Using the association rules of the basic data of the whole link, the operation history law scenarios and the operation status risk perception methods among the distributed data centers are respectively studied. Modeling methods can be used to summarize the normal operation rules of various systems, and the operation rules can be used to find abnormal operation, and then determine the risk content. Its operation can be divided into different regularity stages to be distinguished, grasp the proportional relationship of each resource load in each stage, and realize the quantification of the regularity as a data index. Perform simulation tests to verify the effectiveness of the proposed method. Realize the quantification of abnormalities as data indicators, and then realize the conversion to risk judgments.
The completion of large occluded human body images poses a unique challenge for general image completion methods. The complex shape variations of human bodies make it difficult to establish a consistent understanding of their structures. Furthermore, as human vision is highly sensitive to human bodies, even slight artifacts can significantly compromise image fidelity. To address these challenges, we propose a large occluded human image completion (LOHC) model based on a novel image-prior cooperative completion strategy. Our model leverages human segmentation maps as a prior, and completes the image and prior simultaneously. Compared to the widely adopted prior-then-image completion strategy for object completion, this cooperative completion process fosters more effective interaction between the prior and image information. Our model consists of two stages. The first stage is a transformer-based auto-regressive network that predicts the overall structure of the missing area by generating a coarse completed image at a lower resolution. The second stage is a convolutional network that refines the coarse images. As the coarse result may not always be accurate, we propose a Dynamic Fusion Module (DFM) to selectively fuses the useful features from the coarse image with the original input at spatial and channel levels. Through extensive experiments, we demonstrate our method’s superior performance compared to state-of-the-art methods.
Immersive techniques, such as virtual reality, augmented reality, and mixed reality, take immersive displays as carriers to provide immersive experience. A large number of approaches focus on the visualization of scientific data in immersive environments while just a few methods concentrate on interactive information visualization (InfoVis) in an immersive environment, although InfoVis has been extended to the 3-D space for a long time. In the era of data explosion, the traditional 2-D space is unable to convey large amounts of abstract information in an intuitive way. Meanwhile, desktop-based 3-D InfoVis generally leads to visual conflict and confusion owing to limited display size and field of vision. In this survey, we search for the interactive techniques in immersive InfoVis and summarize their commonalities and discuss their differences and potential trends. The data types of abstract information in InfoVis can be categorized into graph/network data, high-dimensional and multivariate data, time-varying data, and text and document data. Besides, the visual presentation of information in immersive environments is also summarized, especially for charts, plots, and diagrams, which are some basic components of InfoVis techniques. We also described the immersive applications of InfoVis techniques, including the tools or frameworks on immersive analytics and infographics. The discussion about the traditional nonimmersive and the immersive methods in data visualizations show that the latter one has the potential to become an alternative to explore massive information in the future.
This paper investigates the temperature demagnetization modeling method for permanent magnets (PM) in permanent magnet synchronous linear motor (PMSLM). First, the PM characteristics are presented, and finite element analysis (FEA) is conducted to show the magnetic distribution under different temperatures. Second, demagnetization degrees and remanence of the five PMs' experiment sample are actually measured in stove at temperatures varying from room temperature to 300 °C, and to obtain the real data for next-step modeling. Third, machine learning algorithm called extreme learning machine (ELM) is introduced to map the nonlinear relationships between temperature and demagnetization characteristics of PM and build the demagnetization models. Finally, comparison experiments between linear modeling method, polynomial modeling method, and ELM can certify the effectiveness and advancement of this proposed method.
Among data mining algorithms, Apriori association rule data mining algorithm is one of the most widely used algorithms. The algorithm is faced with the problems such as low accuracy of algorithm recommendation, single support and difficulty in setting the support threshold in the operation of current e-commerce recommendation systems. Based on the above problems, this paper proposes an association rule data mining algorithm combining multi-item support tree and support. The algorithm uses recursive iteration to generate conditional database and dynamically adjust the minimum support during mining to obtain frequent item sets, which greatly improves the efficiency of data mining and the accuracy of recommendation. Experiments show that the proposed association rule data mining algorithm with multi-item support greatly changes the number of association rules, thus improving the efficiency of data mining and the accuracy of recommendation.
China is one of the countries with the most serious natural disasters in the world. Hidden dangers and risks of all kinds of accidents are intertwined and superimposed, and the factors affecting public safety are increasing. It is urgent to strengthen the construction of emergency medical rescue capacity. During the nineteenth collective learning of the Central Political Bureau, general secretary Xi Jinping stressed that we should strengthen the capacity building of aviation emergency rescue and improve the airspace guarantee mechanism for emergency rescue. As the most important aerial medical evacuation platform, helicopter has the advantages of fast response, strong mobility, wide rescue range, direct arrival at the first line of treatment, etc. it has become an important means of modern rescue, and also the only way to open life treatment in case of land damage. From the international experience, the helicopter emergency medical system established by most developed countries plays an important role in disaster relief and anti‐terrorism. China has a large population, complex terrain and uneven regional development. In case of disasters, the casualties are often more severe and the demand for treatment is more urgent. It is urgent to establish an aviation medical emergency rescue system suitable for the national conditions and with helicopters as the main carrier, and accelerate the research and development of key technical equipment. In recent years, more and more people realize the necessity and benefits of the application of interactive technology for aviation medical simulation training in major events and disasters. Interaction refers to the transmission and exchange of intelligence, data, data and technical knowledge in all aspects of nature and society. From the perspective of information theory, local chronicles are a tangible text information carrier, which collects all kinds of information in a certain region. Interactive technology is to use certain means to achieve the purpose of interaction, and gradually enter the era of multi field application! In thefield of aviation medical rescue, it is in urgent need of penetration and integration. This paper mainly develops an AR emergency rescue process training system. Through the creation of data sets, action recognition based on computer vision and deep learning, the selection of recognition model and action capture, the action is recognized based on AR, and then 22 joint points of human hand and upper arm skeleton are taken to label the data space position. Through the above learning results, we can get the real‐time hand recognition based on depth information Upper arm skeleton output. Neural network is used to recognize human skeleton action. The skeleton information coordinates are imported into RNN neural network for deep learning. Finally, based on the complementarity of CNN and LSTM, combined with the idea of dual stream structure network, the action recognition is carried out.
In this paper, a modulated signal recognition method based on feature fusion and ResCNN (FF-ResCNN) under generalized fractal noise background is proposed. Firstly, in the feature extraction and fusion part, considering the robustness, anti-noise and interpretability of the features, the fusion feature vectors of generalized fractal spectrum, instantaneous feature and high-order cumulant feature are constructed. Secondly, in order to suppress the possible gradient and degradation problems, the ResCNN model is used to train and classify the fusion features when the CNN model is used to further collect the deep-level abstract features. Finally, feature extraction, feature fusion and classification experiments are carried out for nine modulated signals under two noise types (GWN and fractal noise). Experimental results show that in the case of generalized fractal noise, when SNR=0dB, the recognition accuracy of signal modulation type reaches 95.42%, which verifies the superiority of the proposed method. At the same time, the simulation analysis is carried out for different feature fusion schemes, which provides a valuable reference for feature combination optimization under different noise environment.
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