| 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. |
- 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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| 4. |
- 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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| 5. |
- 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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| 6. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins
- 2024
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Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - : IEEE Computer Society. - 1545-5963 .- 1557-9964. ; 21:4, s. 660-669
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Tidskriftsartikel (refereegranskat)abstract
- With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the dilemma of limited medical resources. Firstly, the Digital Twins module in the Deep Learning algorithm is used to establish the medical care and disease auxiliary diagnosis model. With the help of the digital visualization model of Internet of Things technology, data is collected at the client and server. Based on the improved Random Forest algorithm, the demand analysis and target function design of the medical and health care system are carried out. Based on data analysis, the medical and health care system is designed using the improved algorithm. The results show that the intelligent medical service platform can collect and analyze the clinical trial data of patients. The accuracy of improved ReliefF & Wrapper Random Forest (RW-RF) for sepsis disease recognition can reach about 98%, and the accuracy of algorithm for disease recognition is also more than 80%, which can provide better technical support for disease recognition and medical care services. It provides a solution and experimental reference for the practical problem of scarce medical resources.
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| 7. |
- 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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| 8. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Digital twins for secure thermal energy storage in building
- 2023
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 338
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of this work is to explore the role of the safe and optimal scheduling of thermal energy storage systems in intelligent buildings in promoting sustainable economic development under Digital Twins (DTs) technology. Phase Change Material (PCM) has high energy density, constant temperature storage, small footprint, and long service life. Here, PCM is first placed in the indoor building structure, and the DTs technology is introduced. In the development of intelligent buildings, the data generated by the energy storage system of intelligent buildings in the real space can be mapped to the virtual space in real time for simultaneous analysis. In addition, the PCM wall structure and thermal network DTs model are designed for the intelligent building. In addition, the PCW structure is used to build a thermal energy storage and dispatch model of the smart thermoelectric building based on DTs. Finally, the model is evaluated and analyzed experimentally. The analysis of system optimization power under different schemes indicates that the scheduling operation strategy of thermal energy storage of building walls can avoid overcharging or over-discharging batteries in the microgrid and reduce battery power consumption. Besides, the building wall energy storage capacity is always in the range of 0.2 ∼ 0.8 on the all-weather scale. Moreover, the model constructed here achieves significantly lower economic costs, environmental costs, and energy costs and a better energy-saving effect than the existing model. The model built here can serve as experimental reference for further digital energy storage in intelligent buildings and comprehensive energy utilization because of its superior safety performance and lower consumption.
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| 9. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Digital Twins in the Marine Industry
- 2023
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 12:9
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Tidskriftsartikel (refereegranskat)abstract
- The ocean holds abundant resources, but the utilization of those resources for the marine economy presents a complex and dynamic industrial situation. Exploring sustainable development in this industry is of practical value, as it involves the rational use of marine resources while protecting the environment. This study provides an innovative review of the current application status of Digital Twins Technology (DTT) in various sectors of the marine industry, including the ship-building industry (SBI), Offshore Oil and Gas Industry, marine fishery, and marine energy industry. The findings reveal that DTT offers robust support for full life cycle management (LCM) in SBI, including digital design, intelligent processing, operation, and error management. Furthermore, this work delves into the challenges and prospects of DTT application in the marine industry, aiming to provide reference and direction for intelligent systems in the industry and guide the rational development and utilization of marine resources in the future.
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| 10. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics
- 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. ; 23:12, s. 25106-25114
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Tidskriftsartikel (refereegranskat)abstract
- The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.
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| 11. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Memory‐augmented neural networks based dynamic complex image segmentation in digital twins for self‐driving vehicle
- 2022
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 132
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Tidskriftsartikel (refereegranskat)abstract
- With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.
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| 12. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario
- 2022
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Ingår i: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP). - : Association for Computing Machinery (ACM). - 1551-6857 .- 1551-6865. ; 18:2
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Tidskriftsartikel (refereegranskat)abstract
- With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore the building information modeling (BIM) big data (BD) processing method of digital twins (DTs) of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 +/- 0.013. Through experiments, the BIM BD processing algorithm based on Bayesian Network Structural Learning (BNSL) helps decision-makers use complex data in smart cities efficiently.
