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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A method is developed to analyze vulnerability of gas pipeline networks.  Supply capacity, gas grid performance and demand change are considered.  Indices are developed to measure gas grid vulnerability and pipeline criticality.  Demand site robustness is analyzed by statistics and topology properties.  The application of the method is studied in a complex natural gas pipeline network.

This paper deals with gear pitting fault diagnosis problem and presents a method by integrating convolutional neural network (CNN) and gated recurrent unit (GRU) networks with vibration and acoustic emission signals to solve the problem. The presented method first trains a one-dimensional CNN with acoustic emission signals and a GRU network with vibration signals. Then the gear pitting fault features obtained by the two networks are concatenated to form a deep learning structure for gear pitting fault diagnosis. Seven different gear pitting conditions are used to test the feasibility of the presented method. The diagnosis result of the gear pitting fault shows that the accuracy of the presented method reaches above 98% with only a relatively small number of training samples. In comparison with the results using CNN or GRU network alone, the presented method gives more accurate diagnosis results. By comparing the results of different loads and learning rates, the robustness of the presented method for gear pitting fault diagnosis is proved. Moreover, the presented deep structure can be easily extended to more other sensor input signals for gear pitting fault diagnosis in the future.

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In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

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The crux of Referring Video Object Segmentation (RVOS) lies in modeling dense text-video relations to associate abstract linguistic concepts with dynamic visual contents at pixellevel. Current RVOS methods typically use vision and language models pre-trained independently as backbones. As images and texts are mapped to uncoupled feature spaces, they face the arduous task of learning Vision-Language (VL) relation modeling from scratch. Witnessing the success of Vision-Language Pretrained (VLP) models, we propose to learn relation modeling for RVOS based on their aligned VL feature space. Nevertheless, transferring VLP models to RVOS is a deceptively challenging task due to the substantial gap between the pre-training task (image/region-level prediction) and the RVOS task (pixel-level prediction in videos). In this work, we introduce a framework named VLP-RVOS to address this transfer challenge. We first propose a temporal-aware prompt-tuning method, which not only adapts pre-trained representations for pixel-level prediction but also empowers the vision encoder to model temporal clues. We further propose to perform multi-stage VL relation modeling while and after feature extraction for comprehensive VL understanding. Besides, we customize a cube-frame attention mechanism for spatial-temporal reasoning. Extensive experiments demonstrate that our method outperforms state-of-the-art algorithms and exhibits strong generalization abilities.