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A traceability and analysis method for measurement laboratory testing data based on the intelligent Internet of Things (IoT) and deep belief network (DBN) is proposed to address the issue of low accuracy in identifying anomalies in measurement testing data and difficulty in identifying the causes of anomalies. First, a data analysis system for the metrology laboratory is designed based on an intelligent IoT architecture of “cloud-management-edge-end.” Then, the Gaussian Bernoulli-Restricted Boltzmann machine is introduced to improve the DBN model, which is deployed on the edge side for learning the ledger data sample library to determine the anomaly detection data of the metrology device. Finally, a stacked denoising autoencoder model is used in the cloud center to extract historical electricity consumption curve features, and the cause of anomalies is determined by calculating the cosine similarity between it and the target device feature curve to complete traceability analysis. Based on the selected dataset, the proposed method is experimentally demonstrated, and the results show that its traceability accuracy and time consumption are 88.72% and 3.949 s, respectively, which can meet the detection requirements of the metrology laboratory.

To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues. [ABSTRACT FROM AUTHOR]

Raman spectroscopy has been widely applied in numerous fields including bioanalysis, disease diagnosis, and molecular recognition, owing to its unique advantages of being non-invasive, rapid, and highly specific. However, the acquisition of Raman spectral data is often susceptible to various noise interferences, such as shot noise from the internal detector and dark current noise from the instrument. As a result, the weak Raman signals typically become arduous to discern, which affects the localization and identification of characteristic spectral peaks. This study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. By combining VMD and EWT, the Raman spectra are decomposed into several sub-sequences, which solves the problem of mode mixing between high-frequency signals and noise in empirical mode decomposition, and significantly reduces the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is calculated along with the zero-crossing rate index to categorize the noise and signal sequences. Leveraging the linear differences between the ideal spectral lines and the noisy spectral curves, an encoder-bidirectional long short-term memory (EBiLSTM) denoising network is constructed for hierarchical denoising to extract valid spectral feature information from the high-frequency components, realizing refined adaptive denoising of the Raman spectra. Industry standard objective evaluation metrics on the signal-to-noise ratio and root mean square error are utilized to conduct simulation experiments comparing state-of-the-art algorithms, including empirical mode decomposition, VMD, sliding window averaging, and wavelet thresholding. The experimental results demonstrate that the Raman spectral denoising algorithm combining variational mode decomposition and neural networks improves the denoising performance by 13.38% to 72%, exhibiting higher accuracy and reliability. [ABSTRACT FROM AUTHOR]

Single-lens imaging systems can capture spectral information, but they are affected by aberrations, defocusing, and other factors, leading to spectral data overlap. It is then necessary to perform restoration on overlapped spectral data. The restoration method should not only deblur image information, but also recover spectral information for each wavelength band. In other words, the restoration process needs to handle both spatial and spectral information in parallel while ensuring that neither is distorted. In this study, while considering the characteristics of capturing overlapped information in a single-lens imaging system, a spectrum restoration method based on gradient prior information optimization is proposed. This method is shown to achieve high-quality restoration of both images and spectra. The feasibility of this algorithm is demonstrated through simulation and experimental verification, which show that the restoration quality of spectra using the proposed algorithm is improved compared to that achieved using the MTV algorithm. [ABSTRACT FROM AUTHOR]

AlSi10Mg alloy modified with 4 wt.% cerium was fabricated by selective laser melting and subjected to a T6 solutionizing (2 h at 535°C) and ageing (10 h at 160°C) heat treatment. Microstructure, precipitates and hardness of as-built and heat-treated samples were analysed. Results show that as-built samples contained a refined cellular-dendritic microstructure as well as Ce-containing precipitates at heat-affected zone (HAZ). After T6 heat treatment, cellular-dendritic microstructure converted to fine microstructure containing Si agglomerates and sub-micron Ce-containing precipitates, while precipitates present in as-built HAZ underwent spheroidisation. Hardness testing showed that addition of Ce did not affect alloy hardness. However, the fine dispersion of Ce-containing precipitates and good coarsening resistance shows that Ce is a promising alloying element for the development of heat-resistant alloys. [ABSTRACT FROM AUTHOR]