Browsing by Author "Li, Chong"

Now showing 1 - 7 of 7
  • No Thumbnail Available
    ItemMetadata only
    An AlGaAs/GaAs-Based Planar Gunn Diode Oscillator with a Fundamental Frequency Operation of 120 GHZ
    (Wiley, 2014-07) Maricar, M.; Glover, James; Khalid, A.; Li, Chong; Evans, Gwynne; Cumming, D.; Oxley, C. H.
  • No Thumbnail Available
    ItemOpen Access
    Comparative analysis of properties of weakening buffer operators in time series prediction models
    (Elsevier, 2018-08-23) Li, Chong; Yang, Yingjie; Liu, Sifeng
    Reducing the negative influence of stochastic disturbances in sample data has always been a difficult problem in time series analysis. In this paper, three new fractional weakening buffer operators are proposed, and then some desirable properties of these proposed se- quence operators are investigated. Their potential effect in smoothing unexpected distur- bances while maintaining the normal trend in sample series is analyzed and compared with other widely used sequence operators in time series modeling. Results of theoretical and empirical research show that the proposed novel fractional weakening buffer oper- ators are effective in improving the development pattern analysis of time series in dis- turbance scenarios, while also avoid too subjectively weighting experimental data from collected samples. The robust of the proposed operator-based prediction algorithm against noise effect is tested in five different types of noise scenarios. Result of empirical study demonstrates that the proposed method improves the series prediction performance and it also improves the robustness of corresponding forecasting algorithms. These unique prop- erties of the proposed weakening buffer operators make them more attractive in time se- ries analysis.
  • No Thumbnail Available
    ItemMetadata only
    Extraction of second harmonic from the In0.57Ga0.47As planar Gunn diode using radial stub resonators
    (Elsevier, 2014) Maricar, M.; Khalid, A.; Glover, James; Evans, Gwynne; Vasileious, P.; Li, Chong; Cumming, D.; Oxley, C. H.
  • Thumbnail Image
    ItemOpen Access
    A greyness reduction framework for prediction of grey heterogeneous data
    (Springer, 2020-05-23) Li, Chong; Yang, Yingjie; Liu, Sifeng
    Existing operational rules of interval grey numbers do not make full use of possible background information when determining the interval boundaries, and this may result in inconsistent results if applying different logical operations. This paper finds that multiplication and division rules of interval grey numbers do not meet the calculation rule of inverse operators. Direct solution and inverse solution of the same interval grey number object may differ not only in numerical ranges but also in greyness degrees. To improve the accuracy of grey number calculation, new operational rules for multiplication and division of interval grey numbers are proposed. Then the traditional prediction modeling method of grey heterogeneous data is refined and expanded by integrating a greyness reduction preprocessing, which is based on the proposed calculation rules. Application of the expanded heterogeneous interval grey number prediction model to a stock replenishment scheduling problem in emergency rescue scenarios is included to illustrate the new operational rules of grey numbers and their application in prediction algorithm, and the proposed approach is compared with other existing methods to demonstrate its effectiveness.
  • No Thumbnail Available
    ItemOpen Access
    Micro-macro dynamics of the online opinion evolution: an asynchronous network model approach
    (John Wiley & Sons Ltd, 2020-09-02) Li, Chong; Yang, Yingjie; Liu, Sifeng
    This paper investigates the complex relationship between endogenous and exogenous, deterministic and stochastic stimulating factors in public opinion dynamics. An asynchronous multi-agent network model is proposed to explore the interaction mechanism between individual opinions and the public opinion in online multi-agent network community, including both the micro and the macro patterns of opinion evolution. In addition, based on random network models, a novel algorithm is provided for opinion evolution prediction. The model property analysis and numerical experiments show that the proposed asynchronous multi-agent network model can assimilate and explain some interesting phenomena that are observed in the real world. Further case studies with numerical simulation and real-world applications confirm the feasibility and flexibility of the proposed model in public opinion analysis. The results challenge the common perception that mass media or opinion facilitators play the fundamental role in controlling the development trends of public opinion. This study shows that the formation and evolution of public opinion in the presence of opinion leaders depend also on an individual’s emotional inertia and conformity pressures from peers in the same topic group.
  • No Thumbnail Available
    ItemOpen Access
    A new method to mitigate data fluctuations for time series prediction
    (Elsevier, 2018-08-29) Li, Chong; Yang, Yingjie; Liu, Sifeng
    Although the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctuations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new operator can better capture the real developing trend in raw data and improve forecast accuracy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy.
  • Thumbnail Image
    ItemOpen Access
    Stability of Time Series Models based on Fractional Order Weakening Buffer Operators
    (MDPI, 2023-07-17) Li, Chong; Yang, Yingjie; Zhu, Xinping
    Different weakening butter operators in time series model analysis usually result in different model sensitivity, which sometimes affects the effectiveness of relevant operator-based methods. In this paper, the stability of two classic weakening buffer operator-based series models is studied; then a new data preprocessing method based on a novel fractional bidirectional weakening buffer operator is provided, whose effect in improving model stability is tested and utilized in prediction problems. Practical examples are employed to demonstrate the efficiency of the proposed method in improving model stability in noise scenarios. The comparison indicates that the proposed method overcomes the disadvantage of many weakening buffer operators in too subjectively biased weighting the new or the old information in forecasting. These expand the application of the proposed method in time series analysis