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| 13. |
- Akram, Waseem, et al.
(författare)
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An energy-efficient and secure identity based RFID authentication scheme for vehicular cloud computing
- 2022
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Ingår i: Computer Networks. - : Elsevier. - 1389-1286 .- 1872-7069. ; 217
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Tidskriftsartikel (refereegranskat)abstract
- Vehicular Cloud Computing (VCC) is a contemporary paradigm that includes the Internet of Things (IoT), cloud computing, and vehicular networking technologies. VCC offers vehicle-to-device, vehicle-to-infrastructure, and vehicle-to-vehicle communication in which the vehicles can communicate using sensing abilities. VCC is exploiting the IoT environment, cloud architecture, and vehicle resources. However, the energy-efficient privacy of communicators and security of communication are assertive problems in VCC. To accomplish this goal, we present an identity-based authentication scheme for VCC which also uses radio frequency identification (RFID). The security and robustness of the devised scheme are evaluated using informal and formal analysis. The informal analysis shows that our scheme is vigorous to resist various attacks. The formal analysis is done through Random Oracle Model (ROM) which shows that the scheme is secure and efficient. The performance of our scheme is also determined and compared with various related schemes which clearly illustrate the efficiency of the proposed scheme. Thus, our scheme is very efficient for employment in the VCC environment.
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| 14. |
- Bao, Nan, et al.
(författare)
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Wi-Breath : A WiFi-based Contactless and Real-time Respiration Monitoring Scheme for Remote Healthcare
- 2023
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 27:5, s. 2276-2285
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Tidskriftsartikel (refereegranskat)abstract
- Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.
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| 15. |
- Bian, Zengxue, et al.
(författare)
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The Digital Twins of Thor's Hammer Based on Motion Sensing
- 2022
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Ingår i: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). - : IEEE. - 9781665484022 ; , s. 894-895
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Konferensbidrag (refereegranskat)abstract
- Ancient humans attribute the phenomenon of thunder and lightning to divine power. The power of Thor that can lift Thor's Hammer, the body not be hurt by thunder and lightning. It's not impossible for us to control thunder and lightning like Thor. The Digital Twins system of the robotic arm designed in this paper integrates the physical device of the robotic arm, the digital model of robotic arm, the body sense interaction, and the virtual-reality mapping module. It can digitally control the robotic arm. With this system, we can all lift Thor's hammer in the future.
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| 16. |
- Cao, B., et al.
(författare)
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A Multiobjective Intelligent Decision-Making Method for Multistage Placement of PMU in Power Grid Enterprises
- 2023
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 19:6, s. 7636-7644
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Tidskriftsartikel (refereegranskat)abstract
- The wide area measurement system (WAMS) based on synchronous phasor measurement technology plays an increasingly important role in dynamic monitoring and wide area protection of modern power systems. If the phasor measurement unit (PMU) is placed on all buses of the power system, the voltage and branch current of all buses can be directly observed. However, due to the high placement cost of PMU and its ability to measure the voltage phasor of the installed bus and the current of the associated branch, it is unrealistic and unnecessary to install PMU on all buses of the system. This paper discusses the incomplete observability under single PMU loss (N-1) contingencies and its effect on PMUs placement. An improved two-archive algorithm is proposed to solve the five-objective placement optimization model. In addition, a fuzzy decision-making method combining subjective and objective is proposed to help power grid enterprises select the most appropriate solution. The proposed method is tested on several IEEE bus systems and Polish 2383-bus system, and the test results verify its effectiveness.
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| 17. |
- Cao, B., et al.
