Showing papers by "Xidian University published in 2022"
TL;DR: Wang et al. as mentioned in this paper proposed edge computing based video pre-processing to eliminate the redundant frames, so that they migrate the partial or all the video processing task to the edge, thereby diminishing the computing, storage and network bandwidth requirements of the cloud center, and enhancing the effectiveness of video analyzes.
91 citations
TL;DR: This article focuses on scaled consensus tracking for a class of high-order nonlinear multiagent systems with time delays and external disturbances, and a fully distributed consensus protocol is designed to drive all agents to achieve scaled consensus with preassigned ratios.
Abstract: This article focuses on scaled consensus tracking for a class of high-order nonlinear multiagent systems Different from the existing results, for high-order nonlinear multiagent systems with time delays and external disturbances, a fully distributed consensus protocol is designed to drive all agents to achieve scaled consensus with preassigned ratios The control gains are varying and updated by distributed adaptive laws As a result, the presented protocol is independent of any global information, and thus, could be implemented in a fully distributed manner Simultaneously, the fully distributed control protocol using an adaptive $\sigma$ -modification technique is presented to deal with external disturbances, which can guarantee the tracking errors and coupling weights of all following agents are uniformly ultimately bounded To tackle with the derivatives of the functionals with time delays, the Lyapunov–Krasovskii functional is employed to analyze and compensate them by introducing multiintegral terms Finally, simulation examples are included to verify the effectiveness of the theoretical results
72 citations
TL;DR: In this paper, the static and dynamic lubrication parameters of the floating ring bearing (FRB) were investigated considering the effects of various coupling factors such as clearance ratio, vertical load and rotating speed.
62 citations
TL;DR: In this paper , a 1D/2D TiO2/ZnIn2S4 heterostructure was designed according to the principles of the S-scheme heterojunction.
60 citations
TL;DR: In this article , a multi-mode stretchable and wearable triboelectric nanogenerator (msw-TENG) is presented for biomechanical energy harvesting and physiological functions sensing.
57 citations
TL;DR: In this paper, a multi-mode stretchable and wearable triboelectric nanogenerator (msw-TENG) is presented for biomechanical energy harvesting and physiological functions sensing.
57 citations
TL;DR: In this paper, a frequency up-converting energy harvester based on a component pendulum, a pair of magnetic coupled cantilever beams, and two mechanical stoppers is presented.
53 citations
TL;DR: A comprehensive survey on green UAV communications for 6G is carried out in this paper , where the typical UAVs and their energy consumption models are introduced and the typical trends of green-UAV communications are provided.
51 citations
TL;DR: In this article, the effects of different carbon conditions (types, concentrations, and addition methods) on lipid accumulation in microalgal biomass production and biodiesel production were comprehensively discussed.
49 citations
TL;DR: Wang et al. as mentioned in this paper analyzed the drivers of CO2 emissions and the impact of low-carbon transition in China and showed that the increase of per capita consumption expenditure, energy consumption per unit of GDP, and CO2 consumption per capita is the reason for the continuous growth of CO 2 emissions; the improve of energy efficiency, CO2 output per unit GDP and energy consumption inhibits CO2 emission.
49 citations
TL;DR: In this paper , the effects of different carbon conditions (types, concentrations, and addition methods) on lipid accumulation in microalgal biomass production and biodiesel production were comprehensively discussed, and current challenges and constructive suggestions are proposed on costbenefit concerns in large-scale production of microalgae biodiesel.
TL;DR: In this article , the static and dynamic lubrication parameters of the floating ring bearing (FRB) were investigated considering the effects of various coupling factors such as clearance ratio, vertical load and rotating speed.
TL;DR: In this article , a self-quenching resistant solid-state fluorescent CDs are achieved, which exhibit tunable full-color solidstate emission without any other additional solid matrices, and the CDs were prepared via one-step microwave method using phloroglucinol and urea as sources by regulating reactant ratio and microwave power.
TL;DR: In this article, a novel Fe/Co metal organic complex nanosheet modified by 1,4-dicarboxybenzene (BDC) was prepared, and highly efficient removal performance for trace lead (II) (Pb2+) was demonstrated in the neutral aqueous solutions.
TL;DR: In this paper, three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector crossproduct technique and the LS method.
