Vibration measurement based condition monitoring

Vibrations of the transformers are complex multi-physics phenomena that require a deep understanding of electromagnetic and mechanical principles. Their analysis can be used to assess the condition of the transformer in terms of mechanical fixation quality, buckling or ageing of the components. The article presents the 20 years of efforts of researchers in Xi\u27an Jiaotong University and The University of Queensland on transformer vibration characteristics and its application in the winding mechanical condition monitoring

Formation Control for Unmanned Aerial Vehicles with Directed and Switching Topologies

Formation control problems for unmanned aerial vehicle (UAV) swarm systems with directed and switching topologies are investigated. A general formation control protocol is proposed firstly. Then, by variable transformation, the formation problem is transformed into a consensus problem, which can be solved by a novel matrix decomposition method. Sufficient conditions to achieve formation with directed and switching topologies are provided and an explicit expression of the formation reference function is given. Furthermore, an algorithm to design the gain matrices of the protocol is presented. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results

When Sparsity Meets Dynamic Convolution

Dynamic convolution achieves a substantial performance boost for efficient CNNs at a cost of increased convolutional weights. Contrastively, mask-based unstructured pruning obtains a lightweight network by removing redundancy in the heavy network at risk of performance drop. In this paper, we propose a new framework to coherently integrate these two paths so that they can complement each other compensate for the disadvantages. We first design a binary mask derived from a learnable threshold to prune static kernels, significantly reducing the parameters and computational cost but achieving higher performance in Imagenet-1K(0.6\% increase in top-1 accuracy with 0.67G fewer FLOPs). Based on this learnable mask, we further propose a novel dynamic sparse network incorporating the dynamic routine mechanism, which exerts much higher accuracy than baselines ($2.63\%$ increase in top-1 accuracy for MobileNetV1 with $90\%$ sparsity). As a result, our method demonstrates a more efficient dynamic convolution with sparsity

Whole-Body Lesion Segmentation in 18F-FDG PET/CT

There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers. Recent advances in the medical image segmentation shows the nnUNET is feasible for diverse tasks. However, lesion segmentation in the PET images is not straightforward, because lesion and physiological uptake has similar distribution patterns. The Distinction of them requires extra structural information in the CT images. The present paper introduces a nnUNet based method for the lesion segmentation task. The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. It allows for automated segmentation of potential lesions. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume

Vibration measurement based condition monitoring

Vibrations of the transformers are complex multi-physics phenomena that require a deep understanding of electromagnetic and mechanical principles. Their analysis can be used to assess the condition of the transformer in terms of mechanical fixation quality, buckling or ageing of the components. The article presents the 20 years of efforts of researchers in Xi\u27an Jiaotong University and The University of Queensland on transformer vibration characteristics and its application in the winding mechanical condition monitoring

Batch mode sparse active learning

Abstract-Sparse representation, due to its clear and powerful insight deep into the structure of data, has seen a recent surge of interest in the classification community. Based on this, a family of reliable classification methods have been proposed. On the other hand, obtaining sufficiently labeled training data has long been a challenging problem, thus considerable research has been done regarding active selection of instances to be labeled. In our work, we will present a novel unified framework, i.e. BMSAL(Batch Mode Sparse Active Learning). Based on the existing sparse family of classifiers, we define rigorously the corresponding BMSAL family and explore their shared properties, most importantly (approximate) submodularity. We focus on the feasibility and reliability of the BMSAL family: The first one inspires us to optimize the algorithms and conduct experiments comparing with state-of-the-art methods; for reliability, we give error-bounded algorithms, as well as detailed logical deductions and empirical tests for applying sparse in non-linear data sets

Model and Data Agreement for Learning with Noisy Labels

Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mistakenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our method outperforms state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seamlessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks. The code is available in https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.Comment: Accepted by AAAI2023 Worksho