Dong, HanxuanDing, FanTan, HuachunZhang, Hailong...
16页查看更多>>摘要：Traffic prediction on a large-scale road network is of great importance to various applications. However, many factors such as sensor failure and communication errors inevitably resulted in a sparse distribution of effective detection points with missing data, which resulting adversely affects the accuracy of traffic prediction. This study considers the bidirectional connectivity of road networks to construct a two-way network graph topology. Based on the graph representation, the tensor combined temporal similarity revisited graph convolutional gate recurrent unit (T-TRGCGR), ingeniously combining traffic prediction and data completion through the Graph Laplace, is proposed to predict traffic states under partially input data missing circumstances and sparse detector distribution for a large-scale freeway network. Additionally, the proposed model can not only be applicable to traffic data prediction with missing values but also adaptively extract the spatio-temporal characteristics from various traffic periodicities while retaining the topological information of the large-scale network. Experiments on a large intercity network in Jiangsu, China shows that the proposed method outperforms state-of-art baselines on real-world traffic dataset, which can be well adapted to the prediction task of sparse coverage of road network detectors with missing data. Furthermore, through the comprehensive analysis and visualization of model parameters and results, it can be seen that the model adequately identifies the influential road network nodes and automatically learns to determine the importance of past traffic flow. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Ma, MengdiRen, CuipingXie, Fengjie
16页查看更多>>摘要：In this paper, we integrate the non-Markovian higher-order model with the multilayer network analysis method for the first time to analyse the transportation system with route dependencies. An empirical study on the Chinese high-speed rail (HSR) system was conducted. The non-Markovian higher-order model is used to describe the theoretical high-speed railway network (THSRN), and weighted k-core decomposition is used to divide the THSRN into the core layer, bridge layer and periphery layer. We analyse the importance of cities and HSR routes. Ten important hub cities in the HSR system are discovered, and a finding against the common belief is revealed that the importance rank of cities is not completely consistent with their train flow. In addition, nine complete HSR lines and seven sections of the HSR lines were found to be the most important routes in the HSR system. Finally, the weaknesses of the HSR system are identified and some optimization suggestions are presented. Our work provides important insight for forming a new framework for analysing the transportation systems with route dependencies, which may assist in the future study of transportation systems. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Manimaran, P.Panigrahi, Prasanta K.Pal, Mayukha
17页查看更多>>摘要：It is well known, the Electroencephalogram (EEG) signals are non-stationary signals which helps us in understanding the complex brain dynamics, cognitive processes, etc. In this paper, we have analysed the healthy and Epilepsy seizure subjects through continuous wavelet transform using Morlet wavelet function. We identify the key differences between healthy and epilepsy seizure EEG signals' low frequency periodic modulations and transient time varying patterns. Persistent intermixing of alpha and beta waves is found to be a key characteristic feature of the patients. The frequency intermixing is completely absent in signals from the hippocampal formation of the opposite hemisphere of the brain for the patients without seizure, akin to the healthy subjects. Our study further reveals dominance of frequency broadened gamma waves for seizure patients as compared to the low frequency regular alpha and beta waves for the healthy subjects. The time-frequency localization of wavelet transform clearly shows transfer of power to high frequency beta waves from the low frequency alpha waves in the signals of the epileptogenic zone of the patients. The observed frequency intermixing, reported here, is analogous to the bi-stability behaviour of dynamical systems. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Ning, DiChen, JuanJiang, Meiying
12页查看更多>>摘要：This article mainly investigates pinning impulsive synchronization of the two-layer delayed network with unidirectional interlayer coupling and transmission delays. The intralayer topologies of different layers are independent each other, and the nodal dynamics in each layer are different as well. Using the Lyapunov stability theory and the auxiliary system method, several pinning impulsive strategies for guaranteeing interlayer generalized synchronization of two-layer delayed networks are put forward. When the impulse interval is fixed as a constant value, the smallest number of pinned nodes and largest impulse interval needed theoretically for ensuring interlayer generalized synchronization are obtained. Specially, when the impulse strengths, the impulse interval, and the number of pinned nodes are respectively set to constant values independent of the impulsive instant, the influences of these parameters on the synchronized region are further discussed. Furthermore, it is found that the relatively small interlayer transmission delay is beneficial to interlayer generalized synchronization. Numerical simulations are presented to verify the effectiveness and correctness of the proposed schemes. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Ruan, XiaoliXu, ChenFeng, JianwenWang, Jingyi...
