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Remaining useful life (RUL) is a metric of health state for essential equipment. It plays a significant role in health management. However, RUL is often random and unknown. One type of physics-based method builds a mathematical model for RUL using prior principles, but this is a tough task in real-world applications. Another type of method estimates RUL from available information through condition and health monitoring; this is known as the data-driven method. Traditional data-driven methods require significant human effort in designing health features to represent performance degradation, yet the prediction accuracy is limited. With breakthroughs in various application scenarios in recent years, deep learning techniques provide new insights into this problem. Over the past few years, deep-learning-based RUL prediction has attracted increasing attention from the academic community. Therefore, it is necessary to conduct a survey on deep-learning-based RUL prediction. To ensure a comprehensive survey, the literature is reviewed from three dimensions. Firstly, a unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework. Secondly, detailed estimation processes are compared from the perspective of different deep learning models. Thirdly, the literature is examined from the perspective of specific problems, such as scenarios where the collected data consist of limited labeled data. Finally, the main challenges and future directions are summarized.

Background With the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic. Methods In this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. And then input the word vector to BiLSTM to realize automatic extraction of sentence features. And then CRF performs sentence-level word tagging. Secondly, attention mechanism is added between the BiLSTM and the CRF to construct Attention-BiLSTM-CRF model, which can leverage document-level information to alleviate tagging inconsistency. In addition, this paper proposes an entity auto-correct algorithm to rectify entities according to historical entity information. At last, a drug dictionary and post-processing rules are well-built to rectify entities, to further improve performance. Results The final F1 scores of the BiLSTM-CRF and Attention-BiLSTM-CRF model on given test dataset are 90.15 and 90.82% respectively, both of which are higher than 89.26%, which is the best F1 score on the test dataset except ours. Conclusion Our approach can be used to recognize medical named entity on Chinese electronic medical records and achieves the state-of-the-art performance on the given test dataset.

Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A 2 RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A 2 RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by 5.73 % , 7.06 % , 3.27 % and 2.24 % , respectively.

Water is the source of life. With the development of economy, China has paid more attention to water pollution and adopted a series of scientific solutions. Following the situation, membrane separation technology has made great progress in the research and application of water treatment. In this paper, the principle of membrane separation and the process of membrane separation are briefly introduced, and then the application of membrane separation technology in drinking water purification, industrial wastewater treatment and desalination engineering is introduced, and the discussion and summary are made according to the application.

Object detection based on RGB and infrared images has emerged as a crucial research area in computer vision, and the synergy of RGB-Infrared ensures the robustness of object-detection algorithms under varying lighting conditions. However, the RGB-IR image pairs captured typically exhibit spatial misalignment due to sensor discrepancies, leading to compromised localization performance. Furthermore, since the inconsistent distribution of deep features from the two modalities, directly fusing multi-modal features will weaken the feature difference between the object and the background, therefore interfering with the RGB-Infrared object-detection performance. To address these issues, we propose an adaptive dual-discrepancy calibration network (ADCNet) for misaligned RGB-Infrared object detection, including spatial discrepancy and domain-discrepancy calibration. Specifically, the spatial discrepancy calibration module conducts an adaptive affine transformation to achieve spatial alignment of features. Then, the domain-discrepancy calibration module separately aligns object and background features from different modalities, making the distribution of the object and background of the fusion feature easier to distinguish, therefore enhancing the effectiveness of RGB-Infrared object detection. Our ADCNet outperforms the baseline by 3.3% and 2.5% in mAP50 on the FLIR and misaligned M3FD datasets, respectively. Experimental results demonstrate the superiorities of our proposed method over the state-of-the-art approaches.

