Numerical study on the aerodynamic noise characteristics of CRH2 high-speed trains

The aerodynamic noise of high-speed trains not only causes interior noise pollution and reduces the comfort of passengers, but also seriously affects the normal life of residents. With the increase of running speed of trains, aerodynamic noises will be more than wheel-rail noises and become the main noise source of high-speed trains. This paper established a computational model for the aerodynamic noise of a CRH2 high-speed train with 3-train formation including 3 train bodies and 6 bogies, adopted the detached eddy simulation (DES) to conduct numerical simulation for the flow field around the high-speed train, applied Ffowcs Williams-Hawkings acoustic model to conduct unsteady computation for the aerodynamic noise of high-speed trains, and analyzed the far-field aerodynamic noise characteristics of high-speed trains. Studied results showed: The main energy of the complete train was mainly within the range of 613 Hz-2500 Hz when the high-speed train ran at the speed of 350 km/h. In the whole frequency domain, it was a broadband noise. Regarding the longitudinal observation point which was 25 m away from the center line of track and 6m away from the nose tip of head train, the sound pressure level of total noises reached the maximum value 88.9 dBA. The maximum sound pressure level of the noise observation point which was 7.5 m away from the center line of track was around the first bogie of head train. Various components made different contributions to the aerodynamic noise of the complete train, and the order was head train, mid train, bogie system (6 bogies) and tail train. The first bogie of head train made the greatest contribution to bogie system and was the main aerodynamic noise source of the complete train

LED-Induced Fluorescence System for Tea Classification and Quality Assessment

A fluorescence system is developed by using several light emitting diodes (LEDs) with different wavelengths as excitation light sources. The fluorescence detection head consists of multi LED light sources and a multimode fiber for fluorescence collection, where the LEDs and the corresponding filters can be easily chosen to get appropriate excitation wavelengths for different applications. By analyzing fluorescence spectra with the principal component analysis method, the system is utilized in the classification of four types of green tea beverages and two types of black tea beverages. Qualities of the Xihu Longjing tea leaves of different grades, as well as the corresponding liquid tea samples, are studied to further investigate the ability and application of the system in the evaluation of classification/quality of tea and other foods

Universal Thermoelectric Effect of Dirac Fermions in Graphene

We numerically study the thermoelectric transports of Dirac fermions in graphene in the presence of a strong magnetic field and disorder. We find that the thermoelectric transport coefficients demonstrate universal behavior depending on the ratio between the temperature and the width of the disorder-broadened Landau levels(LLs). The transverse thermoelectric conductivity $\alpha_{xy}$ reaches a universal quantum value at the center of each LL in the high temperature regime, and it has a linear temperature dependence at low temperatures. The calculated Nernst signal has a peak at the central LL with heights of the order of $k_B/e$, and changes sign near other LLs, while the thermopower has an opposite behavior, in good agreement with experimental data. The validity of the generalized Mott relation between the thermoelectric and electrical transport coefficients is verified in a wide range of temperatures.Comment: 4 pages, 4 figures, published versio

Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over a 31.10% ROC score.Comment: Accepted by IEEE ISCAS 2022, 5 pages, 2 figures. arXiv admin note: text overlap with arXiv:2110.0336