The bias drift of a micro-electro-mechanical systems （MEMS） accelerometer suffers from the l/f noise and the tem- perature effect. For massive applications, the bias drift urgently needs to be improved. Conventional...The bias drift of a micro-electro-mechanical systems （MEMS） accelerometer suffers from the l/f noise and the tem- perature effect. For massive applications, the bias drift urgently needs to be improved. Conventional methods often cannot ad- dress the l/f noise and temperature effect in one architecture. In this paper, a combined approach on closed-loop architecture modification is proposed to minimize the bias drift. The modulated feedback approach is used to isolate the l/f noise that exists in the conventional direct feedback approach. Then a common mode signal is created and added into the closed loop on the basis of modulated feedback architecture, to compensate for the temperature drift. With the combined approach, the bias instability is improved to less than 13 μg, and the drift of the Allan variance result is reduced to 17 μg at 100 s of the integration time. The temperature coefficient is reduced from 4.68 to 0.1 mg/℃. The combined approach could be useful for many other closed-loop accelerometers.展开更多
The shear strength parameters of soil （cohesion and angle of internal friction） are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The ...The shear strength parameters of soil （cohesion and angle of internal friction） are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network （ANN） and Regression Tree （CART） techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network （i.e., 6/2/2） were attempted using Connec- tion Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.展开更多
文摘The bias drift of a micro-electro-mechanical systems （MEMS） accelerometer suffers from the l/f noise and the tem- perature effect. For massive applications, the bias drift urgently needs to be improved. Conventional methods often cannot ad- dress the l/f noise and temperature effect in one architecture. In this paper, a combined approach on closed-loop architecture modification is proposed to minimize the bias drift. The modulated feedback approach is used to isolate the l/f noise that exists in the conventional direct feedback approach. Then a common mode signal is created and added into the closed loop on the basis of modulated feedback architecture, to compensate for the temperature drift. With the combined approach, the bias instability is improved to less than 13 μg, and the drift of the Allan variance result is reduced to 17 μg at 100 s of the integration time. The temperature coefficient is reduced from 4.68 to 0.1 mg/℃. The combined approach could be useful for many other closed-loop accelerometers.
文摘The shear strength parameters of soil （cohesion and angle of internal friction） are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network （ANN） and Regression Tree （CART） techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network （i.e., 6/2/2） were attempted using Connec- tion Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.
