scholarly journals Cross-level Correspondence in Q Theory

2020 ◽  
Vol 8 ◽  
Author(s):  
Wm G. Bennett ◽  
Natalie DelBusso
Keyword(s):  
Systems Modeling ◽  
Q Theory ◽  
Embedded Structures ◽  
Feature Values ◽  
Segmental Representations ◽  
Contour Tones

This paper examines cross-level interactions in basic systems modeling segmental harmony in Q theory (Shih & Inkelas 2019, S&I; see also Inkelas & Shih 2015, 2017). Q theory is a theory of segmental representations that decomposes segments (Qs) into linear strings of subsegments (qs). The component qs can differ in feature values, resulting in Qs with contour tones. S&I present Q theory as an alternative to autosegmental representations and use Agreement-by-correspondence (ABC; Rose & Walker 2004, Hansson 2010, Bennett 2015) analyses to derive various kinds of harmony and dissimilation patterns, particularly those involving tones. This paper shows that while the Q theory typologies share the characteristic structures of ABC(D) systems (Bennett & DelBusso 2018, DelBusso & Bennett to appear) at both qand Qlevels, these (sets of) properties interact in more complex embedded structures.  

J. Tobin's Monetary Dynamic Model and the Analysis of Russian Economy

2009 ◽  
pp. 70-93
Author(s):  
V. Manevich
Keyword(s):  
Dynamic Model ◽  
Economic Thought ◽  
Financial Policies ◽  
Russian Economy ◽  
Q Theory ◽  
Keynesian Economic ◽  
The Relationship ◽  
Monetary Assets

The paper considers the monetary dynamic model developed by J. Tobin, the leader of Keynesian economic thought in 1970-1990. Particularly, the author examines q-theory of investment proposed by Tobin which allows to expose the relationship between supply of monetary assets and investment in real capital. Application of various tools of monetary and financial policies is also considered in its different forms. The author aspires to use Tobin's model for the analysis of processes existing in the Russian economy and to test theoretical propositions and relationships elaborated by Tobin on Russian statistics.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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