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Due to the fault vibration signal of the rolling bearing is greatly interfered by the background noise, the fault features are easily submerged and result in a low fault diagnosis accuracy. A novel fault diagnosis method of rolling bearing is proposed based on improved VMD-adaptive wavelet threshold combined with noise reduction in this paper. Firstly, the modal components are obtained based on VMD decomposition; Secondly, the dual determination criteria of sample entropy and correlation coefficient are constructed to filter the components; Subsequently, an adaptive wavelet thresholding function is proposed, and quadratic noise reduction is applied to mixed IMFs, which in turn reconstructs each component to achieve joint noise reduction. Finally, based on traditional machine learning and deep learning diagnosis methods, the features of noise reduction signals are extracted to realize fault diagnosis. By verifying and analyzing the simulated signal with the measured signal, noise components, the expression of fault characteristics, and the accuracy of fault diagnosis are eliminated, enhanced, and improved.

The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications.

Ground deformation poses a serious threat to the safety of subway structures. Consequently, intelligent and efficient automated safety monitoring of ground deformation along the subway has become urgent. Traditional engineering observation methods have the disadvantages of difficulties with datum selection, non-automation, and poor reliability. A ground deformation monitoring system for subway structure safety based on the Global Navigation Satellite System (GNSS) was established and validated through experimental comparisons with traditional precision leveling in this study. Based on the GNSS monitoring points, the continuous kinematic observation GNSS data of ground deformation along the subway line were obtained; a joint robust local mean decomposition (RLMD)–singular value decomposition (SVD) noise-reduction processing method for GNSS signals was proposed to realize the real-time and high-precision monitoring of ground deformation. The results show that the proposed combined noise-reduction method can reduce the maximum noise amplitude by 86%. When compared with the accuracy of the traditional precision leveling method, it was determined that the vertical positioning accuracy of the deformation monitoring system is greater than 2.7 mm, the horizontal positioning accuracy is greater than 1.3 mm, and the measurement error is less than 1.5 mm. The deformation monitoring system has the advantages of convenience, automation, and high accuracy and can be applied to ground deformation monitoring for subway structures.

Objectives To compare the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) MRI sequences with post-processed PROPELLER MRI sequences using deep learning-based (DL) reconstructions. Methods In this prospective study of 30 patients, conventional (19 min 18 s) and accelerated MRI sequences (7 min 16 s) using the PROPELLER technique were acquired. Accelerated sequences were post-processed using DL. The image quality and diagnostic confidence were qualitatively assessed by 2 readers using a 5-point Likert scale. Analysis of the pathological findings of cartilage, rotator cuff tendons and muscles, glenoid labrum and subacromial bursa was performed. Inter-reader agreement was calculated using Cohen’s kappa statistic. Quantitative evaluation of image quality was measured using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Results Mean image quality and diagnostic confidence in evaluation of all shoulder structures were higher in DL sequences ( p value = 0.01). Inter-reader agreement ranged between kappa values of 0.155 (assessment of the bursa) and 0.947 (assessment of the rotator cuff muscles). In 17 cases, thickening of the subacromial bursa of more than 2 mm was only visible in DL sequences. The pathologies of the other structures could be properly evaluated by conventional and DL sequences. Mean SNR ( p value = 0.01) and CNR ( p value = 0.02) were significantly higher for DL sequences. Conclusions The accelerated PROPELLER sequences with DL post-processing showed superior image quality and higher diagnostic confidence compared to the conventional PROPELLER sequences. Subacromial bursa can be thoroughly assessed in DL sequences, while the other structures of the shoulder joint can be assessed in conventional and DL sequences with a good agreement between sequences. Key Points • MRI of the shoulder requires long scan times and can be hampered by motion artifacts. • Deep learning–based convolutional neural networks are used to reduce image noise and scan time while maintaining optimal image quality. The radial k-space acquisition technique (PROPELLER) can reduce the scan time and has potential to reduce motion artifacts. • DL sequences show a higher diagnostic confidence than conventional sequences and therefore are preferred for assessment of the subacromial bursa, while conventional and DL sequences show comparable performance in the evaluation of the shoulder joint.

Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body’s movement intention. In this paper, the OpenSim software is used to determine the muscle sites to be measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, lateral gastrocnemius, and tibial anterior. The surface electromyography (sEMG) signals and inertial data are collected from the lower limbs while the human body is walking, going upstairs, and going uphill. The sEMG noise is reduced by a wavelet-threshold-based complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) reduction algorithm, and the time-domain features are extracted from the noise-reduced sEMG signals. Knee and hip angles during motion are calculated using quaternions through coordinate transformations. The random forest (RF) regression algorithm optimized by cuckoo search (CS), shortened as CS-RF, is used to establish the prediction model of lower limb joint angles by sEMG signals. Finally, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics to compare the prediction performance of the RF, support vector machine (SVM), back propagation (BP) neural network, and CS-RF. The evaluation results of CS-RF are superior to other algorithms under the three motion scenarios, with optimal metric values of 1.9167, 1.3893, and 0.9815, respectively.

We review a simple yet versatile approach for the analysis of multichannel data, focusing in particular on brain signals measured with EEG, MEG, ECoG, LFP or optical imaging. Sensors are combined linearly with weights that are chosen to provide optimal signal-to-noise ratio. Signal and noise can be variably defined to match the specific need, e.g. reproducibility over trials, frequency content, or differences between stimulus conditions. We demonstrate how the method can be used to remove power line or cardiac interference, enhance stimulus-evoked or stimulus-induced activity, isolate narrow-band cortical activity, and so on. The approach involves decorrelating both the original and filtered data by joint diagonalization of their covariance matrices. We trace its origins; offer an easy-to-understand explanation; review a range of applications; and chart failure scenarios that might lead to misleading results, in particular due to overfitting. In addition to its flexibility and effectiveness, a major appeal of the method is that it is easy to understand. •Joint Decorrelation is a powerful, easy to use tool for multichannel data analysis.•It finds optimal weights to be applied to signals to maximize a criterion.•It can factor out noise, enhance weak sources, reveal oscillatory activity, etc.•It has been found effective for EEG, MEG, ECoG, LFP and optical imaging data.•We give examples of useful applications, and review failure scenarios and caveats.

ObjectivesThis study compares metal artifact (MA) reduction in imaging of total hip replacements (THR) using virtual monoenergetic images (VMI), for MA-reduction-specialized reconstructions (MAR) and conventional CT images (CI) from detector-based dual-energy computed tomography (SDCT).MethodsTwenty-seven SDCT-datasets of patients carrying THR were included. CI, MAR and VMI with different energy-levels (60–200 keV) were reconstructed from the same scans. MA width was measured. Attenuation (HU), noise (SD) and contrast-to-noise ratio (CNR) were determined in: extinction artifact, adjacent bone, muscle and bladder. Two radiologists assessed MA-reduction and image quality visually.ResultsIn comparison to CI, VMI (200 keV) and MAR showed a strong artifact reduction (MA width: CI 29.9±6.8 mm, VMI 17.6±13.6 mm, p<0.001; MAR 16.5±14.9 mm, p<0.001; MA density: CI -412.1±204.5 HU, VMI -279.7±283.7 HU; p<0.01; MAR -116.74±105.6 HU, p<0.001). In strong artifacts reduction was superior by MAR. In moderate artifacts VMI was more effective. MAR showed best noise reduction and CNR in bladder and muscle (p<0.05), whereas VMI were superior for depiction of bone (p<0.05). Visual assessment confirmed that VMI and MAR improve artifact reduction and image quality (p<0.001).ConclusionsMAR and VMI (200 keV) yielded significant MA reduction. Each showed distinct advantages both regarding effectiveness of artifact reduction, MAR regarding assessment of soft tissue and VMI regarding assessment of bone.Key Points• Spectral-detector computed tomography improves assessment of total hip replacements and surrounding tissue.• Virtual monoenergetic images and MAR reduce metal artifacts and enhance image quality.• Evaluation of bone, muscle and pelvic organs can be improved by SDCT.