(författare)
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A Resource Allocation Strategy in Fog-Cloud Computing Towards the Internet of Things in the 5G Era
- 2021
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Konferensbidrag (refereegranskat)abstract
- The rapid development of Internet of Things (IoTs) will result in massive amounts of data to be processed. The 5G technology and fog computing can reduce the service delay. A challenging problem in fog computing is how to efficiently allocate resources to guarantee the quality of service (QoS). Therefore, studying the cooperation between fog computing resources and cloud computing resources is of great significance. For resource allocation, four optimization objectives are considered: Minimizing the time delay and cost and maximizing the load balancing and stability of task execution, and an improved Two_Archive2 algorithm using a novel fitness evaluation method and a shift-based density estimation strategy (SDE) is proposed. For the case of resource allocation in fog-cloud computing, the proposed algorithm shows the better performance than the state-of-the-art algorithms and could serve as an effective resource allocation scheme. © 2021 IEEE.
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| 18. |
- Cao, Bin, et al.
(författare)
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Mobility-Aware Multiobjective Task Offloading for Vehicular Edge Computing in Digital Twin Environment
- 2023
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Ingår i: IEEE Journal on Selected Areas in Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0733-8716 .- 1558-0008. ; 41:10, s. 3046-3055
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Tidskriftsartikel (refereegranskat)abstract
- In vehicular edge computing (VEC), vehicle users (VUs) can offload their computation-intensive tasks to edge server (ES) that provides additional computation resources. Due to the edge server being closer to VUs, the propagation delay between the ESs and the VUs is lower compared to cloud computing. Applying digital twin to VEC allows for low-cost trial in task offloading. In real-word, the mobility of VUs cannot be ignored and the downlink delay in receiving process results from ES is related to the mobility of VUs. Therefore, a five-objective optimization model including downlink delay, computation delay, energy consumption, load balancing, and user satisfaction of the VUs is constructed. To solve the above model, an improved CMA-ES algorithm based on the guiding point (GP-CMA-ES) is proposed. When the number of VUs increases, the dimension of variables also increases. Therefore, a convergence-related variable grouping strategy based on the relationship detection between variables and objectives is proposed. The performance of algorithm GP-CMA-ES is compared with five algorithms in the digital twin environment.
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| 19. |
- Cao, Bin, et al.
(författare)
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Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming
- 2022
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Ingår i: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 30:10, s. 4190-4200
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Tidskriftsartikel (refereegranskat)abstract
- The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.
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| 20. |
- Cao, Bin, et al.
(författare)
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Multiobjective Image Compression based on Tensor Decomposition
- 2023
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Ingår i: 2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA. - : IEEE. - 9781665455336 - 9781665455343 ; , s. 545-550
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Konferensbidrag (refereegranskat)abstract
- Most of the traditional image compression methods vectorize the data contained in the image and then compress it. However, this approach does not take into account the high-dimensional information inside the image. To solve this problem, this paper regards the color image as a third-order tensor, and proposes a method of color image compression based on Tucker decomposition and multiobjective optimization. The tensor size compression ratio and Hu invariant moment similarity are proposed to measure the image compression quality. And to more comprehensively consider the sensitivity of human visual system to different visual signals, the five-objective optimization model of image compression is constructed. The five-objective optimization model includes: the above two indexes, information content weighted structure similarity index, color image feature similarity and information fidelity criterion. In addition, an angle-aware opposition-based learning strategy is proposed to improve the reference vector guided selection strategy of RVEA*. In the experiments, this method could effectively solve the problem of color image compression.
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| 21. |
- Cao, Mingwei, et al.
(författare)
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FAPP : Extremely Fast Approach to Boosting Image Matching Precision
- 2024
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 24:4, s. 4907-4919
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Tidskriftsartikel (refereegranskat)abstract
- Feature matching is a critical problem in the field of computer vision, which serves as the foundation for many high-level computer vision applications. This article aims to improve the accuracy of feature matching by eliminating mismatches in putative matches. To achieve this goal, we propose an extremely fast approach to boosting image matching precision (FAPP). The key idea behind FAPP is that correct feature matches have similar Euclidean distances, and the sine values of the angles between correct feature matches and the horizontal axis are also similar. Consequently, putative matches can be represented as 2-D coordinate points (sine value, Euclidean distance), which makes correct and incorrect feature matches have different degrees of clustering. The coordinate points are, furthermore, divided into grid spaces so that the coordinate points are distributed in different grid areas. Through adaptive parameter estimation, we determine a threshold for the number of correct feature matches within each grid, thereby eliminating false feature matches. In addition, to validate the effectiveness of FAPP, we conducted experiments on two public datasets and compared the results with several existing classical methods. The experimental outcomes demonstrate the superior performance of FAPP over the existing classical methods. Furthermore, the method has been applied to 3-D reconstruction with good results. Source code: https://github.com/caomw/fapp.
