A transformation technique to estimate the process capability index for non-normal processes

Estimating the process capability index (PCI) for non-normal processes has been discussed by many researches. There are two basic approaches to estimating the PCI for non-normal processes. The first commonly used approach is to transform the non-normal data into normal data using transformation techniques and then use a conventional normal method to estimate the PCI for transformed data. This is a straightforward approach and is easy to deploy. The alternate approach is to use non-normal percentiles to calculate the PCI. The latter approach is not easy to implement and a deviation in estimating the distribution of the process may affect the efficacy of the estimated PCI. The aim of this paper is to estimate the PCI for non-normal processes using a transformation technique called root transformation. The efficacy of the proposed technique is assessed by conducting a simulation study using gamma, Weibull, and beta distributions. The root transformation technique is used to estimate the PCI for each set of simulated data. These results are then compared with the PCI obtained using exact percentiles and the Box-Cox method. Finally, a case study based on real-world data is presented.     

Novel RAM cell designs based on inherent capabilities of quantum-dot cellular automata

Quantum Cellular Automata (QCA) is a novel and attractive method which enables designing and implementing high-performance and low-power consumption digital circuits at nano-scale. Since memory is one of the most applicable basic units in digital circuits, having a fast and optimized QCA-based memory cell is remarkable. Although there are some QCA structures for a memory cell in the literature, however, QCA characteristics may be used in designing a more optimized memory cell than blindly modeling CMOS logics in QCA. In this paper, two improved structures have been proposed for a loop-based Random Access Memory (RAM) cell. In the proposed methods, the inherent capabilities of QCA, such as the programmability of majority gate and the clocking mechanism have been considered. The first proposed method enjoys smaller number of cells and the wasted area has been reduced compared to traditional loop-based RAM cell. For the second proposed method, the memory access time has been duplicated in presence of smaller number of cells. Irregular placement of QCA cells in a QCA layout makes its realization troublesome. So, we have proposed alternative versions of the proposed methods that exploit regularity of clock zones in design and have compared them to each other. QCA designer has been employed for simulation of the proposed designs and proving their validity.     

Nonlinear vibration of embedded smart composite microtube conveying fluid based on modified couple stress theory

Electro‐thermo‐mechanical nonlinear vibration and instability of a fluid conveying smart composite microtube made of polyvinylidene fluoride (PVDF) are investigated in this article based on the modified couple stress theory and Timoshenko beam model. The composite matrix is reinforced by double‐walled boron nitride nanotubes (BNNTs). Mechanical, electrical, and thermal characteristics of equivalent composite are determined based on micromechanical model. The surrounded elastic medium is taken into account using Winkler and Pasternak models. Considering the small‐size effects and slip boundary conditions of microflow through Knudsen number and applying Hamilton's principle, the coupled differential equations, containing displacement and electric potential terms, are obtained. The differential quadrature method is applied to discretize the coupled governing equations and boundary conditions, which are then solved to obtain the nonlinear frequency and critical fluid velocity of the fluid‐conveying microtube. The detailed parametric study is conducted, focusing on the combined effects of the Knudsen number, nonlocal parameter, BNNT volume percent, temperature change, elastic medium, and aspect ratio on the nonlinear frequency and critical fluid velocity. Results indicate that the natural frequency and the critical fluid velocity of the smart composite microtube increase with increasing the small‐scale parameter. POLYM. COMPOS., 36:1314–1324, 2015. © 2014 Society of Plastics Engineers     

Low temperature operation of UHF power static induction transistors

This paper describes the results of recent experiments with high power surface gate static induction transistors (SITs) operated at and near liquid nitrogen temperatures. The temperature dependence of important large signal and small signal device parameters over a wide range of operating temperatures is described. UHF power performance of liquid nitrogen cooled SIT power amplifiers is described as well. At 425 MHz, a single transistor power amplifier has been fabricated which exhibits output power levels > 350 W with 8 dB gain and nearly 80% drain efficiency. Smaller test devices have been used in an 850 MHz amplifier, which exhibits 30 W c.w. with 7.8 dB gain and 64% drain efficiency.     

A transformation-based multivariate chart to monitor process dispersion

Multivariate monitoring techniques such as multivariate control charts are used to control the processes that contain more than one correlated characteristic. Although the majority of previous researches are focused on controlling only the mean vector of multivariate processes, little work has been performed to monitor the covariance matrix. In this research, a new method is presented to detect possible shifts in the covariance matrix of multivariate processes. The basis of the proposed method is to eliminate the correlation structure between the quality characteristics by transformation technique and then use an S chart for each variable. The performance of the proposed method is then compared to the ones from other existing methods and a real case is presented.     

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.     

Camera Motion-Based Analysis of User Generated Video

In this paper we propose a system for the analysis of user generated video (UGV). UGV often has a rich camera motion structure that is generated at the time the video is recorded by the person taking the video, i.e., the ?camera person.? We exploit this structure by defining a new concept known as camera view for temporal segmentation of UGV. The segmentation provides a video summary with unique properties that is useful in applications such as video annotation. Camera motion is also a powerful feature for identification of keyframes and regions of interest (ROIs) since it is an indicator of the camera person's interests in the scene and can also attract the viewers' attention. We propose a new location-based saliency map which is generated based on camera motion parameters. This map is combined with other saliency maps generated using features such as color contrast, object motion and face detection to determine the ROIs. In order to evaluate our methods we conducted several user studies. A subjective evaluation indicated that our system produces results that is consistent with viewers' preferences. We also examined the effect of camera motion on human visual attention through an eye tracking experiment. The results showed a high dependency between the distribution of fixation points of the viewers and the direction of camera movement which is consistent with our location-based saliency map.     

Transient response of FG higher-order nanobeams integrated with magnetostrictive layers using modified couple stress theory

The present paper studies the transient response of a functionally graded nanobeam integrated with magnetostrictive layers. The material properties of sandwich nanobeam are temperature dependent and assumed to vary in the thickness direction. In order to consider small-scale effects, the modified couple stress theory is also taken into consideration. Using a unified beam theory that contains various beam models and energy method as well as Hamilton's principle, the governing motion equations and related boundary conditions are obtained. The obtained results in this paper can be used as sensors and actuators in sensitive applications.     

Determining quality risk for correlated bivariate normal characteristics

Most multivariate process capability indices proposed by other researchers provide quality measure in losses due to variability and being off-target. Some suggested loss function models that estimate losses due to rejection and quality costs but failed to consider the correlation between the multivariate quality characteristics. In this paper, we propose a novel new approach for estimating the total expected quality cost that provides quality cost measure not only in losses due to variability and being off-target but also losses due to rejection. The proposed model also estimates the quality costs for correlated quality characteristics with nominal specifications and provides the estimated costs due to individual characteristics; it helps to identify those process quality characteristics which have high variation when compared with their specification spread. The model will also improve the estimate of the rejection cost suggested by earlier researchers as it identifies the reject region as the region outside of the modified tolerance region. We present four different correlated bivariate normal process scenarios where the proposed method can be applicable and demonstrate that our approach provides a robust tool in estimating the risk of the product being rejected and performs as well as existing methods in estimating the total quality cost.