The effect of Mg content on crystal transformation and electrochemical characteristics of La_2-xMg_xNi₇ (x = 0.40–0.60) alloys has been studied. It is found that alloys with allotropic structures Ce₂Ni₇-type (2H) and Gd₂Co₇-type (3R) phases exist in a range of Mg concentration from 0.48 to 0.50. Beyond that range, further increasing Mg to 0.60 is favorable for the formation of PuNi₃-type phase due to the rising ratio of [A₂B₄] to [AB₅] slabs in regard to the demand of La and Ni atoms for Mg to assemble [A₂B₄] slabs. Conversely, the surplus of La and Ni atoms for Mg to assemble [A₂B₄] slabs lowers the ratio of [A₂B₄] to [AB₅] slabs, which helps the formation of CaCu₅-type phase as Mg decreases to 0.40. Electrochemical studies show that the electrochemical properties of the alloy electrodes with allotropic phases are superior in discharge capacity (388 mAh g⁻¹) and cycling stability (78% capacity retaining rate at 100^th cycle). Appearance of PuNi₃-type or CaCu₅-type phase reduces the cycling stability, while CaCu₅-type phase can significantly improve the high rate dischargeablity (HRD) of the alloy electrode.

When the wearer of Wearable Human-Machine Interaction System (WHMIS) is talking a strenuous walk, the Inertial Measurement Units (IMUs) installed on the extremities are likely to be beyond the range of design specification of the IMU sensors. This overrange leads to poor navigation performances of the system. To overcome the problem, this paper proposes a deep learning-based method to realize robust autonomous navigation. Specifically, the Faster Region-Convolutional Neural Network (R-CNN) is built to recognize the gait types of the wearer. For each gait type, the inertial data of the wearer's thigh and the foot are collected at the same frequency as the training samples, and then used to construct a correspondent Gated Recurrent Unit-Support Vector Regression (GRU-SVR) deep hybrid neural network model for virtual IMU simulation. Then, the virtual foot-mounted IMU will be constructed in real-time by the physical thigh-mounted IMU based on the neural network models mentioned above. Once the over-range of the physical foot-mounted IMU is detected, the virtual foot inertial information will be used to reconstruct the WHMIS navigation system. Experimental results show that the proposed navigation method has the ability of fault tolerance in complex gaits, and the 3D positioning performance of the reconstructed navigation system is approximately equivalent to that of the fault-free WHMIS navigation system.

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.