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Zhang Ying,Niu Dongxiao 인하대학교 정석물류통상연구원 2009 인하대학교 정석물류통상연구원 학술대회 Vol.2009 No.10
This paper attempts to provide direct evidence on the effectiveness of internal control and its determinants. Since there are four objectives for internal control according to COSO, we examine the effectiveness of internal control and its determinants from four aspects. Using a sample of 126 Chinese firms collected by questionnaire. we find that; (1) Effective internal control over compliance is more likely for firms that are larger, at maturity state, financially stronger, with more decentralized ownership, with more centralized management mode, with stronger enterprise culture and better management philosophy, with internal auditing of higher quality. The nature of largest shareholder is not significantly correlated to the effectiveness of internal control over compliance. (2) Effective internal control over reporting is more likely for firms that are larger, older, financially stronger, with core centralized management mode, with weaker control power of largest shareholder, with better management philosophy. (3) Effective internal control over operation is more likely for firms that are larger, older, financially stronger, with more centralized management mode, with weaker control power of largest shareholder, with stronger enterprise culture and better management philosophy. (4) Effective internal control over strategy is more likely for firms that are larger and at maturity stage.
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JunSong Qin,Yan Lu,Dongxiao Niu,Guodong Zhu 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.8
The medium and long term load forecasting is the basis of power planning, investment, production, scheduling and trade, which plays an important role in electric power safety and economic operation. In China, it has the increasing uncertainty and the uncertainty of random variation to forecast the medium and long term load. Thus we can regard it as a typical grey system. However, the traditional grey prediction method cannot be adapt to the needs of the load forecasting gradually. It need to be rich and perfect with the continuous improvement of power system complexity and power marketization degree. This paper studied the modelling mechanism of grey prediction model. Then we analyzed the problems existing in the model, including the boundary value problem, the background value structure problem and the least squares parameter identification problem. This paper put forward an optimization method to directly identify the boundary value x(0)(1), the developing coefficient a and grey coefficient b using ant colony algorithm according to the time response expression of GM(1,1) model, so that it established an optimized GM(1,1) prediction model based on ant colony algorithm. This model can fix the impact of boundary value, and also avoid the errors brought by the background value construction and the least squares parameter estimation. It can verify the effectiveness of the proposed optimization model through the load data simulation. And it can improve the prediction accuracy effectively.
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