Optimal motion planning for parallel robots via convex optimization and receding horizon

In this paper, the problem of motion planning for parallel robots in the presence of static and dynamic obstacles has been investigated. The proposed algorithm can be regarded as a synergy of convex optimization with discrete optimization and receding horizon. This algorithm has several advantages, including absence of trapping in local optimums and a high computational speed. This problem has been fully analyzed for two three-DOF parallel robots, ie 3s-RPR parallel mechanism and the so-called Tripteron, while the shortest path is selected as the objective function. It should be noted that the first case study is a parallel mechanism with complex singularity loci expression from a convex optimization problem standpoint, while the second case is a parallel manipulator for which each limb has two links, an issue which increases the complexity of the optimization problem. Since some of the constraints are non-convex, two approaches are introduced in order to convexify them: (1) A McCormick-based relaxation merged with a branch-and-prune algorithm to prevent it from becoming too loose and (2) a first-order approximation which linearizes the non-convex quadratic constraints. The computational time for the approaches presented in this paper is considerably low, which will pave the way for online applications.     

Using correlation coefficients for locating partial discharge in power transformer

Most serious failure of power transformers is due to the insulation breakdown. Partial discharge (PD) that damages insulation by gradual erosion is major source of insulation failure. The effective ability of the wavelet packets analysis as a tool for disk-to-disk partial discharge faults detection and localization in transformer windings is shown in this paper. Techniques for locating a PD source are of the major importance in both the maintenance and repair of a transformer. One of the most well-known methods of PD localization in transformers is based on winding modeling and current of neutral point analysis. Since the impedance between PD location and neutral point of winding depends on the PD location in respect to neutral point, the frequency spectrum of neutral point current varies when the PD location changes. In the other word, the current components of neutral points vary according to the place where PD occurs. So in this paper, detailed model of transformer winding is modeled and the neutral point current is studied for locating PD. The used method is validated by the simulated model of transformer windings. This model produces a very acceptable current when compared to the experimental data. In this paper for locating partial discharge (PD) in transformer windings, a simulated model is developed for the transformer winding and the PD phenomenon mechanism. The impulse current test and wavelet packets transformation are used to locate PD. It is shown that the neutral current measurement of the transformer winding has useful information about PD location.     

New Approaches in Monitoring Multivariate Categorical Processes based on Contingency Tables in Phase II

In some statistical process control (SPC) applications, quality of a process or product is characterized by contingency table. Contingency tables describe the relation between two or more categorical quality characteristics. In this paper, two new control charts based on the WALD and Stuart score test statistics are designed for monitoring of contingency table‐based processes in Phase‐II. The performances of the proposed control charts are compared with the generalized linear test (GLT) control chart proposed in the literature. The results show the better performance of the proposed control charts under small and moderate shifts. Moreover, new schemes are proposed to diagnose which cell corresponding to different levels of categorical variables is responsible for out‐of‐control signal. In addition, we propose EWMA–WALD and EWMA–Stuart score test control charts to improve the performance of Shewhart‐based control charts in detecting small and moderate shifts in contingency table parameters. Meanwhile, we compare the performances of two proposed EWMA‐based control charts with the ones of three existing control charts called EWMA–GLT, EWMA–GLRT and an EWMA‐type control chart for multivariate binomial/multinomial processes along with the ones of the corresponding Shewhart‐based control charts. A numerical example is given to show the efficiency of the proposed methods. Finally, the effect of parameter estimation in Phase I based on m historical contingency table on the performance of the Shewhart‐based control charts is studied. Copyright © 2016 John Wiley & Sons, Ltd.     

Experimental investigation of the flow-induced motion of a square-section cylinder

The flow-induced motion(FIM)of an elastically mounted square-section cylinder is experimentally investigated over a wide range of Reynolds numbers(1.5×104＜Re＜7....     

Experimental studies on the catalytic behavior of alloy and core-shell supported Co-Ni bimetallic nano-catalysts for hydrogen generation by hydrolysis of sodium borohydride

Monometallic (Co) and bimetallic (Co-Ni and Co-Cu) oxides catalysts supported on the almond based activated carbon (AC) were prepared by the heterogeneous deposition-precipitation method. The activity of these catalysts was evaluated as a function of reaction temperature, NaOH, and NaBH₄ concentration. Several analysis methods including XRD, XPS, FTIR, TEM, FESEM, ICP-OES, and BET were applied to characterize the structure of prepared samples. Well-dispersed supported bimetallic nano-catalysts with the size of particles below 20 nm were formed by using nickel and copper oxides as a promoter which was confirmed by XRD and TEM techniques. Surface composition of alloy and core-shell cobalt-nickel oxides catalysts was analyzed by ICP-OES which was in a good agreement with nominal content during catalyst preparation. The performance of bimetallic cobalt-nickel oxides catalysts indicated the synergic effect between cobalt and nickel in comparison with monometallic and bimetallic cobalt-copper samples for hydrogen production. Maximum hydrogen generation rate was measured for the supported core-shell catalyst as named Ni1/Co3/AC. The reaction rate increased with increasing the temperature of the alkaline solution as a significant parameter while other operating conditions were kept constant. The optimal values for NaOH and NaBH₄ content were calculated to be 10 wt % for both variables at 30 °C. Hydrogen production rates were calculated to be 252.0, 310.8 and 658.8 mL min^?1.g^?1 by applying Co3/Ni1/AC, Co3-Ni1/AC (alloy) and Ni1/Co3/AC at 30 °C in 5 wt % NaBH₄ and 5 wt % NaOH solutions, respectively. Obtained activation energy (50 kJ mol^?1) illustrated that the suitable catalysts were synthesized for hydrogen generation. The experimental study showed that the hydrolysis of NaBH₄ was a zero-order type reaction with the respect to the sodium borohydride concentration. A semi empirical kinetic model was derived at the various temperatures and NaOH concentrations.     

