The Skill-Task Matching Model: Mechanism, Model Structure, and Algorithm

We distinguished between the expected and actual profit of a firm. We proposed that, beyond maximizing profit, a firm's goal also encompasses minimizing the gap between expected and actual profit. Firms strive to enhance their capability to transform projects into reality through a process of trial and error, evident as a cyclical iterative optimization process. To characterize this iterative mechanism, we developed the Skill-Task Matching Model, extending the task approach in both multidimensional and iterative manners. We vectorized jobs and employees into task and skill vector spaces, respectively, while treating production techniques as a skill-task matching matrix and business strategy as a task value vector. In our model, the process of stabilizing production techniques and optimizing business strategies corresponds to the recalibration of parameters within the skill-task matching matrix and the task value vector. We constructed a feed-forward neural network algorithm to run this model and demonstrated how it can augment operational efficiency.Comment: 23 pages, 6 figure

A Study on the Location Determinants of the US FDI in China

he US foreign direct investment in China plays a leading role in the process of introducing FDI to China. This paper carries on an empirical research dynamically on the location factors of US foreign direct investment in China, adopting Johansen cointegration test, the VEC model, Granger causality test and variance decomposition technology, based on analytical data in the period from 1983 to 2006. The studying result demonstrates that there is a stable relationship among the US foreign direct investment in China, China’s GDP, fixed asset investment in China and the prophase stock of the US foreign direct investment in the long-run. And China’s GDP is the major power to induce the US FDI to bias the long-term equilibrium

RVM-based adaboost scheme for stator interturn faults of the induction motor

This paper presents an AdaBoost method based on RVM (Relevance Vector Machine) to detect and locate an interturn short circuit fault in the stator windings of IM (Induction Machine). This method is achieved through constructing an Adaboost combined with a weak RVM multiclassifier based on a binary tree, and the fault features are extracted from the three phase shifts between the line current and the phase voltage of IM by establishing a global stator faulty model. The simulation results show that, compared with other competitors, the proposed method has a higher precision and a stronger generalization capability, and it can accurately detect and locate an interturn short circuit fault, thus demonstrating the effectiveness of the proposed method