RESUMEN
Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.
Asunto(s)
Electromiografía , Redes Neurales de la Computación , Articulación de la Muñeca , Humanos , Electromiografía/métodos , Articulación de la Muñeca/fisiología , Rango del Movimiento Articular/fisiología , Movimiento/fisiología , Procesamiento de Señales Asistido por Computador , Algoritmos , Adulto , Masculino , Muñeca/fisiologíaRESUMEN
Metal vanadates as negative electrode materials for lithium-ion batteries have attracted widespread attention, attributed to their substantial capacity, broad availability, and exceptional safety. In this study, NiCo2V2O8@NC microspheres featuring a yolk-double shell structure were successfully synthesized via ion exchange reactions and surface deposition techniques, employing metal glycerolate as a template. Owing to the bimetallic cobalt-nickel synergistic effect and the N-doped carbon network, this configuration not only optimizes the pore structure but also enhances conductivity, thereby augmenting the stability of the overall structure. The unique yolk-double shell design significantly enhances the utilization of active components and reduces the ion transport distance, thereby achieving high capacity. Thanks to the synergistic effects of this bimetallic and intricate structure, the material demonstrates exceptional capacity and cycle stability in lithium storage. The initial discharge capacity possesses 1522 mAh g-1 at a current density of 0.2 A g-1, with the reversible capacity still maintained at 1197 mAh g-1 after 100 cycles. In addition, at a high current density of 0.5 A g-1, the initial discharge capacity is 1487 mAh g-1, with a reversible capacity of 747 mAh g-1 maintained after 500 cycles. This study offers a perspective and methodology for the design and fabrication of complex porous double shell nanostructures.
RESUMEN
An eco-friendly superabsorbent polymer (SAP) was prepared by grafting 2-acrylamido-2-methyl-1-propanesulfonic acid onto microcrystalline cellulose in lithium chloride/N, N-dimethylacetamide system. The synthesized SAP (cellulose-g-PAMPS) was characterized by FTIR, TGA, SEM, 1H NMR, 13C NMR and XRD. The water absorption equilibrium of cellulose-g-PAMPS could be achieved within 10 min in distilled water. Moreover, the maximum water absorption capacities of cellulose-g-PAMPS in distilled water, 0.9 wt% NaCl solution and 3.2 wt% Na2CO3 solution were 648.9, 298.4 and 207.3 g·g-1, respectively. The water absorption behavior of cellulose-g-PAMPS was interpreted by the pseudo-second-order model. Furthermore, cellulose-g-PAMPS could be used in some extreme conditions due to its high acid and alkali resistance. The water retention rate of cellulose-g-PAMPS could be maintained above 90 % at 25 °C for 6 h. As a consequence, the synthesized SAP can be applied to increase the plant growth and survival time under drought conditions, even under saline alkali conditions.