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Article
Publication date: 11 April 2008

Chihli Hung and Stefan Wermter

The purpose of this paper is to examine neural document clustering techniques, e.g. self‐organising map (SOM) or growing neural gas (GNG), usually assume that textual…

Abstract

Purpose

The purpose of this paper is to examine neural document clustering techniques, e.g. self‐organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity.

Design/methodology/approach

The authors propose a novel dynamic adaptive self‐organising hybrid (DASH) model, which adapts to time‐event news collections not only to the neural topological structure but also to its main parameters in a non‐stationary environment. Based on features of a time‐event news collection in a non‐stationary environment, they review the main current neural clustering models. The main deficiency is a need of pre‐definition of the thresholds of unit‐growing and unit‐pruning. Thus, the dynamic adaptive self‐organising hybrid (DASH) model is designed for a non‐stationary environment.

Findings

The paper compares DASH with SOM and GNG based on an artificial jumping corner data set and a real world Reuters news collection. According to the experimental results, the DASH model is more effective than SOM and GNG for time‐event document clustering.

Practical implications

A real world environment is dynamic. This paper provides an approach to present news clustering in a non‐stationary environment.

Originality/value

Text clustering in a non‐stationary environment is a novel concept. The paper demonstrates DASH, which can deal with a real world data set in a non‐stationary environment.

Details

The Electronic Library, vol. 26 no. 2
Type: Research Article
ISSN: 0264-0473

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Abstract

Details

New Directions in Macromodelling
Type: Book
ISBN: 978-1-84950-830-8

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Article
Publication date: 2 October 2020

Cheng Chen and Honghua Wang

Stimulated by previous reference, which proposed making straight line of regression to test gear gravimetric wear loss sequence distribution, this paper aims to propose…

Abstract

Purpose

Stimulated by previous reference, which proposed making straight line of regression to test gear gravimetric wear loss sequence distribution, this paper aims to propose using straight line of regression to fit gear gravimetric wear loss sequence based on stationary random process suppose. Faced to that the stationary random sequence suppose had not been proved by previous reference, and that prediction did not present high precision, this paper proposes a method of fitting non-stationary random process probability distribution function.

Design/methodology/approach

Firstly, this paper proposes using weighted sum of Gauss items to fit zero-step approximate probability density. Secondly, for the beginning, this paper uses the method with few Gauss items under low precision. With the amount of points increasing, this paper uses more Gauss items under higher precision, and some Gauss items and some former points are deleted under precision condition. Thirdly, for particle swarm optimization with constraint problem, this paper proposed improved method, and the stop condition is under precision condition.

Findings

In experiment data analysis section, gear wear loss prediction is done by the method proposed by this paper. Compared with the method based on the stationary random sequence suppose by prediction relative error, the method proposed by this paper lowers the relative error whose absolute values are more than 5%, except when the current point sequence number is 2, and retains the relative error, whose absolute values are lower than 5%, still lower than 5%.

Originality/value

Finally, the method proposed by this paper based on non-stationary random sequence suppose is proved to be the better method in gear gravimetric wear loss prediction.

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Article
Publication date: 8 May 2009

Igor Ye. Korotyeyev and Zbigniew Fedyczak

The purpose of this paper is to introduce methods for calculating steady‐state and transient processes in a symmetrical three‐phase matrix‐reactance frequency converter…

Abstract

Purpose

The purpose of this paper is to introduce methods for calculating steady‐state and transient processes in a symmetrical three‐phase matrix‐reactance frequency converter (MRFC). The MRFC in question makes it possible to obtain a load output voltage much greater than the input voltage.

Design/methodology/approach

MRFCs based on a matrix‐reactance chopper are used for both frequency and voltage transformation. The processes in a MRFC system are described by nonstationary differential equations. A two‐frequency complex function method is proposed for solving non‐stationary equations in steady‐state. The method is applied to a state‐space averaged mathematical model used in the analysis of the discussed MRFC. A two‐frequency matrix transform is proposed for solving non‐stationary equations. This method can be used to find both transient and steady‐state processes.

Findings

The two‐frequency complex function method permits the reduction from 12 non‐stationary differential equations to four stationary differential equations. The two‐frequency matrix transform allows the transformation of non‐stationary differential equations to stationary ones. By using these methods descriptions of steady‐state and transient properties of buck‐boost MRFCs are obtained.

Originality/value

A new method of solving of nonstationary differential equations is presented. The method is useful for process analyses in nonstationary power electronic converters.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 28 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 21 October 2013

C.S. Agnes Cheng, Bong-Soo Lee and Simon Yang

Prior studies provide mixed propositions on whether earnings levels or earnings changes provide the better explanatory power for variations of stock returns and whether…

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2064

Abstract

Purpose

Prior studies provide mixed propositions on whether earnings levels or earnings changes provide the better explanatory power for variations of stock returns and whether the time-series behavior of earnings affects the value relevance of both earnings variables. This paper aims to compare the value relevance of earnings levels with that of earnings changes in the return-earnings relations.

Design/methodology/approach

The unobservable components model is used to estimate permanent and transitory components of earnings.

Findings

The finding shows that the proxy ability of earnings changes for unexpected earnings is sensitive to a firm's time-series earnings permanence property and is unstable and noisy when earnings contain predominantly transitory components, but that of earnings levels is not. The results support earnings levels are a stable and better value relevant proxy in the return-earnings relations.

