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To address the issues of frequency switching differences between communication and sensing systems in traditional integrated sensing and communication (ISAC) scenarios, as well as the susceptibility of communication to obstruction by obstacles, the integration of full-duplex (FD) technology and intelligent reflecting surface (IRS) into the ISAC system was studied. The design of FD beamforming assisted by IRS in the ISAC scenario was investigated. An optimization problem for maximizing the user rates of uplink and downlink beamforming was formulated, considering constraints on IRS phase shift, base station receiving beamforming, the maximum transmission power of the base station, the maximum transmission power of uplink users, and the radar sensing threshold. The original non-convex problem was solved by being transformed into a convex optimization problem through the use of alternating optimization, continuous convex approximation, semi-definite relaxation, variable substitution, and semi-definite programming. Simulation results show that the proposed scheme resulted in an 89.02% enhancement under the communication performance indicators considered compared to the corresponding half-duplex scheme and a 70.15% improvement over the random IRS scheme.

Vehicle trajectory prediction is one of the key technologies to realize autonomous driving, which provides an important guarantee for the safety of vehicles in the process of autonomous driving. In this paper, with this as the starting point, a graph convolutional neural network is introduced through a graph attention mechanism to obtain scene features by modeling the temporal Transformer model of surrounding information. Based on the temporal convolutional model to obtain scene features, new feature vectors are calculated by aggregating the weights for the features of nodes and neighboring nodes. Then the input feature dimensions are transformed into the weight matrix of the output feature dimensions, and the output feature vector corresponding to the attention coefficients is calculated by using weighted summation. Then the effect of multiple training of the model is evaluated by taking the mean value and defining its structural relationship. The experimental results show that the prediction error of the proposed method is significantly smaller than that of the comparison method in scenarios with speeds less than or equal to 5m/s and greater than 5m/s. The prediction error based on target detection is reduced by 58.95%, indicating that the proposed method is more consistent with the operation scenarios of autonomous driving.

Non-intrusive load monitoring (NILM) technology is only based on the current and voltage information of the main entrance of home power supply to obtain the electrical information of indoor electrical equipment. Improving the accuracy of load identification is of great significance to optimize the energy structure, improve the efficiency of power utilization and reduce energy consumption. Firstly, the normalized current signal is decomposed by using variational mode decomposition (VMD), and then the correlation coefficients between each component and the original current signal are calculated. The two components with the largest correlation coefficients are selected as the load characteristics and input into the trained LSTM neural network for identification. The test results of an example show that the recognition rate of this method is up to 99% on public data set PLAID and 96.6% on laboratory data set, which proves the effectiveness of this method.

Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction., Author(s): Wenhao Jiang [1,2]; Yunpeng Xiao [3]; Yanbing Liu [1]; Qilie Liu [3]; Zheng Li (corresponding author) [3] 1. Introduction Traffic flow prediction is to predict the future traffic flow [...]

Vehicle to grid (V2G) technology is a highly emerging technology in the energy Internet, which is widely studied for the rational use of renewable energy. However, due to the centralized management of V2G network and the openness of communication, V2G network has some problems, such as single point of failure, privacy disclosure, unbalanced scheduling of distributed energy and so on. To solve the above problems, a localized vehicle to vehicle (V2V) energy trading system model based on alliance blockchain is proposed. In addition, considering the price and power demand matching between electric vehicles, a new secondary double auction mechanism is proposed to match the transaction vehicle pairs and determine the final transaction price, and make the profits of both parties equal. The security analysis shows that the blockchain network improves the security of V2V energy transaction. Finally, the numerical results show that the proposed double auction mechanism improves the income of both parties and the income is equal, and improves the success rate of the auction compared with the traditional auction scheme. [ABSTRACT FROM AUTHOR]