Appl. Sci.2016, 6(7), 181; doi:10.3390/app6070181 (registering DOI) - published 1 July 2016 Show/Hide Abstract
Abstract: Portable articulated coordinate measuring machine (PACMM) is a kind of high accuracy coordinate measurement instrument and it has been widely applied in manufacturing and assembly. A number of key problems should be taken into consideration to achieve the required accuracy, such as structural design, mathematical measurement model and calibration method. Although the classical kinematic model of PACMM is the Denavit-Hartenberg (D-H) model, the representation of D-H encounters the badly-conditioned problem when the consecutive joint axes are parallel or nearly parallel. In this paper, a new kinematic model of PACMM based on a generalized geometric error model which eliminates the inadequacies of D-H model has been proposed. Furthermore, the generalized geometric error parameters of PACMM are optimized by the Levenberg-Marquard (L-M) algorithm. The experimental result demonstrates that the measurement of standard deviation of PACMM based on the generalized geometric error model has been reduced from 0.0627 mm to 0.0452 mm with respect to the D-H model.
Appl. Sci.2016, 6(7), 192; doi:10.3390/app6070192 - published 30 June 2016 Show/Hide Abstract
Abstract: In this paper, a flexible control strategy for a synthesis model dedicated to nonlinear friction phenomena is proposed. This model enables to synthesize different types of sound sources, such as creaky doors, singing glasses, squeaking wet plates or bowed strings. Based on the perceptual stance that a sound is perceived as the result of an action on an object we propose a genuine source/filter synthesis approach that enables to elude physical constraints induced by the coupling between the interacting objects. This approach makes it possible to independently control and freely combine the action and the object. Different implementations and applications related to computer animation, gesture learning for rehabilitation and expert gestures are presented at the end of this paper.
Appl. Sci.2016, 6(7), 193; doi:10.3390/app6070193 - published 30 June 2016 Show/Hide Abstract
Abstract: Efforts to simulate heat transfer in a PCM (Phase Change Material) storage device are generally led by considerations of Biot number and material thickness, of 2D versus 1D representation, and of possible hysteresis effects arising from the characterisation of the PCM using differential scanning calorimetry (DSC). In this paper we present a numerical treatment of heat conduction in a paraffin-based storage brick, based on experimental data for a full scale, heat storage component studied under laboratory conditions. The PCM was modelled adopting equivalent thermophysical properties during the phase change. An equivalent heat capacity and thermal conductivity were provided for an appropriate description of energy release and storage in the process of solidification and melting. The geometry of the metal container induces 2D effects that are generally neglected in numerical modelling. The thickness of the plates (about 2 cm) is sufficiently large to require the modelling of conduction in the PCM, but can also induce convection that has been neglected in this study. Experimental results are presented and compared for both a 1D and 2D model of the PCM device. It was concluded that a 2D representation is essential for configurations; like the case study and geometry we had; with a large difference in thermal conductivity between PCM and metal casing. Two curves of equivalent heat capacity (measured via DSC) were introduced for heating and cooling phases. Comparisons to experimental results indicated significant errors in the models during melting and solidification of the PCM, which could be reduced by instead adopting the mean of the two heat capacity curves. The rate of temperature change during the experiments and for the DSC characterisation was analysed and found to explain well the observations. In particular, as novelty, two peaks of equivalent heat capacity have been observed with DSC when the rate is very low instead of only one peak using current rate: and that explains the real behaviour in the experiments.
Appl. Sci.2016, 6(7), 195; doi:10.3390/app6070195 - published 30 June 2016 Show/Hide Abstract
Abstract: Recently, high-frame-rate ultrasound has been extensively studied for measurement of tissue dynamics, such as pulsations of the carotid artery and heart. Motion estimators are very important for such measurements of tissue dynamics. In high-frame-rate ultrasound, the tissue displacement between frames becomes very small owing to the high temporal resolution. Under such conditions, the speckle tracking method requires high levels of interpolation to estimate such a small displacement. A phase-sensitive motion estimator is feasible because it does not suffer from the aliasing effect by such a small displacement and does not require interpolation to estimate a sub-sample displacement. In the present study, two phase-sensitive 2D motion estimators, namely, paired 1D motion estimators and 2D motion estimator with shifted cross spectra, were developed. Phase-sensitive motion estimators using frequency spectra of ultrasonic echoes have already been proposed in previous studies. However, such methods had not taken into account the ambiguity of the frequency of each component of the spectrum. We have proposed a method, which estimates the mean frequency of each component of the spectrum, and the proposed method was validated by a phantom experiment. The experimental results showed that the bias errors in the estimated motion velocities of the phantom were less than or equal to (11.5% in lateral, 2.0% in axial) by the proposed 1D paired motion estimators and (3.0%, 2.0%) by the proposed 2D motion estimators, both of which were significantly smaller than (14.0%, 3.0%) of the conventional phase-sensitive 2D motion estimator.
Appl. Sci.2016, 6(7), 189; doi:10.3390/app6070189 - published 29 June 2016 Show/Hide Abstract
Abstract: In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the “vague” concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.
Appl. Sci.2016, 6(7), 188; doi:10.3390/app6070188 - published 29 June 2016 Show/Hide Abstract
Abstract: Polymer flooding is now considered a technically- and commercially-proven method for enhanced oil recovery (EOR). The viscosity of the injected polymer solution is the key property for successful polymer flooding. Given that the viscosity of a polymer solution has a non-linear relationship with various influential parameters (molecular weight, degree of hydrolysis, polymer concentration, cation concentration of polymer solution, shear rate, temperature) and that measurement of viscosity based on these parameters is a time-consuming process, the range of solution samples and the measurement conditions need to be limited and precise. Viscosity estimation of the polymer solution is effective for these purposes. An artificial neural network (ANN) was applied to the viscosity estimation of FlopaamTM 3330S, FlopaamTM 3630S and AN-125 solutions, three commonly-used EOR polymers. The viscosities measured and estimated by ANN and the Carreau model using Lee’s correlation, the only method for estimating the viscosity of an EOR polymer solution in unmeasured conditions, were compared. Estimation accuracy was evaluated by the average absolute relative deviation, which has been widely used for accuracy evaluation of the results of ANN models. In all conditions, the accuracy of the ANN model is higher than that of the Carreau model using Lee’s correlation.