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This paper presents an acoustic emission (AE) detection method for refined oil storage tanks which is aimed towards specialized places such as oil storage tanks with high explosion-proof requirements, such as cave oil tanks and buried oil tanks. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. An independent channel AE detection system is designed, which can store the collected data in a piece of independent explosion-proof equipment, and can analyze and process the data in a safe area after the detection, solving the problems of a short signal acquisition distance and the weak safety protection applied to traditional AE instruments. A location analysis of the AE sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open-tank tests confirms that using AE technology effectively captures corrosion signals from oil storage tanks' bottoms. The feasibility of carrying out online inspection under the condition of oil storage in vertical steel oil tanks was verified through a comparison with open inspections, which provided a guide for determining the inspection target and opening order of large-scale oil tanks. [ABSTRACT FROM AUTHOR]

The comprehension of spoken language is a crucial aspect of dialogue systems, encompassing two fundamental tasks: intent classification and slot filling. Currently, the joint modeling approach for these two tasks has emerged as the dominant method in spoken language understanding modeling. However, the existing joint models have limitations in terms of their relevancy and utilization of contextual semantic features between the multiple tasks. To address these limitations, a joint model based on BERT and semantic fusion (JMBSF) is proposed. The model employs pre-trained BERT to extract semantic features and utilizes semantic fusion to associate and integrate this information. The results of experiments on two benchmark datasets, ATIS and Snips, in spoken language comprehension demonstrate that the proposed JMBSF model attains 98.80% and 99.71% intent classification accuracy, 98.25% and 97.24% slot-filling F1-score, and 93.40% and 93.57% sentence accuracy, respectively. These results reveal a significant improvement compared to other joint models. Furthermore, comprehensive ablation studies affirm the effectiveness of each component in the design of JMBSF. [ABSTRACT FROM AUTHOR]

Object detection based on deep learning is one of the most important and fundamental tasks of computer vision. High-performance detection algorithms have been widely used in many practical fields. For the management of workers wearing helmets in construction scenarios, this paper proposes a framework model based on the YOLOv5 detection algorithm, combined with multi-object tracking algorithms, to monitor and track whether workers wear safety helmets in real-time video. The improved StrongSORT tracking algorithm of DeepSORT is selected to reduce the loss of the tracked object caused by the occlusion, trajectory blur, and motion scale of the object. The safety helmet dataset is trained with YOLOv5s, and the best result of training is used as the weight model in the StrongSORT tracking algorithm. The experimental results show that the mAP@0.5 of all classes in the YOLOv5s model can reach 95.1% in the validation dataset, mAP@0.5:0.95 is 62.1%, and the precision of wearing helmet is 95.7%. After the box regression loss function was changed from CIOU to Focal-EIOU, the mAP@0.5 increased to 95.4%, mAP@0.5:0.95 increased to 62.9%, and the precision of wearing helmet increased to 96.5%, which were increased by 0.3%, 0.8% and 0.8%, respectively. StrongSORT can update object trajectories in video frames at a speed of 0.05 s per frame. Based on the improved YOLOv5s combined with the StrongSORT tracking algorithm, the helmet-wearing tracking detection can achieve better performance. [ABSTRACT FROM AUTHOR]

In this study, nanostructured gold was successfully prepared on a bare Au electrode using the electrochemical deposition method. Nanostructured gold provided more exposed active sites to facilitate the ion and electron transfer during the electrocatalytic reaction of organophosphorus pesticide (methyl parathion). The morphological and structural characterization of nanostructured gold was conducted using field-emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD), which was further carried out to evaluate the electrocatalytic activity towards methyl parathion sensing. The electrochemical performance of nanostructured gold was investigated by electrochemical measurements (cyclic voltammetry (CV) and differential pulse voltammetry (DPV)). The proposed nanostructured gold-modified electrode exhibited prominent electrochemical methyl parathion sensing performance (including two linear concentration ranges from 0.01 to 0.5 ppm (R2 = 0.993) and from 0.5 to 4 ppm (R2 = 0.996), limit of detection of 5.9 ppb, excellent selectivity and stability), and excellent capability in determination of pesticide residue in real fruit and vegetable samples (bok choy and strawberry). The study demonstrated that the presented approach to fabricate a nanostructured gold-modified electrode could be practically applied to detect pesticide residue in agricultural products via integrating the electrochemical and gas chromatography coupled with mass spectrometry (GC/MS-MS) analysis. [ABSTRACT FROM AUTHOR]

