approximation as a conventional eigenvalue problem facilitates computation of the eigenvalues or attenuation coefficients. This approach is used to determine the attenuation coefficients in the asymptotic regime over the full values of the scattering albedo and reduced scattering albedo (0 to 1) and all positive values of the asymmetry factor (0 to 1). Frequency-domain measurements yield a sensitivity to turbid media optical properties for reduced scattering albedos as small as 0.2. P(N) calculations are used to assess the magnitude of errors associated with the P1 and P3 approximations over a range of scattering albedo, phase function, and modulation frequency.     

Preparation of membranes by electropolymerization of pyrrole functionalized by a ferrocene group

The preparation of dense membranes by the electropolymerization of functionalized pyrrole (Py) by ferrocene units was carried out. The synthesis of N‐[3‐(pyrrol‐1‐yl)propyl]ferrocene‐1‐carboxamide (or [(ferrocenyl)amidopropyl] pyrrole, FAPP) is described. The electropolymerization of the monomer on platinum electrode and on stainless‐steel meshes was studied. The electroactivity of the grafted ferrocenyl group of the FAPP monomer was confirmed and the electrochemical properties of the electrogenerated FAPP film were investigated. The stability in potentiostatic and potentiodynamic modes of FAPP films was low and, to improve the latter, the electrochemical preparation of the copolymer between the pyrrole and the FAPP was performed. The electrochemical characterization of the copolymer showed that it was possible to control the oxidation state of both electroactive compounds of the film. The FAPP/Py copolymer was then prepared on stainless‐steel meshes to produce membranes, whose transport properties were electrochemically controllable. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91: 3947–3958, 2004     

Synchronization and wave propagation in neuronal network under field coupling

Electric and chemical synapse play important role in connecting neurons and thus signal propagation can be realized between neurons. External electric stimulus can change the excitability of neuron and then the electrical activities can be modulated completely. Continuous fluctuation of ion concentration in cell can induce complex time-varying electromagnetic field during the exchange of charged ions across the membrane of neuron. Polarization and magnetization in the media(and neuron), which exposed to electromagnetic radiation, can modulate the dynamical response and mode transition in electrical activities of neurons. In this paper, magnetic flux is used to describe the effect of electromagnetic field, and the three-variable HindmarshRose neuron model is updated to propose a four-variable neuron model that the effect of electromagnetic induction and radiation can be explained. Based on the physical law of electromagnetic induction, exchange of charged ions and flow of ion currents will change the distribution of electromagnetic filed in cell, and each neuron will be exposed to the superimposed field triggered by other neurons. Therefore, signal exchange could occur even synapse coupling between neurons is removed in the case of field coupling. A chain network is proposed to investigate the signal exchange between neurons under field coupling when synapse coupling is not available. It is found that field coupling between neurons can change the collective behaviors in electrical activities. A statistical factor of synchronization and spatial patterns are calculated, these results confirmed that field coupling is effective for signal communication between neurons. In the end, open problems are suggested for readers' extensive guidance in this field.     

Image processing for chatter identification in machining processes

Identifying chatter or intensive self-excited relative tool–workpiece vibration is one of the main challenges in the realization of automatic machining processes. Chatter is undesirable because it causes poor surface finish and machining accuracy, as well as reducing tool life. The identification of chatter is performed by evaluating the surface roughness of a turned workpiece undergoing chatter and chatter-free processes. In this paper, an image-processing approach for the identification of chatter vibration in a turning process was investigated. Chatter is identified by first establishing the correlation between the surface roughness and the level of vibration or chatter in the turning process. Images from chatter-free and chatter-rich turning processes are analyzed. Several quantification parameters are utilized to differentiate between chatter and chatter-free processes. The arithmetic average of gray level G _a is computed. Intensity histograms are constructed and then the variance, mean, and optical roughness parameter of the intensity distributions are calculated. The surface texture analysis is carried out on the images using a second-order histogram or co-occurrence matrix of the images. Analysis is performed to investigate the ability of each technique to differentiate between a chatter-rich and a chatter-free process. Finally, a machine vision system is proposed to identify the presence of chatter vibration in a turning process.