Accurate and fast-convergent time synchronization is very important for wireless sensor networks. The flooding time synchronization converges fast, but its transmission delay and by-hop error accumulation seriously reduce the synchronization accuracy. In this paper, a rapid-flooding multiple one-way broadcast time-synchronization (RMTS) protocol for large-scale wireless sensor networks is proposed. To minimize the by-hop error accumulation, the RMTS uses maximum likelihood estimations for clock skew estimation and clock offset estimation, and quickly shares the estimations among the networks. As a result, the synchronization error resulting from delays is greatly reduced, while faster convergence and higher-accuracy synchronization is achieved. Extensive experimental results demonstrate that, even over 24-hops networks, the RMTS is able to build accurate synchronization at the third synchronization period, and moreover, the by-hop error accumulation is slower when the network diameter increases.

Cs₂LiYCl₆: Ce³⁺ (CLYC) is a dual-mode gamma-neutron scintillator with a medium gamma-ray resolution and pulse-shape discrimination (PSD) capability. The PSD performance of CLYC is greatly weakened when coupled with silicon photomultipliers (SiPMs) because of SiPMs’ low detection efficiency for the ultrafast Core-Valence-Luminescence (CVL) component under gamma excitation. In our previous work, the PSD Figure-of-Merit (FoM) value was optimized to 2.45 at the gamma-equivalent energy region of the thermal neutron by using the charge comparison method. However, this value was reduced to 1.37 at the lower gamma-equivalent energy region of more than 325 keV, and neutrons were difficult to distinguish from gamma rays. Hence, new algorithms should be studied to improve the PSD performance at low gamma-equivalent energy regions. Convolutional Neural Networks (CNNs) have excellent image recognition capabilities, and thus, neutron and gamma-ray waveforms can be discriminated by their characteristics through a known training set. In this study, neutron and gamma-ray waveforms were measured with a ¹³⁷Cs source and moderated ²⁵²Cf source via an SiPM array-coupled CLYC detector and divided into two groups: training and PSD testing. The CNN training set comprised ¹³⁷Cs characteristic gamma-ray waveforms and thermal neutron waveforms that were discriminated by the charge comparison method from the training group. A CNN with two convolution-pooling layers was designed to accomplish PSD with the test group. The PSD FoM value of the CNN method was calculated to be 37.20 at the gamma-equivalent energy region of more than 325 keV. This result was much higher than that of the charge comparison method, indicating that neutrons and gamma rays could be better distinguished with the CNN method, especially at low gamma-equivalent energy regions.

Rockburst is a common geological disaster in mines, tunnels, deep underground engineering, and during excavation, mining, and construction. Rockburst frequently occurs as the depth of burial increases, and its early warning technology is in urgent need of further development. At present, the most effective monitoring and analysis method of rockburst is microseismic technology, which detects a large number of rock micro-fracture signals through geophones. The identification of microseismic monitoring data is an essential part of microseismic data processing. It is necessary to identify effective microseismic signals from considerable monitoring data for subsequent early warning. Aiming at the identification of rock micro-fracture signals, this thesis proposes a microseismic data identification method based on the Deep Convolution Neural Network Inception (DCNN-Inception) algorithm. The algorithm uses an existing Convolutional Neural Network (CNN) model, adding Inception structure in the middle of the model to form a DCNN-Inception model. A data set was established depending on the actual measured data of Baihetan Hydropower Station, and CNN and DCNN-Inception were employed to identify effective microseismic signals. The results demonstrate that the DCNN-Inception algorithm is better than CNN in recognition accuracy and can effectively identify effective microseismic signals. It provides an essential foundation for the identification of microseismic abnormal signals of rock microfracture and the early warning of rock rupture precursors and is of practical significance for the study of rockburst warning technology.

Rock burst early warning technology is currently applied mainly in microseismic monitoring. Rock burst signals indicate the micro-fracture phenomena of a rock and can transmit earthquake waves through the rock before they are finally received by a detector. A characteristic decomposition of rock micro-fracture signals was conducted by the singular value Empirical Mode Decomposition (EMD) algorithm to effectively decompose the characteristic signals of a rock micro-fracture from mixed microseismic signals, with a low signal to noise ratio to ensure prediction precision. When comparing the proposed method with wavelet decomposition and EMD, it was found that the local characteristics of the signals were retained effectively. The proposed algorithm was verified by applying it in a laboratory simulation and to the decomposition of microseismic signals from a hydro-power station. It was concluded that the improved algorithm had a better decomposition precision than wavelet decomposition and EMD decomposition and could effectively separate the characteristic signals of micro-earthquakes. This could provide a significant basis for the identification of the abnormal microseismic signals of rock micro-fractures as well as a pre-warning of rock fractures. It is therefore of practical significance to study rock fracture early warning technology.

We present an open‐source and MATLAB‐based tool with an easy‐to‐use graphical user interface (GUI) consisting of four polarization analysis approaches: the particle‐motion trajectory (a hodogram in a 3D plane), eigenvalue decomposition (EVD) based on the covariance matrix (including two calculation methods), singular value decomposition using principal component analysis, and EVD based on a constructed analytic signal matrix (EVD‐ASM). We review the calculation processes and features of the four cited methods. The eigenvalue and eigenvector are applied to obtain the polarization attributes of the three‐component (3C) seismic data. Using rose graphs and histograms, the corresponding azimuth and incidence angle are calculated to determine the propagation direction of the seismic wave. Statistical distribution curves of the corresponding rectilinearity and planarity of the waves are also plotted. The polarization analysis GUI can simultaneously analyze two selected data sections in a seismic recording corresponding to P and S waves. We evaluate the performance of these algorithms using real 3C earthquake datasets. Comparison tests indicate that the aforementioned four methods have different time consumption, and the differences between the results of the EVD‐ASM and those of the other methods are very small.

Foam mixers are classified as low-pressure and high-pressure types. Low-pressure mixers rely on agitator rotation, facing cleaning challenges and complex designs. High-pressure mixers are simple and require no cleaning but struggle with uneven mixing for high-viscosity substances. Traditionally, increasing the working pressure resolved this, but material quality limits it at higher pressures. To address the issues faced by high-pressure mixers when handling high-viscosity materials and to further improve the mixing performance of the mixer, this study focuses on a polyimide high-pressure mixer, identifying four design variables: impingement angle, inlet and outlet diameters, and impingement pressure. Using a Full Factorial Design of Experiments (DOE), the study investigates the impacts of these variables on mixing unevenness. Sample points were generated using Optimal Latin Hypercube Sampling—OLH. Combining the Sparrow Search Algorithm (SSA), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM), the SSA-CNN-LSTM model was constructed for predictive analysis. The Whale Optimization Algorithm (WOA) optimized the model, to find an optimal design variable combination. The Computational Fluid Dynamics (CFD) simulation results indicate a 70% reduction in mixing unevenness through algorithmic optimization, significantly improving the mixer’s performance.