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| 22. |
- Chen, D., et al.
(författare)
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MeetDurian : Can Location-Based Games be Used to Improve COVID-19 Hygiene Habits?
- 2022
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Ingår i: Games and Culture. - : Sage Publications. - 1555-4120 .- 1555-4139. ; 17:5, s. 679-702
-
Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 problem has not gone away with the passing of the seasons. Although most countries have achieved remarkable results in fighting against epidemic diseases and controlling viruses, the general public is still far from understanding the new crown virus and lack imagination on its transmission law. Location-based games (LBGs) have been challenged during the on-going pandemic. No research has shown that LBGs can be used to help prevent COVID-19 infection. Therefore, we designed the game MeetDurian, which integrates entertainment, sports, and education. For investigating factors influencing intention to play the MeetDurian, we proposed some comparative evaluation. Data were gathered from participants who participated in capturing virtual durians and completed questionnaires about immersion into the game, workload assessment, user’s emotions, learning outcomes, and personal hygiene. These results proved the acceptability and usability of the mobile game-based MeetDurian for preventing the infection and severity of the COVID-19 pandemic. © The Author(s) 2022.
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| 23. |
- Fang, Bo, et al.
(författare)
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Dual-Channel Neural Network for Atrial Fibrillation Detection From a Single Lead ECG Wave
- 2023
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 27:5, s. 2296-2305
-
Tidskriftsartikel (refereegranskat)abstract
- With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.
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| 24. |
- Feng, Hailin, et al.
(författare)
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Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins
- 2023
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 136
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Tidskriftsartikel (refereegranskat)abstract
- The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.
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| 25. |
- Guo, J., et al.
(författare)
-
Application of Digital Twins in multiple fields
- 2022
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Ingår i: Multimedia tools and applications. - : Springer. - 1380-7501 .- 1573-7721.
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Tidskriftsartikel (refereegranskat)abstract
- With the development of science and technology, the high-tech industry is developing rapidly, and various new-age technologies continue to appear, and Digital Twins (DT) is one of them. As a brand-new interactive technology, DT technology can handle the interaction between the real world and the virtual world well. It has become a hot spot in the academic circles of all countries in the world. DT have developed rapidly in recent years result from centrality, integrity and dynamics. It is integrated with other technologies and has been applied in many fields, such as smart factory in industrial production, digital model of life in medical field, construction of smart city, security guarantee in aerospace field, immersive shopping in commercial field and so on. The introduction of DT is mostly a summary of concepts, and few practical applications of Digital Twins are introduced. The purpose of this paper is to enable people to understand the application status of DT technology. At the same time, the introduction of core technologies related to DT is interspersed in the application introduction. Finally, combined with the current development status of DT, predict the future development trend of DT and make a summary. © 2022, The Author(s).
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| 26. |
- Guo, Z., et al.
(författare)
-
DS-CNN : Dual-Stream Convolutional Neural Networks based Heart Sound Classification for Wearable Devices
- 2023
-
Ingår i: IEEE transactions on consumer electronics. - : IEEE. - 0098-3063 .- 1558-4127. ; 69:4, s. 1186-1194
-
Tidskriftsartikel (refereegranskat)abstract
- Cardiovascular diseases (CVDs) is considered a serious public health problem due to the uncertainty of its onset. Consuming wearable devices have increasing popularities for healthcare monitoring, and many of them are capable of continuous monitoring and early detection of CVDs. This paper proposes a framework for heart sound detection that can be considered for deployment on smart wearable devices to screen CVDs conveniently. A dual-stream convolutional neural network (DS-CNN) is developed to detect abnormal ones from short-term heart sound recordings. Preprocessing module is first employed for noise filtering and amplitude normalization. Then short-time Fourier transform and higher-order spectral are introduced for feature extraction, whose products are subsequently fed into the DS-CNN for screening abnormal heart sound signals. Two open accessible datasets are employed for performance evaluation. The results well demonstrate the classification accuracy of the proposed DS-CNN, and also indicate its advantages for adapting to heart sound recordings collected by different equipments. IEEE
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| 27. |
- He, Tongyue, et al.