TL;DR: An edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids and a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution.
Abstract: With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.
TL;DR: In this article , the authors improved the Faster Region-based Convolutional Neural Network (Faster R-CNN) model by embedding Gabor kernels into the network, which is termed the Genetic Algorithm Gabor Faster-R-CNN.
TL;DR: In this article, the authors improved the Faster Region-based Convolutional Neural Network (Faster R-CNN) model by embedding Gabor kernels into the network, which is termed the Genetic Algorithm Gabor Faster-R-CNN.
TL;DR: In this paper , an improved version of the MK-CKKS multi-key homomorphic encryption protocol is proposed to design a novel privacy-preserving federated learning scheme, where model updates are encrypted via an aggregated public key before sharing with a server for aggregation.
Abstract: With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data. Federated learning mitigates this need to transfer local data by sharing model updates only. However, privacy leakage remains an issue. This paper proposes xMK-CKKS, an improved version of the MK-CKKS multi-key homomorphic encryption protocol, to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, a collaboration among all participating devices is required. Our scheme prevents privacy leakage from publicly shared model updates in federated learning and is resistant to collusion between k < N − 1 participating devices and the server. The evaluation demonstrates that the scheme outperforms other innovations in communication and computational cost while preserving model accuracy.
01 Jan 2022
TL;DR: In this paper , a Fe/Co metal organic complex nanosheet modified by 1,4-dicarboxybenzene (BDC) was prepared, and highly efficient removal performance for trace lead (II) (Pb2+) was demonstrated in the neutral aqueous solutions.
Abstract: A novel Fe/Co metal organic complex nanosheet modified by 1,4-dicarboxybenzene (BDC), i.e., [email protected], was prepared, and highly efficient removal performance for trace lead (II) (Pb2+) was demonstrated in the neutral aqueous solutions. The removal rates were higher than 95% and the adsorption was equilibrated in 15 min. The isotherms and kinetics for the adsorption Pb2+ by the [email protected] adsorbents followed Langmuir model and pseudo-second-order model, respectively. The maximum adsorption capacity was 220.48 mg g−1. The [email protected] adsorbents also own a prominent regeneration performance. The prominent performance of in the removal of trace Pb2+ makes [email protected] an ideal candidate as commercial adsorbent materials.
TL;DR: In this article, the degradation rate of tetracycline (TC) was investigated using Bi2WO6/TiO2 nanocomposites (BWO/TNTAs).
TL;DR: Wang et al. as mentioned in this paper proposed a spatio-temporal consistency-enhanced network to generate spatiotemporal consistency predictions, where a 3D CNN-based encoder and 2D CNNbased decoder constitute the main part of the model.
TL;DR: Zhang et al. as discussed by the authors proposed a novel method to classify vague labeled data based on evidential fusion, where vague data are divided into several small data groups by the proposed valid label-set cover assignment algorithm.
TL;DR: Zhang et al. as mentioned in this paper proposed a novel method to classify vague labeled data based on evidential fusion, where vague data are divided into several small data groups by the proposed valid label-set cover assignment algorithm.
TL;DR: In this paper , the degradation rate of tetracycline (TC) was investigated using Bi2WO6/TiO2 nanocomposites (BWO/TNTAs).
TL;DR: The proposed method is lightweight and computationally efficient by utilizing a sampled 3D point cloud as input combined with a graph convolutional neural network (GCNN) and achieves outstanding performance in head pose estimation.
TL;DR: Zhang et al. as discussed by the authors proposed a semantic SLAM system named Blitz-SLAM to remove the noise blocks in the local point cloud by combining the advantages of semantic and geometric information of mask, RGB and depth images.
TL;DR: In this paper , N/F co-doped TiO2/carbon microspheres were synthesized to achieve high volumetric capacity and high tap density simultaneously, and they achieved an ultrahigh power density of 25.2 kW kg−1.
TL;DR: In this paper, a dynamic optimization problem was formulated to explore prosumers' economic potentials, and the size parameter of WTTESs was swept in prosumers to obtain the optimal storage size considering the trade-off between the payback period and the heating cost saving.
TL;DR: In this paper , a QEPAS-based sensor system for the sub-ppm level H2O detection in SF6 buffer gas was developed by use of a near-infrared commercial DFB diode laser.