ABSTRACT
LaFe x Ni1- x O3 perovskite oxides were prepared by the sol-gel method under various conditions, including different pH values (pH 0 and pH 7) and different calcination temperatures (500-800 °C) as well as different Fe/Ni ratios (1/9, 3/7, 5/5, 7/3, 9/1). The samples were examined by XRD, DRS, BET, and SEM to reveal their crystallinity, light-absorption ability, specific surface area, and surface features, respectively. The photocatalytic Fenton reaction was conducted using various LaFe x Ni1- x O3 perovskite oxides to decompose the methylene blue molecules. Accordingly, the synthesis condition of pH 0, calcination temperature at 700 °C, and Fe/Ni ratio = 7/3 could form LaFe0.7Ni0.3O3 perovskite oxides as highly efficient photocatalysts. Moreover, various conditions during the photocatalytic degradation were verified, such as pH value, catalyst dosage, and the additional amount of H2O2. LaFe0.7Ni0.3O3 perovskite oxides could operate efficiently under pH 3.5, catalyst dosage of 50 mg/150 mL, and H2O2 concentration of 133 ppm to decompose the MB dye in the 1st order kinetic rate constant of 0.0506 s-1.
ABSTRACT
In this research, a novel sound source localization model is introduced that integrates a convolutional neural network with a regression model (CNN-R) to estimate the sound source angle and distance based on the acoustic characteristics of the interaural phase difference (IPD). The IPD features of the sound signal are firstly extracted from time-frequency domain by short-time Fourier transform (STFT). Then, the IPD features map is fed to the CNN-R model as an image for sound source localization. The Pyroomacoustics platform and the multichannel impulse response database (MIRD) are used to generate both simulated and real room impulse response (RIR) datasets. The experimental results show that an average accuracy of 98.96% and 98.31% are achieved by the proposed CNN-R for angle and distance estimations in the simulation scenario at SNR = 30 dB and RT60 = 0.16 s, respectively. Moreover, in the real environment, the average accuracies of the angle and distance estimations are 99.85% and 99.38% at SNR = 30 dB and RT60 = 0.16 s, respectively. The performance obtained in both scenarios is superior to that of existing models, indicating the potential of the proposed CNN-R model for real-life applications.
Subject(s)
Neural Networks, Computer , Sound , Computer Simulation , Databases, FactualABSTRACT
Incorporation of carbon nanotubes into nylon-6 creates interfacial phonons; phonon occupation at low frequency regime is therefore enhanced, leading to thermal conductivity improvement by 74-78%.