With the advances in Internet-of-Things and data mining technologies, deep learning-based approaches have been widely used for intelligent fault diagnosis of manufacturing assets. However, uncertainty caused by the non-stationary process data such as vibration signal and noise interference in practical working environments will greatly affect the performance and reliability of predictions. The present paper develops a trustworthy and intelligent fault diagnosis framework based on a two-stage joint denoising method and evidential neural networks. The proposed denoising method integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the independent component analysis (ICA) method can effectively reduce data uncertainty caused by noise interference. The stacked gated recurrent unit (SGRU) model has been incorporated into the evidential neural networks as a deep classifier. The proposed evidential SGRU (ESGRU) method can quantify the prediction uncertainty, which estimates the prediction trustworthiness. Predictive entropy and reliability diagrams are used as calibration methods to validate the effectiveness of uncertainty estimation. The proposed framework is validated by two case studies of rolling bearing fault diagnosis in variable noise conditions. Experimental results demonstrate that the proposed method can achieve a high denoising effect and provide reliable uncertainty prediction results which are significant for practical applications.

The limited occupied space and various noise spectrum requires an adjustable sound absorber with a smart structure and tunable sound absorption performance. The hexagonal acoustic metamaterial cell of the multiple parallel-connection resonators with tunable perforating rate was proposed in this research, which consisted of six triangular cavities and six trapezium cavities, and the perforation rate of each cavity was adjustable by moving the sliding block along the slideway. The optimal geometric parameters were obtained by the joint optimization of the acoustic finite element simulation and cuckoo search algorithm, and the average sound absorption coefficients in the target frequency ranges of 650–1150 Hz, 700–1200 Hz and 700–1000 Hz were up to 0.8565, 0.8615 and 0.8807, respectively. The experimental sample was fabricated by the fused filament fabrication method, and its sound absorption coefficients were further detected by impedance tube detector. The consistency between simulation data and experimental data proved the accuracy of the acoustic finite element simulation model and the effectiveness of the joint optimization method. The tunable sound absorption performance, outstanding low-frequency noise reduction property, extensible outline structure and efficient space utilization were favorable to promote its practical applications in noise reduction.

Background Clinical notes are documents that contain detailed information about the health status of patients. Medical codes generally accompany them. However, the manual diagnosis is costly and error-prone. Moreover, large datasets in clinical diagnosis are susceptible to noise labels because of erroneous manual annotation. Therefore, machine learning has been utilized to perform automatic diagnoses. Previous state-of-the-art (SOTA) models used convolutional neural networks to build document representations for predicting medical codes. However, the clinical notes are usually long-tailed. Moreover, most models fail to deal with the noise during code allocation. Therefore, denoising mechanism and long-tailed classification are the keys to automated coding at scale. Results In this paper, a new joint learning model is proposed to extend our attention model for predicting medical codes from clinical notes. On the MIMIC-III-50 dataset, our model outperforms all the baselines and SOTA models in all quantitative metrics. On the MIMIC-III-full dataset, our model outperforms in the macro-F1, micro-F1, macro-AUC, and precision at eight compared to the most advanced models. In addition, after introducing the denoising mechanism, the convergence speed of the model becomes faster, and the loss of the model is reduced overall. Conclusions The innovations of our model are threefold: firstly, the code-specific representation can be identified by adopted the self-attention mechanism and the label attention mechanism. Secondly, the performance of the long-tailed distributions can be boosted by introducing the joint learning mechanism. Thirdly, the denoising mechanism is suitable for reducing the noise effects in medical code prediction. Finally, we evaluate the effectiveness of our model on the widely-used MIMIC-III datasets and achieve new SOTA results.