15页查看更多>>摘要：This paper deals with the consensus problem of linear multi-agent systems (MASs) with matched uncertainties on directed topologies via a dynamic event-triggered control. To reduce the redundant communication, a distributed dynamic event-triggered (DET) protocol with a dynamic threshold is proposed, which makes the average time interval of controller update have a positive lower bound. The Zeno behavior can also be excluded. Furthermore, to avoid the assuming availability of full state information, an observer-based adaptive dynamic event-triggered (ADET) protocol is introduced. The proposed adaptive a-modification technique for the time-varying coupling gain can render smaller control gain and achieve better regulatory effects. Compared with the existing DET scheme, the dynamic threshold with a positive lower bound can reach consensus with larger inter-execution times and less communication energy among agents. By using the Riccati equation and inequality techniques, some simple and convenient sufficient conditions are derived to guarantee the stability of the closed-loop system. Finally, two numerical examples are given to verify the effectiveness of the obtained theoretical results. (C) 2021 Published by Elsevier B.V.
原文链接:- NSTL
- Elsevier
Shoreh, A. A-HKuznetsov, N., VMokaev, T. N.
16页查看更多>>摘要：The aim of this report is to investigate an adaptive synchronization (AS) for the general class of complex hyperchaotic models with unknown parameters and a new algorithm to achieve this type of synchronization is proposed. Owing to the intricacy behavior of hyperchaotic models that could be effective in secure communications, the special control based on adaptive laws of parameters is constructed analytically, and the corresponding simulated results are performed to validate the algorithm's accuracy. The complex Rabinovich model is utilized as an enticing example to examine the proposed synchronization technique. A strategy for secure communication improving the overall cryptosystem is proposed; the scheme is designed to split the message and insert some bit of information signal into the modulation parameters and the other bit into the transmitter system's states, making decryption by intruders more difficult. Meanwhile, adaptive techniques and a decryption function on the receiver side can accurately retrieve the message. Different types of encoded messages are examined for testing the robustness of the proposed scheme (e.g., text and gray images with diverse scales of white Gaussian noise). (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Huang, Qi-AnZhao, Jun-ChanWu, Xiao-Qun
13页查看更多>>摘要：Stock networks, which are constructed from stock price time series, are useful tools for analyzing complex behaviors in stock markets. Following former researches, the epidemic model has been usually used to detect dynamic characteristics in a stock price complex systems. Recently, multilayer networks have been demonstrated well when working on heterogeneous nodes rather than integrated networks. In this paper, we proposed a two-layer SIR propagation model with an infective medium to analyze the spread of financial shocks. In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng(medium), we established a two-layer Granger networks. Betweenness showed that the Hong Kong stock market had a promoting transition function of financial shocks between the US stock markets and the mainland China stock markets. In addition, with a big basic reproduction number, stock markets system appeared to be vulnerable during extreme financial shock such as the outbreak of COVID-19 epidemic and the meltdown of stock markets. Furthermore, sensitivity analysis and the spreading simulation indicated that the US stock markets were much more robust to financial shocks than the mainland China stock markets. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Lin, DanWu, JiajingChen, Jialan
15页查看更多>>摘要：Cryptocurrency exchanges, which act as a platform for cryptocurrency trading, play a vital role in the ever-growing cryptocurrency market. However, with the rapid development of this emerging market, some unethical phenomena including faking trading volume have also appeared in cryptocurrency exchanges. To this end, this paper proposes a data mining-based method based on off-chain data and on-chain transaction data to detect the exchanges that fake trading volume. In particular, we first collect off-chain data from the websites of five exchanges and the on-chain data provided by a blockchain browser, and then analyze them from two perspectives, including transaction number and transaction amount. The empirical results suggest that Huobi exchange fakes trading volume most obviously, while Binance trading is relatively the most honest. In addition, different exchanges adopt distinct counterfeiting strategies when creating wash trading. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Jiang, Liang-ChaoLiu, Run-RanJia, Chun-Xiao
7页查看更多>>摘要：Personalized recommender system is a powerful method to solve the problem of information overload, which has been widely applied in a variety of scenarios, such as e-commerce, video platforms and social networks, to help users find relevant items or friends of interest. Collaborative filtering is the most successful and widely used algorithm in the recommender systems as its powerful capability of generating recom-mendations by sharing collective experiences of users. In recent years, the use of mobile devices and the rapid development of internet infrastructures provide the possibility to analyze regional features of items based on user locations. Here we improve the performance of collaborative filtering by using user-location distribution to uncover the potential similarities between items. We find that the similarity of user-location distribution is one efficient measure for the item-item similarities in the framework of collaborative filtering to generate personalized recommendation for users. Furthermore, we have also mixed similarity measures of user-location distribution and the traditional method based on the number of common users linearly to optimize the performance of collaborative filtering. Based on the Movielens data set, we show that the performance of our methods could be improved in terms of the metrics of accuracy and diversity simultaneously. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
