| 1. |
- Chen, Dongliang, et al.
(författare)
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The Scanner of Heterogeneous Traffic Flow in Smart Cities by an Updating Model of Connected and Automated Vehicles
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 23:12, s. 25361-25370
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Tidskriftsartikel (refereegranskat)abstract
- The problems of traditional traffic flow detection and calculation methods include limited traffic scenes, high system costs, and lower efficiency over detecting and calculating. Therefore, in this paper, we presented the updating Connected and Automated Vehicles (CAVs) model as the scanner of heterogeneous traffic flow, which uses various sensors to detect the characteristics of traffic flow in several traffic scenes on the roads. The model contains the hardware platform, software algorithm of CAV, and the analysis of traffic flow detection and simulation by Flow Project, where the driving of vehicles is mainly controlled by Reinforcement Learning (RL). Finally, the effectiveness of the proposed model and the corresponding swarm intelligence strategy is evaluated through simulation experiments. The results showed that the traffic flow scanning, tracking, and data recording performed continuously by CAVs are effective. The increase in the penetration rate of CAVs in the overall traffic flow has a significant effect on vehicle detection and identification. In addition, the vehicle occlusion rate is independent of the CAV lane position in all cases. The complete street scanner is a new technology that realizes the perception of the human settlement environment with the help of the Internet of Vehicles based on 5G communications and sensors. Although there are some shortcomings in the experiment, it still provides an experimental reference for the development of smart vehicles.
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| 2. |
- Feng, Hailin, et al.
(författare)
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Resilience towarded Digital Twins to improve the adaptability of transportation systems
- 2023
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Ingår i: Transportation Research Part A. - : Elsevier BV. - 0965-8564 .- 1879-2375. ; 173
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Tidskriftsartikel (refereegranskat)abstract
- This work aims to investigate the role of the resilience of Digital Twins on the applicability of the transportation system. A literature study is conducted to review the current status of research on transportation systems and Digital Twins. It is found that the current research on Digital Twins technology has achieved different degrees of success in different aspects of transportation sys-tems. Yet, the system performance of Digital Twins has to be optimized. First, the application of Digital Twins in intelligent transportation systems is analyzed. Then, how the changes in traveler behavior patterns reflect the extent to which the traffic network is affected by uncertain events is analyzed from the traveler's perspective. Finally, an Internet of Vehicles (IoV) system based on Digital Twins and blockchain is established to solve the data redundancy and high computational volume problems of in-vehicle data sharing common in the IoV system. Moreover, the perfor-mance of the twin system is optimized by proposing a multi-intelligence body algorithm based on local perception, and a case validation is performed. The results demonstrate that the adaptability of the transportation system to uncertain events and its response and recovery measures taken are reflected to some extent in the traveler behavior model. Besides, data sharing between vehicles and infrastructure in the transportation network can be well solved by Digital Twins Blockchain. The locally-aware multi-intelligent body algorithm saves more than 50% communication over-head and improves operational efficiency by nearly 20% over traditional algorithms by increasing intelligent body infrastructure units. It is adequately suited for large-scale vehicle traffic twins. It can be seen that improving the resilience of Digital Twins is a very obvious change in the adaptability of the traffic system.
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| 3. |
- Li, Xiaoming, et al.
(författare)
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Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning
- 2022
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Ingår i: Future generations computer systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 128, s. 167-177
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Tidskriftsartikel (refereegranskat)abstract
- The study aims to conduct big data analysis (BDA) on the massive data generated in the smart city Internet of things (IoT), make the smart city change to the direction of fine governance and efficient and safe data processing. Aiming at the multi-source data collected in the smart city, the study introduces the deep learning (DL) algorithm while using BDA, and puts forward the distributed parallelism strategy of convolutional neural network (CNN). Meantime, the digital twins (DTs) and multi-hop transmission technology are introduced to construct the smart city DTs multi-hop transmission IoT-BDA system based on DL, and further simulate and analyze the performance of the system. The results reveal that in the energy efficiency analysis of model data transmission, the energy efficiency first increases and then decrease as the minimum energy collected α0 increases. But a more suitable power diversion factor ρ is crucial to the signal transmission energy efficiency of the IoT-BDA system. The prediction accuracy of the model is analyzed and it suggests that the accuracy of the constructed system reaches 97.80%, which is at least 2.24% higher than the DL algorithm adopted by other scholars. Regarding the data transmission performance of the constructed system, it is found that when the successful transmission probability is 100% and the exponential distribution parameters λ is valued 0.01∼0.05, it is the closest to the actual result, and the data delay is the smallest, which is maintained at the ms level. To sum up, improving the smart city’s IoT-BDA system using the DL approach can reduce data transmission delay, improve data forecasting accuracy, and offer actual efficacy, providing experimental references for the digital development of smart cities in the future.
