Sleep Apnea Detection Based on Multi-Scale Residual Network

Aiming at the fact that traditional convolutional neural networks cannot effectively extract signal features in complex application scenarios, a sleep apnea (SA) detection method based on multi-scale residual networks is proposed. First, we analyze the physiological mechanism of SA, which uses the RR interval signals and R peak signals derived from the ECG signals as input. Then, a multi-scale residual network is used to extract the characteristics of the original signals in order to obtain sensitive characteristics from various angles. Because the residual structure is used in the model, the problem of model degradation can be avoided. Finally, a fully connected layer is introduced for SA detection. In order to overcome the impact of class imbalance, a focal loss function is introduced to replace the traditional cross-entropy loss function, which makes the model pay more attention to learning difficult samples in the training phase. Experimental results from the Apnea-ECG dataset show that the accuracy, sensitivity and specificity of the proposed multi-scale residual network are 86.0%, 84.1% and 87.1%, respectively. These results indicate that the proposed method not only achieves greater recognition accuracy than other methods, but it also effectively resolves the problem of low sensitivity caused by class imbalance.

Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

The Inhibition Effect of Linezolid With Reyanning Mixture on MRSA and its Biofilm is More Significant than That of Linezolid Alone

Methicillin-resistant Staphylococcus aureus (MRSA) is a superbacterium, and when it forms biofilms, it is difficult to treat even with the first-line of antibiotic linezolid (LNZ). Reyanning mixture (RYN), a compound-based Chinese medicine formula, has been found to have inhibitory effects on biofilms. This study aims to explore the synergistic inhibitory effect and corresponding mechanisms of their (LNZ&RYN) combination on the planktonic as well as biofilm cells of MRSA. Broth microdilution and chessboard methods were employed for the determination of minimum inhibitory concentrations (MICs) and synergistic concentration of LNZ&RYN, respectively. The effect of the combined medication on biofilm and mature biofilm of MRSA were observed by biofilm morphology and permeability experiments, respectively. To unveil the molecular mechanism of action of the synergistic combination of LNZ and RYN, RT-PCR based biofilm-related gene expression analysis and ultra-high pressure liquid chromatography-time-of-flight mass spectrometry based endogenous metabonomic analysis were deployed. The results indicated that 1/16RYN as the best combined dose reduced LNZ (4 μg/ml) to 2 μg/ml. The combined treatment inhibited living MRSA before and after biofilm formation, removed the residual structure of dead bacteria in MRSA biofilms and affected the shape and size of bacteria, resulting in the improvement of biofilm permeability. The mechanism was that biofilm-related genes such as agrC, atlA, and sarA, as well as amino acid uptake associated with the metabolism of 3-dehydrocarnitine, kynurenine, L-leucine, L-lysine and sebacic acid were inhibited. This study provides evidence for the treatment of MRSA and its biofilms with LNZ combined with RYN.

Research on the Basketball Goal Recognition Method Based on Improved MobileNet

Moving target detection is involved in many engineering projects, but it is difficult because of the strong time-varying speed and uncertain path. Goal recognition is the key technology of the basketball goal automatic test. Also, accurate and timely judgment of basketball goals has important practical value. Therefore, a basketball goal recognition method based on an improved lightweight deep learning network model (L-MobileNet) is proposed. First of all, the basket detection is carried out by the Hough circle transform algorithm. Then, in order to further improve the detection speed of basketball goals, based on the lightweight network MobileNet, an improved lightweight network (L-MobileNet) is proposed. First of all, for deeply separable convolution, channel compression and block convolution reduce the parameters and computational complexity of the module. At the same time, because block convolution will hinder the information exchange between characteristic channels, an improved channel shuffling method, IShuffle, is introduced. Then, combined with the residual structure to improve the generalization ability of the network, the RLDWS module is constructed. Finally, a more lightweight network L-MobileNet is constructed by using the RLDWS module. The experimental results show that the proposed method can effectively realize the judgment of basketball goals, and the judgment accuracy is improved by 8.35%. At the same time, the amount of parameters and computation is only 29.7% and 53.2% of the original, and it also has certain advantages compared with other lightweight networks.

Machine remaining life prediction based on multi-layer self-attention and temporal convolution network

Convolution neural network (CNN) has been widely used in the field of remaining useful life (RUL) prediction. However, the CNN-based RUL prediction methods have some limitations. The receptive field of CNN is limited and easy to happen gradient vanishing problem when the network is too deep. The contribution differences of different channels and different time steps to RUL prediction are not considered, and only use deep learning features or handcrafted statistical features for prediction. These limitations can lead to inaccurate prediction results. To solve these problems, this paper proposes an RUL prediction method based on multi-layer self-attention (MLSA) and temporal convolution network (TCN). The TCN is used to extract deep learning features. Dilated convolution and residual connection are adopted in TCN structure. Dilated convolution is an efficient way to widen receptive field, and the residual structure can avoid the gradient vanishing problem. Besides, we propose a feature fusion method to fuse deep learning features and statistical features. And the MLSA is designed to adaptively assign feature weights. Finally, the turbofan engine dataset is used to verify the proposed method. Experimental results indicate the effectiveness of the proposed method.

