Browsing by Author "Lin, Changhai"

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    On Spectral Analysis and New Research Directions in Grey System Theory
    (Research Information Ltd, 2020-03-15) Liu, Sifeng; Lin, Changhai; Tao, Liangyan; Javed, Saad Ahmed; Fang, Zhigeng; Yang, Yingjie
    Spectral analysis as a powerful mean to identify the characteristics of data series is introduced in this paper. The problems requiring further explorations in grey system theory are also identified. This includes discrimination of various factors of a data sequence in frequency domain, spectral analysis of various sequence operators, the synthesis axiom of degree of greyness for “multiplication” and “division” etc. Further, how to select and test a grey prediction model? How to select and test an inverse grey incidence analysis model? The test rules and criteria of grey clustering evaluation models, etc.
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    Several problems need to be studied in grey system theory
    (IEEE, 2017-10-26) Liu, Sifeng; Lin, Changhai; Yang, Yingjie
    The problems which remain for further studying in grey system theory are identified in this paper. Including the synthesis axiom of degree of greyness for “multiplication” and “division”, how to construct and select a suitable buffer operator? how to select and test a grey prediction model? how to select and test a negative grey incidence analysis models? the test rules and criteria of grey clustering evaluation models, and on consensus and unified definition of grey combined models, etc..
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    Spectrum analysis of moving average operator and construction of time-frequency hybrid sequence operator
    (Emerald, 2021-02-22) Lin, Changhai; Liu, Sifeng; Fang, Zhigeng; Yang, Yingjie
    Purpose – The purpose of this paper is to analyze the spectral characteristics of moving average operator and to propose a novel time-frequency hybrid sequence operator. Design/methodology/approach – Firstly, the complex data is converted into frequency domain data by Fourier transform. An appropriate frequency domain operator is constructed to eliminate the impact of disturbance. Then, the inverse Fourier transform transforms the frequency domain data in which the disturbance is removed, into time domain data. Finally, an appropriate moving average operator of N items is selected based on spectral characteristics to eliminate the influence of periodic factors and noise. Findings – Through the spectrum analysis of the real-time data sensed and recorded by microwave sensors, the spectral characteristics and the ranges of information, noise and shock disturbance factors in the data can be clarified. Practical implications – The real-time data analysis results for a drug component monitoring show that the hybrid sequence operator has a good effect on suppressing disturbances, periodic factors and noise implied in the data. Originality/value – Firstly, the spectral analysis of moving average operator and the novel time-frequency hybrid sequence operator were presented in this paper. For complex data, the ideal effect is difficult to achieve by applying the frequency domain operator or time domain operator alone. The more satisfactory results can be obtained by time-frequency hybrid sequence operator.
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    Study on mechanism and filter efficacy of AGO/IAGO in the frequency domain
    (Emerald, 2020-05-20) Lin, Changhai; Song, Zhengyu; Liu, Sifeng; Yang, Yingjie; Forrest, Jeffrey
    Purpose – The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain. Design/methodology/approach – The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/ IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain. Findings – Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system’s behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity. Practical implications – The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are shortperiod factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1- AGO acting, the information mining and extrapolation prediction will have a good effect. Originality/value – The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.