Real-time identification of threats has been the focus of attention in the security field in recent years. Researchers have aimed to build a fast, efficient, and accurate model to address risks in threat event identification. This study proposes a fast, effective deep learning model-the squeeze and excitation WaveNet (SE-WaveNet). WaveNet is a 1-D convolutional neural network (CNN) model with a large receptive field. It is small, fast, and highly accurate. Thus, it has a great advantage in processing time series data. On this basis, the squeeze and excitation (SE) structure of the attention mechanism is introduced to adapt to matrix data with complete information. To ensure the integrity of information in the experimental verification, the distributed optical fiber vibration sensing (DVS) system is used to collect the data near the vibration point and form the time-space matrix as the input of the model. SE-WaveNet is compared with WaveNet and related models. Results show that the accuracy of SE-WaveNet based on WaveNet is increased by approximately 3.5%, the model size is reduced by 39%, and the speed is increased by 32%. Compared with the five other related models, the average performance is 3%-9% higher, and the final test accuracy rate can reach approximately 97.73%.

Background: In medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion speed but the fusion image texture is blurred; the latter has a better fusion effect but requires higher machine computing capabilities. Therefore, how to find a balanced algorithm in terms of image quality, speed and computing power is still the focus of all scholars.Methods: We built an end-to-end Hahn-PCNN-CNN. The network is composed of feature extraction module, feature fusion module and image reconstruction module. We selected 8000 multi-modal brain medical images downloaded from the Harvard Medical School website to train the feature extraction layer and image reconstruction layer to enhance the network’s ability to reconstruct brain medical images. In the feature fusion module, we use the moments of the feature map combined with the pulse-coupled neural network to reduce the information loss caused by convolution in the previous fusion module and save time.Results: We choose eight sets of registered multi-modal brain medical images in four diease to verify our model. The anatomical structure images are from MRI and the functional metabolism images are SPECT and 18F-FDG. At the same time, we also selected eight representative fusion models as comparative experiments. In terms of objective quality evaluation, we select six evaluation metrics in five categories to evaluate our model.Conclusions: The fusion image obtained by our model can retain the effective information in source images to the greatest extent. In terms of image fusion evaluation metrics, our model is superior to other comparison algorithms. In terms of time computational efficiency, our model also performs well. In terms of robustness, our model is very stable and can be generalized to multi-modal image fusion of other organs.

Magnetic resonance sounding (MRS) is a novel geophysical method to detect groundwater directly. By applying this method to underground projects in mines and tunnels, warning information can be provided on water bodies that are hidden in front prior to excavation and thus reduce the risk of casualties and accidents. However, unlike its application to ground surfaces, the application of MRS to underground environments is constrained by the narrow space, quite weak MRS signal, and complex electromagnetic interferences with high intensities in mines. Focusing on the special requirements of underground MRS (UMRS) detection, this study proposes the use of an antenna with different turn numbers, which employs a separated transmitter and receiver. We designed a stationary coil with stable performance parameters and with a side length of 2 m, a matching circuit based on a Q-switch and a multi-stage broad/narrowband mixed filter that can cancel out most electromagnetic noise. In addition, noises in the pass-band are further eliminated by adopting statistical criteria and harmonic modeling and stacking, all of which together allow weak UMRS signals to be reliably detected. Finally, we conducted a field case study of the UMRS measurement in the Wujiagou Mine in Shanxi Province, China, with known water bodies. Our results show that the method proposed in this study can be used to obtain UMRS signals in narrow mine environments, and the inverted hydrological information generally agrees with the actual situation. Thus, we conclude that the UMRS method proposed in this study can be used for predicting hazardous water bodies at a distance of 7–9 m in front of the wall for underground mining projects.

In order to obtain the physiological information and key features of source images to the maximum extent, improve the visual effect and clarity of the fused image, and reduce the computation, a multi-modal medical image fusion framework based on feature reuse is proposed. The framework consists of intuitive fuzzy processing (IFP), capture image details network (CIDN), fusion, and decoding. First, the membership function of the image is redefined to remove redundant features and obtain the image with complete features. Then, inspired by DenseNet, we proposed a new encoder to capture all the medical information features in the source image. In the fusion layer, we calculate the weight of each feature graph in the required fusion coefficient according to the trajectory of the feature graph. Finally, the filtered medical information is spliced and decoded to reproduce the required fusion image. In the encoding and image reconstruction networks, the mixed loss function of cross entropy and structural similarity is adopted to greatly reduce the information loss in image fusion. To assess performance, we conducted three sets of experiments on medical images of different grayscales and colors. Experimental results show that the proposed algorithm has advantages not only in detail and structure recognition but also in visual features and time complexity compared with other algorithms.

Semi-airborne Electromagnetic Method (SAEM) is an emerging geophysical exploration method, which can be efficiently carried out in complex terrain conditions.SAEM signals are often mixed with various noise interference, which seriously affects the signal quality. However, the amount of SAEM data is extremely large, and the number of signals of a single survey line is in the millions, and the existing noise elimination methods usually intercept the signals in segments and then process them, which is inefficient and has strong subjective factors. To address the above problems, this paper proposes a recurrent self-coding neural network (RSCN) for one-stop noise cancellation of the whole measurement line signal. The method adopts the Gated Recurrent Unit(GRU) to obtain the full waveform state, and adopts the data segmentation technique to obtain the small-scale data and perform the convolutional coding. The state information as well as the coded data is used to extract important features using an attention mechanism and decoded. The self-encoding structure is updated cyclically to realize one-stop noise cancellation for the whole measurement line data. The test results of simulated and measured data show that the method proposed in this paper has a good denoising effect on the full survey line SAEM data, and is more adaptable to the SAEM data than the existing noise reduction methods.