Search

Traffic flow prediction is an important and challenging task in building smart city. Accurate forecasting requires consideration of spatio-temporal characteristics consisting of multiple influences such as holiday, similar node, and weather. In order to accurately capture the spatio-temporal characteristics of road network intersections, this paper proposed a prediction model based on graph convolutional neural network, temporal algorithm Prophet and Pearson correlation coefficient to achieve accurate prediction of intersection traffic considering spatial structure, similar nodes, holidays and other influencing factors. Firstly, it introduced pearson correlation coefficient to reduce the influence of similar nodes to improve the temporal algorithm for capturing temporal features. Secondly, it used the graph convolution neural network to capture spatial features; Finally, it determined the fusion weights of the graph convolutional network and the temporal algorithm by linear regression to obtain the results of spatio-temporal fusion prediction. This paper finally extracted the intersection traffic data based on the analysis of Chengdu taxi trajectory data and conducted traffic prediction experiments. The results show that the accuracy of the model proposed in this paper is better than that of most existing baseline methods, compared with the T-GCN, ASTGCN, and AGCRN models, the MAE is reduced by 1.623, 0.724, 0.161, respectively, and the accuracy is improved by 0. 144, 0. 068, and 0. 021, respectively, which verifies the effectiveness of the model in traffic intersection flow prediction. [ABSTRACT FROM AUTHOR]

How to realize safety data sharing and promote the integration of multi-source data is one of the important technical challenges faced by academic and industrial circles. In recent years,federated learning has received widespread attention, which is a new technology to deal with this challenge. Federated learning has been applied in fields such as smart healthcare and smart city construction, but there is little research in the field of potential trajectory data mining. To solve this problem, this paper proposed a distributed and secure framework named federated spectral clustering( FSC), and applied it to the spectral clustering of ship AIS trajectory data. In the FSC framework, it used the encrypted sample alignment technology and a homomorphic encryption scheme as building blocks for the clustering algorithm, guaranteeing the security of the data in the process offederal training executed by multi-participants. To illustrate the effect of this algorithm, this paper conducted the experiments on both synthetic datasets and ships AIS trajectory datasets. The comparisons of experiments results with other similar clustering algorithms demonstrate that, besides its security advantage, this algorithm performs well in terms of clustering effect. The results indicate that the FSC can obtain the main route in the marine navigation area, which can provide specialized support for the intelligence of maritime supervision systems. [ABSTRACT FROM AUTHOR]

License plate recognition is a significant technology to build a smart city transportation system. The current license plate recognition systems have achieved a good recognition and application effect for single-row license plates, nonetheless, these can’t satisfy the recognition need for license plates which contain stacked characters. Based on this problem, this paper proposed a method based on deep learning and independent of character segmentation to identify the license plates containing stacked characters. Firstly, the method corrected the tilted or distorted license plate, and used MobileNet-SSD algorithm to detect single-row characters and stacked characters in the license plate. Then, it sent stacked characters to the stacked character recognition network based on CTC loss and conducted the end-to-end identification without character segmentation. Result of the experiment indicates that this algorithm not only has high recognition accuracy for the license plate with stacked characters, but also adapts to the license plate under the complex environment such as tilt and distortion, greatly improves the generality of the license plate recognition system. [ABSTRACT FROM AUTHOR]

Software defined network(SDN) has attracted much attention in recent years.Some scholars have integrated it with smart city construction.In view of the current situation of decentralized traffic management and combined with the characteristics of SDN forwarding and control separation and centralized control,this paper introduced it into intelligent transport system(ITS),and proposed a collaborative management architecture to achieve collaborative management and improve traffic.Firstly,it expounded the characteristics of SDN,and proposed an intelligent traffic architecture based on SDN by using ITS characteristics and combining with ITS.Secondly,it built a collaborative management strategy model combining factors such as dynamic speed limit,path guidance and weather and compared with a single control strategy.Finally,through simulation experiments,it verified and analyzed the proposed architecture strategy.The experimental results show that compared with the single control strategy,the management under this architecture smooths the traffic flow,reduces the average delay at the intersection by 20.7%,reduces the average queue length by 35%. [ABSTRACT FROM AUTHOR]

For the multi-attribute group decision-making problem in Pythagoras environment, firstly, this paper combined the Pythagorean fuzzy number and the power-average operator to creatively expand a new improved weighted support, then based on this, it proposed a Pythagorean fuzzy interaction power-average operator with improved weighted support, and discussed the properties of the operator. Then it established a decision-making method that could reflect the interaction between decision attributes under Pythagoras fuzzy background. Finally, it was applied to the evaluation of smart cities. The case study shows that the method can solve the problem of actual multi-attribute group decision-making and can be further applied to the fields of intelligent logistics, pattern recognition and artificial intelligence. [ABSTRACT FROM AUTHOR]

The lightweight block ciphers could be widely used in various applications, such as smart cities, internet of things and cloud computation and so on, in order to protect data and information secure. Midori was a lightweight block cipher proposed in ASIACRYPT 2015. Its block size had two scenarios, i. e, 64 bit and 128 bit, denoted by Midori 64 and Midori 128 respectively. Up to now, there were no results about meet-in-the-middle attacks on Midori 128. This paper developed a meet-inthe-middle attack on 10-round Midori 128 for the first time. Specifically, studying the basic construction and key schedule of Midori 128, this paper constructed a 7-round distinguisher on Midori 128 by using the differential enumeration and key-dependent sieve techniques. Through appending one round at its top and two rounds at its bottom, this paper mounted a meet-in-themiddle attack on 10-round Midori 128. In the attack, time-memory tradeoff technique and some weak subkeys were considered so as to reduce the time complexity of online phase. Finally, the data, time and memory complexities of our attack are 2125 chosen plaintexts, 2126. 510-round encryptions and 2105128-bit blocks, respectively. [ABSTRACT FROM AUTHOR]