(författare)
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Toward Wearable Sensors : Advances, Trends, and Challenges
- 2023
-
Ingår i: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 55:14S
-
Forskningsöversikt (refereegranskat)abstract
- Sensors suitable for wearable devices have many special characteristics compared to other sensors, such as stability, sensitivity, sensor volume, biocompatibility, and so on. With the development of wearable technology, amazing wearable sensors have attracted a lot of attention, and some researchers have done a large number of technology explorations and reviews. However, previous surveys generally were concerned with a specified application and comprehensively reviewed the computing techniques for the signals required by this application, as well as how computing can promote data processing. There is a gap in the opposite direction, i.e., the fundamental data source actively stimulates application rather than from the application to the data, and computing promotes the acquisition of data rather than data processing. To fill this gap, starting with different parts of the body as the source of signal, the fundamental data sources that can be obtained and detected are explored by combining the three sensing principles, as well as discussing and analyzing the existing and potential applications of machine learning in simplifying sensor designs and the fabrication of sensors.
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| 28. |
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| 29. |
- Kim, J. K., et al.
(författare)
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Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke
- 2022
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Ingår i: European Neurology. - : S. Karger. - 0014-3022 .- 1421-9913. ; 85:4, s. 273-279
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Machine learning (ML) is an artificial intelligence technique in which a system learns patterns and rules from a given data. Objectives: The objective of the study was to investigate the potential of ML for predicting motor recovery in stroke patients. Methods: We analyzed data from 833 consecutive stroke patients using 3 ML algorithms: deep neural network (DNN), random forest, and logistic regression. We created a practical ML model using the most common data measured in almost all rehabilitation hospitals as input data. Demographic and clinical data, including modified Brunnstrom classification (MBC) and functional ambulation classification (FAC), were collected when patients were transferred to the rehabilitation unit (8-30 days) and 6 months after stroke onset and were used as input data. Motor outcomes at 6 months after stroke onset of the affected upper and lower extremities were classified according to MBC and FAC, respectively. Patients with an MBC of <5 and an FAC of <4 at 6 months after stroke onset were considered to have a "poor"outcome, whereas those with MBC ≥5 and FAC ≥4 were considered to have a "good"outcome. Results: The area under the curve (AUC) for the DNN model for predicting motor function was 0.836 for the upper and lower limb motor functions. For the random forest and logistic regression models, the AUCs were 0.736 and 0.790 for the upper and lower limb motor functions, respectively. The AUCs for the random forest and logistic regression models were 0.741 and 0.795 for the upper and lower limb motor functions, respectively. Conclusion: Although we used simple and common data that can be obtained in clinical practice as variables, our DNN algorithm was useful for predicting motor recovery of the upper and lower extremities in stroke patients during the recovery phase.
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| 30. |
- Li, Mianjie, et al.
(författare)
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A dual-embedded tamper detection framework based on block truncation coding for intelligent multimedia systems
- 2023
-
Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 649
-
Tidskriftsartikel (refereegranskat)abstract
- The rich intelligent multimedia systems provide great convenience and efficiency. Unfortunately, it faces a series of security challenges and threats in developing and deploying multimedia ser-vices, such as tampering, hijacking, and adversarial attacks. Therefore, this paper proposes a dual -embedding framework based on block truncation coding to improve the security of intelligent multimedia systems. First, the signal is decomposed in frequency domain by using approximate translation invariance to obtain multi-layer frequency-domain parameters; then, this paper hides the encrypted data in two layers of low-frequency coefficients through fragile and robust embedding algorithms, respectively. In addition, in order to further improve the security per-formance, this paper adopts the method of block truncation coding to encrypt the embedded data. On the basis of performance analysis, the superiority of this method is illustrated by comparing with the existing methods.
