Selection of a Resource Estimation Method for Monywa K and L Copper Deposits in Myanmar

The cognitive theory of conceptual blending may be employed to understand the way music becomes meaningful and, at the same time, it may form a basis for musical creativity per se. This work constitutes a case study whereby conceptual blending is used as a creative tool for inventing musical cadences. Specifically, the perfect and the renaissance Phrygian cadential sequences are used as input spaces to a cadence blending system that produces various cadential blends based on musicological and blending optimality criteria. A selection of “novel” cadences is subject to empirical evaluation in order to gain a better understanding of perceptual relationships between cadences. Pairwise dissimilarity ratings between cadences are transformed into a perceptual space and a verbal attribute magnitude estimation method on six descriptive axes (preference, originality, tension, closure, expectancy, and fit) is used to associate the dimensions of this space with descriptive qualities (closure and tension emerged as the most prominent qualities). The novel cadences generated by the computational blending system are mainly perceived as single-scope blends (i.e., blends where one input space is dominant), since categorical perception seems to play a significant role (especially in relation to the upward leading note movement). Insights into perceptual aspects of conceptual bending are presented and ramifications for developing sophisticated creative systems are discussed.

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Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances

Mathematical Problems in Engineering ◽

10.1155/2018/5303694 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Vladimir Shin ◽

Rebbecca T. Y. Thien ◽

Yoonsoo Kim

Keyword(s):

Least Squares ◽

Estimation Method ◽

Estimation Accuracy ◽

Receding Horizon ◽

Covariance Estimator ◽

Noise Covariance ◽

Experimental Errors ◽

And Performance ◽

Theoretical Results ◽

Selection Of

This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices. A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed. The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator. The estimator uses a set of recent information with appropriately chosen horizon conditions. Of special interest is a constant rectangular noise gain matrices for which the key theoretical results are obtained. They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length. Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.

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On the selection of fill rate estimation method to determine the optimal base stock in periodic review systems

2015 International Conference on Logistics, Informatics and Service Sciences (LISS) ◽

10.1109/liss.2015.7369623 ◽

2015 ◽

Cited By ~ 1

Author(s):

Ester Guijarro ◽

Manuel Cardos ◽

Eugenia Babiloni

Keyword(s):

Estimation Method ◽

Base Stock ◽

Periodic Review ◽

Fill Rate ◽

Rate Estimation ◽

Selection Of

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A change-point model for detecting heterogeneity in ordered survival responses

Statistical Methods in Medical Research ◽

10.1177/0962280217707231 ◽

2017 ◽

Vol 27 (12) ◽

pp. 3595-3611 ◽

Cited By ~ 3

Author(s):

Olivier Bouaziz ◽

Grégory Nuel

Keyword(s):

Disease Onset ◽

Estimation Method ◽

Survival Times ◽

Time To Event ◽

Calendar Time ◽

Wide Range ◽

Change Point Model ◽

Hidden States ◽

Number Of Segments ◽

Selection Of

In this article, we suggest a new statistical approach considering survival heterogeneity as a breakpoint model in an ordered sequence of time-to-event variables. The survival responses need to be ordered according to a numerical covariate. Our estimation method will aim at detecting heterogeneity that could arise through the ordering covariate. We formally introduce our model as a constrained Hidden Markov Model, where the hidden states are the unknown segmentation (breakpoint locations) and the observed states are the survival responses. We derive an efficient Expectation-Maximization framework for maximizing the likelihood of this model for a wide range of baseline hazard forms (parametrics or nonparametric). The posterior distribution of the breakpoints is also derived, and the selection of the number of segments using penalized likelihood criterion is discussed. The performance of our survival breakpoint model is finally illustrated on a diabetes dataset where the observed survival times are ordered according to the calendar time of disease onset.

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