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Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library). [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been widely used in breast cancer diagnosis, and many high-performance models have emerged. However, most of the existing deep learning models are mainly based on static breast ultrasound (US) images. In actual diagnostic process, contrast-enhanced ultrasound (CEUS) is a commonly used technique by radiologists. Compared with static breast US images, CEUS videos can provide more detailed blood supply information of tumors, and therefore can help radiologists make a more accurate diagnosis. In this paper, we propose a novel diagnosis model based on CEUS videos. The backbone of the model is a 3D convolutional neural network. More specifically, we notice that radiologists generally follow two specific patterns when browsing CEUS videos. One pattern is that they focus on specific time slots, and the other is that they pay attention to the differences between the CEUS frames and the corresponding US images. To incorporate these two patterns into our deep learning model, we design a domain-knowledge-guided temporal attention module and a channel attention module. We validate our model on our Breast-CEUS dataset composed of 221 cases. The result shows that our model can achieve a sensitivity of 97.2% and an accuracy of 86.3%. In particular, the incorporation of domain knowledge leads to a 3.5% improvement in sensitivity and a 6.0% improvement in specificity. Finally, we also prove the validity of two domain knowledge modules in the 3D convolutional neural network (C3D) and the 3D ResNet (R3D). [ABSTRACT FROM AUTHOR]

Rain streaks in the air show diverse characteristics with different shapes, directions, densities, even the complex overlapped phenomenon, causing great challenges for the deraining task. Recently, deep learning based image deraining methods have been extensively investigated due to their excellent performance. However, most of the existing algorithms still have limitations in removing rain streaks while preserving rich textural details under complicated rain conditions. To this end, we propose to decompose rain streaks into multiple rain layers and individually estimate each of them along the network stages to cope with the increasing abstracts. To better characterize rain layers, an improved non-local block is designed to exploit the self-similarity of rain information by learning the holistic spatial feature correlations while reducing the calculation complexity. Moreover, a mixed attention mechanism is applied to guide the fusion of rain layers by focusing on the local and global overlaps among these rain layers. Extensive experiments on both synthetic rainy/rain-haze/raindrop datasets, real-world samples, the haze, and low-light scenarios show substantial improvements both on quantitative indicators and visual effects over the current state-of-the-art technologies. The source code is available at https://github.com/kuihua/IADN. [ABSTRACT FROM AUTHOR]

• Propose a deep-learning intelligent system incorporating data augmentation for STVS assessment. • Build a BiGRU-attention-based assessment model to extract temporal dependencies. • Effectiveness and superiority of the approach is verified by the New England 39-bus system. • Statistical tests have been performed to examine the performance of the proposal. Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on a small training dataset is a challenging and urgent problem. Although a big enough dataset can be directly generated by contingency simulation, this data generation process is usually cumbersome and inefficient; while data augmentation provides a low-cost and efficient way to artificially inflate the representative and diversified training datasets with label preserving transformations. In this respect, this paper proposes a novel deep-learning intelligent system incorporating data augmentation for STVSA of power systems. First, due to the unavailability of reliable quantitative criteria to judge the stability status for a specific power system, semi-supervised cluster learning is leveraged to obtain labeled samples in an original small dataset. Second, to make deep learning applicable to the small dataset, conditional least squares generative adversarial networks (LSGAN)-based data augmentation is introduced to expand the original dataset via artificially creating additional valid samples. Third, to extract temporal dependencies from the post-disturbance dynamic trajectories of a system, a bi-directional gated recurrent unit with attention mechanism based assessment model is established, which bi-directionally learns the significant time dependencies and automatically allocates attention weights. The test results demonstrate the presented approach manages to achieve better accuracy and a faster response time with original small datasets. Besides classification accuracy, this work employs statistical measures to comprehensively examine the performance of the proposal. [ABSTRACT FROM AUTHOR]

Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (COD eff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SS eff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production. [Display omitted] • A new deep learning model was proposed for real-time estimation of water quality. • The new model was based on convolutional neural network, long-term short-term memory and attention mechanism. • The new model can guarantee wastewater reuse and reduce the wastewater treatment cost. [ABSTRACT FROM AUTHOR]