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| 31. |
- Li, X., et al.
(författare)
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Application of effective water-energy management based on digital twins technology in sustainable cities construction
- 2022
-
Ingår i: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 87
-
Tidskriftsartikel (refereegranskat)abstract
- This work aims to effectively resolve the problem of waterlogging in cities and manage water resources in sustainable cities. Digital Twins (DTs) technology was applied to the Urban Drainage System (UDS) and solves the modeling and scheduling problems of emergency drainage through the core model construction method of DTs. Firstly, the components of the UDS that are necessary in the process of building the model were listed according to the entity elements of the five-dimensional (5D) DTs model. Then, this work analyzed the essential data of the DTs of UDS and the Data Collection method according to the data elements of the 5D DTs model. Finally, the Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm was proposed based on the Fixed Priority Scheduling (FPS) algorithm, which was verified by the simulation experiment. The experimental results indicated that the MDPIS algorithm showed significant performance in the rainfall scene with large fluctuations compared with the FPS algorithm. Specifically, the average improvement ratio was the highest, reaching 49.81%; the overall improvement rate was constant at about 48%. The operation results showed an apparent correlation between the catchment parameters and the overflow loss of the pumping station. The improved MDPIS algorithm can effectively utilize the water storage capacity and drainage capacity of the pumping station and reduce overflow losses during rainfall by dynamically adjusting the priority to solve the problem of urban inland inundation. The DTs-based UDS proposed here can effectively mitigate the overflow loss and improve the working efficiency of the pumping station cluster, promoting the development of Substainable Cities.
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| 32. |
- Li, Xin, et al.
(författare)
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Towards a sustainable city : Deciphering the determinants of restorative park and spatial patterns
- 2024
-
Ingår i: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 104
-
Tidskriftsartikel (refereegranskat)abstract
- Urban parks have been found to provide mental health benefits. Some empirical studies have tested natural features and perceptual measures respectively, announcing their contribution to psychological restoration. However, inconsistent findings were occasionally reported, whereas few attempts have been made to combine both observed and perceptual factors for validation. Little is known about the variation of restorative drivers and their spatial patterns. To address these problems, this study combined public participation geographic information system (PPGIS) and deep learning method to capture visual qualities of landscape features along with several important perceptual measures. A typical urban park in Wuhan, China, was selected for a pilot study, and 1560 crowdsourced on-site images were collected, with thematic and geographic information being integrated. A series of statistical models, e.g., OLS, QRM, and MGWR, were employed successively for validation. The results showed that landscape preference, place attachment, greenery and water were validated as the global explanatory factors to estimate the conditional mean of psychological restoration. The variation of influential effects of these factors were detected at different restoration levels. There exist spatial heterogeneity for these influential factors on restorative effects. Findings provided new knowledge on a deeper understanding of the subtlety of restoration drivers and their spatial patterns. The findings offered useful insights and guidance for urban planners in creating high-quality green parks with restorative values.
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| 33. |
- Liu, Xin, et al.
(författare)
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Federated Neural Architecture Search for Medical Data Security
- 2022
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 18:8, s. 5628-5636
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Tidskriftsartikel (refereegranskat)abstract
- Medical data widely exist in the hospital and personal life, usually across institutions and regions. They have essential diagnostic value and therapeutic significance. The disclosure of patient information causes people's panic, therefore, medical data security solution is very crucial for intelligent health care. The emergence of federated learning (FL) provides an effective solution, which only transmits model parameters, breaking through the bottleneck of medical data sharing, protecting data security, and avoiding economic losses. Meanwhile, the neural architecture search (NAS) has become a popular method to automatically search the optimal neural architecture for solving complex practical problems. However, few papers have combined the FL and NAS for simultaneous privacy protection and model architecture selection. Convolutional neural network (CNN) has outstanding performance in the image recognition field. Combining CNN and fuzzy rough sets can effectively improve the interpretability of deep neural networks. This article aims to develop a multiobjective convolutional interval type-2 fuzzy rough FL model based on NAS (CIT2FR-FL-NAS) for medical data security with an improved multiobjective evolutionary algorithm. We test the proposed framework on the LC25000 lung and colon histopathological image dataset. Experimental verification demonstrates that the designed multiobjective CIT2FR-FL-NAS framework can achieve high accuracy superior to state-of-the-art models and reduce network complexity under the condition of protecting medical data security.