Formaldehyde (HCHO) gas sensors with high performance based on the ZnO/CuO heterostructure (ZC) were designed, and the sensing mechanism was explored. FTIR results show that more OH− and N–H groups appeared on the surface of ZC with an increase in Cu content. XPS results show that ZC has more free oxygen radicals (O*) on its surface compared with ZnO, which will react with more absorbed HCHO molecules to form CO2, H2O and, electrons, accelerating the oxidation-reduction reaction to enhance the sensitivity of the ZC sensor. Furthermore, electrons move from ZnO to CuO in the ZC heterostructure due to the higher Fermi level of ZnO, and holes move from CuO to ZnO until the Fermi level reaches an equilibrium, which means the ZC heterostructure facilitates more free electrons existing on the surface of ZC. Sensing tests show that ZC has a low detection limit (0.079 ppm), a fast response/recovery time (1.78/2.90 s), and excellent selectivity and sensitivity for HCHO detection at room temperature. In addition, ambient humidity has little effect on the ZC gas sensor. All results indicate that the performance of the ZnO sensor for HCHO detection can be improved effectively by ZC heterojunction. [ABSTRACT FROM AUTHOR]

In recent years, deep learning-based speech synthesis has attracted a lot of attention from the machine learning and speech communities. In this paper, we propose Mixture-TTS, a non-autoregressive speech synthesis model based on mixture alignment mechanism. Mixture-TTS aims to optimize the alignment information between text sequences and mel-spectrogram. Mixture-TTS uses a linguistic encoder based on soft phoneme-level alignment and hard word-level alignment approaches, which explicitly extract word-level semantic information, and introduce pitch and energy predictors to optimally predict the rhythmic information of the audio. Specifically, Mixture-TTS introduces a post-net based on a five-layer 1D convolution network to optimize the reconfiguration capability of the mel-spectrogram. We connect the output of the decoder to the post-net through the residual network. The mel-spectrogram is converted into the final audio by the HiFi-GAN vocoder. We evaluate the performance of the Mixture-TTS on the AISHELL3 and LJSpeech datasets. Experimental results show that Mixture-TTS is somewhat better in alignment information between the text sequences and mel-spectrogram, and is able to achieve high-quality audio. The ablation studies demonstrate that the structure of Mixture-TTS is effective. [ABSTRACT FROM AUTHOR]

The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm–artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space–time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction's NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution. [ABSTRACT FROM AUTHOR]

Stress detection of the conical frustum window is a very important issue to ensure the safety of deep manned submersibles. In this paper, we propose a method based on polarization imaging to evaluate the stress accumulation and recovery in the conical frustum window. An experimental setup of Mueller matrix polarimetry is built, and the samples are made by referring to the typical conical frustum windows in submersibles. By pressurizing different pressures on the samples, we can find the changes of their Mueller matrix images and further derived polarization parameters. The results show that the polarization parameters can characterize the stress transfer process and the elastic–plastic transformation process of the window under different pressurization pressures. We also use a two-layered wave plate model to simulate the stress distribution in the window, which reveals different performances of the former and latter layers of the window under pressurization. Finally, we use a finite element model to simulate and understand some of the above experimental results. This proposed method is expected to provide new possibilities for monitoring the window stress and further ensure the safety of deep manned submersibles. [ABSTRACT FROM AUTHOR]