A final optics assembly (FOA) is located at the output end of an inertial confinement fusion (ICF) experimental device. There are 48 FOAs in the Shenguang-III (SG-III) laser facility, each containing nine large aperture optics, the aperture size of which is $430\mathrm{mm}\times 430\mathrm{mm}$. Laser-induced damage is easily generated under 351-nm high-power laser irradiation.¹ 2. ^{– 3} When the number of damaged sites increases to a certain magnitude, these sites modulate the high-power UV lasers in ICF experiments, thereby significantly affecting the beam quality.⁴ 5. 6. ^{– 7} The best way to rapidly and efficiently detect and track the damage on an FOA surface is to design an online imaging system for observing tiny defects in the center of the target chamber. From the literature published in recent years, there are mainly two research teams in the field of damage online detection in ICF experiments for large aperture optics: the Lawrence Livermore National Laboratory (LLNL) and the China Academy of Engineering Physics (CAEP). Each team has developed its own experimental version of the final optics damage inspection (FODI) system, namely, the NIF FODI developed for the United States National Ignition Facility (NIF) by LLNL scientists^8,9 and the SG-III FODI we developed for the SG-III laser facility at the CAEP.¹⁰ In general, FODI is an online imaging system based on machine vision for rapidly observing laser-induced damage. With edge illumination technology in the dark field, FODI captures images of the damage on an FOA surface and obtains the damage information by image processing. LLNL scientists have conducted much meaningful research in this field. Kegelmeyer et al.¹¹ used the local area signal-to-noise ratio (LASNR) algorithm to process FODI images to obtain all possible damage sites; these sites are also called candidate sites or LASNR markers. The FOA damage condition can be roughly obtained using these LASNR markers. However, due to the presence of stray light, a significant amount of noise is present in FODI images in addition to true damage, as shown in Fig. 1(b), which is referred to as false damage.^6,12,13 These candidate sites can generally be divided into these categories: damage site, hardware reflection (HR), damaged CCD pixels (DC), reflection of a damage site (RD), and attachments (Att).¹⁴ Damage sites are also called true damage sites or true sites, and the others are called false damage sites or false sites. How to accurately remove all kinds of false damage while retaining the true damage is a hot and challenging topic in the present research. Abdulla et al. conducted machine learning of an ensemble of decision trees to identify HR-type false sites from LASNR markers with very high accuracy (99.8%, 10-fold crossvalidation). After the removal of HR-type sites, the remaining false sites (Abdulla et al.¹⁵ referred to them as being of the unknown type, here abbreviated as UN-type) and true sites were mixed together. DC-type false sites are caused by radiation or thermal noise generated by long-term operation in a vacuum environment and generally appear as randomly distributed isolated pixels with high gray values. These DC can be easily identified via machine learning or other pattern recognition methods. To save time for later machine learning classifications, we have identified these pixels in the data-preprocessing step and removed them from the FODI image. RD-type false sites are also easy to identify and will be discussed in Sec. 2.1. Att-type false sites are more similar to true sites than any other types of false site when observed by the naked eye. The traditional machine learning methods often struggle to identify them, and almost no related studies exist in the field of damage online inspection. To solve this problem, we establish our FODI detection system in the SG-III laser facility and present a new solution for the classification of true and false sites. Limited by the size of the workspace, the SG-III FODI system is smaller than the NIF FODI system, as shown in Fig. 1(a); its size is 0.35 m. The SG-III FODI system obtains online images in a vacuum target chamber, in which the distance between the FOA and FODI camera usually ranges from 3.7 to 5.1 m. This system can detect damage larger than $100\mu \mathrm{m}$ with a sizing accuracy of at most $\pm 15\%$ and damage ranging from 50 to $100\mu \mathrm{m}$ with a sizing accuracy of at most $\pm 30\%$. For convenience of description, FODI is herein used to refer to SG-III FODI unless specified as NIF FODI.

Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.

Weakly supervised anomalous behavior detection is a popular area at present. Compared to semi-supervised anomalous behavior detection, weakly-supervised learning both eliminates the need to crop videos and solves the problem of semi-supervised learning’s difficulty in handling long videos. Previous work has used graph convolution or self-attention mechanisms to model temporal relationships. However, these methods tend to model temporal relationships at a single scale and lack consideration of the aggregation problem for different temporal relationships. In this paper, we propose a weakly supervised anomaly detection framework, MTDA-Net, with emphasis on modeling different temporal relationships and enhanced semantic discrimination. To this end, we construct a new plug-and-play module, MTDA, which uses three branches, Multi-headed Attention (MHA), Temporal Shift (TS), and Dilated Aggregation (DA), to extract different temporal sequences. Specifically, the MHA branch can globally model the video information and project the features into different semantic spaces to enhance the expressiveness and discrimination of the features. The DA branch extracts temporal information of different scales via dilated convolution and captures the temporal features of local regions in the video. The TS branch can fuse the features of adjacent frames on a local scale and enhance the information flow. MTDA-Net can learn the temporal relationships between video segments on different branches and learn powerful video representations based on these relationships. The experimental results on the XD-Violence dataset show that MTDA-Net can significantly improve the detection accuracy of abnormal behaviors.

The Chinese South–North Water Transfer Project is an important project to improve the freshwater supply environment in the Chinese interior and greatly alleviates the water shortage in the Chinese North China Plain; its sustainable, healthy, and safe operation guarantees ecological protection and economic development. However, due to the special expansive soil and deep excavation structure, the first section of the South–North Water Transfer Project canal faces serious disease risk directly manifested by cracks in the slope of the canal. Currently, relying on manual inspection not only consumes a lot of human resources but also unnecessarily repeats and misses many inspection areas. In this paper, a monitoring method combining depth learning and Uncrewed Aerial Vehicle (UAV) high-definition remote sensing is proposed, which can detect the cracks of the channel slope in time and accurately and can be used for long-term health inspection of the South–North Water Transfer Project. The main contributions are as follows: (1) aiming at the need to identify small cracks in reinforced channels, a ground-imitating UAV that can obtain super-clear resolution remote-sensing images is introduced to identify small cracks on a complex slope background; (2) to identify fine cracks in massive images, a channel crack image dataset is constructed, and deep-learning methods are introduced for the intelligent batch identification of massive image data; (3) to provide the geolocation of crack-extraction results, a fast field positioning method for non-modeled data combined with navigation information is investigated. The experimental results show that the method can achieve a 92.68% recall rate and a 97.58% accuracy rate for detecting cracks in the Chinese South–North Water Transfer Project channel slopes. The maximum positioning accuracy of the method is 0.6 m, and the root mean square error is 0.21 m. It provides a new technical means for geological risk identification and health assessment of the South–North Water Transfer Central Project.

Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion.

In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching for an optimal LED for each UE. Due to the non-convex and non-continuous nature of this security maximization problem, we propose an LED selection algorithm based on tabu search to avoid getting trapped in local optima and expedite the search process by managing trial vectors from previous iterations. Moreover, we introduce three LED selection strategies with a low computational complexity. The simulation results demonstrate that the proposed algorithm achieves a secrecy performance very close to the global optimal value, with a gap of less than 1%. Additionally, the proposed strategies exhibit a performance gap of 28% compared to the global optimal.