This paper presents a hydrogel-based flexible sensor array to detect plantar pressure distribution and recognize the gait patterns to assist those who suffer from gait disorders to rehabilitate better. The traditional pressure detection array is composed of rigid metal sensors, which have poor biocompatibility and expensive manufacturing costs. To solve the above problems, we have designed and fabricated a novel flexible sensor array based on AAM/NaCl (Acrylamide/Sodium chloride) hydrogel and PI (Polyimide) membrane. The proposed array exhibits excellent structural flexibility (209 KPa) and high sensitivity (12.3 mV·N⁻¹), which allows it to be in full contact with the sole of the foot to collect pressure signals accurately. The Wavelet Transform-Random Forest (WT-RF) algorithm is introduced to recognize the gaits based on the plantar pressure signals. Wavelet transform realizes the signal filtering and normalization, and random forest is responsible for the classification of the processed signals. The classification accuracy of the WT-RF algorithm reaches 91.9%, which ensures the precise recognition of gaits.

This paper proposes a bidirectional LC resonance DC–DC converter for wide-range applications. By introducing an auxiliary Boost bridge arm on the HVS of the conventional structure and multiplexing the resonant inductor, the proposed converter extends the voltage gain range. Utilizing a combined PWM and PFM control strategy, the converter operates in multiple modes: two gain modes (medium and high) during battery discharge, and three gain modes (low, medium, and high) during battery charging. This multi-mode mechanism effectively extends the voltage gain range, narrows the switching frequency variation, and simplifies magnetic component design. Furthermore, ZCS operation is confirmed under the tested representative operating conditions, significantly reducing switching losses. Finally, an experimental prototype with a rated power of 750 W was developed to verify the performance for 40–120 V battery charging and discharging requirements. The experimental results demonstrate the effectiveness and validity of the proposed topology and control strategy for high-efficiency, wide-range power conversion applications.

Existing data-fusion-driven photovoltaic (PV) power-forecasting methods do not fully exploit cloud-image information. Simple feature concatenation cannot explicitly describe high-order interactions between cloud imagery and historical power, whereas full high-order fusion tensors introduce substantial parameter and computational overhead. This paper proposes a short-term PV power-forecasting method that fuses ground-based cloud-image features with historical-power data. MAGAN is used to enhance the brightness and texture representation of ground-based cloud images. An improved CNN-Transformer with a multi-scale separable convolution (MSC) module extracts multi-scale spatial cloud features. Low-rank tensor fusion (LTF) models cross-modal interactions between cloud-image and power features under a low-rank constraint and the fused sequence is fed into an improved Pyraformer with an i-Pyra module to forecast four 15 min steps over the next hour. Experiments using 2021 measurements from a 100 kW PV station in Jiangxi Province show that MAGAN-enhanced cloud images reach a PSNR of 7.25 dB and an SSIM of 0.75. On the independent test set, the proposed method obtains MAE, MAPE, and RMSE values of 21.8%, 17.6%, and 32.4%, respectively, reducing the three errors by 13.1%, 15.8%, and 15.2% compared with a cloud-feature and sample-weighted Pyraformer, while reducing fusion-layer parameters by 46.7% relative to full bilinear fusion.