Yuan, Yali

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[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","IEEE/ACM Transactions on Networking"],["dc.bibliographiccitation.lastpage","14"],["dc.contributor.author","Ren, Bangbang"],["dc.contributor.author","Guo, Deke"],["dc.contributor.author","Yuan, Yali"],["dc.contributor.author","Tang, Guoming"],["dc.contributor.author","Wang, Weijun"],["dc.contributor.author","Fu, Xiaoming"],["dc.date.accessioned","2022-01-11T14:05:56Z"],["dc.date.available","2022-01-11T14:05:56Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1109/TNET.2021.3105959"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97783"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","1558-2566"],["dc.relation.issn","1063-6692"],["dc.title","Optimal Deployment of SRv6 to Enable Network Interconnection Service"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","15685"],["dc.bibliographiccitation.issue","17"],["dc.bibliographiccitation.journal","IEEE Internet of Things Journal"],["dc.bibliographiccitation.lastpage","15696"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Yuan, Yachao"],["dc.contributor.author","Yuan, Yali"],["dc.contributor.author","Memarmoshrefi, Parisa"],["dc.contributor.author","Baker, Thar"],["dc.contributor.author","Hogrefe, Dieter"],["dc.date.accessioned","2022-10-04T10:22:13Z"],["dc.date.available","2022-10-04T10:22:13Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1109/JIOT.2022.3153271"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/114614"],["dc.notes.intern","DOI-Import GROB-600"],["dc.relation.eissn","2327-4662"],["dc.relation.eissn","2372-2541"],["dc.title","LbSP: Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","ACM transactions on internet technology"],["dc.bibliographiccitation.lastpage","20"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Yuan, Yali"],["dc.contributor.author","Liang, Chencheng"],["dc.contributor.author","Chen, Xu"],["dc.contributor.author","Baker, Thar"],["dc.contributor.author","Fu, Xiaoming"],["dc.date.accessioned","2022-06-08T07:57:06Z"],["dc.date.available","2022-06-08T07:57:06Z"],["dc.date.issued","2022"],["dc.description.abstract","Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay."],["dc.identifier.doi","10.1145/3406533"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/109997"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1557-6051"],["dc.relation.issn","1533-5399"],["dc.title","Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","527"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Computer-Aided Civil and Infrastructure Engineering"],["dc.bibliographiccitation.lastpage","544"],["dc.bibliographiccitation.volume","33"],["dc.contributor.author","Li, Ruoxing"],["dc.contributor.author","Yuan, Yachao"],["dc.contributor.author","Zhang, Wei"],["dc.contributor.author","Yuan, Yali"],["dc.date.accessioned","2018-09-06T10:46:16Z"],["dc.date.available","2018-09-06T10:46:16Z"],["dc.date.issued","2018"],["dc.description.abstract","Vision‐based autonomous inspection of concrete surface defects is crucial for efficient maintenance and rehabilitation of infrastructures and has become a research hot spot. However, most existing vision‐based inspection methods mainly focus on detecting one kind of defect in nearly uniform testing background where defects are relatively large and easily recognizable. But in the real‐world scenarios, multiple types of defects often occur simultaneously. And most of them occupy only small fractions of inspection images and are swamped in cluttered background, which easily leads to missed and false detections. In addition, the majority of the previous researches only focus on detecting defects but few of them pay attention to the geolocalization problem, which is indispensable for timely performing repair, protection, or reinforcement works. And most of them rely heavily on GPS for tracking the locations of the defects. However, this method is sometimes unreliable within infrastructures where the GPS signals are easily blocked, which causes a dramatic increase in searching costs. To address these limitations, we present a unified and purely vision‐based method denoted as defects detection and localization network, which can detect and classify various typical types of defects under challenging conditions while simultaneously geolocating the defects without requiring external localization sensors. We design a supervised deep convolutional neural network and propose novel training methods to optimize its performance on specific tasks. Extensive experiments show that the proposed method is effective with a detection accuracy of 80.7% and a localization accuracy of 86% at 0.41 s per image (at a scale of 1,200 pixels in the field test experiment), which is ideal for integration within intelligent autonomous inspection systems to provide support for practical applications."],["dc.identifier.doi","10.1111/mice.12351"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15669"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.title","Unified Vision-Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.firstpage","8243"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","IEEE Transactions on Vehicular Technology"],["dc.bibliographiccitation.lastpage","8256"],["dc.bibliographiccitation.volume","69"],["dc.contributor.author","Wu, Tong"],["dc.contributor.author","Zhou, Pan"],["dc.contributor.author","Liu, Kai"],["dc.contributor.author","Yuan, Yali"],["dc.contributor.author","Wang, Xiumin"],["dc.contributor.author","Huang, Huawei"],["dc.contributor.author","Wu, Dapeng Oliver"],["dc.date.accessioned","2021-04-14T08:24:11Z"],["dc.date.available","2021-04-14T08:24:11Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1109/TVT.2020.2997896"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81197"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1939-9359"],["dc.relation.issn","0018-9545"],["dc.title","Multi-Agent Deep Reinforcement Learning for Urban Traffic Light Control in Vehicular Networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.firstpage","385"],["dc.bibliographiccitation.journal","Future Generation Computer Systems"],["dc.bibliographiccitation.lastpage","398"],["dc.bibliographiccitation.volume","125"],["dc.contributor.author","Yuan, Yachao"],["dc.contributor.author","Yuan, Yali"],["dc.contributor.author","Baker, Thar"],["dc.contributor.author","Kolbe, Lutz Maria"],["dc.contributor.author","Hogrefe, Dieter"],["dc.date.accessioned","2021-10-01T09:57:32Z"],["dc.date.available","2021-10-01T09:57:32Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.future.2021.06.035"],["dc.identifier.pii","S0167739X21002302"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89861"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-469"],["dc.relation.issn","0167-739X"],["dc.title","FedRD: Privacy-preserving adaptive Federated learning framework for intelligent hazardous Road Damage detection and warning"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]