|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Qo S-Based Service Selection with Lightweight Description for Large-Scale Service-Oriented Internet of Things显示文摘Quality of Service(Qo S)-based service selection is the key to large-scale service-oriented Internet of Things(IOT), due to the increasing emergence of massive services with various Qo S. Current methods either have low selection accuracy or are highly time-consuming(e.g., exponential time complexity), neither of which are desirable in large-scale IOT applications. We investigate a Qo S-based service selection method to solve this problem. The main challenges are that we need to not only improve the selection accuracy but also decrease the time complexity to make them suitable for large-scale IOT applications. We address these challenges with the following three basic ideas. First, we present a lightweight description method to describe the Qo S, dramatically decreasing the time complexity of service selection. Further more, based on this Qo S description, we decompose the complex problem of Qo S-based service selection into a simple and basic sub-problem. Finally, based on this problem decomposition, we present a Qo S-based service matching algorithm, which greatly improves selection accuracy by considering the whole meaning of the predicates. The traces-driven simulations show that our method can increase the matching precision by 69% and the recall rate by 20% in comparison with current methods.Moreover, theoretical analysis illustrates that our method has polynomial time complexity, i.e., O.m2 n/, where m and n denote the number of predicates and services, respectively. | Chaocan Xiang Panlong Yang Xuangou Wu Hong He Shucheng Xiao | 2015 | Tsinghua Science and Technology2015，20，4： | 5 |
| 2 | Surface Coverage Algorithm in Directional Sensor Networks for Three-Dimensional Complex Terrains显示文摘Coverage is an important issue in the area of wireless sensor networks, which reflects the monitoring quality of the sensor networks in scenes. Most sensor coverage research focuses on the ideal two-dimensional(2-D) plane and full three-dimensional(3-D) space. However, in many real-world applications, the target field is a3-D complex surface, which makes conventional methods unsuitable. In this paper, we study the coverage problem in directional sensor networks for complex 3-D terrains, and design a new surface coverage algorithm. Based on a 3-D directional sensing model of nodes, this algorithm employs grid division, simulated annealing, and local optimum ideas to improve the area coverage ratio by optimizing the position coordinates and the deviation angles of the nodes, which results in coverage enhancement for complex 3-D terrains. We also conduct extensive simulations to evaluate the performance of our algorithms. | Fu Xiao Xiekun Yang Meng Yang Lijuan Sun Ruchuan Wang Panlong Yang | 2016 | Tsinghua Science and Technology2016，21，4： | 4 |
| 3 | Robust and Passive Motion Detection with COTS WiFi Devices显示文摘Device-free Passive(DfP) detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength(RSS), most recent studies rely on finer-grained Channel State Information(CSI). However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight(LOS) or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection(R-PMD). Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis(PCA)-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%. | Hai Zhu Fu Xiao Lijuan Sun Xiaohui Xie Panlong Yang Ruchuan Wang | 2017 | Tsinghua Science and Technology2017，22，4： | 3 |
| 4 | Synergistic Effects of Salt Concentration and Working Temperature towards Dendrite-Free Lithium Deposition显示文摘The lithium-(Li-)metal anode is crucial for developing high-energy-density batteries,while its dendritic growth and the low charge/discharge Coulombic efficiency in organic electrolytes hinder its practical application.Herein,we employed an in situ optical microscope to investigate the effect of the electrolyte concentration and the working temperature on the Li-plating/-stripping process.It is found that a higher concentration electrolyte can suppress its side reaction to improve the charge/discharge Coulombic efficiency,and a higher temperature can help lithium plate/strip uniformly with less lithium dendritic growth.An average Coulombic efficiency was obtained as high as 99.2%for over 150 cycles with a fixed plating capacity of 2 mAh cm^(-2) on copper foil in a 3 mol/kg ether-based electrolyte under 60℃,which provides an efficient and facile strategy for developing high-performance Li-metal batteries. | Panlong Li Chao Li Yang Yang Chanyuan Zhang Renhe Wang Yao Liu Yonggang Wang Jiayan Luo Xiaoli Dong Yongyao Xia | 2019 | Research2019，，1： | 1 |
