Intelligent Early Fault Diagnosis of Space Flywheel Rotor System.

Three frequently encountered problems-a variety of fault types, data with insufficient labels, and missing fault types-are the common challenges in the early fault diagnosis of space flywheel rotor systems. Focusing on the above issues, this paper proposes an intelligent early fault diagnosis method based on the multi-channel convolutional neural network with hierarchical branch and similarity clustering (HB-SC-MCCNN). First, a similarity clustering (SC) method is integrated into the parameter-shared dual MCCNN architecture to set up as the basic structural block. The hierarchical branch model and additional loss are then added to SC-MCCNN to form a hierarchical branch network, which simplifies the problem of fault multi-classification into binary classification with multi-steps. Based on the self-learning characteristics of the proposed model, the unlabeled data and the missing fault types in the training set are re-labeled to realize the re-training of the network. The results of the experiments for comparing the abilities between the proposed method and several advanced deep learning models confirm that on the established early fault dataset of the space flywheel rotor system, the proposed method successfully achieves the hierarchical diagnosis and presents stronger competitiveness in the case of insufficient labeled data and missing fault types at the same time.

Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer.

OBJECTIVE: To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC). METHODS: 106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness. RESULTS: LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05). CONCLUSION: The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.

[Lorenz plot and its application in automatic diagnoses of atrial fibrillation ECG signals].

In this paper is presented the Lorenz plot, the qualitative analyses and quantitative parameters of the plot. The long axis vs short axis(LVS) parameter is given. Atrial Fibrillation Electrocardiogram (AF ECG) is compared with sinus ECG by use of Lorenz plot. Then the difference in their plots and the parameter of LVS is pointed out. Lorenz plot finding is useful for the AF disease in automatic diagnoses of ECG signals.

[A simple and accurate algorithm for identifying the R waves].

QRS complex has its distinct characteristics as compared with other waves in Electrocardiogram (ECG). In this paper is introduced a kind of algorithm to identify the R waves, which combines forward-backward difference algorithm, half-wave width algorithm and R-R interval algorithm. The algorithm is simple and reliable. It is in possession of the ability of anti-interference such as baseline drifting, high P waves or high T waves, and severe high-frequency interference. The average accuracy for identification is higher than 99.5%. The results of the experiment and the analyses are also presented.