Applying Cross-Modality Data Processing for Infarction Learning in Medical Internet of Things

Xu, Chenchu; Gao, Zhifan; Zhang, Dong; Zhang, Jinglin; Xu, Lei; Li, Shuo

doi:10.1109/jiot.2021.3068775

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…In the DC stage, the main content of the geospatial framework is the basic geographic information database and geographic information public platform. Xu et al [44] mentioned that with the progression of IoT and cloud computing, basic geographic information databases are transformed into spatiotemporal information databases, and geographic information public platforms are upgraded to spatiotemporal information cloud platforms. The spatiotemporal information database consists of spatiotemporal information data, IoT node address data, a spatiotemporal information database management system, and a support environment.…”

Section: The Main Content Of DC Construction In the Iot Environmentmentioning

confidence: 99%

Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

Shang

Guizani

2022

Applied Sciences

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To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a detailed review of the impact of DTs and digitalization on cities is made to assess the progression of cities and standardization of their management mode. Combined with the technical elements of DTs, the coupling effect of DTs technology and urban construction and the internal logic of DTs technology embedded in urban construction are discussed. Relevant literature covering the full range of DTs technologies and their applications is collected, evaluated, and collated, relevant studies are concatenated, and relevant accepted conclusions are summarized by modules. First, the historical process and construction content of a Digital City (DC) under modern demand are analyzed, and the main ideas of a DC design and construction are discussed in combination with the key technology of DTs. Then, the metaverse is the product of the combination of various technologies in different scenes. It is a key component to promote the integration of the real world and the digital world and can provide more advanced technical support in the construction of the DC. DTs urban technology architecture is composed of an infrastructure terminal information center terminal and application server end. Urban intelligent management is realized through physical urban data collection, transmission, processing, and digital urban visualization. The construction of DTs urban platform can improve the city’s perception and decision-making ability and bring a broader vision for future planning and progression. The interactive experience of the virtual world covered by the metaverse can effectively support and promote the integration of the virtual and real, and will also greatly promote the construction of SCs. In summary, this work is of important reference value for the overall development and practical adoption of DTs cities, which improves the overall operation efficiency and the governance level of cities.

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Section: The Main Content Of DC Construction In the Iot Environmentmentioning

confidence: 99%

Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

Shang

Guizani

2022

Applied Sciences

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“…Towards the smart and wireless-Internet era, it is favored and required to introduce Artificial Intelligence (AI) technologies into the real-world applications of Internet of Things (IoT) [1]- [3]. As a widely used AI technology, Generative Adversarial Networks (GANs) [4] are feasible for complex or high-dimensional data processing that is commonly suffered in IoT [5], [6]. In practice, there are a great deal of applications based on GANs [7]- [11], such as image reconstruction, behavior imitation, pedestrian reidentification and secure steganography, especially anomaly detection.…”

Section: A Backgroundmentioning

confidence: 99%

From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks

Sheng¹,

Fan²

2022

Preprint

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In order to introduce deep learning technologies into anomaly detection, Generative Adversarial Networks (GANs) are considered as important roles in the algorithm design and realistic applications.In terms of GANs, event probability reflected in the objective function, has an impact on the event generation which plays a crucial part in GAN-based anomaly detection. The information metric, e.g. Kullback-Leibler divergence in the original GAN, makes the objective function have different sensitivity on different event probability, which provides an opportunity to refine GAN-based anomaly detection by influencing data generation. In this paper, we introduce the exponential information metric into the GAN, referred to as MIM-based GAN, whose superior characteristics on data generation are discussed in theory. Furthermore, we propose an anomaly detection method with MIM-based GAN, as well as explain its principle for the unsupervised learning case from the viewpoint of probability event generation. Since this method is promising to detect anomalies in Internet of Things (IoT), such as environmental, medical and biochemical outliers, we make use of several datasets from the online ODDS repository to evaluate its performance and compare it with other methods.

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From MIM-Based GAN to Anomaly Detection: Event Probability Influence on Generative Adversarial Networks

Sheng

Fan

2022

IEEE Internet Things J.

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Applying Cross-Modality Data Processing for Infarction Learning in Medical Internet of Things

Cited by 6 publications

References 38 publications

Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks

From MIM-Based GAN to Anomaly Detection: Event Probability Influence on Generative Adversarial Networks

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