CloudNavi

Teng, Xiaoqiang; Guo, Deke; Guo, Yulan; Zhou, Xiaolei; Liu, Zhong

doi:10.1145/3216722

Cited by 21 publications

(6 citation statements)

References 56 publications

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“…The purpose of intelligent network optimization based on artificial intelligence technology is to improve and maintain the communication quality of intelligent network. From the perspective of network, the purpose of intelligent network optimization based on artificial intelligence technology is to improve the quality of mobile communication, while minimizing the cost of network maintenance 4–7 …”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of network, the purpose of intelligent network optimization based on artificial intelligence technology is to improve the quality of mobile communication, while minimizing the cost of network maintenance. [4][5][6][7] In this paper, the ubiquitous clouds framework is proposed. The application of this cloud computing model in transmission and communication network optimization is an innovation and conducive to the healthy development of the communication industry.…”

Section: Introductionmentioning

confidence: 99%

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Retracted: Artificial intelligence point‐to‐point signal communication network optimization based on ubiquitous clouds

Lin

Deng

et al. 2020

Int J Communication

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Summary At present, the application of communication network has spread to every area of social life. The progress of network technology has driven the development of information technology industry and the advancement of cloud computing. With the application of artificial intelligence and deep learning technology, the network becomes more and more intelligent, so the application of artificial intelligence theory in communication network optimization modeling is more extensive. In this paper, we propose the ubiquitous clouds framework and apply the artificial intelligence optimization scheme to the point‐to‐point (P2P) signal transmission network optimization scheme. For different application scenarios, this paper analyzes and experiments the proposed method. The experimental results show that the proposed method makes full use of the advantages of communication network resources which improves the efficiency of communication network optimization and also reduces the optimization cost to a certain extent compared with the state‐of‐the‐art approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retracted: Artificial intelligence point‐to‐point signal communication network optimization based on ubiquitous clouds

Lin

Deng

et al. 2020

Int J Communication

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“…Moreover, there are some existing approaches on indoor localization [2,6,8] and navigation [10,12,19,20] that are related to this work. A major category of indoor localization approaches is fingerprint-based, which collects measurements at known locations in advance.…”

Section: Related Workmentioning

confidence: 99%

Start from Scratch: A Crowdsourcing-Based Data Fusion Approach to Support Location-Aware Applications

Jiang

Liu

Wang

et al. 2019

Sensors

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As one of the most important breakthroughs for modern transportation, the indoor location-based technology has been gradually penetrating into our daily lives and underlines the foundation of the Internet of Things (IoT). To improve the positioning accuracy and efficiency, crowdsourcing has been widely applied in indoor localization in recent years. However, the crowdsourced data can hardly be fused easily to enable usable applications for the reason that the data are collected by different users, in different locations, at different times, with different noises and distortions. Although different data fusing methods have been implemented in different crowdsourcing services, we find that they may not fully leverage the data collected from multiple dimensions that can potentially lead to a better fusion results. In order to address this problem, we propose a more general solution, which can fuse the multi-dimensional crowdsourced data together and align them with the consistent time and location stamps, by using the features of the sensory data only, and thus build high quality crowdsourcing services from the raw data samplings collected from the environment. Finally, we conduct extensive evaluations and experiments using different commercial devices to validate the effectiveness of the method we proposed.

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“…The digital archiving was the important measurement for the protection of historical buildings [3,4] and 3D point cloud which can provide the extract spatial geometry of built heritage with a complex shape has been widely used in the documentation of Chinese ancient architectures [5]. However, the captured original point cloud lacked the structured information, such as semantics and hierarchy between parts, which disturbed the usage of point cloud in other application fields [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

MP-DGCNN for the Semantic Segmentation of Chinese Ancient Building Point Clouds

Dong,

Hou,

Zhou

et al. 2024

Preprint

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Point cloud semantic segmentation is a key step in the scan-to-HBIM process. In order to reduce the information in the process of DGCNN, this paper proposes a Mix Pooling Dynamic Graph Convolutional Neural Network (MP-DGCNN) for the segmentation of ancient architecture point clouds. The proposed MP-DGCNN differs from DGCNN mainly in two aspects: (1) to more comprehensively characterize the local topological structure of points, the edge features are redefined, and distance and neighboring points are added to the original edge features; (2) based on a Multilayer Perceptron (MLP), an internal feature adjustment mechanism is established, and a learnable mix pooling operator is designed by fusing adaptive pooling, max pooling, average pooling, and aggregation pooling, to learn local graph features from the point cloud topology. To verify the proposed algorithm, experiments are conducted on the Qutan Temple point cloud dataset, and the results show that compared with PointNet, PointNet++, DGCNN, and LDGCNN, the MP-DGCNN segmentation network achieves the highest OA and mIOU, reaching 90.19% and 65.34%, respectively.

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CloudNavi

Cited by 21 publications

References 56 publications

Retracted: Artificial intelligence point‐to‐point signal communication network optimization based on ubiquitous clouds

Retracted: Artificial intelligence point‐to‐point signal communication network optimization based on ubiquitous clouds

Start from Scratch: A Crowdsourcing-Based Data Fusion Approach to Support Location-Aware Applications

MP-DGCNN for the Semantic Segmentation of Chinese Ancient Building Point Clouds

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