In this study, we have theoretically designed a micro-structured photoconductive antenna based on the combination of split-ring resonator (SRR) and dipole photoconductive antenna. Further, we have used numerical simulations to prove that the proposed antenna can enhance the terahertz radiation characteristics. According to the equivalent circuit model analysis, the terahertz radiation power and the resonance frequency of this antenna were found to be closely related with the structural parameters of the SRR. A numerical model was established to simulate the effect of the structural parameters of SRR on the terahertz radiation characteristics of the proposed antenna. The results indicated that an increase in the width and length of SRR caused an increase in the peak power intensity and a decrease in the resonance frequency. Moreover, as the gap width of SRR was increased, the peak power intensity was decreased and the resonance frequency was increased. Furthermore, the terahertz radiation power increased with increase in the gap width of antennas. Overall, we show that high-power terahertz radiation sources with specific frequency can be obtained by optimizing the structural parameters of SRR, and such sources are potentially useful in a variety of fields such as biomedicine, material analysis, and detection systems.

Metamaterials can freely control terahertz waves by designing the geometric shape and direction of the unit structure to obtain the desired electromagnetic characteristics, so they have been widely used in sensing, communication and radar stealth technology. The traditional design of terahertz metamaterial absorber usually requires continuous structural adjustment and a large number of simulations to meet the expected requirements. The process largely relies on the experience of researchers, and the physical modeling and simulation solution process is time-consuming and inefficient, greatly hindering the development of metamaterial absorbers. Therefore, due to its powerful learning ability, deep learning has been used to predict the structural parameters or spectra of metamaterial absorbers. However, when designing a new structure, it is necessary to prepare a large number of training samples again, which is both time-consuming and not universal. Particle swarm optimization algorithm can quickly converge to the optimal solution through the sharing and cooperation of individual information in the group, with no need for prior preparation. Therefore, a method of fast designing terahertz metamaterial absorber is proposed based on multi-objective particle swarm optimization algorithm in this work. Taking a new center symmetric absorber structure composed of four Ls for example, the structure parameters are optimized to achieve rapid and automatic design of metamaterial absorber. The multi-objective particle swarm optimization algorithm takes the absorptivity and quality factor as independent targets to design the structure parameters of the absorber, realizing the dual-objective optimization of the absorber, and overcoming the shortcoming of the multi-objective conflicts that cannot be solved by PSO. When used for refractive index sensing, the optimally-designed absorber achieves perfect absorption at 1.613 THz with a quality factor of 319.72 and a sensing sensitivity of 264.5 GHz/RIU. In addition, the reasons of absorption peaks are analyzed in detail through impedance matching, surface current, and electric field distribution. By studying the polarization characteristics of the absorber, it is found that the absorber is not sensitive to polarization, which is more stable in practical application. In summary, the multi-objective particle swarm optimization algorithm can realize the design according to the requirements, reduce the experience requirement of researchers in the design of metamaterial absorber, thereby improving design efficiency and performance, and has great potential for application in the design of terahertz functional devices.

Terahertz metamaterial dual-band absorbers are used for multi-target detection and high-sensitivity sensing in complex environments by enhancing information that reflects differences in the measured substances. Traditional design processes are complex and time-consuming. Machine learning-based methods, such as neural networks and deep learning, require a large number of simulations to gather training samples. Existing design methods based on single-objective optimization often result in uneven multi-objective optimization, which restricts practical applications. In this study, we developed a metamaterial absorber featuring a circular split-ring resonator with four gaps nested in a “卍” structure and used the Multi-Objective Firefly Algorithm based on Multiple Cooperative Strategies to achieve fast optimization of the absorber’s structural parameters. A comparison revealed that our approach requires fewer iterations than the Multi-Objective Particle Swarm Optimization and reduces design time by nearly half. The absorber designed using this method exhibited two resonant peaks at 0.607 THz and 0.936 THz, with absorptivity exceeding 99%, indicating near-perfect absorption and quality factors of 31.42 and 30.08, respectively. Additionally, we validated the absorber’s wave-absorbing mechanism by applying impedance-matching theory. Finally, we elucidated the resonance-peak formation mechanism of the absorber based on the surface current and electric-field distribution at the resonance frequencies. These results confirmed that the proposed dual-band metamaterial absorber design is efficient, representing a significant step toward the development of metamaterial devices.

To address the high time cost and low efficiency of traditional terahertz (THz) metamaterial absorber (MMA) design, an adaptive method based on multi-objective particle swarm optimization (PSO) was proposed. First, we presented a symmetric absorber of a four-split-ring resonator with a cross-shaped top layer. The structural parameters were optimized by the PSO and multi-objective PSO (MOPSO) algorithms. The simulation results indicated that the MOPSO rapidly obtained geometric parameters that balance the high absorptivity and high quality factors. This method quickly identified a symmetric structure with absorptivity greater than 99% and quality factor of 377.45 at 1.584 THz. Additionally, the asymmetric structure achieved near-perfect absorption at 1.585 THz with a Q value of 281.32. By studying the electric field distribution and the surface current distribution, the physical mechanism of the absorber designed has been explained. This adaptive assisted method based on MOPSO can efficiently design MMAs and offer an artificial intelligence strategy for THz functional device design.

A reliable aging-prediction method is significant for lithium-ion batteries (LIBs) to prolong the service life and increase the efficiency of operation. In this paper, an improved Gaussian-process regression (GPR) is proposed to predict the degradation rate of LIBs under coupled aging stress to simulate working conditions. The complicated degradation processes at different ranges of the state of charge (SOC) under different discharge rates were analyzed. A composed kernel function was conducted to optimize the hyperparameter. The inputs for the kernel function of GPR were improved by coupling the constant and variant characteristics. Moreover, previous aging information was employed as a characteristic to improve the reliability of the prediction. Experiments were conducted on a lithium–cobalt battery at three different SOC ranges under three discharge rates to verify the performance of the proposed method. Some tips to slow the aging process based on the coupled stress were discovered. Results show that the proposed method accurately estimated the degradation rate with a maximum estimation root-mean-square error of 0.14% and regression coefficient of 0.9851. Because of the proposed method’s superiority to the exponential equation and GPR by fitting all cells under a different operating mode, it is better for reflecting the true degradation in actual EV.