Few-shot learning for image-based bridge damage detection

Autonomous bridge visual inspection is a real-world challenge due to various materials, surface coatings, and changing light and weather conditions. Traditional supervised learning relies on massive annotated data to establish a robust model, which requires a time-consuming data acquisition process. This work proposes a few-shot learning (FSL) approach based on improved ProtoNet for damage detection with just a few labeled examples. Feature embedding is achieved through cross-domain transfer learning from ImageNet instead of episodic training. The ProtoNet is improved with embedding normalization to enhance transduction performance based on Euclidean distance and a linear classifier for classification. The approach is explored on a public dataset through different ablation experiments and achieves over 94% mean accuracy for 2-way 5-shot classification via the pre-trained GoogleNet after fine-tuning. Moreover, the proposed fine-tuning methods based on a fully connected layer (FCN) and Hadamard product are demonstrated with better performance than the previous method. Finally, the approach is validated using real bridge inspection images, demonstrating its capability of fast implementation for practical damage inspection with weakly supervised information

Fluorescent Probes for Molecular Imaging of ROS/RNS Species in Living Systems

Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS) are highly reactive species which play crucial roles in many fundamental physiological processes including cellular signalling pathways. Over-production of these reactive species by various stimuli leads to cellular oxidative stress which is linked to various disease conditions. Therefore, the development of novel detection methods for ROS and RNS is of great interest and indispensable for monitoring the dynamic changes of ROS and RNS in cells and for elucidating their mechanisms of trafficking and connections to diseases. We have been recently developing various fluorescent sensors which can selectively detect metal ions, ROS or RNS species in live cells or animals. Our turn-on profluorescent sensors are capable of imaging oxidative stress promoted by metal and H2O2 (i.e. the Fenton Reaction conditions) in living cells (Chem Commun 2010); our highly selective and sensitive iron sensors can image the endogenous exchangeable iron pools and their dynamic changes with subcellular resolution in living neuronal cells (ChemBioChem 2012 and unpublished data), and so do our superoxide sensors (ChemBioChem 2012 and unpublished data). Moreover, we have recently developed nitric oxide (NO) sensors for molecular imaging of stimulated NO production in live cells with subcellular resolution as well as novel near infra red (NIR) sensors for NO imaging in live animals

A deep learning framework for intelligent fault diagnosis using AutoML-CNN and image-like data fusion

Intelligent fault diagnosis (IFD) is essential for preventative maintenance (PM) in Industry 4.0. Data-driven approaches have been widely accepted for IFD in smart manufacturing, and various deep learning (DL) models have been developed for different datasets and scenarios. However, an automatic and unified DL framework for developing IFD applications is still required. Hence, this work proposes an efficient framework integrating popular convolutional neural networks (CNNs) for IFD based on time-series data by leveraging automated machine learning (AutoML) and image-like data fusion. After normalisation, uniaxial or triaxial signals are reconstructed into -channel pseudo-images to satisfy the input requirements for CNNs and achieve data-level fusion simultaneously. Then, the model training, hyperparameter optimisation, and evaluation can be taken automatically based on AutoML. Finally, the selected model can be deployed on a cloud server or an edge device (via tiny machine learning). The proposed framework and method were validated via two case studies, demonstrating the framework’s availability for the automatic development of IFD applications and the effectiveness of the proposed data-level fusion method

Numerical investigation on the dynamic response characteristics of a thermoelectric generator module under transient temperature excitations

In this work, a three-dimensional transient numerical model of a thermoelectric generator module considering the temperature-dependent properties and the topological connection of load resistance is proposed to study its dynamic response characteristics. The dynamic output power and conversion efficiency of the thermoelectric generator module under steady and different transient temperature excitations are compared and studied. A time delay exists in the output response of the thermoelectric generator module, and the time delay increases when the temperature rate increases. When the heat source temperature changes rapidly, the corresponding output power, conversion efficiency, and other thermal responses will show a more stable change trend. Moreover, the dynamic response characteristic of the output power is synchronous with that of the conversion efficiency. The periodic temperature excitation may amplify the output power, where the average output power of the sine and triangle waves are 4.93% and 2.82% respectively higher than the steady-state output power. However, the average conversion efficiency of both is almost identical to the steady-state conversion efficiency. The proposed model contributes to predicting the dynamic performance of thermoelectric generators, and can be further extended to the whole thermoelectric generator system

