Ar, N₂, O₂, and N₂-O₂ mixture under different O₂ components were separately used as the working medium gas of a plasma igniter to study the influence of working-medium-gas type and O₂ concentration on emission spectral and temperature characteristics of a direct-current arc plasma igniter. The emission spectra of the plasma jet were also analyzed using the optical emission spectroscopy method, and the influence rule of working medium gas and O₂ concentration on electron temperature and vibrational temperature of the plasma jet was calculated and analyzed. The experimental results show that the emission spectra of the plasma jet had significant differences when diverse working gases were used as the working medium of the igniter. With increasing O₂ concentration in the working medium gas, the spectral line intensity of oxygen-containing particles in emission spectra of the plasma jet was significantly enhanced, and the spectral intensities of nitrogen-containing particles, NO molecular bands, the second positive system of N2C3∏u−B3∏g, and the first negative system of N2+B2∑u+−X2∑u+ were also enhanced obviously. Both electron and vibrational temperature of the plasma jet increased gradually with increasing O₂ concentration in the working medium gas, and first increased and then decreased with increasing axial distance from the plasma jet.

The mechanism of plasma-assisted combustion at increasing discharge voltage is investigated in detail at two distinctive system schemes (pretreatment of reactants and direct in situ discharge). OH-planar laser-induced fluorescence (PLIF) technique is used to diagnose the turbulent structure methane-air flame, and the experimental apparatus consists of dump burner, plasma-generating system, gas supply system and OH-PLIF system. Results have shown that the effect of pretreatment of reactants on flame can be categorized into three regimes: regime I for voltage lower than 6.6 kV; regime II for voltage between 6.6 and 11.1 kV; and regime III for voltage between 11.1 and 12.5 kV. In regime I, aerodynamic effect and slower oxidation of higher hydrocarbons generated around the inner electrode tip plays a dominate role, while in regime III, the temperature rising effect will probably superimpose on the chemical effect and amplify it. For wire-cylinder dielectric barrier discharge reactor with spatially uneven electric field, the amount of radicals and hydrocarbons are decreased monotonically in radial direction which affects the flame shape. With regard to in situ plasma discharge in flames, the discharge pattern changes from streamer type to glow type. Compared with the case of reactants pretreatment, the flame propagates further in the upstream direction. In the discharge region, the OH intensity is highest for in situ plasma assisted combustion, indicating that the plasma energy is coupled into flame reaction zone.

To enhance path planning efficiency in unmanned aerial vehicle (UAV) search missions in complex environments, this paper proposes a coverage path planning (CPP) algorithm for a UAV that integrates the deep Q-network (DQN) with the A* algorithm (DQN-A*). In the proposed DQN-A* algorithm, a dual-driven reward mechanism is established, comprising a probability-weighted reward and a step-dependent reward, steering the UAV toward high-probability regions. Furthermore, to handle previously unknown obstacles in real time, the algorithm employs a multi-stage obstacle-identification strategy, enabling the UAV to improve coverage of traversable cells by dynamically adjusting its local path when newly detected obstacles are encountered. A theoretical analysis derives a principled recommended range for the UAV positional identifier based on statistical feature analysis; this range is then validated through extensive simulations. Additionally, Hamiltonian path pre-training is introduced to accelerate convergence. Comparative simulations demonstrate that the proposed DQN-A* algorithm achieves higher area-coverage and target-detection probabilities than benchmark algorithms in environments with unknown obstacles, offering valuable insights for positional encoding in deep reinforcement learning (DRL)-based robotic coverage problems.

Ultrasonic guided wave monitoring signals contain critical information for structural damage assessment. However, these signals are inevitably contaminated by noise in real-world acquisition environments, significantly compromising monitoring reliability. Therefore, this paper proposes a multi-modal multi-scale dual residual network for ultrasonic guided wave signal denoising. First, the original ultrasonic guided wave monitoring signal and its gramian angular difference field image are utilized as multi-modal inputs to realize complementary feature learning among different modalities. Second, the channel and spatial features of the signal are extracted by the sequential modality branch and the spatial modality branch, respectively. And the multi-scale feature extraction module and the multiple residual module in two branches are designed to enhance the feature extraction capability. In addition, a loss function is integrated by root mean square error, Jensen-Shannon divergence and extreme difference to enhance the robustness, interpretability and generalization ability of the network. Experimental results demonstrate that the proposed method improves denoising performance by 29.87%, 22.23%, 12.80%, and 5.38% compared to the second-best compared method for noise signals with 3dB, 6dB, 9dB and 12dB levels, which demonstrates the effectiveness and advancement of our proposed method.