| 5 | Sparsest random scheduling for compressive data gathering in wireless sensor networks显示文摘 | Wu Xuangou Yan Xiong Yang Panlong | 2014 | IEEE Transactions on Wireless Communications2014，13，10： | 1 |
| 6 | Dynamic User DemandDriven Online Network Selection显示文摘 | DU Zhiyong WU Qihui YANG Panlong | 2014 | IEEE Communications Let-ters2014，18，3： | 1 |
| 7 | The clinical implication of gamma-glutamyl transpeptidase in COVID-19显示文摘Background and aim:Coronavirus disease 2019(COVID-19)is a life-threatening disease that predomi-nantly causes respiratory failure.The impact of COVID-19 on other organs remains elusive.Herein,we aimed to investigate the effects of COVID-19 on the hepatobiliary system.Methods:In the current study,we obtained the clinical records and laboratory results from 66 laboratory-confirmed patients with COVID-19 at the Wuhan Tongji Hospital between 10 February 2020 and 28 February 2020.The detailed clinical features and laboratory findings were collected for analysis.Bioinformatics analysis was conducted to evaluate the correlation between gamma-glutamyl transferase(GGT)and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)entry receptor angiotensin-converting enzyme 2(ACE2).Results:In this cohort,30(51.7%)patients had abnormal liver function on admission,which was asso-ciated with disease severity and enriched in the male and diabetic patients.The elevated levels of direct bilirubin(P¼0.029)and GGT(P¼0.004)were common in patients with severe pneumonia when compared with those with mild pneumonia.In addition,elevated levels of GGT(P¼0.003)and aspartate aminotransferase(AST)(P¼0.007)were positively associated with longer hospital stay.The expression of ACE2 was closely associated with GGT in various human tissues because they shared the common transcriptional regulator hepatic nuclear factor-1 b(HNF1B).Conclusions:Increased GGT levels were common in severe cases and elevated GGT levels were positively associated with prolonged hospital stay and disease severity.Due to the consistent expression with ACE2,GGT is a potent biomarker indicating the susceptibility of SARS-CoV-2 infection. | Jianrong Liu Chao Yu Qing Yang Xiaofeng Yuan Fan Yang Panlong Li Guihua Chen Weicheng Liang Yang Yang | 2021 | Liver Research2021，5，4： | 0 |
| 8 | Seismic ahead-prospecting based on deep learning of retrieving seismic wavefield显示文摘Unknown geology ahead of the tunnel boring machine(TBM)brings a large safety risk for tunnel construction.Seismic ahead-prospecting using TBM drilling noise as a source can achieve near-real-time detection,meeting the requirements of TBM rapid drilling.Seismic wavefield retrieval is the key data processing step for the efficient utilization of TBM drilling noise.The traditional solution is based on cross-correlation to extract reflected waves,but the reference waves remain in the result,disturbing the imaging and interpre-tation of the adverse geology.To solve this problem,the deep learning method was introduced in wavefield retrieval to improve the accu-racy of geological prospecting.We trained a deep neural network(DNN)with its strong nonlinear mapping capability to transform seismic data from TBM drilling noise to data from the active source.The issue lies in its features for this specific tunnel task,including the decay of the seismic signal with time and the incomplete spatial correspondence.Thus,we improved a classical DNN with the time constraint as an additional input,and an additional pre-decoder to enlarge the receptive field.Additionally,a loss function weighted by the ground truth and time constraint is improved to achieve an accurate retrieval of the effective signal,considering the little effective information in tunnel data.Finally,the workflow of the proposed method was given,and a dataset designed with reference to the field case was employed to train the network.The proposed method accurately retrieved the reflection signal with higher dominant frequen-cies,which helped improve the accuracy of imaging.Numerical simulations and imaging on typical geological models show that the pro-posed method can suppress reference waves and get more accurate results with fewer artifacts.The proposed method has been applied in the Gaoligongshan Tunnel and imaged two abnormal zones,providing meaningful geological information for TBM drilling and tunnel construction. | Lei Chen Senlin Yang Lei Guo Panlong Zhang Kai Li Wei Shao Xinji Xu ⇑Fahe Sun | 2023 | Underground Space2023，，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员