Chunli Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Chunli Liu has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Baoqing Sun
School of Information Science and Engineering, Shandong University, Qingdao, China
1 shared publication - Duan-Lu Zhou
Chinese Academy of Sciences
1 shared publication - Hailong Liu
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
1 shared publication - Jinghu Li
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
1 shared publication - Miaomiao Meng
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
1 shared publication - Qianguo Xing
Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
1 shared publication
Latest Publications
- Open Access
Insufficiency of Pure-State Ensembles in Characterizing Transformations of Entangled States under LOCC
The conditions for transforming pure entangled states under local operations and classical communication (LOCC) are well understood. A natural question then arises: Can we determine the transformation conditions for mixed entangled states under LOCC based on the properties of their pure-state ensembles? While much effort has been devoted to this issue, in this paper, we rule out this possibility. Our findings address several open questions, including: (i) The conditions \( E_f^{cr}(ρ) \geq E_f^{cr}(σ) \) for all convex roof entanglement measures \(E_f^{cr}\) is insufficient to guarantee the existence of an LOCC transformation \(Λ^L(\cdot)\) from \(ρ\) to \(σ\); and (ii) The inequalities \(\sum_j p_j E(\varphi_j) \geq \sum_l q_l E(ψ_l)\) for all entanglement monotones \(E\) are not sufficient to ensure the existence of an LOCC transformation from \(\{p_j, \ket{\varphi_j}\}\) to \(\{q_l, \ket{ψ_l}\}\).Open AccessInsufficiency of Pure-State Ensembles in Characterizing Transformations of Entangled States under LOCC
The conditions for transforming pure entangled states under local operations and classical communication (LOCC) are well understood. A natural question then arises: Can we determine the transformation conditions for mixed entangled states under LOCC based on the properties of their pure-state ensembles? While much effort has been devoted to this issue, in this paper, we rule out this possibility. Our findings address several open questions, including: (i) The conditions \( E_f^{cr}(ρ) \geq E_f^{cr}(σ) \) for all convex roof entanglement measures \(E_f^{cr}\) is insufficient to guarantee the existence of an LOCC transformation \(Λ^L(\cdot)\) from \(ρ\) to \(σ\); and (ii) The inequalities \(\sum_j p_j E(\varphi_j) \geq \sum_l q_l E(ψ_l)\) for all entanglement monotones \(E\) are not sufficient to ensure the existence of an LOCC transformation from \(\{p_j, \ket{\varphi_j}\}\) to \(\{q_l, \ket{ψ_l}\}\). - Open Access
A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning
Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available.
Open AccessA Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning
Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available.