Feedback control of coronal plane hip angle in paraplegic subjectsusing functional neuromuscular stimulation

This paper reports on an investigation of feedback control of coronal plane posture in paraplegic subjects who stand using functional neuromuscular stimulation (FNS). A feedback control system directed at regulating coronal plane hip angle in neutral position was designed, implemented, and evaluated in two paraplegic subjects. The control system included sensor mounting and signal processing techniques, a two-stage feedback controller, stimulation hardware, and a set of percutaneous intramuscular electrodes. The feedback controller consisted of two-stages in cascade: a modified discrete-time proportional-integral-derivative (PID) stage and a nonlinear single-input, multiple-output stage to determine the stimulation to be sent to several muscles. The focus of this work was on evaluating the performance of the feedback controller by comparing the response of the feedback-controlled system to that of an open-loop stimulation system. In an evaluation based on temporal response characteristics the controlled system exhibited a 41% reduction in root-mean-squared (rms) error (where error is defined as the deviation from the desired angle), a 52% reduction in steady-state error, and a 22% reduction in hip compliance. In addition, the feedback-controlled system exhibited significant reductions in variability of these measures on several days. These results demonstrate the ability of the feedback controller to improve the temporal response characteristics of the FNS control system.     

Flexible polymer composite electromagnetic crystals

The dielectric properties of a ceramic powder (BaTiO₃) filled thermoplastic elastomer (EPDM TPE) were investigated for use in a flexible electromagnetic crystal. Materials were produced that had a high dielectric constant (approximately 9) and low loss tangent (less than 0.01). Materials were extruded and injection molded so as to povide low‐cost processing. Mechanical and electromagnetic test results showed the effect of processing conditions on the final quality of the composite. The shear rate during processing and the number of mixing cycles were found to affect the final material characteristics significantly. An electromagnetic crystal woven from extruded rods showed good reflectivity in the 10–15 GHz region.     

Surface modification of starch nanocrystals through ring‐opening polymerization of ε‐caprolactone and investigation of their microstructures

Bionanoparticles of starch obtained by submitting native potato starch granules to acid hydrolysis conditions. The resulted starch nanoparticles were used as core or macro initiator for polymerization of ε‐caprolactone (CL). Starch nanoparticle‐g‐polycaprolactone was synthesized through ring‐opening polymerization (ROP) of CL in the presence of Sn(Oct)₂ as initiator. The detailed microstructure of the resulted copolymer was characterized with NMR spectroscopy. Thermal characteristic of the copolymer was investigated using DSC and TGA. By introducing PCL, the range of melting temperature for starch was increased and degradation of copolymer occurred in a broader region. X‐ray diffraction and TEM micrographs confirmed that there was no alteration of starch crystalline structure and morphology of nanoparticles, respectively. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Dielectric loaded HTS resonators as frequency standards andlow-phase noise oscillators

High-quality, high-temperature superconducting (HTS) thin-films exhibit very low losses at microwave frequencies and, as a result, allow very high Q resonators to be produced. The size of such resonators may be significantly reduced by loading with low-loss single crystal dielectric. The potential for HTS dielectric loaded resonators as practical frequency standards and reference elements for low-phase-noise oscillators is assessed, with emphasis on operation at 60 K, a temperature readily attainable with compact Stirling cycle coolers     

Statistically correlated multi-task learning for autonomous driving

Neural Computing and Applications - Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning...     

Bayesian estimation based on vague lifetime data

In Classical Bayesian approach, estimation of lifetime data usually is dealing with precise information. However, in real world, some informations about an underlying system might be imprecise and represented in the form of vague quantities. In these situations, we need to generalize classical methods to vague environment for studying and analyzing the systems of interest. In this paper, we propose the Bayesian estimation of failure rate and mean time to failure based on vague set theory in the case of complete and censored data sets. To employ the Bayesian approach, model parameters are assumed to be vague random variables with vague prior distributions. This approach will be used to induce the vague Bayes estimate of failure rate and mean time to failure by introducing and applying a theorem called “Resolution Identity” for vague sets. In order to evaluate the membership degrees of vague Bayesian estimate for these quantities, a computational procedure is investigated. In the proposed method, the original problem is transformed into a nonlinear programming problem which is then divided into eight subproblems to simplifying computations.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Rheological and extrusion characteristics of glass fiber-reinforced polycarbonate

An experimental investigation of the rheological properties of glass fiber-reinforced polycarbonate melts and the extrusion of such compounds through capillary and slit dies is presented. The viscosity–shear rate function seems independent of instrument for cone-plate and capillary investigations. The presence of fibers increases the level of the viscosity. Normal stresses at fixed shear stress are also increased by the presence of fibers. The extrudate swell is decreased by the presence of fibers and surface roughness is increased. Fiber orientation increases and surface roughness decreases with increasing extrusion rate.     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.