Dan Yang

With the continuous evolution of human-machine interaction (HMI) technologies, natural and intuitive gesture recognition has emerged as a pivotal interaction modality. However, constrained by inter-individual differences in hand dimensions and wearing habits, existing gesture recognition techniques still suffer from limited generalization performance in cross-subject recognition. This paper proposes a flexible impedance glove based on MXene/graphene composites with wide linear response range, which enables lightweight and zero-shot cross-subject gesture recognition. The sensing layer of the glove is fabricated using MXene/graphene/spandex composites, delivering a wide linear tensile range of 0-90% (R² = 0.995), and exceptional dual-modal sensing capability, effectively suppressing baseline fluctuations induced by individual differences. To enable high-precision gesture signal acquisition and intelligent classification, a self-designed miniaturized, low-cost impedance measurement module and the Random Forest (RF) algorithm were integrated. The customized hardware module achieves a high signal-to-noise ratio (SNR) of 61.6 dB and an average measurement accuracy of 99.79%. By employing a computationally lightweight Random Forest (RF) algorithm, the integrated system delivers an intra-subject recognition accuracy of 99.43% in classifying 12 complex gestures. Remarkably, it achieves a zero-shot cross-subject recognition accuracy of 91.04%, which further improved to 97.85% with the introduction of merely 5% individual calibration data. Furthermore, the successfully developed applications in intelligent sign language translation and low-latency game control demonstrate the promising application potential of the proposed system in barrier-free communication, HMI and other related fields.

Inverters generate significant EMI due to their rapid switching operations. This paper proposes an EMI suppression scheme at the source level based on a series IGBT topology and voltage shaping. By reshaping the output voltage waveform, the scheme periodically suppresses the EMI source. Furthermore, staircase voltage shaping is implemented using a CPLD-based sequential gate drive circuit, eliminating the need for complex modifications to existing gate drive circuits. Experimental results on an 1140 V explosion-proof induction motor drive platform demonstrate that this method achieves harmonic attenuation of 4-20 dBμV within a 2 MHz bandwidth period. Compared to traditional two-level inverters, total switching losses are reduced by around 70%. Periodic EMI suppression also simplifies the design complexity of other EMI mitigation methods within the system. Its periodic nature also enhances design predictability. Moreover, this approach leverages the flexibility and scalability of the series IGBT structure to provide a practical solution for high-voltage inverter systems. It enables source-level EMI suppression without significantly increasing system complexity or cost.

In this paper, a liquid-filled photonic crystal fiber (PCF) with elliptical air holes for supercontinuum (SC) generation is proposed. Due to infiltrating ethylene glycol C 2 H 4 ( OH ) 2 liquid, restricted power handling capacity, excessive confinement losses and low optical transmission range have been improved compared to solid-core fibers. Also, the addition of elliptical air holes can help to better regulate the dispersion curves as well as reduce confinement losses. The proposed PCF is modeled in COMSOL Multiphysics 6.2 software and numerical analysis is accomplished by finite element method. Dispersion, effective mode area, nonlinear coefficient, and confinement loss are investigated for a specific mid-infrared wavelength range. The proposed PCF exhibits zero dispersion at 4487 nm and 4575 nm, nonlinear coefficient of 3276 W − 1 k m − 1 and confinement loss around   10 − 9   order in dB m ⁻¹ . These results indicates that the proposed PCF produces a SC bandwidth of 4800 nm, which is approximately a few times more effective than the earlier reported fiber designs. The designed PCF achieves flat dispersion and high nonlinearity, and can find extensive applications in various fields such as telecommunication and optical coherence tomography, frequency comb generation, and optical metrology.

Stacking SiC mosfets in series is an effective approach to enhance voltage rating and simplify topologies of high-power converters. Beyond the dynamic voltage balancing during normal switching operations, short-circuit (SC) detection and protection are equally critical. Discrepancies in gate drive timing and inherent mismatches in the I–V characteristics among series-connected devices not only complicate SC detection but also induce imbalanced voltage sharing with elevated current, posing a significant challenge for conventional SC detection circuits and snubbers. To solve these issues, a novel SC detector (SCD) and soft turn-off (STO) circuit designed for series-connected SiC mosfets are proposed in this article. Based on the voltage slope of the entire power stack, the SCD is compatible with both hard switching fault and fault under load (FUL) conditions. The STO circuit can be directly integrated into conventional concentrated clamping snubber. It can actively limit the current through each series-stacked SiC mosfet during an SC event, while balancing the fault energy distribution among devices and mitigating the risk of excessive power dissipation. The design guidelines as well as detailed selection principles for the components of each sub-circuit are specified. Furthermore, the performance of the proposed SC detection and protection scheme is experimentally verified based on the established test platform.

In this paper, a bend-resistant photonic crystal fiber inverse design using a deep neural network (DNN) with feature analysis is proposed for higher efficiency. First, a DNN model is trained to design structures of the bend-resistant PCF according to the given optical performance. Then, Shapley additive interpretation (SHAP), a kind of XAI algorithm is employed to optimize the pre-trained DNN model with feature analysis. The features with higher SHAP values are selected from the original input data to reduce the input data dimension of the pre-trained DNN. When the input data dimension is reduced from 33 to 11, the number of model parameters is decreased by 87.9%. The design accuracy of the optimized DNN is 94.4%, and the total design time of the optimized model is only 0.024 seconds. The results show that the proposed method reduces the computational load and improves the efficiency of photonic crystal fiber design. The designed bend-resistant photonic crystal fibers have excellent performance and are easy to be manufactured. This can also offer an alternative method for the design of various optical devices.

This paper investigates the problem of practical prescribed-time trajectory tracking consensus for higher-order heterogeneous multi-agent systems (MASs) with unknown time-varying control gains and non-vanishing uncertainties, while also taking external disturbances into account. A directed graph is employed to characterize the communication topology. By introducing a time-varying scaling function and adopting an adaptive backstepping design, a distributed controller is developed that achieves practical prescribed-time trajectory tracking consensus of heterogeneous MASs, while avoiding unbounded control gains and preserving the continuity of control inputs. Furthermore, to optimize the use of communication resources, an event-triggered mechanism is incorporated to reduce the update frequency of controllers. Finally, numerical simulations are carried out to verify the effectiveness of the proposed method.