Delamination detection in composite beams using a transient wave analysis method

In this paper, delamination detection of composite beams is investigated through a transient wave analysis method. A higher-order beam theory is proposed to model the Lamb wave propagation behavior. Wavelet Transform (WT) is used to localized the delamination. The reflection ratios and transmission ratios are found to depend strongly on the frequency of the incident flexural waves, as well as the size of the delamination. So it can be well used to detect the small size of the delamination, which is important for the Structural Health Monitoring (SHM). The numerical results show that the localization and identification of the size of the delamination are feasible by the proposed approach, which is the essential first step for the enhancement of safety and reliability of composite structures. The results are being verified by the experiments

The influence of viscous liquid to the propagation of torsional wave in pipes

This paper deals with the torsional waves propagating in a pipe contacting with viscous liquid. The expression describing the movement of viscous liquid near the pipe is presented. Both the pipe is filled with and immersed in the liquid are considered and both the isotropic and transversely isotropic pipes are investigated. Numerical calculations were carried out to investigate the influence of viscosity of liquid on the dispersion and attenuation curves. The first torsional wave mode is investigated individually by varying the viscosity and the density of the liquid. It is concluded that the viscosity of the liquid has little influence on the dispersion curves, while its main effect is the attenuation. When the frequency and the thickness of the pipe are not large, the attenuation ratio between the two cases of pipe immersed in liquid and pipe filled with liquid is approximately a constant, which is determined by the ratio of inner radius and the thickness

Rotor fault classification technique and precision analysis with kernel principal component analysis and multi-support vector machines

To solve the diagnosis problem of fault classification for aero-engine vibration over standard during test, a fault diagnosis classification approach based on kernel principal component analysis (KPCA) feature extraction and multi-support vector machines (SVM) is proposed, which extracted the feature of testing cell standard fault samples through exhausting the capability of nonlinear feature extraction of KPCA. By computing inner product kernel functions of original feature space, the vibration signal of rotor is transformed from principal low dimensional feature space to high dimensional feature spaces by this nonlinear map. Then, the nonlinear principal components of original low dimensional space are obtained by performing PCA on the high dimensional feature spaces. During muti-SVM training period, as eigenvectors, the nonlinear principal components are separated into training set and test set, and penalty parameter and kernel function parameter are optimized by adopting genetic optimization algorithm. A high classification accuracy of training set and test set is sustained and over-fitting and under-fitting are avoided. Experiment results indicate that this method has good performance in distinguishing different aero-engine fault mode, and is suitable for fault recognition of a high speed rotor

Multi-damage detection in composite structure

In this paper a pre-stack reverse-time migration concept of signal processing techniques is developed and adapted to guided-wave propagation in composite structure for multi-damage imaging by experimental studies. An anisotropic laminated composite plate with a surface-mounted linear piezoelectric ceramic (PZT) disk array is studied as an example. At first, Mindlin Plate Theory is used to model Lamb waves propagating in laminates. The group velocities of flexural waves are also derived from dispersion relations and validated by experiments. Then reconstruct the response wave fields with reflected data collected by the linear PZT array. Reverse-time migration technique is then performed to back-propagate the reflected energy to the damages using a two-dimensional explicit finite difference algorithm and damages are imaged. Stacking these images together gets the final image of multiple damages. The results show that the pre-stack migration method is hopeful for damage detection in composite structures

Delamination detection in composite beams using a transient wave analysis method

In this paper, delamination detection of composite beams is investigated through a transient wave analysis method. A higher-order beam theory is proposed to model the Lamb wave propagation behavior. Wavelet Transform (WT) is used to localized the delamination. The reflection ratios and transmission ratios are found to depend strongly on the frequency of the incident flexural waves, as well as the size of the delamination. So it can be well used to detect the small size of the delamination, which is important for the Structural Health Monitoring (SHM). The numerical results show that the localization and identification of the size of the delamination are feasible by the proposed approach, which is the essential first step for the enhancement of safety and reliability of composite structures. The results are being verified by the experiments

Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning

Self-supervised audio-visual source localization aims to locate sound-source objects in video frames without extra annotations. Recent methods often approach this goal with the help of contrastive learning, which assumes only the audio and visual contents from the same video are positive samples for each other. However, this assumption would suffer from false negative samples in real-world training. For example, for an audio sample, treating the frames from the same audio class as negative samples may mislead the model and therefore harm the learned representations e.g., the audio of a siren wailing may reasonably correspond to the ambulances in multiple images). Based on this observation, we propose a new learning strategy named False Negative Aware Contrastive (FNAC) to mitigate the problem of misleading the training with such false negative samples. Specifically, we utilize the intra-modal similarities to identify potentially similar samples and construct corresponding adjacency matrices to guide contrastive learning. Further, we propose to strengthen the role of true negative samples by explicitly leveraging the visual features of sound sources to facilitate the differentiation of authentic sounding source regions. FNAC achieves state-of-the-art performances on Flickr-SoundNet, VGG-Sound, and AVSBench, which demonstrates the effectiveness of our method in mitigating the false negative issue. The code is available at \url{https://github.com/OpenNLPLab/FNAC_AVL}.Comment: CVPR202

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8+ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC)

Controller Performance Assessment and Data Reconciliation for Artificial Pancreas

Artificial pancreas (AP) systems are implemented as a treatment for type 1 diabetes (T1D) patients to regulate blood glucose concentration (BGC). With continuous glucose monitoring (CGM), information related to BGC can be measured at a high frequency. It is widely known that besides meals, BGC is also influenced by many other factors such as exercise, sleep, and stress. In order to get information about these factors, different kinds of measurements such as heart rate, acceleration and derived variables such as energy expenditure (EE) should also be collected using equipment like armband and chest band devices to be used as inputs for AP systems. With adequate information about patients, BGC, and other related factors, the controller in AP systems is able to calculate insulin infusion rate for patients based on the model and control algorithm. The insulin pump will deliver the calculated amount of insulin to patient's body to close the loop of BGC regulating. For AP systems, the performance of model-based control systems depends on the accuracy of the models and may be affected when large dynamic changes in the human body occur or when the equipment performance varies. And those factors may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors such as signal bias and missing data can mislead or stop the calculation of insulin infusion rate. All of these possible performance failures can make AP systems unreliable and endanger the safety of patients. This project aims to develop additional modules focused on fault detection and diagnosis of the controller and the sensors of the AP system. A controller performance assessment module (CPAM) is developed to generate several indexes to monitor different aspects of controller performance and retune the controller parameters according to different types of controller performance deterioration. A sensor error detection and reconciliation module (SED&RM) is developed to detect sensor error in CGM measurements. The SED&RM is based on two model estimation technologies, outlier-robust Kalman filter (ORKF) and locally weighted partial least squares (LW-PLS) to replace the erroneous sensor signal with the model estimated value. A novel method, the nominal angle analysis (NAA) is introduced to solve problems of false positive and candidate selection for signal reconciliation. SED&RM is extended to multi-sensor error detection and reconciliation module (MSED&RM), which also includes error detection and reconciliation for other sensor signals such as galvanic skin response (GSR) and values derived from original sensor signals such as EE. A multi-level supervision and controller modification (ML-SCM) module integrates CPAM and MSED&RM together and extends the controller modification into different time scales including sample level, period level, and day level. CPAM is tested with a single input and single output (SISO) version of AP system in UVa/Padova simulator. The results indicate that a generalized predictive control (GPC) with the proposed CPAM has a safer range of glucose concentration variation and more reasonable insulin suggestions than a GPC without controller retuning guided by the proposed CPAM. The performance of SED&RM and MSED&RM is tested with data from clinical experiments. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable model estimation values. The ML-SCM is tested with both simulation and clinical experiments. The results indicate that the AP system with ML-SCM module has a safer range of glucose concentration distribution and more reasonable insulin infusion rate suggestions than an AP system without the ML-SCM module