The influence of activated carbon as an additive in anode materials for low temperature solid oxide fuel cells

The effects of activated carbon (AC) as an additive in multi-oxide nano composite LiNiCuZn–O for application as anode in solid oxide fuel cell (SOFC) is reported. The composite was synthesized using solid state reactions method with varying content of AC in range 0.1%–0.9% for use as anode in the cell. The cell was composed of the synthesized composite as anode, LiNiCuZn–O as cathode and Samaria doped ceria (SDC) as electrolyte. The prepared composites were characterized for morphology and crystal structure by scanning electron microscope (SEM) and x-ray diffraction (XRD) respectively. Furthermore, the crystallite sizes of LiNiCuZn–O and LiNiCuZn–O with AC as an additive have been found in the range from 50 nm to 70 nm. The prepared composite materials were observed porous and the porosity of the sample having 0.5% additive was found highest. The conductivity and power density of the SOFC were studied at temperature of 600 °C. The maximum value of conductivity was found as 4.79 S/cm for the composite containing 0.5% AC as measured by using 4-probe method. The maximum value of power density of the fuel cell with anode comprising of 0.5% AC along with the mentioned cathode and the electrolyte was 455 mW/cm². Therefore, out of the compositions studied, the composite comprising of LiNiCuZn–O with 0.5% AC offered best performance for anode in the cell. This oxide composite is reported as a potential candidate for use as anode in low temperature SOFCs.     

Hydroxyethyl cellulose filled with M2+ chelate complexes with ethylenediaminetetraacetic acid (EDTA) as an effective electron-injection layer for polymer light-emitting diodes

An effective electron-injection layer (EIL) is crucial to the development of highly efficient polymer light-emitting diodes (PLEDs) using stable, high work-function aluminium as the cathode. This work presents the first investigation using hydroxyethyl cellulose (HEC), filled with chelate complexes [(CH₃COO)₂-M, EDTA-M; M: Ca²⁺, Mg²⁺], as an electron-injection layer (EIL) to fabricate multilayer polymer light-emitting diodes (ITO/PEDO:PSS/HY-PPV/EIL/Al) by spin-coating processes. Devices based on HEC doped with EDTA-M provided the best performance. The maximum luminance and maximum current efficiency of polymer light-emitting diodes with EDTA-Ca in an HEC layer were 7502 cd/m² and 2.85 cd/A, respectively, whereas those with EDTA-Mg were 8443 cd/m² and 3.12 cd/A, which was approximately seven- to eight-fold of that without EIL. This performance enhancement was attributed to electron donation from the chelator that reduces metal cations to a “pseudo-metallic state”, enabling it to act as an intermediate step to facilitate electron injection. The results demonstrate that chelates of bivalent cations with EDTA can potentially serve as electron-injection materials for optoelectronic applications.     

A mixed collector system for phosphate flotation

Flotation has been used in industry for more than a half century as the primary technique for upgrading phosphate. While the flotation of phosphate was inefficient when oleic acid was used alone as a collector, therefore a mixed collector of oleic acid (HOl), linoleic acid (LA) and linolenic acid (LNA) was employed to improve the recovery of phosphate flotation. The batch flotation results showed that the optimal composition of the mixed collector was 54 wt.% HOl, 36 wt.% LA and 10 wt.% LNA. Additionally, the effect of pH on the mixed collector application was studied while considering the surface tension, contact angle and micro-flotation. The results showed that the mixed collector should be used at a pH of 9.5. Above a pH of 9.5, the adsorption of fatty acids dimers on the apatite surface hindered phosphate flotation. The influence of the mixed collector assembly on apatite flotation was also investigated. It was demonstrated that due to its low critical micelle concentration, a sufficiently hydrophobic apatite surface could be generated at a collector concentration of 60 mg/L. In addition, zeta potential experiments suggested that collector adsorption was governed by chemisorption. FTIR and XPS spectra studies further indicated that the chemical reaction involved the carboxyl groups of fatty acids and Ca species at the apatite surface for each fatty acid in the mixed collector.     

