Incorporating higher-order point distribution model priors into MRFs using convex quadratic programming

Recently, with the advent of powerful optimisation algorithms for Markov random fields (MRFs), priors of high arity (more than two) have been put into practice more widely. The statistical relationship between object parts encoding shape in a covariant space, also known as the point distribution model (PDM), is a widely employed technique in computer vision which has been largely overlooked in the context of higher-order MRF models. This paper focuses on such higher-order statistical shape priors and illustrates that in a spatial transformation invariant space, these models can be formulated as convex quadratic programmes. As such, the associated energy of a PDM may be optimised efficiently using a variety of different dedicated algorithms. Moreover, it is shown that such an approach in the context of graph matching can be utilised to incorporate both a global rigid and a non-rigid deformation prior into the problem in a parametric form, a problem which has been rarely addressed in the literature. The paper then illustrates an application of PDM priors for different tasks using graphical models incorporating factors of different cardinalities.     

A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA,Canada, Singapore,Pakistan and Iran

Utilization of small data sets for energy consumption forecasting is a major problem because it could create large noise. This study presents a hybrid framework for improvement of energy consumption estimation with small data sets. The framework is based on fuzzy regression, conventional regression and design of experiment (DOE). The hybrid framework uses analysis of variance (ANOVA) and minimum absolute percentage error (MAPE) to select between fuzzy and conventional regressions. The significance of the proposed framework is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE. Second, the framework may identify conventional regression as the best model for future energy consumption forecasting because of its dynamic structure, whereas in the case of uncertainty and ambiguity, previous studies assume that fuzzy regression provides better solutions and estimation. Third, it is ideal candidate for short data sets. To show the applicability of the hybrid framework, the data for energy consumption in Canada, United States, Singapore, Pakistan and Iran from 1995 to 2005 are considered and tested. This is the first study which introduces a hybrid fuzzy regression-design of experiment for improvement of energy consumption estimation and forecasting with relatively small data sets.     

A receiver aware H.264/AVC encoder for decoder complexity control in mobile applications

Due to the power limitations of mobile devices, high-quality video decoding is still a main concern, because it quickly drains battery. In this paper, an H.264/AVC receiver aware encoder has been designed that (1) takes into account all of the decoder modules of a receiver, unlike existing RAEs that only consider some of these modules and are therefore sub optimal, and (2) is independent of decoder implementations and platforms. Furthermore, a decoder complexity controller has been proposed that reduces the complexity of different decoder modules, while minimum distortion is achieved. Finally, we formulate and solve a generic RAE optimization problem, and apply this solution to control the computational resource allocation at the macroblock level of a RAE. Our experiments indicate that the proposed approach can reduce the complexity of different modules by up to 10 % with no quality degradation. In addition, the average error of the proposed complexity controller is 0.8 %, making the accuracy of the system very close to 1.     

Comparison Autocorrelation Method and SVD Method for Plasma Mode Analysis in Tokamaks

Autocorrelation method (Single time series) is new method for analysis of plasma mode in Tokamaks. In this article autocorrelation method has been compared with SVD method. Energy of the modes which obtained by SVD analysis showed that the autocorrelation method is a cited method for mode detection.     

Mode Analysis with Autocorrelation Method (Single Time Series) in Tokamak

In this paper plasma mode analyzed with statistical method that designated Autocorrelation function. Auto correlation function used from one time series, so for this purpose we need one Minov coil. After autocorrelation analysis on mirnov coil data, spectral density diagram is plotted. Spectral density diagram from symmetries and trends can analyzed plasma mode. RHF fields effects with this method ate investigated in IR-T1 tokamak and results corresponded with multichannel methods such as SVD and FFT.     

Cause-selecting control charts based on Huber’s M-estimator

Cause-selecting chart (CSC) is effective in monitoring and diagnosing multistage processes. It discriminates between the overall and specific qualities by establishing the relationship between input and output measurements. In practice, the model relating the input and output variables must be estimated. To this end, historical data are used, which often contain outliers. The presence of outliers has a deleterious effect on the control charting procedure. To alleviate the encountered problem, a robust monitoring approach based on Huber’s M-estimator is proposed. Subsequently, the performance of the robust and non-robust CSCs is investigated using the average run length criterion while conducting a simulation study. The results reveal that the Huber-based CSC is superior to the traditional CSC due to its prompt detection of out-of-control conditions.     

Controlling autocorrelated data in multistage manufacturing processes with an application to textile industry

In recent years, much attention has been given to monitoring multistage processes in order to effectively improve the product reliability. To this end, the output of the process is investigated under special circumstances, and the values corresponding to reliability‐related quality characteristic are measured. However, analyzing reliability data is quite complicated because of their unique features such as being censored and obeying nonnormal distributions. A more sophisticated picture arises when the observations of the process are autocorrelated in some cases, which makes the application of previous control procedures futile. In this paper, the accelerated failure time (AFT) regression models have been modified in order to account for autocorrelated data. Then, a cumulative sum (CUSUM) control chart and an exponentially weighted moving average (EWMA) control chart based on conditional expected values have been proposed to monitor the quality variable with Weibull distribution while taking the effective covariates into consideration. Extensive simulation studies reveal that the CUSUM control chart outperforms its counterpart in detecting out‐of‐control conditions. Finally, a real case study in a textile industry has been provided to investigate the application of the CUSUM control scheme.