A stochastic reward net-based assessment of reliability,availability and operational cost for a software-defined network infrastructure

The Journal of Supercomputing - The networking infrastructure of a software-defined network (SDN) requires further study to achieve continuity and high availability of data transactions for cloud...     

Reliability-based design optimization with confidence level under input model uncertainty due to limited test data

For obtaining a correct reliability-based optimum design, the input statistical model, which includes marginal and joint distributions of input random variables, needs to be accurately estimated. However, in most engineering applications, only limited data on input variables are available due to expensive testing costs. The input statistical model estimated from the insufficient data will be inaccurate, which leads to an unreliable optimum design. In this paper, reliability-based design optimization (RBDO) with the confidence level for input normal random variables is proposed to offset the inaccurate estimation of the input statistical model by using adjusted standard deviation and correlation coefficient that include the effect of inaccurate estimation of mean, standard deviation, and correlation coefficient.     

Companion robots for older adults: Rodgers’ evolutionary concept analysis approach

Intelligent Service Robotics - This study aims to analyze the concept of companion robots for older adults from the perspective of nursing. This study employed a concept analysis. The literature...     

Reliability-based design optimization of problems with correlated input variables using a Gaussian Copula

The reliability-based design optimization (RBDO) using performance measure approach for problems with correlated input variables requires a transformation from the correlated input random variables into independent standard normal variables. For the transformation with correlated input variables, the two most representative transformations, the Rosenblatt and Nataf transformations, are investigated. The Rosenblatt transformation requires a joint cumulative distribution function (CDF). Thus, the Rosenblatt transformation can be used only if the joint CDF is given or input variables are independent. In the Nataf transformation, the joint CDF is approximated using the Gaussian copula, marginal CDFs, and covariance of the input correlated variables. Using the generated CDF, the correlated input variables are transformed into correlated normal variables and then the correlated normal variables are transformed into independent standard normal variables through a linear transformation. Thus, the Nataf transformation can accurately estimates joint normal and some lognormal CDFs of the input variable that cover broad engineering applications. This paper develops a PMA-based RBDO method for problems with correlated random input variables using the Gaussian copula. Several numerical examples show that the correlated random input variables significantly affect RBDO results.     

Discrimination of visual and haptic rendering delays in networked environments

Many networked human-machine interface systems have a distributed structure for certain purposes such as more computational power, tele-presence, collaboration, and portability. However, network delays are inevitable in the distributed structure, and often make sensory information delivered behind time to the user. In the literature, the effect of network delays on the quality of information presentation has been considered with respect to task performances in most cases. In this paper, we pay attention to a more stringent criterion, namely whether perceptual artifacts caused by network delays are perceptible by the user. We examined minimum perceptible visual and/or haptic rendering delays by measuring their discrimination thresholds between normal and delayed virtual environments with and without a task, and report the results in this paper. We also provide a simple guideline for determining whether active delay compensation algorithms are required in a networked human-machine interface system by comparing representative network delays to the measured discrimination thresholds. Recommended by Guest Editor Phill Kyu Rhee. This work was supported in parts by a grant R01-2006-000-10808-0 and a NRL grant R0A-2008-000-20087-0 both from KOSEF and by a ITRC support program C1090-0804-0002 from IITA, all funded by the Korea government. In Lee is a Ph.D student in Computer Science and Engineering at POSTECH, of Korea. He received the B.S. degree in Information and Communication Engineering from Sungkyunkwan University in 2006. His research interests include haptics, virtual reality, and human-computer interaction. Seungmoon Choi is an Assistant Professor in Computer Science and engineering at POSTECH, Korea. He received the B.S. and M.S. degrees in Control and Instrumentation Engineering from Seoul National University in 1995 and 1997, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Purdue University in 2003. His research areas include haptics, virtual reality, data perceptualization, and applied perception.     

A modified OSEM algorithm for PET reconstruction using wavelet processing

Ordered subset expectation–maximization (OSEM) method in positron emission tomography (PET) has been very popular recently. It is an iterative algorithm and provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. Due to the lack of smoothness in images in OSEM iterations, however, some type of inter-smoothing is required. For this purpose, the smoothing based on the convolution with the Gaussian kernel has been used in clinical PET practices. In this paper, we incorporated a robust wavelet de-noising method into OSEM iterations as an inter-smoothing tool. The proposed wavelet method is based on a hybrid use of the standard wavelet shrinkage and the robust wavelet shrinkage to have edge preserving and robust de-noising simultaneously. The performances of the proposed method were compared with those of the smoothing methods based on the convolution with Gaussian kernel using software phantoms, physical phantoms, and human PET studies. The results demonstrated that the proposed wavelet method provided better spatial resolution characteristic than the smoothing methods based on the Gaussian convolution, while having comparable performance in noise removal.     

Object tracking based on an online learning network with total error rate minimization

This paper presents a visual object tracking system which is tolerant to external imaging factors such as illumination, scale, rotation, occlusion and background changes. Specifically, an integration of an online version of total-error-rate minimization based projection network with an observation model of particle filter is proposed to effectively distinguish between the target object and the background. A re-weighting technique is proposed to stabilize the sampling of particle filter for stochastic propagation. For self-adaptation, an automatic updating scheme and extraction of training samples are proposed to adjust to system changes online. Our qualitative and quantitative experiments on 16 public video sequences show convincing performances in terms of tracking accuracy and computational efficiency over competing state-of-the-art algorithms.     

Development and optimal design of a HDD spindle motor with pulling magnet to reduce electrical loss

A conventional hard disk drive (HDD) spindle motor has a pulling plate to generate the axial magnetic force. However, the pulling plate consumes significant amount of iron loss due to the alternating magnetic field on the pulling plate. We propose the new design of a HDD spindle motor with pulling magnet to generate the pre-load as well as to eliminate the iron loss of the pulling plate. We also develop an optimal design methodology to minimize iron and copper losses from the spindle motor of a computer HDD while maintaining the same level of torque ripple and pulling force. The new design is optimized by the developed optimal design methodology. A metamodel is constructed from the three-dimensional finite element analysis of the magnetic field and the meta-modeling techniques, and the accuracies of the metamodels are discussed. The proposed optimal design problem is solved by the progressive quadratic approximation method. The proposed design reduces the electrical loss of the HDD spindle motor by 30.42?% while maintaining the same level of torque ripple and pulling force.     

A new current‐mirror pixel circuit employing poly‐Si TFTs for active‐matrix organic light‐emitting‐diode displays

Abstract— A novel active‐matrix organic light‐emitting‐diode (AMOLED) display employing a new current‐mirror pixel circuit, which requires four‐poly‐Si TFTs and one‐capacitor and no additional signal lines, has been proposed and sucessfully fabricated. The experimental results show that a new current mirror can considerably compensate luminance non‐uniformity and scale down a data current more than a conventional current‐mirror circuit in order to reduce the pixel charging time and increase the minimum data current. Compared with a conventional two‐TFT pixel, the luminance non‐uniformity induced by the grain boundaries of poly‐Si TFTs can be decreased considerably from 41% to 9.1%.