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| 4. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Artificial Intelligence in Underwater Digital Twins Sensor Networks
- 2022
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Ingår i: ACM transactions on sensor networks. - : Association for Computing Machinery (ACM). - 1550-4867 .- 1550-4859. ; 18:3
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Tidskriftsartikel (refereegranskat)abstract
- The particularity of the marine underwater environment has brought many challenges to the development of underwater sensor networks (UWSNs). This research realized the effective monitoring of targets by UWSNs and achieved higher quality of service in various applications such as communication, monitoring, and data transmission in the marine environment. After analysis of the architecture, the marine integrated communication network system (MICN system) is constructed based on the maritime wireless Mesh network (MWMN) by combining with the UWSNs. A distributed hybrid fish swarm optimization algorithm (FSOA) based on mobility of underwater environment and artificial fish swarm (AFS) theory is proposed in response to the actual needs of UWSNs. The proposed FSOA algorithm makes full use of the perceptual communication of sensor nodes and lets the sensor nodes share the information covered by each other as much as possible, enhancing the global search ability. In addition, a reliable transmission protocol NC-HARQ is put forward based on the combination of network coding (NC) and hybrid automatic repeat request (HARQ). In this work, three sets of experiments are performed in an area of 200 x 200 x 200 m. The simulation results show that the FSOA algorithm can fully cover the events, effectively avoid the blind movement of nodes, and ensure consistent distribution density of nodes and events. The NC-HARQ protocol proposed uses relay nodes for retransmission, and the probability of successful retransmission is much higher than that of the source node. At a distance of more than 2,000 m, the successful delivery rate of data packets is as high as 99.6%. Based on the MICN system, the intelligent ship constructed with the digital twins framework can provide effective ship operating state prediction information. In summary, this study is of great value for improving the overall performance of UWSNs and advancing the monitoring of marine data information.
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| 5. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model
- 2023
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Ingår i: Philosophical Transactions. Series A. - : The Royal Society. - 1364-503X .- 1471-2962. ; 381:2254
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Tidskriftsartikel (refereegranskat)abstract
- The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future.This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
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| 6. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Blockchain-Based Decentralized Learning for Security in Digital Twins
- 2023
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:24, s. 21479-21488
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Tidskriftsartikel (refereegranskat)abstract
- This work aims to analyze malicious communication behaviors that pose a threat to the security of digital twins (DTs) and safeguard user privacy. A unified and integrated multidimensional DTs Network (DTN) architecture is constructed. On this basis, the propagation process model of malware in the network is built to analyze the malicious propagation behavior that threatens network security. This model ensures the protection of mobile distributed machine learning system security. Blockchain technology is a distributed data protection mechanism with broad prospects. It is characterized by decentralization, transparency, and anonymity, which can help ensure secure network data sharing and privacy protection. Based on this, this work designs a secure distributed data sharing (DDS) architecture based on blockchain to improve the security and reliability of data protection with the support of the Internet of Things (IoT). Then, digital resource allocation based on semi-distributed learning is examined to propose a broad learning federated continuous learning (BL-FCL) algorithm combining blockchain and DTs. This algorithm significantly speeds up the model training process. Broad learning technology supports incremental learning. In this way, each client does not need to retrain when learning the newly generated data. In the experimental part, the prediction accuracy of BL-FCL on the mixed national institute of standards and technology data set is similar to that of the FedAvg-50 and FedAvg-80 schemes. As the number of devices increases from 1 to 6, the detection probability exhibits a rapid decrease. However, as the number of devices further increases from 6 to 10, the detection probability gradually decreases at a slower rate until it reaches 0. Comparatively, the prediction accuracy of the BL-FCL outperforms the federated averaging algorithm-based scheme by 20%-60%. The BL-FCL reported here can deal with the problem of inaccurate training while ensuring the privacy and security of users. This work is of great significance for ensuring the security of the DTN and promoting the development of the digital economy. The results can provide references for applying blockchain and distributed learning in the DT field.
