Jinwen University of Science and Technology

About: Jinwen University of Science and Technology is a education organization based out in Xindian District, Taiwan. It is known for research contribution in the topics: Tourism & Monopole antenna. The organization has 272 authors who have published 446 publications receiving 6172 citations. The organization is also known as: JUST.

Determinants of the Adoption of Enterprise Resource Planning within the Technology-Organization-Environment Framework: Taiwan's Communications Industry

Abstract: Enterprise resource planning (ERP) systems are costly and complex but vital for companies having to face a rapidly changing business environment and an increasingly competitive marketplace. As the first study to examine the factors within the technology-organization-environment (TOE) framework that affect the decision to adopt ERP in Taiwan's communications industry, the empirical tests conducted here are based on personal interviews with a sample of 99 firms in Taiwan's communications industry. Eight factors in three broad categories are tested using logistic regression, and four of these, technology readiness, size, perceived barriers and production and operations improvements, are found to be important determinants of the adoption of ERP. This model correctly classifies 79.8% of the decisions made with respect to the adoption of ERP. The results substantiate the usefulness of this model which may be interesting to managers seeking to be more proactive in planning for their adoption of an ERP system.

Power-Spectrum-Based Wavelet Transform for Nonintrusive Demand Monitoring and Load Identification

Abstract: Though the wavelet transform coefficients (WTCs) contain plenty of information needed for turn-on/off transient signal identification of load events, adopting the WTCs directly requires longer computation time and larger memory requirements for the nonintrusive load monitoring identification process. To effectively reduce the number of WTCs representing load turn-on/off transient signals without degrading performance, a power spectrum of the WTCs in different scales calculated by Parseval's theorem is proposed and presented in this paper. The back-propagation classification system is then used for artificial neural network construction and load identification. The high success rates of load event recognition from both experiments and simulations have proved that the proposed algorithm is applicable in multiple load operations of nonintrusive demand monitoring applications.

Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses

Abstract: Energy management systems strive to use energy resources efficiently, save energy, and reduce carbon output. This study proposes transient feature analyses of the transient response time and transient energy on the power signatures of non-intrusive demand monitoring and load identification to detect the power demand and load operation. This study uses the wavelet transform (WT) of the time-frequency domain to analyze and detect the transient physical behavior of loads during the load identification. The experimental results show the transient response time and transient energy are better than the steady-state features to improve the recognition accuracy and reduces computation requirements in non-intrusive load monitoring (NILM) systems. The discrete wavelet transform (DWT) is more suitable than short-time Fourier transform (STFT) for transient load analyses.