Z
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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Proceedings ArticleDOI
Serious game based dysphonic rehabilitation tool
TL;DR: The purpose of this work is designing and implementing a rehabilitation software for dysphonic patients that can play the game as well as conduct the voice training simultaneously guided by therapists at clinic or exercise independently at home.
Journal ArticleDOI
A robust energy-efficient routing algorithm to cloud computing networks for learning
TL;DR: The REERA is able to dynamically change the link weight, ensure that most of the link can be used uniformly, and avoid the traffic congestion when certain links of cloud computing networks was excessively used, and improves the performance of the entire network.
Posted Content
Preprint: Intuitive Evaluation of Kinect2 based Balance Measurement Software.
TL;DR: In this paper, a balance measurement software based on Kinect2 sensor is evaluated by comparing to the golden standard balance measure platform intuitively, the software analyzes the tracked body data from the user by Kinect 2 sensor and get user's center of mass(CoM) as well as its motion route on a plane.
Journal ArticleDOI
Many-Objective Deployment Optimization of Edge Devices for 5G Networks
TL;DR: An improved optimization algorithm named grouping-based many-objective evolutionary algorithm (GMEA) is proposed, and the experimental results demonstrate that GMEA performs better than the other methods in both visualization results and hypervolume (HV) indicators.
Journal ArticleDOI
Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing.
TL;DR: In this article, a multi-stage combination-based service deployment optimization model is proposed to solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively.