Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and ...Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components （PCs） to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network （ANN） to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody＇s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.展开更多
In view of limitalions function, cost and application in domestic meter at present, this paper was designed the low power consumption, low cost smart meters and PC system based on microprocessor MSP430F4794. It suppor...In view of limitalions function, cost and application in domestic meter at present, this paper was designed the low power consumption, low cost smart meters and PC system based on microprocessor MSP430F4794. It supports multi rate, step rate and power factor influence factor of electricity metering method, and can realize the real-time clock, timing backup data, monitoring the temperature and humidity of the environment and other functions, has the ability to forecast the next period of time energy consumption and carbon dioxide emissions of electrical appliances. Combined with the PC software, the backup data of smart meters to report, detailed list, and the data curve method is presented to the user, and facilitate to analysis of user. The whole embedded in smart meters and the host computer system provides a good human-computer interface, can realize the user personalized service configuration and software calibration function.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 40974039)High-Tech Research and Development Program of China (Grant No.2006AA06205)Leading Strategic Project of Science and Technology, Chinese Academy of Sciences (XDA08020500)
文摘Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components （PCs） to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network （ANN） to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody＇s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.
文摘In view of limitalions function, cost and application in domestic meter at present, this paper was designed the low power consumption, low cost smart meters and PC system based on microprocessor MSP430F4794. It supports multi rate, step rate and power factor influence factor of electricity metering method, and can realize the real-time clock, timing backup data, monitoring the temperature and humidity of the environment and other functions, has the ability to forecast the next period of time energy consumption and carbon dioxide emissions of electrical appliances. Combined with the PC software, the backup data of smart meters to report, detailed list, and the data curve method is presented to the user, and facilitate to analysis of user. The whole embedded in smart meters and the host computer system provides a good human-computer interface, can realize the user personalized service configuration and software calibration function.