In Situ Remediation of Phosphogypsum with Water-Washing Pre-Treatment Using Cemented Paste Backfill: Rheology Behavior and Damage Evolution

The accumulation of original phosphogypsum (OPG) has occupied considerable land resources, which have induced significant environmental problems worldwide. The OPG-based cemented paste backfill (OCPB) has been introduced as a promising solution. In this study, a water-washing pre-treatment was used to purify OPG, aiming to optimize the transport performance and mechanical properties of backfills. The overall results proved that in treated phosphogypsum-based cemented paste backfill (TCPB), the altered particle size distribution can alleviate the shear-thinning characteristic. The mechanical properties were significantly optimized, of which a maximum increase of 183% of stress value was observed. With more pronounced AE signals, the TCPB samples demonstrated better residual structures after the ultimate strength values but with more unstable cracks with high amplitude generated during loading. Principal component analysis confirmed the adverse effects of fluorine and phosphorus on the damage fractal dimensions. The most voluminous hydration products observed were amorphous CSH and ettringite. The interlocked stellate clusters may be associated with the residual structure and the after-peak AE events evident in TCPB, indicate that more significant stress should be applied to break the closely interlocked stitches. Ultimately, the essential findings in this experimental work can provide a scientific reference for efficient OPG recycling.

ACRnet: Adaptive Cross-transfer Residual neural network for chest X-ray images discrimination

Background: Cardiothoracic diseases are a serious threat to human health and chest X-ray images have great reference value for the diagnosis and treatment. However, it is difficult for professional doctors to accurately diagnose cardiothoracic diseases through chest X-ray images sometimes and there will be different understanding based on human subjective differences, which will affect the judgment and treatment of diseases. Therefore, it is very necessary to develop a high-precision neural network to recognize chest X-ray images of cardiothoracic diseases.Methods: In this work, we cross-transfer the information extracted by the residual block and by the adaptive structure to different levels, which avoids the reduction of the adaptive function by residual structure and improves the recognition performance of the model. To evaluate the recognition ability of ACRnet, VGG16, InceptionV2, ResNet101 and CliqueNet are used for comparison. In addition, we use the deep convolution generative adversarial network (DCGAN) to expand the original dataset.Result: We find ACRnet has better recognition ability than other networks in identifying cardiomegaly, emphysema and normal. Besides, ACRnet's recognition ability has been greatly improved after data expansion. Conclusions: The experimental result indicates that emphysema and cardiomegaly can be effectively identified by ACRnet. Using DCGAN technology to expand the dataset can further improve the recognition ability of the model.

ERDBNet: Enhanced Residual Dense Block Net --A New Net to Rich ESRGAN Image Details

Super resolution is applied in many digital image fields. In many cases, only a set of low-resolution images can be obtained, but the image needs a higher resolution, and then SR needs to be applied. SR technology has undergone years of development. Among them, SRGAN is the key work to introduce GAN into the SR field, which can truly restore a large number of details on the basis of low-pixel pictures. ESRGAN is a further improvement on SRGAN. By removing the BN layer in SRGAN, the effect of artifacts in SRGAN is eliminated. However, there is still a problem that the restoration of information on small and medium scales is not accurate enough. The proposed ERDBNet improve the model on the basis of ESRGAN, and use the ERDB block to replace the original RRDB block. The new structure uses a three-layer dense block to replace the original dense block, and a residual structure of the starting point is added to each dense block. The pre-trained network can reach a PSNR of 30.425 after 200k iterations, and the minimum floating PSNR is only 30.213. Compared with the original structure, it is more stable and performs better in the detail recovery of many low-pixel images.

Improved U-Net3+ with Stage Residual for Brain Tumor Segmentation

Background For the coding part of U-Net3+, the brain tumor feature extraction ability is insufficient, leading to insufficient feature fusion when sampling on the network and reducing the segmentation accuracy. Methods In this study, we propose an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to reduce the degradation problem caused by the increase in network depth and enhance the feature extraction ability of the encoder, which is convenient for full feature fusion when sampling on the network. Besides, we used a filter response normalization (FRN) layer instead of a batch normalization layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We explore appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network. Results The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus. Conclusion In the segmentation task of brain tumor brats2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3 +, the proposed network has smaller parameters and significantly improved accuracy.