Paving the way for CRM success: The mediating role of knowledge management and organizational commitment

Customer relationship management (CRM) has become one of the most influential technologies in the world, and companies are increasingly implementing it to create value. However, despite significant investment in CRM technology infrastructure, empirical research offers inconsistent support for its positive impact on performance. This study develops and tests a research model analyzing the process through which CRM technology infrastructure translates into organizational performance, drawing on the resource-based view (RBV) and the knowledge-based view (KBV) of the firm. Based on an international sample of 125 hotels, the results suggest that organizational commitment and knowledge management fully mediate this process.     

Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine

It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.     

基于混沌系统和极化恒等式的数字图像加密算法

混沌系统具有初值敏感性,参数敏感性和类随机性等特性。将图像分成大小为8 8的图像子块,利用经典的Logistic方程产生的混沌序列并对其排序生成匹配模板来改变图像子块的位置,然后利用极化恒等式内积分解加密算法改变图像像素的值。因此,基于混沌系统和极化恒等式的数字图像加密算法使图像的像素的位置和值都发生了变化,实现了图像的混淆和扩散。仿真实验表明该方法进行图像加密具有可行性和安全性。     

模糊综合评判理论在研究性学习系统中的研究及应用

介绍了工程数学中的模糊综合评判理论以及相应树型向量推理算法,并将模糊综合推理机制运用到研究性学习系统中进行研究型应用。该系统中运用模糊综合评判对学习效果进行合理评判,得出综合成绩,以其作为改进研究性学习模式的参考评证,从而使研究性学习过程良性发展。     

New receipt-free voting scheme using double-trapdoor commitment

It is considered to be the most suitable solution for large scale elections to design an electronic voting scheme using blind signatures and anonymous channels. Based on this framework, Okamoto first proposed a receipt-free voting scheme [30] for large scale elections. However, in the following paper, Okamoto [31] proved that the scheme [30] was not receipt-free and presented two improved schemes. One scheme requires the help of the parameter registration committee and the other needs a stronger physical assumption of the voting booth. In this paper, we utilize the double-trapdoor commitment to propose a new receipt-free voting scheme based on blind signatures for large scale elections. Neither the parameter registration committee nor the voting booth is required in our scheme. We also present a more efficient zero-knowledge proof for secret permutation. Therefore, our scheme is much more efficient than Okamoto’s schemes [30] and [31] with the weaker physical assumptions. Moreover, we prove that our scheme can achieve the desired security properties.     

Conservative and aggressive rough SVR modeling

Support vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use ?-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders.     

Using immune genetic algorithm based hybrid techniques for short-term unit commitment problem

This paper presents a hybrid chaos search (CS), immune algorithm (IA)/genetic algorithm (GA), and fuzzy system (FS) method (CIGAFS) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shut-down schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve, and individual units. First, we combined the IA and GA, then we added the CS and the FS approach. This hybrid system was then used to solve the UC problems. Numerical simulations were carried out using three cases: 10, 20, and 30 thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as dynamic programming (DP), Lagrangian relaxation (LR), standard genetic algorithm (SGA), traditional simulated annealing (TSA), and traditional Tabu search (TTS). A comparison with an immune genetic algorithm (IGA) combined with the CS and FS was carried out. The results show that the CS and FS all make substantial contributions to the IGA. The result demonstrated the accuracy of the proposed CIGAFS approach.     

基于矢量合成的平衡机最小可达剩余不平衡度测试方法的研究

通过对国标规定的平衡机最小可达剩余不平衡度评定方法的分析和研究，指出了该方法在原理和方法上所存在的问题，并且提出了一种全新的评定方法。这种基于矢量分析的新的评定方法能够快速、准确地计算出转子的最小可达剩余不平衡度，提高平衡机测试的准确性和现场测试效率，为更加客观地评价被测平衡机的综合性能提供了依据。     

Pseudo-Stokes Vector Correlation From Complex Signal Representation of a Speckle Pattern and Its Applications to Micro-Displacement Measurement

Abstract:  As an improvement of the intensity correlation used widely in conventional electronic speckle photography, we propose a new technique for displacement measurement based on correlating Stokes-like parameters derivatives for transformed speckle patterns. The method is based on a Riesz transform of the intensity speckle pattern, which converts the original real-valued signal into a complex signal. In closest analogy to the polarisation of a vector wave, the Stokes-like vector constructed from the spatial derivative of the generated complex signal has been applied for correlation. Experimental results are presented that demonstrate the validity and advantage of the proposed pseudo-Stokes vector correlation technique over conventional intensity correlation technique.     

混合式步进电动机的矢量控制分析

混合式步进电动机与传统的异步、同步电动机相比,由于定子磁势、转子磁钢的非线性分布以及磁饱和等因素的存在而没有简单的线性化数学模型.根据已有数学模型,利用电动机矢量控制原理,在d-q坐标系下推导出混合式步进电动机电磁转矩与电流关系、静态控制与动态控制的数学模型,以及电动机定子电流细分算法,最后通过搭建实验样机验证其控制方法的有效性.