A5100343939- High-Temperature Coating Behaviors
- Nuclear Materials and Properties
- Advanced materials and composites
- Privacy-Preserving Technologies in Data
- Catalytic Processes in Materials Science
- Cooperative Communication and Network Coding
- Metal and Thin Film Mechanics
- UAV Applications and Optimization
- Caching and Content Delivery
- Advanced ceramic materials synthesis
- Mobile Ad Hoc Networks
- Advanced Wireless Network Optimization
- Wireless Networks and Protocols
- Advanced Wireless Communication Technologies
- IPv6, Mobility, Handover, Networks, Security
- IoT and Edge/Fog Computing
- Mobile Crowdsensing and Crowdsourcing
- GNSS positioning and interference
- High Entropy Alloys Studies
- Advanced MIMO Systems Optimization
- Geophysics and Gravity Measurements
- Intermetallics and Advanced Alloy Properties
- Ionosphere and magnetosphere dynamics
- Recommender Systems and Techniques
- Particle Dynamics in Fluid Flows
University of Chinese Academy of Sciences
2005-2025
Institute of Computing Technology
2015-2025
Chinese Academy of Sciences
2015-2025
Chongqing University of Posts and Telecommunications
2022-2025
Guangdong Institute of New Materials
2015-2024
Guangdong Academy of Sciences
2019-2024
Central South University
2019-2024
State Key Laboratory of Powder Metallurgy
2019-2024
Huazhong University of Science and Technology
2024
Institute of Information Engineering
2024
Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) Convolutional (CNN) proposed. improve the accuracy minimize dependence aperiodic data, this article, Beijing...
Abstract Thermal barrier coatings (TBCs) can effectively protect the alloy substrate of hot components in aeroengines or land-based gas turbines by thermal insulation and corrosion/erosion resistance ceramic top coat. However, continuous pursuit a higher operating temperature leads to degradation, delamination, premature failure Both new materials coating structures must be developed meet demand for future advanced TBC systems. In this paper, latest progress some is first reviewed. Then,...
Dispatching flexible unmanned aerial vehicles (UAVs) to collect data from distributed Internet-of-Things devices (IoTDs) is expected be a promising technology support time-critical applications. However, in urban environments, the communication links between UAV and IoTDs are prone frequently blocked by buildings, which severely impairs freshness of information collected UAV. Thus, how overcome building blockages ensure fresh collection quite important but neglected existing works. In this...
In heterogeneous wireless networks, handoff can be separated into two parts: horizontal (HHO) and vertical (VHO). VHO plays an important role to fulfill seamless data transfer when mobile nodes cross access networks with different link layer technologies. Current algorithms mainly focus on trigger VHO, but neglect the problem of how synthetically consider all currently available (homogeneous or heterogeneous) choose optimal network for HHO from candidates. this paper, we present analytical...
Abstract Plasma spray-physical vapor deposition (PS-PVD), called the third-generation method for thermal barrier coatings (TBCs) fabrication, has great potential their using in gas-turbine engines. Compared to atmospheric plasma spray (APS), first-generation TBCs, and electron beam-PVD (EB-PVD), second generation, PS-PVD many interesting features, including non-line sight deposition, high rate, microstructural flexibility, among others. Such advantages make them a promising approach prepare...
The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning Multi-access Edge Computing (MEC). Diverse user behaviors call personalized with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task (FMTL) is proposed train related but ML devices, whereas previous works suffer from excessive communication overhead during training neglect model heterogeneity among MEC....
Federated learning (FL) is a privacy-preserving machine paradigm in which the server periodically aggregates local model parameters from cli ents without assembling their private data. Constrained communication and personalization requirements pose severe challenges to FL. distillation (FD) proposed simultaneously address above two problems, exchanges knowledge between clients, supporting heterogeneous models while significantly reducing overhead. However, most existing FD methods require...
Edge Intelligence (EI) allows Artificial (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time close sources. To protect privacy unify silos distributed among end devices EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple without compromising privacy. However, prevailing FL approaches cannot guarantee model generalization adaptation on heterogeneous clients. Recently, Personalized (PFL)...
In a competitive electricity market, generation company (Genco) can manage its trading risk through among multiple markets such as spot and contract markets. The question is how to decide the proportion of each market in order maximize Genco's profit minimize associated risk. Based on mean-variance portfolio theory, this paper proposes sequential optimization approach electric energy allocation between markets, taking into consideration risks price, congestion charge, fuel price. Especially,...
Mobile P2P networking is an enabling technology for mobile devices to self-organize in unstructured style and communicate a peer-to-peer fashion. Due user mobility and/or the unrestricted switching on/off of devices, links are intermittently connected end-to-end paths may not exist, causing routing very challenging problem. Moreover, limited wireless spectrum device resources together with rapidly growing number portable amount transmitted data make even harder. To tackle these challenges,...
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in privacy-preserving manner. However, noisy labels are ubiquitous reality since high-quality require expensive human efforts, which cause severe performance degradation. Although lot methods proposed directly deal labels, these either excessive computation overhead or violate the privacy protection principle FL. To this end, we focus on issue FL purpose alleviating degradation...
This letter presents a wideband quad-polarization-reconfigurable bidirectional antenna using simple switchable feeding network. By cutting symmetrical fractal slot on the ground, dual-polarized differential circular monopole with high isolation is realized. To achieve reconfigurable quad-polarization diversity, fed by network, which provides four output states only six PIN diodes. For verification, prototype operating at 1.85 GHz designed and fabricated. two linear polarization states,...
With the ubiquitous demand for wireless communications, researchers have studied heterogeneous networks (HetNets) years. Often HetNets include a macrocell base station (MBS), several sets of users (MUs), large number femtocell stations (FBSs), and (secondary users), where femtocells help system relay uplink or downlink traffic between MUs MBS. In this article, we propose novel joint information energy cooperation method, with aim enhancing spectrum efficiency (EE) cognitive HetNets....