P21cip-Overexpression in the Mouse β Cells Leads to the Improved Recovery from Streptozotocin-Induced Diabetes

Under normal conditions, the regeneration of mouse β cells is mainly dependent on their own duplication. Although there is evidence that pancreatic progenitor cells exist around duct, whether non-β cells in the islet could also potentially contribute to β cell regeneration in vivo is still controversial. Here, we developed a novel transgenic mouse model to study the pancreatic β cell regeneration, which could specifically inhibit β cell proliferation by overexpressing p21cip in β cells via regulation of the Tet-on system. We discovered that p21 overexpression could inhibit β-cell duplication in the transgenic mice and these mice would gradually suffer from hyperglycemia. Importantly, the recovery efficiency of the p21-overexpressing mice from streptozotocin-induced diabetes was significantly higher than control mice, which is embodied by better physiological quality and earlier emergence of insulin expressing cells. Furthermore, in the islets of these streptozotocin-treated transgenic mice, we found a large population of proliferating cells which expressed pancreatic duodenal homeobox 1 (PDX1) but not markers of terminally differentiated cells. Transcription factors characteristic of early pancreatic development, such as Nkx2.2 and NeuroD1, and pancreatic progenitor markers, such as Ngn3 and c-Met, could also be detected in these islets. Thus, our work showed for the first time that when β cell self-duplication is repressed by p21 overexpression, the markers for embryonic pancreatic progenitor cells could be detected in islets, which might contribute to the recovery of these transgenic mice from streptozotocin-induced diabetes. These discoveries could be important for exploring new diabetes therapies that directly promote the regeneration of pancreatic progenitors to differentiate into islet β cells in vivo

Damage volumetric assessment and digital twin synchronization based on LiDAR point clouds

Point clouds are widely used for structure inspection and can provide damage spatial information. However, how to update a digital twin (DT) with local damage based on point clouds has not been sufficiently studied. This research presents an efficient framework for assessing and DT synchronizing local damage on a planar surface using point clouds. The pipeline starts from damage detection via DeepLabV3+ on the pseudo grayscale images from the point depth. It avoids the drawbacks of image and point cloud fusion. The target point cloud is separated according to the detected damage. Then, it can be converted into a 3D binary matrix through voxelization and binarization, which is highly lightweight and can be losslessly compressed for DT synchronization. The framework is validated via two case studies, demonstrating that the proposed voxel-based method can be easily applied to real-world damage with non-convex geometry instead of convex-hull fitting; finite-element (FE) models and BIM models can be updated automatically through the framework

Transient numerical modelling of a thermoelectric generator system used for automotive exhaust waste heat recovery

The automotive thermoelectric generator system is a promising technology of exhaust waste heat recovery, but reasonable theoretical models to predict its dynamic performance are lacking. In this work, a transient fluid-thermal-electric multiphysics coupling field numerical model is proposed for the first time, and the model is used to evaluate the dynamic performance of a simplified automotive thermoelectric generator system under vehicle driving cycles. The transient numerical model, which takes into account the dynamic characteristics, fluid-thermal-electric multiphysics field coupling effects, and material temperature dependence, is thus far the most complete model ever. Numerical results reveal that there is a delay in output response with the change of exhaust temperature, and the change of output voltage and output power is often accompanied by the change of exhaust mass flow rate. The small and short-term fluctuation of exhaust gases has a slight influence on output performance. With the transient variation of exhaust characteristics, the output voltage and output power show more stable changes and slower responses, but the situation is the opposite for conversion efficiency. The output power predicted by steady-state numerical simulation is 12.6% higher than that of transient numerical simulation. Moreover, the proposed transient numerical model is recommended to investigate the dynamic performance of automotive thermoelectric generator systems