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Zhihan Lv

Researcher at Qingdao University

Publications -  460
Citations -  14707

Zhihan Lv is an academic researcher from Qingdao University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 54, co-authored 313 publications receiving 8997 citations. Previous affiliations of Zhihan Lv include Chinese Academy of Sciences & Warsaw University of Technology.

Papers
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Guest Editorial: Secure Communications Over the Internet of Artificially Intelligent Things

TL;DR: Wang et al. as mentioned in this paper used machine learning (ML) technology to solve practical problems that are difficult for simple rules using data and is widely used in complex tasks such as search engine (SE), autonomous driving (AD), machine translation (MT), medical diagnosis, spam filtering, computer games, face recognition, data matching, and credit rating.
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Intelligent Fire Information System Based on 3D GIS

TL;DR: In this article , a smart fire protection information system was designed based on the Internet of Things (IoT) and achieved an actual intelligent fire rescue. But this system is not suitable for large-scale fire scenes.
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From Three-Body game to Metaverse

Zhihan Lv
- 15 May 2023 - 
TL;DR: In this article , the authors study the concept of the metaverse as reflected in the TV series The Three-Body Problem, as well as the current development status of metaverse in the real world.
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Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

TL;DR: In this paper , the authors summarized the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status.
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Nonrigid Optical Flow Ground Truth for Real-World Scenes with Time-Varying Shading Effects

TL;DR: This letter presents a dense ground truth dataset of nonrigidly deforming real-world scenes, and presents an RGB-NIR multispectral optical flow model allowing for energy optimization by adoptively combining featured information from both the RGB and the complementary NIR channels.