Based on the study of the mechanics features of pneumatic artificial muscle including the nonlinearities in material, geometry, and with the method of nonlinear finite element analysis, the three-dimensional finite element model of a pneumatic artificial muscle was established. The analytical results were confirmed by the experiment with the use of a pneumatic artificial muscle test bench.

Surface-borehole induced polarization method is a common geophysical method in metal exploration. The forward calculation results of the borehole observation responses can provide reference and support for practical work. In particular, the forward results of spherical polarized target can be equivalent to massive ore body or karst caves and other low-resistivity anomalies. In this paper, the forward simulation of borehole induced polarization responses is carried out by using the electric dipole model equivalent spherical polarized body. The results show that the polarization degree of the excitation source at different positions is different, and the closer the proximity is, the stronger the secondary field anomaly is. Borehole observation has advantages for the identification of deep ore bodies. Under the same offset condition, the closer the distance from the borehole is, the stronger the responses will be. The results of this paper can provide reference and basis for relevant research work.

Identification of time spectrum is one of the core issues of the time-domain induced polarization (IP) method, which can be considered as one of the bases for distinguishing various polarized rocks. Fitting the IP data based on the spectrum forward model and obtaining optimal solution of the model parameters is a key step in spectrum identification. However, the suitable forward model should consider the observation conditions such as the charging-time and the time-window. The time spectrum identification may be difficult to implement stably due to the lack of objective reference for the optimal solution. Our purpose is therefore to improve the forward model and implement spectrum identification for IP data. First, using the Weibull (WB) distribution function as the basis, a time spectrum forward model considering the charging-time and observation time-window is provided according to the typical measurement mode. Then, based on the WB spectrum model and Barzilai-Borwein gradient optimization, a method for solution of apparent spectral model parameters for spectrum identification is developed. Finally, this method is used to process the IP data from a mine where the anomalies related to ore bearing beds are identified based on the processing results. Results obtained demonstrate that the spectrum forward model based on the WB function is feasible in describing the IP data. The limited charging-time and a wide observed time-window should be considered to realize accurate simulations and description of the time-domain IP data. The essence of the time spectrum identification is to comprehensively reflect the time-varying state of whole time-channel through the spectral model parameters, wherein the decay field ratio of adjacent time-channel can be used as an objective reference. The parameters of the spectrum model characterizing the time-varying state are independent of polarization and resistivity, and thus can be directly used for identification of IP anomalies.

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Dielectric elastomers as a soft active material have been widely used in the field of artificial muscle actuator, acoustic actuator, loudspeaker, active control of vibration, soft robots and membrane resonators. Compared with traditional materials, there are many unknown uncertainties in the properties of the DE actuators. In this work, a viscoelastic dynamic model of dielectric elastomer is proposed with considering the uncertainties in material parameters, external mechanical load and voltage. By introducing the interval perturbation method and first-order Taylor series expansion method, the creep analysis, relaxation analysis and dynamic analysis of the dielectric elastomer with interval uncertain parameters are implemented. The effectivity of the proposed interval method is verified by the Monte Carlo simulation. This uncertain prediction method could be used in the design of active control systems with dielectric elastomers as actuators or sensors in the future.