Application of classification and regression trees for sensitivity analysis of the Escherichia coli O157:H7 food safety process risk model

Microbial food safety process risk models are simplifications of the real world that help risk managers in their efforts to mitigate food safety risks. An important tool in these risk assessment endeavors is sensitivity analysis, a systematic method used to quantify the effect of changes in input variables on model outputs. In this study, a novel sensitivity analysis method called classification and regression trees was applied to safety risk assessment with the use of portions of the Slaughter Module and Preparation Module of the E. coli O157:H7 microbial food safety process risk as an example. Specifically, the classification and regression trees sensitivity analysis method was evaluated on the basis of its ability to address typical characteristics of microbial food safety process risk models such as nonlinearities, interaction, thresholds, and categorical inputs. Moreover, this method was evaluated with respect to identification of high exposure scenarios and corresponding key inputs and critical limits. The results from the classification and regression trees analysis applied to the Slaughter Module confirmed that the process of chilling carcasses is a critical control point. The method identified a cutoff value of a 2.2-log increase in the number of organisms during chilling as a critical value above which high levels of contamination would be expected. When classification and regression trees analysis was applied to the cooking effects part of the Preparation Module, cooking temperature was found to be the most sensitive input, with precooking treatment (i.e., raw product storage conditions) ranked second in importance. This case study demonstrates the capabilities of classification and regression trees analysis as an alternative to other statistically based sensitivity analysis methods, and one that can readily address specific characteristics that are common in microbial food safety process risk models.     

Generalized linear mixed model for monitoring autocorrelated logistic regression profiles

Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems. In these instances, classic methods cannot be used for monitoring the profiles. For example, when the response variable is binary, logistic regression methods should be used rather than ordinary least square or other classic regression methods. There are some methods for monitoring logistic profiles in the literature, but the basic assumption of these methods is the independency of the consecutive observations, while this assumption is violated in some instances for example when the successive samples are taken in short intervals. This paper considers the effect of autocorrelation presence between the observations in different levels of the independent variable in a logistic regression profile on the monitoring procedure (T ² control chart) and proposes two remedies to account for the autocorrelation within logistic profiles. In one of the remedies, upper control limit of the traditional T ² control chart is modified. In the second one, we use a generalized linear mixed model (GLMM) to estimate the regression parameters and then use the T ² control chart for monitoring autocorrelated logistic regression profiles. Simulation studies show the better performance of T ² control chart when the regression parameters are estimated by the GLMM method under both step shifts and drifts.     

A critical review of bioceramics for magnetic hyperthermia

Magnetic hyperthermia (HT) using biocompatible ceramics is a ground-breaking, competent, and safe thermo-therapeutic strategy for cancer treatment. The magnetic properties of bioceramics, along with their structure and synthesis parameters, are responsible for the controlled heating of malignant tumors and are the key to clinical success. After providing a brief overview of magnetism and its significance in biomedicine, this review deals with materials selection and synthesis methods of bioceramics/glasses used for HT. Relevant research carried out on promising bioceramics for magnetic HT, with a focus on their size, shape, surface functionalization, magnetic field parameters, and in vitro/in vivo properties to optimize cancer therapy, is also discussed. Recent progress in magnetic HT combined with chemotherapy and phototherapy is especially highlighted, with the aim to provide interdisciplinary knowledge to advance further the applications of bioceramics in this field.     

Matrix based method for synthesis of main intensified and integrated distillation sequences

The objective of many studies in this area has involved access to a column-sequencing algorithm enabling designers and researchers alike to generate a wide range of sequences in a broad search space, and be as mathematically and as automated as possible for programing purposes and with good generality. In the present work an algorithm previously developed by the authors, called the matrix method, has been developed much further. The new version of the algorithm includes thermally coupled, thermodynamically equivalent, intensified, simultaneous heat and mass integrated and divided-wall column sequences which are of gross application and provide vast saving potential both on capital investment, operating costs and energy usage in industrial applications. To demonstrate the much wider searchable space now accessible, a three component separation has been thoroughly examined as a case study, always resulting in an integrated sequence being proposed as the optimum.