Volume 52, Nº 6 (2018)

The logical purpose for a statistical tolerance limit is to predict future outcomes for some (say, production) process. The coverage value γ is the percentage of the future process outcomes to be captured by the prediction, and the confidence level (1 – α) is the proportion of the time we hope to capture that percentage γ. Tolerance limits of the type mentioned above are considered in this paper, which presents a new technique for constructing exact statistical (lower and upper) tolerance limits on outcomes (for example, on order statistics) in future samples. Attention is restricted to the two-parameter Weibull distribution under parametric uncertainty. The technique used here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. It does not require the construction of any tables and is applicable whether the experimental data are complete or Type II censored. The exact tolerance limits on order statistics associated with sampling from underlying distributions can be found easily and quickly making tables, simulation, Monte Carlo estimated percentiles, special computer programs, and approximation unnecessary. The proposed technique is based on a probability transformation and pivotal quantity averaging. It is conceptually simple and easy to use. The discussion is restricted to one-sided tolerance limits. Finally, we give numerical examples, where the proposed analytical methodology is illustrated in terms of the two-parameter Weibull distribution. Applications to other log-location-scale distributions could follow directly.

The problem of planning supercomputer computations is considered using the example of computing the characteristics of a quantum rotation sensor model. A methodology for applying the task scheduling algorithm is proposed. For this purpose, time estimates were obtained for computations at cluster nodes. The possibility of dividing settlements by cluster nodes without additional synchronization costs is shown. A start-up algorithm for improving the efficiency of multivariate computations is presented, and the efficiency of the planning algorithm in its use is estimated.

Water body extraction from remote sensing image data has been becoming a really hot topic. Recently, researchers put forward numerous methods for water body extraction, while most of them rely on elaborative feature selection and enough number of training samples. Convolution Neural Network (CNN), one of the implementation models of deep learning, has strong capability for two-dimension images’ classification. A new water body extraction model based on CNNs is established for deep multi-feature learning. Before experiment, image enhancement will be done by Dark Channel Prior. Then we concatenate three kinds of features: spectral information, spatial information that is extracted by Extended Multi-attribute Profile (EMAP) and various water indexes firstly. Next, feature matrixes are acted as the input of CNN-based model for training and classifying. The experimental results showed that the proposed model has better classification performance than Support Vector Machine (SVM) and artificial neural network (ANN). On very limited training set, our model could learn unique and representative features for better water body extraction.

The focus of this paper is on the control design and simulation of twin rotor aero-dynamical system (TRAS). The challenges for control design in these systems lie in their nonlinearity and the inherent cross coupling between motion in the vertical and horizontal directions. Working from a highly nonlinear, dynamically coupled, mathematical model, controller design is presented for the angular position/velocity in vertical and horizontal planes of motion. Three linear control methods were developed and optimized to control the TRAS, namely full state feedback (FSF), linear quadratic regulator (LQR), and PID control. Simulation and experimental results show trade-off between these control methods. Although better performance was achieved with LQR, more effort was needed. The PID control has always proved to be a simple approach that works well with linear models. However, in this study case the performance was strongly affected by the coupling effect as demonstrated by simulation and experimental results

This paper proposes a computational auditory scene analysis approach to separation of room reverberant speech, which performs multi-pitch tracking and supervised classification. The algorithm trains speech and non-speech model separately, which learns to map from harmonic features to grouping cue encoding the posterior probability of time-frequency unit being dominated by the target and periodic interference. Then, a likelihood ratio test selects the correct model for labeling time-frequency unit. Experimental results show that the proposed approach produces strong pitch tracking results and leads to significant improvements of predicted speech intelligibility and quality. Compared with the classical Jin-Wang algorithm, the average SNR of this algorithm is improved by 1.22 dB.