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| 7. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Cognitive Computing for Brain-Computer Interface-Based Computational Social Digital Twins Systems
- 2022
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X. ; 9:6, s. 1635-1643
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Tidskriftsartikel (refereegranskat)abstract
- To accurately and effectively analyze electroencephalogram (EEG) with high complexity, large amount of data, and strong uncertainty, brain-computer interface (BCI) cognitive computing and its signal analysis algorithms are studied based on the digital twins (DTs) cognitive computing platform. To avoid the influence of noise on EEG analysis results, it is necessary to use filtering and defalsification methods to process EEG. Four methods, including Butterworth filter, finite impulse response (FIR) filter, elliptic filter, and wavelet decomposition, are summarized. Based on the Riemann manifold theory, a feature extraction algorithm under transfer learning based on tangent space selection (TL-TSS) is proposed. In the process of decoding EEG, an EEG decoding method combining entropy measure and singular spectrum analysis (SSA) is proposed. An algorithm performance is tested on the motor imagery dataset of the two International BCI Competitions. It is found that when the training sample size accounts for 5%, the TL-TSS algorithm proposed in this work is superior to other algorithms in classification accuracy. In particular, compared with common spatial pattern (CSP) algorithm, it has great advantages. The classification accuracy of A2, A4, A8, and A9 users is the best, and especially for A8 users, the classification accuracy reaches 97.88%. In summary, in the EEG interface technology of DT cognitive computing platform, the combination of cognitive computing and deep learning can improve the recognition and analysis effect of EEG, which is of great value for further optimization of DT cognitive computing system.
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| 8. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Computational Intelligence in Security of Digital Twins Big Graphic Data in Cyber-physical Systems of Smart Cities
- 2022
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Ingår i: ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS. - : Association for Computing Machinery (ACM). - 2158-656X .- 2158-6578. ; 13:4
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Tidskriftsartikel (refereegranskat)abstract
- This investigation focuses on the application of computational intelligence to the security of Digital Twins (DTs) graphic data of the Cyber-physical System (CPS). The intricate and diverse physical space of CPS in the smart city is mapped in virtual space to construct the DTs CPS in the smart city. Besides, Differential Privacy Frequent Subgraph-Big Multigraph (DPFS-BM) is employed to ensure data privacy security. Moreover, the analysis and prediction model for the DTs big graphic data (BGD) in the CPS is built based on Differential Privacy-AlexNet (DP-AlexNet). Alexnet successfully solves the gradient dispersion problem of the Sigmoid function of deep network structures. Finally, the comparative analysis approach is utilized to verify the performance of the model reported here by comparing it with Long Short-Term Memory, Convolutional Neural Network, Recurrent Neural Network, original AlexNet, and Multi-Layer Perceptron in a simulation experiment. Through the comparison in the root mean square error, the mean absolute error, the mean absolute percentage error, training time, and test time, the model proposed here outperforms other models regarding errors, time delay, and time consumption. In the same environment, the system performs better with multi-hop paths, extra relays, and a high fading index; in that case, the outage probability is minimal. Therefore, the DP-AlexNet model is suitable for processing BGD. Moreover, its speed acceleration is more apparent than that of other models, with a higher SpeedUp indicator. The research effectively combines data mining and data security, which is of significant value for optimizing the privacy protection technology of frequent subgraph mining on a single multi-graph. Besides, the constructed DTs of CPS can provide excellent accuracy and a prominent acceleration effect on the premise of low errors. In addition, the model reported here can provide reference for the intelligent and digital development of smart cities.
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| 9. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Deep Learning for Security in Digital Twins of Cooperative Intelligent Transportation Systems
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16666-16675
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Tidskriftsartikel (refereegranskat)abstract
- The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy's effects become more apparent. When MPR ranges between 40% similar to 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.
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| 10. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Digital Twins Based VR Simulation for Accident Prevention of Intelligent Vehicle
- 2022
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Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 71:4, s. 3414-3428
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Tidskriftsartikel (refereegranskat)abstract
- This work aims to prevent Traffic Accident (TA) and ensure drivers' and pedestrians' life and property safety. A TA prevention and prediction system is established based on Digital Twins (DTs) and Artificial Intelligence (AI). Firstly, the double-scale decomposition equation decomposes the original TA Time Series Data (TSD) into multiple sub-layers. The Long-Short Term Memory (LSTM) network is used to predict the low-frequency sub-layers. Then, the double-scale LSTM network prediction model is constructed based on the prediction results. Secondly, a Particle Filter (PF) is proposed based on target block tracking and improved resampling against the possible occlusion problem in target tracking. The proposed PF can improve particle dilution. Finally, the proposed target tracking algorithm and DTs are combined and applied to TA processing, and a motor vehicle road TA-oriented video analysis system is designed. Then, the proposed system is tested. The results corroborate that the proposed research model can effectively predict the TSD of TA compared with other models and has strong robustness. Compared with the original LSTM model and Stacked Auto Encoders (SAEs) prediction model, the prediction accuracy of the proposed model is improved by 6% and 8%, respectively. Besides, the training and prediction time of the proposed model is less than the original LSTM and SAEs models. The optimized Particle Swarm Optimization (PSO) model makes the target identification easier. Additionally, the proposed model has good generalization performance. In short, the proposed system can effectively improve the efficiency of TA handling and ensure accuracy and fairness, which provides some data support for applying DTs in intelligent transportation.
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