Research limitations/implications

The findings imply that the valuation role of earnings levels is important in the research relating to earnings components, earnings innovations, and equity valuation, especially when earnings permanence is of interest.

Practical implications

The results provide a new understanding on the role of earnings levels in many business decisions such as executive compensations, institutional investment and conservative accounting where they often involve the choice of using levels and/or changes of earnings variables in making decisions.

Originality/value

The paper contributes to the accounting literature by providing a new insight into the valuation role of earnings levels in the return-earnings relations. The stable value relevance of earnings levels also has important implications, especially for studies that use only earnings levels to assess earnings quality and earnings attributes.

Details

International Journal of Accounting and Information Management, vol. 21 no. 4
Type: Research Article
ISSN: 1834-7649

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Article
Publication date: 2 January 2009

Zbigniew Leonowicz, Tadeusz Lobos and Krzysztof Wozniak

The purpose of this paper is to compare the accuracy of tracking the amplitude and frequency changes of non‐stationary electric signals.

Abstract

Purpose

The purpose of this paper is to compare the accuracy of tracking the amplitude and frequency changes of non‐stationary electric signals.

Design/methodology/approach

Short‐time fourier transform (STFT) and S‐transform algorithms were applied to analyze non‐stationary signals originating from switching of capacitor banks in a power system.

Findings

The S‐transform showed possibilities of sharp localization of the basic component, and allowed improvement of tracking dynamism the transient components in comparison to STFT.

Practical implications

S‐transform is a better tool for the analysis of non‐stationary waveforms in power systems and its properties can be used for diagnostic and power quality applications.

Originality/value

The dynamic tracking of the changes in time and frequency of real‐like signals originating from a power system are investigated in this paper.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 28 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 13 November 2017

Jianhua Cai

This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method.

Abstract

Purpose

This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method.

Design/methodology/approach

By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal.

Findings

The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method.

Originality/value

Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.

Details

Industrial Lubrication and Tribology, vol. 69 no. 6
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 1 December 2003

Y. Zhan, V. Makis and A.K.S. Jardine

Due to the non‐stationarity of vibration signals resulting from either varying operating conditions or natural deterioration of machinery, both the frequency components…

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1502

Abstract

Due to the non‐stationarity of vibration signals resulting from either varying operating conditions or natural deterioration of machinery, both the frequency components and their magnitudes vary with time. However, little research has been done on the parameter estimation of time‐varying multivariate time series models based on adaptive filtering theory for condition‐based maintenance purposes. This paper proposes a state‐space model of non‐stationary multivariate vibration signals for the online estimation of the state of rotating machinery using a modified extended Kalman filtering algorithm and spectral analysis in the time‐frequency domain. Adaptability and spectral resolution capability of the model have been tested by using simulated vibration signal with abrupt changes and time‐varying spectral content. The implementation of this model to detect machinery deterioration under varying operating conditions for condition‐based maintenance purposes has been conducted by using real gearbox vibration monitoring signals. Experimental results demonstrate that the proposed model is able to quickly detect the actual state of the rotating machinery even under highly non‐stationary conditions with abrupt changes and yield accurate spectral information for an early warning of incipient fault in rotating machinery diagnosis. This is achieved through combination with a change detection statistic in bi‐spectral domain.

Details

Journal of Quality in Maintenance Engineering, vol. 9 no. 4
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 1 March 1996

Fuchiao Chyr

The concept of zero inventory (ZI) is a powerful tool to improve production economics. The major factor in ZI is set‐up cost reduction. Examines what will happen when…

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810

Abstract

The concept of zero inventory (ZI) is a powerful tool to improve production economics. The major factor in ZI is set‐up cost reduction. Examines what will happen when set‐up costs are stationarily and non‐stationarily reduced by mathematical presentations and simulation. The results are useful for real practice. Zangwill observes that reducing set‐up costs need not decrease inventory by a special example of non‐stationary cases. Likewise, set‐up cost reduction need not decrease total production and inventory costs. By using simulation, obtains results contrary to Zangwill. Most presentations of set‐up cost reduction consider the stationary case. It is hard to find the degree of cost variations by mathematical models. Thus uses a mathematical approach and a few simulation results that varying set‐up costs are provided. Reduces set‐up costs stationarily and non‐stationarily to examine the effects on total costs and total holding costs.

Details

International Journal of Operations & Production Management, vol. 16 no. 3
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 1 April 2008

A. Bezuidenhout, C. Mlambo and W.D. Hamman

In financial analysis, forecasting often involves regressing one time series variable on another. However, to ensure that the models are correctly specified, one needs to…

Abstract

In financial analysis, forecasting often involves regressing one time series variable on another. However, to ensure that the models are correctly specified, one needs to first test for stationarity, co‐integration and causality. In testing for causality, the variables should be stationary. If non‐stationary, one can estimate the model in difference form, unless the variables are co‐integrated. This article determines whether cash flow and earnings variables are stationary, and which variable causes the other, using econometric analysis. In most cases, cash flow variables are found to cause earnings variables. This is so when the models are estimated in levels. However, when estimated in first differences, the causal relationship tends to be reversed such that earnings cause cash flows. Further study is recommended, whereby panel data could be used to improve the power of the tests.

Details

Meditari Accountancy Research, vol. 16 no. 1
Type: Research Article
ISSN: 1022-2529

Keywords

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