In this paper, a broadband metamaterial absorber suitable for the S, C, and X bands is designed and manufactured. The absorber is made of FR-4 substrate, resonant metal structure, lumped resistance, and metal backplate, has a unit size of 0.11λL, and a total thickness of 0.084λL. The absorption principle of the absorber is analyzed using equivalent medium theory and parametric research. The calculation results show that the absorber achieves more than 90% broadband absorption (relative bandwidth of 130%) between 2.7 GHz and 12.7 GHz, has a good polarization angle and incidence angle insensitivity, uses electromagnetic resonance to explain its absorption characteristics, and experiments confirm that the absorber has good broadband absorption. The proposed absorber has higher absorption and simpler construction than the previously described broadband absorber, and it has the potential for practical applications in EMC, radar, and electromagnetic protection.

Non-intrusive load monitoring (NILM), monitoring single-appliance consumption level by decomposing the aggregated energy consumption, is a novel and economic technology that is beneficial to energy utilities and energy demand management strategies development. Hardware costs of high-frequency sampling and algorithm’s computational complexity hampered NILM large-scale application. However, low sampling data shows poor performance in event detection when multiple appliances are simultaneously turned on. In this paper, we contribute an iterative disaggregation approach that is based on appliance consumption pattern (ILDACP). Our approach combined Fuzzy C-means clustering algorithm, which provide an initial appliance operating status, and sub-sequence searching Dynamic Time Warping, which retrieves single energy consumption based on the typical power consumption pattern. Results show that the proposed approach is effective to accurately disaggregate power consumption, and is suitable for the situation where different appliances are simultaneously operated. Also, the approach has lower computational complexity than Hidden Markov Model method and it is easy to implement in the household without installing special equipment.

It is an important engineering challenge to detect a weak magnetic field ${H}_{AC}$ at low frequencies (1-100 Hz) due to large noise. In this work, a magnetoelectric (ME) sensor using single-crystal quartz material as the piezoelectric phase and optimized magnetostrictive layers has been presented to sense the low-frequency magnetic field signals. Firstly, the ME response properties of the bulk Metglas/quartz/Metglas laminate sensor were studied and a high ME coefficient of 3038 V/(cm $\cdot $ Oe) has been attained in its electromechanical resonance frequency. With frequency modulation technique, which a magnetic low-frequency signal can be transferred to around resonance frequency of the sensor, the proposed sensor was used to detect the low frequency magnetic field signals under zero bias conditions. It is experimentally found that the absolute resolution with respect to a 1 Hz magnetic field can be decreased as low as 44 pT ± 1.41 pT with 20 layers of Metglas. More importantly, the ME coefficients with different layers under zero bias and the effect of Metglas layers on the limit of the detection at 1 Hz also have been investigated. The results show that the limit of the detection at 1 Hz can be decreased to 10 pT ± 0.32 pT with 14 layers of Metglas which indicates an enhancement by 4.4 times in comparison with 20 layers. This simple bulk Metglas/quartz/Metglas laminate sensor with optimized layers has a great potential to be used for low-frequency bio-magnetic signals measurement at room temperature.

Considering the low‐frequency and large‐amplitude vibration characteristics of the high‐rise structure, a tuned magnetic fluid rolling‐ball damper is proposed to suppress the vibration of the structure. By adjusting the external magnetic field to control the natural rolling frequency of the ball, the purpose of tuning vibration reduction is achieved. Firstly, the working principle of the damper is theoretically analysed, a three‐dimensional (3D) magnetic‐fluid‐solid multiphysical field coupling mathematical model of the damper is established and the governing equations of multiphysical field coupling are derived. Secondly, the magnetic field distribution and operating characteristics of the damper are simulated and analysed. Finally, the effectiveness of the model is verified by experiments, and the damping performance of the damper with two kinds of magnetic fluid is tested and compared. The results show that the magnetic‐fluid‐solid multiphysical field coupling model can accurately simulate the working characteristics of the damper. The maximum damping force of the damper is about 12% of the elastic force of the structure, which can increase the damping ratio of the structure by about two times, effectively reduce the vibration response time, and suppress the vibration of the high‐rise structure.

In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to the smart grid environment. However, methods using low-frequency features are poorly-suited when several appliances are switched on at the same time, or devices with similar power values are used. In response to these problems, this paper proposes a load disaggregation method based on the power consumption patterns of appliances, combining an improved mathematical optimization model and optimized bird swarm algorithm (OBSA) for load disaggregation. Experiments show that the method can effectively identify the operating states of appliances, and deal with situations in which multiple instruments have similar power characteristics or are simultaneously switching. The performance comparison proves that the improved model is more efficient than the traditional active and reactive power (PQ) optimization model in load disaggregation performance and computation time, and also verifies the robustness of the proposed method and the convergence of OBSA. As an inexpensive method without extra measurement hardware installed, the process is suitable for large-scale applications in smart grids.