Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately −10 °C to 60 °C, the maximum relative error in the force measurement is within ±0.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.

An elasto-magnetic (EM) and magneto-electric (ME) effect based elasto-magneto-electric (EME) sensor has been proposed recently by the authors for stress monitoring of steel cables with obvious superiorities over traditional elasto-magnetic sensors. For design optimization and engineering application of the EME sensor, the design theory is interpreted with a developed model taking into account the EM coupling effect and ME coupling effect. This model is able to approximate the magnetization changes that a steel structural component undergoes when subjected to excitation magnetic field and external stress, and to simulate the induced ME voltages of the ME sensing unit located in the magnetization area. A full-scale experiment is then carried out to verify the model and to calibrate the EME sensor as a non-destructive evaluation (NDE) tool to monitor the cable stress. The experimental results agree well with the simulation results using the developed model. The proposed EME sensor proves to be feasible for stress monitoring of steel cables with high sensitivity, fast response, and ease of installation.

The work is for the purpose of studying the influence of drying temperature and ultrasound on drying kinetics, antioxidant enzymes, and germination performance of pea seeds. The drying experiments were performed at air temperature of 30, 35, and 40°C without ultrasound and with three ultrasonic levels of 28 kHz + 60 W, 28 kHz + 100 W, and 40 kHz + 60 W. The antioxidant enzymes including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) and the toxic substances such as malondialdehyde (MDA) of the dried seeds were determined, and germination percentage (GP), germination index (GI), and mean germination time (MGT) were measured. The result showed that high temperature and ultrasound application had significant (P < 0.05) enhancing of pea seed drying kinetics, which shortened the drying time and improved the diffusion coefficient from 3.528 × 10⁻¹¹ to 5.668 × 10⁻¹¹ m²/s. Page model can well describe the drying curves under different experiment conditions. Ultrasound application significantly (P < 0.05) improved the activities of SOD, POD, and CAT and reduced the MDA content. In addition, high ultrasonic power contributed to the increase in GP and GI and the reduction of MGT of seeds.