Measuring the noise emitted by power equipment requires measuring the total environment noise and background noise. Due to it being impossible to shut down the power equipment to measure background noise, the noise emission of power equipment cannot be measured accurately. This work conducts research on noise emission of power equipment based on coherent power. Firstly, a test in the lab was conducted to measure the noise emitted by two speakers based on coherent power. This test showed that the measurement error was less than 1 dB(A). Subsequently, the measurement of noise emitted by power equipment was carried out in a 500 kV substation, and the results showed that the measurement error was also less than 1 dB(A). Through measurements in the lab and substation, it has been verified that the coherent power method can accurately measure the noise emitted by power equipment, avoiding the error caused by inaccurate measurement of background noise.
The substation’s production of low-frequency noise will have a significant impact on individuals’ physical and emotional well-being, as well as their everyday functioning, resulting in detrimental effects on the human body. This research aims to examine the classification, preparation method, and key parameters that influence the damping performance of damping materials in order to mitigate the vibration and noise associated with transformer equipment. These criteria include compatibility, temperature, copolymerization, crosslinking degree, and filler. This study investigates and develops a magnetic damping material that utilizes waste rubber as a resource, along with the corresponding preparation technique. By improving the material ratio, the temperature range of the damping material is expanded, resulting in enhanced efficacy in suppressing low frequency vibrations within this temperature range. The simulation study of the damping material laying is conducted, taking into account the physical properties of the damping material and the actual structure of the transformer. This study aims to optimize the vibration and noise reduction effect of the transformer surface, enabling effective noise control within the frequency range of 100-1000 Hz. The overall noise level experiences a decrease of 5.1 dB(A), whereas the highest reduction in peak noise can exceed 10 dB(A). Enhancing and refining the damping patch material can significantly enhance the performance of vibration and noise reduction, thereby serving as a crucial technical avenue for mitigating transformer noise pollution.
Visual relationship detection aims to locate objects in images and recognize the relationships between objects. Traditional methods treat all observed relationships in an image equally, which causes a relatively poor performance in the detection tasks on complex images with abundant visual objects and various relationships. To address this problem, we propose an attention based model, namely AVR, to achieve salient visual relationships based on both local and global context of the relationships. Specifically, AVR recognizes relationships and measures the attention on the relationships in the local context of an input image by fusing the visual features, semantic and spatial information of the relationships. AVR then applies the attention to assign important relationships with larger salient weights for effective information filtering. Furthermore, AVR is integrated with the priori knowledge in the global context of image datasets to improve the precision of relationship prediction, where the context is modeled as a heterogeneous graph to measure the priori probability of relationships based on the random walk algorithm. Comprehensive experiments are conducted to demonstrate the effectiveness of AVR in several real-world image datasets, and the results show that AVR outperforms state-of-the-art visual relationship detection methods significantly by up to 87.5% in terms of recall.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.