The key identification element of the smart factory is the interconnection between devices, which solves the production method and development dilemma of the factory under Industry 3.0 and previous models. The new article published by Xinhua News Agency on January 16, 2022, advocates the idea of industry+ Internet, and its main goal is to realize intelligent production. At present, smart factories have become the main development direction of industrial enterprises in the world. 5G technology has been rapidly deployed with the development of mobile communication, and its performance has also been greatly improved compared to previous communication technologies. After China put forward the smart manufacturing 2025 plan, although a large number of enterprises are still lingering in the process of research, learning, and exploration, the idea of Industry 4.0 has taken root in various intelligent manufacturing enterprises, and the development route of Chinese manufacturing enterprises in the future has been ahead of schedule. Direction. Based on the basic theory of Industry 4.0, 5G wireless communication, Internet of Things, and smart factories, this paper firstly distributed questionnaires to 20 enterprises with smart factories by means of network and interview and reanalyzed the collected questionnaires by regression analysis method and then used the questionnaire scale analysis method to analyze the reliability and validity of the questionnaire and combined with the results of the questionnaire analysis to analyze the problems existing in the current smart factory. Finally, based on the background of Industry 4.0 and 5G communication technology, combined with the Internet of Things technology, the development layout of the smart factory is designed; that is, based on the elliptic curve encryption algorithm, the signature mechanism of mutual trust of all electronic devices in the IoT smart factory is set to improve the smart factory.
The diversity of big data in Internet of Things is one of the important characteristics that distinguish it from traditional big data. Big data of Internet of Things is often composed of a variety of data with different structural forms. The description of the same thing by these different modal data has certain independence and strong relevance. Accurately and efficiently extracting and processing the hidden fusion information in the big data of the Internet of Things is helpful to solve various multimodal data analysis tasks at present. In this paper, a multimodal interactive function fusion model based on attention mechanism is proposed, which provides more efficient and accurate information for emotion classification tasks. Firstly, a sparse noise reduction self-encoder is used to extract text features, Secondly, features are extracted by encoder. Finally, an interactive fusion module is constructed, which makes text features and image features learn their internal information then the combination function is applied to the emotion classification task.
Computer vision systems cannot function without visual target tracking. Intelligent video monitoring, medical treatment, human-computer interaction, and traffic management all stand to benefit greatly from this technology. Although many new algorithms and methods emerge every year, the reality is complex. Targets are often disturbed by factors such as occlusion, illumination changes, deformation, and rapid motion. Solving these problems has also become the main task of visual target tracking researchers. As with the development for deep neural networks and attention mechanisms, object-tracking methods with deep learning show great research potential. This paper analyzes the abovementioned difficult factors, uses the tracking framework based on deep learning, and combines the attention mechanism model to accurately model the target, aiming to improve tracking algorithm. In this work, twin network tracking strategy with dual self-attention is designed. A dual self-attention mechanism is used to enhance feature representation of the target from the standpoint of space and channel, with the goal of addressing target deformation and other problems. In addition, adaptive weights and residual connections are used to enable adaptive attention feature selection. A Siamese tracking network is used in conjunction with the proposed dual self-attention technique. Massive experimental results show our proposed method improves tracking performance, and tracking strategy achieves an excellent tracking effect.
The electronic health record (EHR) surveillance process relies on wireless security administered in application technology, such as the Internet of Things (IoT). Automated supervision with cutting-edge data analysis methods may be a viable strategy to enhance treatment in light of the increasing accessibility of medical narratives in the electronic health record. EHR analysis structured data structure code was used to obtain data on initial fatality risk, infection rate, and hazard ratio of death from EHRs for prediction of unexpected deaths. Patients utilizing EHRs in general must keep in mind the significance of security. With the rise of the IoT and sensor-based Healthcare 4.0, cyber-resilience has emerged as a need for the safekeeping of patient information across all connected devices. Security for access, amendment, and storage is cumulatively managed using the common paradigm. For improving the security of surveillance in the aforementioned services, this article introduces an endorsed joint security scheme (EJSS). This scheme recognizes the EHR utilization based on the aforementioned processes. For each process, different security measures are administered for sustainable security. Access control and storage modification require relative security administered using mutual key sharing between the accessing user and the EHR database. In this process, the learning identifies the variations in different processes for reducing adversarial interruption. The federated learning paradigm employed in this scheme identifies concurrent adversaries in the different processes initiated at the same time. Differentiating the adversaries under each process strengthens mutual authentication using individual attributes. Therefore, individual surveillance efficiency through log inspection and adversary detection is improved for heterogeneous and large-scale EHR databases.
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