Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Articles by Jing Shi
Total Records ( 3 ) for Jing Shi
  Gong Li and Jing Shi
  Wind speed forecasting is critical for wind energy conversion systems since it greatly influences the issues such as the scheduling of a power system, and the dynamic control of the wind turbine. In this paper, we present a comprehensive comparison study on the application of different artificial neural networks in 1-h-ahead wind speed forecasting. Three types of typical neural networks, namely, adaptive linear element, back propagation, and radial basis function, are investigated. The wind data used are the hourly mean wind speed collected at two observation sites in North Dakota. The performance is evaluated based on three metrics, namely, mean absolute error, root mean square error, and mean absolute percentage error. The results show that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics. Moreover, the selection of the type of neural networks for best performance is also dependent upon the data sources. Among the optimal models obtained, the relative difference in terms of one particular evaluation metric can be as much as 20%. This indicates the need of generating a single robust and reliable forecast by applying a post-processing method.
  Sining Yun , Xiaoli Wang , Juanfei Li , Jing Shi and Delong Xu
  The temperature dependence of dielectric constant was investigated for (Ba0.90Ca0.10)1−2x(Na0.5Bi0.5)2xTiO3 (BCT-NBT5 (x = 0.05), BCT-NBT15 (x = 0.15)) and (Ba0.90Ca0.10)0.925Bi0.05TiO3 (BCT-BiT5) ceramic samples prepared using the solid-state reaction technique. The dielectric relaxation behavior was observed in BCT-NBT15 and BCT-BiT5. The different dielectric relaxation mechanisms have been discussed for these two samples. In view of the defect chemistry, Raman spectroscopy was made on BCT-NBT5, BCT-NBT15, BCT-BiT5 and (Ba0.925Bi0.05)(Ti0.90Ca0.10)O3 (BTC-BiT5). The development of the new Raman bands at 827 and 825 cm−1 for BTC-BiT5 and BCT-BiT5, respectively, indicated that Ca2+ ions substitution for B-site Ti4+ ions has happened in BCT-BiT5 ceramics, giving the evidence for the formation of O2− vacancies. Raman spectroscopy and the temperature dependence of dielectric studies suggest that the dielectric relaxor behavior of Bi doped barium calcium titanate ceramics is related with the Bi3+ ions and the Ca2+ ions.
  Jing Shi , Xiangjun Xu , Jialai Wang and Gong Li
  In this paper, a new approach for efficient damage detection in engineering structures is introduced. The key concept is to use the mature computer vision technology to capture the static deformation profile of a structure, and then employ profile analysis methods to detect the locations of the damages. By combining with wireless communication techniques, the proposed approach can provide an effective and economical solution for remote monitoring of structure health. Moreover, a preliminary experiment is conducted to verify the proposed concept. A commercial computer vision camera is used to capture the static deformation profiles of cracked cantilever beams under loading. The profiles are then processed to reveal the existence and location of the irregularities on the deformation profiles by applying fractal dimension, wavelet transform and roughness methods, respectively. The proposed concept is validated on both one-crack and two-crack cantilever beam-type specimens. It is also shown that all three methods can produce satisfactory results based on the profiles provided by the vision camera. In addition, the profile quality is the determining factor for the noise level in resultant detection signal.
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility