Polarization Decomposition Algorithm for Detection Efficiency Enhancement

In the paper, a new polarization decomposition of the optimal detection algorithm in the partially homogeneous environment is presented. Firstly, the detectors Matched Subspace Detector (MSD) and Adaptive Subspace Detector (ASD) are adopted to deal with detection problems in the partially homogeneous environment. Secondly, the fitness function with polarization parameters is equivalently decomposed to enhance time detection efficiency in the algorithm. It makes the multiplication number of the fitness function from square to a linear increase along with the increase in parameters. Simulation results indicate that the proposed decomposition is much more efficient than direct use of the fitness function

Determinants and Impacts of the Relative Use of Depository Receipts and Euro Convertible Bonds by High-tech Corporations: An Empirical Study

This paper adopts Taiwan's high-tech companies as the sample to address and examine four new determinants of various foreign financing instruments and test their impacts on the issuing firms. Our empirical findings are consistent with the following notions. First, the firms with higher foreign holding and foreign investment will be likely to adopt foreign financing policy. Moreover, the firms with higher stock dividend payment in Taiwan will adopt both of ECB (Euro convertible bond) and DR (depository receipt). Firm managers with better education background will prefer DR. Second, the use of DR can effectively decrease the volatility of stock returns but also pronounce a negative influence on the mean of stock returns. In contrast, the use of ECB can effectively increase the mean but can not significantly decrease the volatility.

Data preprocessing for artificial neural network applications in prioritizing railroad projects â a practical experience in Taiwan

[[abstract]]Financial constraints necessitate the tradeoff among proposed railroad projects, so that the project priorities for implementation and budget allocation need to be determined by the ranking mechanisms in the government. At present, the Taiwan central government prioritizes funding allocations primarily using the analytic hierarchy process (AHP), a methodology that permits the synthesizing of subjective judgments systematically and logically into objective consensus. However, due to the coopetition and heterogeneity of railway projects, the proper priorities of railroad projects could not be always evaluated by the AHP. The decision makers prefer subjective judgments to referring to the AHP evaluation re- sults. This circumstance not only decreased the AHP advantages, but also raised the risk of the policies. A method to con- sider both objective measures and subjective judgments of project attributes can help reduce this problem. Accordingly, combining the AHP with the artificial neural network (ANN) methodologies would theoretically be a proper solution to bring a ranking predication model by creating the obscure relations between objective measures by the AHP and subjec- tive judgments. However, the inconsistency between the AHP evaluation and subjective judgments resulted in the inferior soundness of the AHP/ANN ranking forecast model. To overcome this problem, this study proposes the data prepro- cessing method (DPM) to calculate the correlation coefficient value using the subjective and objective ranking incidence matrixes; according to the correlation coefficient value, the consistency between the AHP rankings and subjective judg- ments of railroad projects can be evaluated and improved, so that the forecast accuracy of the AHP/ANN ranking forecast model can also be enhanced. Based on this concept, a practical railroad project ranking experience derived from the Insti- tute of Transportation of Taiwan is illustrated in this paper to reveal the feasibility of applying the DPM to the AHP/ANN ranking prediction model.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]LT