In the marine systems, engines represent the most important part of ships, the probability of the bearings fault is the highest in the engines, so in the bearing vibration analysis, early weak fault detection is very important for long term monitoring. In this paper, we propose a novel method to solve the early weak fault diagnosis of bearing. Firstly, we should improve the alternating direction method of multipliers (ADMM), structure of the traditional ADMM is changed, and then the improved ADMM is applied to the compressed sensing (CS) theory, which realizes the sparse optimization of bearing signal for a mount of data. After the sparse signal is reconstructed, the calculated signal is restored with the minimum entropy de-convolution (MED) to get clear fault information. Finally we adopt the sample entropy. Morphological mean square amplitude and the root mean square (RMS) to find the early fault diagnosis of bearing respectively, at the same time, we plot the Boxplot comparison chart to find the best of the three indicators. The experimental results prove that the proposed method can effectively identify the early weak fault diagnosis.

Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems.

Recent trend in medical imaging modalities and their applications in disease diagnosis: a review

There are two interesting properties in human brain. One is its massively interconnected structure. Another one is that human can handle outlier data effectively. For instance, human is able to recognize an object from an image with non-Gaussian noise. Artificial neural network is one of biologically inspired techniques. From the structural point of view, many neural network models have massively interconnected structures. Since the traditional analog neural network approach cannot handle an l
 1-norm-like objective function, it cannot be used to handle outlier data. This paper proposes two neural network models for the robust source localization problem in the time-of-arrival (TOA) model. Our development is based on the Lagrange programming neural network (LPNN) approach. To alleviate the influence of outliers, this paper introduces an l
 1-norm objective function. However, in the traditional LPNN approach, the constraints and the objective function must be differentiable. We devise two methods to handle the non-differentiable l
 1-norm term. The first method introduces an approximation to replace the l
 1-norm term. The second one uses the concept of hidden state from the locally competitive algorithm (LCA) to avoid the computation of the gradient vector at non-differentiable points. We also present the local stability of the two proposed models. From the simulations, our proposed methods are capable to handle the outliers and their error performances are better than many existing TOA algorithms.

Lagrange Programming Neural Network Approaches for Robust Time-of-Arrival Localization

Deep learning shows excellent performance in medical image segmentation. However, a pooling operation in its encoding stage leads to feature loss and the ability of multi-scale contextual information extraction is insufficient. In addition, there is a semantic gap between low-level features and high-level features in the encoder–decoder. Inspired by an U-shaped network (UNet), this work proposes a multi-scale dilation attention network (MSDANet) for medical image segmentation. Specifically, it is mainly composed of a parallel dilation pooling module (PD), a multi-scale channel attention mechanism (MSCA), a multilayer perceptron squeeze and excitation module (MSE), a big kernel convolution module (BC), and a skip feature pyramid module (SFP). The proposed PD reduces the loss of subtle features during downsampling. In order to improve the extraction of effective features, MSCA, MSE and BC are used in encoding and decoding stages. In the skip connection stage, SPF is used to reduce the difference between the low-level and high-level semantic information to accelerate the network learning. We validate the proposed model on two publicly available medical image datasets. The results show that MSDANet has superior performance on several evaluation metrics compared to state-of-the-art methods. Code is available at https://github.com/1999luan/MSDANet. 

MSDANet: A multi-scale dilation attention network for medical image segmentation

Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment.To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques.We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM).The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task.The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information. 

Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans

AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation

Lasso-type variable selection has been demonstrated to be effective in handling high-dimensional data. From the biological perspective, traditional Lasso-type models are capable of learning which stimuli are valuable while ignoring the many that are not, and thus perform feature selection. Traditional Lasso has the tendency to over-emphasize sparsity and to overlook the correlations between features. These drawbacks have been demonstrated to be critical in limiting its performance on real-world feature selection problems. Although some work has considered the problem of correlation, the issue of discriminative ability resulting from sparsity has been overlooked. To overcome this shortcoming, we propose a discriminative Lasso (referred to as dLasso) in which sparsity and correlation are jointly considered. Specifically, the new method can select features (or stimuli) that are correlated more strongly with the response but are less correlated with each other. Moreover, an efficient alternating direction method of multipliers (ADMM) is presented to solve the resulting sparse non-convex optimization problem. Extensive experiments on different datasets show that although our proposed model is not a convex problem, it outperforms both its approximately convex counterparts and a number of state-of-the-art feature selection methods.

Discriminative Lasso

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AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation

Discriminative Lasso