Alkyl sulfobetaine shows a strong advantage in the compounding of surfactants due to the defects in the size matching of hydrophilic and hydrophobic groups. The interfacial tensions (IFTs) of alkyl sulfobetaine (ASB) and xylene-substituted alkyl sulfobetaine (XSB) with oil-soluble (Span80) and water-soluble (Tween80) nonionic surfactants on a series of n-alkanes were studied using a spinning drop tensiometer to investigate the mechanism of IFT between nonionic and betaine surfactants. The two betaine surfactants’ IFTs are considerably impacted differently by Span80 and Tween80. The results demonstrate that Span80, through mixed adsorption with ASB and XSB, can create a relatively compacted interfacial film at the n-alkanes–water interface. The equilibrium IFT can be reduced to ultra-low values of 5.7 × 10−3 mN/m at ideal concentrations by tuning the fit between the size of the nonionic surfactant and the size of the oil-side vacancies of the betaine surfactant. Nevertheless, Tween80 has minimal effect on the IFT of betaine surfactants, and the betaine surfactant has no vacancies on the aqueous side. The present study provides significant research implications for screening betaine surfactants and their potential application in enhanced oil recovery (EOR) processes.

Removing raindrops from a single image is a challenging problem due to the complex changes in shape, scale, and transparency among raindrops. Previous explorations have mainly been limited in two ways. First, publicly available raindrop image datasets have limited capacity in terms of modeling raindrop characteristics (e.g., raindrop collision and fusion) in real-world scenes. Second, recent deraining methods tend to apply shape-invariant filters to cope with diverse rainy images and fail to remove raindrops that are especially varied in shape and scale. In this paper, we address these raindrop removal problems from two perspectives. First, we establish a large-scale dataset named RaindropCityscapes, which includes 11,583 pairs of raindrop and raindrop-free images, covering a wide variety of raindrops and background scenarios. Second, a two-branch Multi-scale Shape Adaptive Network (MSANet) is proposed to detect and remove diverse raindrops, effectively filtering the occluded raindrop regions and keeping the clean background well-preserved. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art raindrop removal methods. Moreover, the extension of our method towards the rainy image segmentation and detection tasks validates the practicality of the proposed method in outdoor applications.

Although many problems in computer vision and natural language processing have made breakthrough progress with neural networks, adversarial attack is a serious potential problem in many neural network- based applications. Attackers can mislead classifiers with slightly perturbed examples, which are called adversarial examples. As the existing adversarial attacks are specific to application and have difficulty in general usage, we propose a multimodal adversarial attack framework to attack both text and image classifiers. The proposed framework firstly generates candidate set to find the substitution words or pixels and generate candidate adversarial examples. Secondly, the framework updates candidate set and search adversarial examples with three local or random search methods [beam search, genetic algorithm (GA) search, particle swarm optimization (PSO) search]. The experiments demonstrate that the proposed framework effectively generates image and text adversarial examples. Comparing the proposed methods with other image adversarial attacks in MNIST dataset, the PSO search in the framework has 98.4% attack success rate which outperforms other methods. Besides, the beam search has the best attack efficiency and human imperception in both MNIST and CIFAR-10 dataset. Comparing with other text adversarial attacks, the beam search in the framework has an attack success rate of 91.5%, which outperforms other existing and the proposed search methods. In attack efficiency, the beam search also outperforms other methods, meaning that we can craft text adversarial examples with less perturbation using beam search.

To answer questions, visual question answering systems (VQA) rely on language bias but ignore the information of the images, which has negative information on its generalization. The mainstream debiased methods focus on removing language prior to inferring. However, the image samples are distributed unevenly in the dataset, so the feature sets acquired by the model often cannot cover the features (views) of the tail samples. Therefore, language bias occurs. This paper proposes a language bias-driven self-knowledge distillation framework to implicitly learn the feature sets of multi-views so as to reduce language bias. Moreover, to measure the performance of student models, the authors of this paper use a generalization uncertainty index to help student models learn unbiased visual knowledge and force them to focus more on the questions that cannot be answered based on language bias alone. In addition, the authors of this paper analyze the theory of the proposed method and verify the positive correlation between generalization uncertainty and expected test error. The authors of this paper validate the method’s effectiveness on the VQA-CP v2, VQA-CP v1 and VQA v2 datasets through extensive ablation experiments.