Deep Generative Fixed-filter Active Noise Control

Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises. Selective fixed-filter active noise control (SFANC) can significantly reduce response time by selecting appropriate pre-trained control filters for different noises. Nonetheless, the limited number of pre-trained control filters may affect noise reduction performance, especially when the incoming noise differs much from the initial noises during pre-training. Therefore, a generative fixed-filter active noise control (GFANC) method is proposed in this paper to overcome the limitation. Based on deep learning and a perfect-reconstruction filter bank, the GFANC method only requires a few prior data (one pre-trained broadband control filter) to automatically generate suitable control filters for various noises. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.Comment: Accepted by ICASSP 2023. Code will be available after publicatio

Effect of hyperbaric oxygen therapy on cognitive impairment after aneurysm subarachnoid hemorrhage

Purpose: To evaluate the effect of hyperbaric oxygen therapy (HBOT) on cognitive impairment after aneurysm subarachnoid hemorrhage (aSAH). Methods: The current study was carried out in a regional neurosurgical center in Taiyuan, Shanxi Province of China from January 2019 to September 2020. A total of 150 patients with persistent cognitive dysfunction at 3 months after aSAH onset were enrolled, which were randomly classified into group A (HBOT) and group B (control) via the random number table method. The outcome was evaluated by Montreal cognitive assessment (MoCA). Results: There were no significant differences between group A and group B with regard to MoCA score and proportions of normal MoCA patients at 3 months after HBOT (p > 0.05). Both groups showed no significant differences in proportions of normal MoCA patients at 6 months after HBOT (p > 0.05). However, there were significant differences between group A and group B with MoCA score of patients at 6 months after HBOT (p < 0.05). There were also significant differences in MoCA score and proportions of normal MoCA patients at 9 months after HBOT. Conclusion: HBOT alleviates cognitive impairment after aSAH, and thus may be used to manage cognitive impairment in patients after aSAH. However, further clinical trials are required prior to application in clinical practice

Experimental observation of Dirac-like surface states and topological phase transition in Pb1−x_{1-x}Snx_xTe(111) films

The surface of a topological crystalline insulator (TCI) carries an even number of Dirac cones protected by crystalline symmetry. We epitaxially grew high quality Pb$_{1-x}$Sn$_x$Te(111) films and investigated the TCI phase by in-situ angle-resolved photoemission spectroscopy. Pb$_{1-x}$Sn$_x$Te(111) films undergo a topological phase transition from trivial insulator to TCI via increasing the Sn/Pb ratio, accompanied by a crossover from n-type to p-type doping. In addition, a hybridization gap is opened in the surface states when the thickness of film is reduced to the two-dimensional limit. The work demonstrates an approach to manipulating the topological properties of TCI, which is of importance for future fundamental research and applications based on TCI

AB-GRU: An attention-based bidirectional GRU model for multimodal sentiment fusion and analysis

Multimodal sentiment analysis is an important area of artificial intelligence. It integrates multiple modalities such as text, audio, video and image into a compact multimodal representation and obtains sentiment information from them. In this paper, we improve two modules, i.e., feature extraction and feature fusion, to enhance multimodal sentiment analysis and finally propose an attention-based two-layer bidirectional GRU (AB-GRU, gated recurrent unit) multimodal sentiment analysis method. For the feature extraction module, we use a two-layer bidirectional GRU network and connect two layers of attention mechanisms to enhance the extraction of important information. The feature fusion part uses low-rank multimodal fusion, which can reduce the multimodal data dimensionality and improve the computational rate and accuracy. The experimental results demonstrate that the AB-GRU model can achieve 80.9% accuracy on the CMU-MOSI dataset, which exceeds the same model type by at least 2.5%. The AB-GRU model also possesses a strong generalization capability and solid robustness

Identification and optimal selection of temperature-sensitive measuring points of thermal error compensation on a heavy-duty machine tool

Thermal error compensation is considered as an effective and economic method to improve the machining accuracy for a machine tool. The performance of thermal error prediction mainly depends on the accuracy and robustness of predictive model and the input temperature variables. Selection of temperature-sensitive measuring points is the premise of thermal error compensation. In the thermal error compensation scheme for heavy-duty computer numerical control (CNC) machine tools, the identification of temperature-sensitive points still lacks an effective method due to its complex structure and heat generation mechanisms. In this paper, an optimal selection method of temperature-sensitive measuring points has been proposed. The optimal measuring points are acquired through three steps. First, the degree of temperature sensitivity is defined and used to select the measuring points with high sensitivity to thermal error. Then, the first selected points are classified with fuzzy clustering and grey correlation grade. Finally, the temperature-sensitive measuring points are selected with analysis of location of temperature sensors. In order to verify the method above, an experiment is carried out on the CR5116 of flexible machining center. A novel temperature sensor, fiber Bragg grating (FBG) sensor, is used to collect the surface temperature of the machine. A thermal error compensation model is developed to analyze the prediction accuracy based on four sequences of measuring points, which are generated by different selection approaches. The results show that the number of the measuring points is reduced from 27 to 5 through the proposed selection method, and the thermal error compensation model based on the optimum temperature-sensitive measuring points has the best performance of prediction effect

Mechanically Robust and Spectrally Selective Convection Shield for Daytime Subambient Radiative Cooling

As a passive cooling strategy, radiative cooling is becoming anappealing approach to dissipate heat from terrestrial emitters to the outer space. However, the currently achieved cooling performance is still underperforming due to considerable solar radiation absorbed by the emitter and nonradiative heat transferred from the surroundings. Here, we proposed a mechanically robust and spectrally selective convection shield composed of nanoporous composite fabric (NCF) to achieve daytime subambient radiative cooling. By selectively reflecting ∼95% solar radiation, transmitting ∼84% thermal radiation, and suppressing the nonradiative heat transferred from warmer surroundings, the NCF-based radiative cooler demonstrated an average daytime temperature reduction of ∼4.9 °C below the ambient temperature, resulting in an average net radiative cooling power of ∼48 W/m2 over a 24 h measurement. In addition, we also modeled the potential cooling capacity of the NCF-based radiative cooler and demonstrated that it can cover the cooling demands of energy-efficient residential buildings in most regions of China. Excellent spectral selectivity, mechanical strength, and weatherability of the NCF cover enable a much broader selection for the emitters, which is promising in the real-world deployment of direct daytime subambient radiative cooling