Transmission lines are often located in complex environments and are susceptible to the presence of foreign objects. Failure to promptly address these objects can result in accidents, including short circuits and fires. Existing foreign object detection networks face several challenges, such as high levels of memory consumption, slow detection speeds, and susceptibility to background interference. To address these issues, this paper proposes a lightweight detection network based on deep learning, namely YOLOv5 with an improved version of CSPDarknet and a Swin Transformer (YOLOv5-IC-ST). YOLOv5-IC-ST was developed by incorporating the Swin Transformer into YOLOv5, thereby reducing the impact of background information on the model. Furthermore, the improved CSPDarknet (IC) enhances the model’s feature-extraction capability while reducing the number of parameters. To evaluate the model’s performance, a dataset specific to foreign objects on transmission lines was constructed. The experimental results demonstrate that compared to other single-stage networks such as YOLOv4, YOLOv5, and YOLOv7, YOLOv5-IC-ST achieves superior detection results, with a mean average precision (mAP) of 98.4%, a detection speed of 92.8 frames per second (FPS), and a compact model size of 10.3 MB. These findings highlight that the proposed network is well suited for deployment on embedded devices such as UAVs.

The internal electric field coupling noise of a quartz flexible accelerometer (QFA) restricts the improvement of the measurement accuracy of the accelerometer. In this paper, the internal electric field coupling mechanism of a QFA is studied, an electric field coupling detection noise model of the accelerometer is established, the distributed capacitance among the components of the QFA is simulated, the structure of the detection noise transfer system of different carrier modulation differential capacitance detection circuits is analyzed, and the influence of each transfer chain on the detection noise is discussed. The simulation results of electric field coupling detection noise show that the average value of detection noise can reach 41.7 μg, which is close to the effective resolution of the QFA, 50 μg. This confirms that electric field coupling detection noise is a non-negligible factor affecting the measurement accuracy of the accelerometer. A method of adding a high-pass filter to the front of the phase-shifting circuit is presented to suppress the noise of electric field coupling detection. This method attenuates the average value of the detected noise by about 78 dB, and reduces the average value of the detected noise to less than 0.1 μg, which provides a new approach and direction for effectively breaking through the performance of the QFA.

Quartz flexible accelerometers (QFAs) are a type of temperature-sensitive sensor, whereby a change in temperature will cause the key parameters of the accelerometer to drift and cause stability errors. Due to the absence of effective methods for sensing the temperature of internal accelerometer components, existing temperature error correction approaches primarily rely on shell temperature measurements to establish correction models. Consequently, most correction methods achieve higher accuracy during the steady-state heat conduction phase of the accelerometer, whereas the correction error markedly increases during the transient heat conduction phase. To elucidate the temperature discrepancy between the QFA shell and its internal components and to support the development of a temperature error correction method for QFAs based on the internal temperature as a reference, this paper investigated the heat exchange dynamics between the interior and exterior of a QFA. A thermal conduction simulation model of the QFA was established, from which the spatiotemporal distribution patterns of the internal temperature field were derived. The results indicate that the temperature of the QFA shell changes significantly faster than that of the internal meter head in the early stage of the temperature change. The temperature gradient between the shell and the meter head first increases and then decreases, and the rate of temperature change in the upper part of the accelerometer is faster than that in the lower part. Before thermal equilibrium is reached, the temperature distribution inside the accelerometer is uneven in terms of time and space. Inside the accelerometer, the yoke iron, swing plate assembly, servo circuit, and magnetic steel assembly are the main components that exhibit differences in the internal temperature change in the QFA. When developing the temperature error correction method, it was crucial to address and mitigate the impact of temperature variations among these components. The average RMSE between the predicted temperature from the heat transfer model established in this paper and the experimental results was 0.4 °C. This indicates that the model can accurately predict the temperature variation within the QFA, thereby providing robust support for investigating the temperature behavior inside the QFA and offering essential technical foundations for enhancing the accuracy of the temperature error correction method.

This research endeavors to delve comprehensively into the ramifications of behavioral guidance policies pertaining to construction waste resource utilization (CWRU) on the behavioral awareness and attitudes of Chinese construction contractors. The implicit attitudes of 98 construction contractor workers toward construction waste collection, sorting, and reuse were quantitatively assessed by simulating different types of behavior-guided policies using the External Affective Simon Task (EAST) method with unconscious reaction time as an indicator. At the same time, the questionnaire survey was combined to collect and analyze the scores of these workers’ exogenous attitudes toward CWRU, with a view to revealing the contractors’ intrinsic behavioral awareness and exogenous attitudinal tendencies. The study reveals that economic incentive behavioral guidance policies are pivotal in fostering the collection, sorting, and reuse of construction waste. Furthermore, it novelly uncovers the distinct advantages of laws and regulations frameworks in safeguarding the fundamental aspects of construction waste collection. Notably, publicity and education behavioral policies emerge as a cornerstone in elevating the significance of construction waste collection and sorting, highlighting the cruciality of enhancing public awareness and knowledge to propel CWRU. The results of the study not only provide empirical evidence to understand the inherent attitudes of construction contractor workers toward CWRU behavior but also provide practical support for future policymakers. By advocating for economic incentives, strengthening regulations, and leading education, the multi-pronged approach promotes China’s CWRU accelerates the green transformation of the construction industry.