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties

This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy.     

Thermodynamic analysis of natural gas reciprocating compressors based on real and ideal gas models

The accurate modelling and investigating effects of various parameters of the reciprocating compressors are important subjects. In this work, based on first law of thermodynamics, conversation of mass and real and ideal gas assumptions, a theoretical analysis has been constructed to simulate natural gas reciprocating compressors. For computing the thermodynamic properties of natural gas based on real gas model, the AGA8 equation of state has been used. Numerical results validated with previous measured values and showed a good agreement. The effects of important parameters such as: angular speed, clearance and pressure ratio have been studied on the performance of the compressors. The results reveal the in-control volume temperature for ideal gas is more than real gas model but the mass flow rate and work for real gas is higher than ideal gas model. On the other hand, the indicated work that required for compression is greater for ideal gas model.     

A new MM algorithm for constrained estimation in the proportional hazards model

The constrained estimation in Cox’s model for the right-censored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear inequality restrictions in the proportional hazards model. The first M-step of the proposed MM algorithm is to construct a surrogate function with a diagonal Hessian matrix, which can be reached by utilizing the convexity of the exponential function and the negative logarithm function. The second M-step is to maximize the surrogate function with a diagonal Hessian matrix subject to box constraints, which is equivalent to separately maximizing several one-dimensional concave functions with a lower bound and an upper bound constraint, resulting in an explicit solution via a median function. The ascent property of the proposed MM algorithm under constraints is theoretically justified. Standard error estimation is also presented via a non-parametric bootstrap approach. Simulation studies are performed to compare the estimations with and without constraints. Two real data sets are used to illustrate the proposed methods.     

A hand gesture recognition technique for human–computer interaction

We propose an approach to recognize trajectory-based dynamic hand gestures in real time for human–computer interaction (HCI). We also introduce a fast learning mechanism that does not require extensive training data to teach gestures to the system. We use a six-degrees-of-freedom position tracker to collect trajectory data and represent gestures as an ordered sequence of directional movements in 2D. In the learning phase, sample gesture data is filtered and processed to create gesture recognizers, which are basically finite-state machine sequence recognizers. We achieve online gesture recognition by these recognizers without needing to specify gesture start and end positions. The results of the conducted user study show that the proposed method is very promising in terms of gesture detection and recognition performance (73% accuracy) in a stream of motion. Additionally, the assessment of the user attitude survey denotes that the gestural interface is very useful and satisfactory. One of the novel parts of the proposed approach is that it gives users the freedom to create gesture commands according to their preferences for selected tasks. Thus, the presented gesture recognition approach makes the HCI process more intuitive and user specific.     

Self‐similarity analysis of vehicle driver's electrodermal activity

This paper characterizes stress levels via a self‐similarity analysis of the electrodermal activity (EDA) collected in a real‐world driving context. To characterize the EDA richness over scales, the fractional Brownian motion (FBM) process and its corresponding exponent H, estimated via a wavelet‐based approach, are used. Specifically, an automatic scale range selection is proposed in order to detect the linearity in a log scale diagram. The procedure is applied to the EDA signals, from the open database drivedb, originally captured on the foot and the hand of the drivers during a real‐world driving experiment, designed to evoke different levels of arousal and stress. The estimated Hurst exponent H offers a distinction in stress levels when driving in highway versus city, with a reference to restful state of minimal stress level. Specifically, the estimated H values tend to decrease when the driving environmental complexity increases. In addition, the estimated H values on the foot EDA signals allow a better characterization of the driving task than that of hand EDA. The self‐similarity analysis was applied to various physiological signals in literature but not to the EDA so far, a signal which was found to correlate most with human affect. The proposed analysis could be useful in real‐time monitoring of stress levels in urban driving spaces, among other applications.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.