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| 34. |
- Liu, Xiaochena, et al.
(författare)
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Intelligence Visualization for Wave Energy Power Generation
- 2022
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Ingår i: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). - : IEEE. - 9781665484022 ; , s. 986-987
-
Konferensbidrag (refereegranskat)abstract
- Ocean waves provide a large amount of renewable energy, and Wave energy converter (WEC) can convert wave energy into electric en-ergy. This paper proposes a visualization platform for wave power generation. The platform can monitor various indicators of wave power generation in real time, combined with Long Short-Term Memory (LSTM) neural network to predict wave power and electric-ity consumption in real time and visualize monitoring data. The plat-form can intelligently allocate power generation equipment based on the power generation forecast data to achieve precise matching of power generation and power consumption, thereby improving overall power generation efficiency.
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| 35. |
- Liu, Yuqi, et al.
(författare)
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Digital Twins of Wave Energy Generation Based on Artificial Intelligence
- 2022
-
Ingår i: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665484022 - 9781665484039 ; , s. 718-719
-
Konferensbidrag (refereegranskat)abstract
- Ocean waves provide a large amount of renewable energy, and Wave energy converter (WEC) can convert wave energy into electric energy. This paper proposes a visualization platform for wave power generation. The platform can monitor various indicators of wave power generation in real time, combined with Long Short-Term Memory (LSTM) neural network to predict wave power and electricity consumption. We make digital twins of a wave power plant in a computer, allowing users to remotely view the factory through VR glasses.
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| 36. |
- Liu, Yu, et al.
(författare)
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Ensemble Learning-Based Atrial Fibrillation Detection From Single Lead ECG Wave for Wireless Body Sensor Network
- 2023
-
Ingår i: IEEE Transactions on Network Science and Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4697. ; 10:5, s. 2627-2636
-
Tidskriftsartikel (refereegranskat)abstract
- With the popularity of wireless techniques and portable devices, continuous and real-time monitoring patient’s health status by collecting physiological signals is becoming more popular. Data mining of electrocardiograph (ECG) attracts widespread attention, where automatic atrial fibrillation (AF) detection has become a hot research field. In this paper, an ensemble learning algorithm is proposed for AF detection from single lead ECG recordings collected by wearable devices. This algorithm includes two modules. First, denoised 1-D time series (ECG), time-frequency spectrum and Poincare plot are used to train three component learners through a parallel style, respectively, and each component learner produces four probability values. Then, all the outputs are combined using a weighted matrix constructed by a Bayesian optimization algorithm, which is capable of producing the final classification result. Quantities of experiments have been implemented, and the results well prove the algorithm’s effectiveness and its advantage over some state-of-the-art counterparts.
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| 37. |
- Liu, Yuqi, et al.
(författare)
-
Liquid Digital Twins Based on Magnetic Fluid Toys
- 2022
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Ingår i: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). - : IEEE. - 9781665484022 ; , s. 988-989
-
Konferensbidrag (refereegranskat)abstract
- As a new type of functional material, magnetic fluid has both the fluidity of liquid and the magnetic properties of solid magnetic material. By controlling the magnets, one can simulate the effect of manipulating liquids like a sea emperor. This will provide new ideas for the multiverse of the metaverse. Not only that, magnetic fluids also have very important applications in astrophysics, controlled thermonuclear reactions and even the medical industry. Therefore, this paper hopes to provide a control idea for the future application of magnetic fluid by performing Digital Twins simulation of magnetic fluid.
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| 38. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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5G for mobile augmented reality
- 2022
-
Ingår i: International Journal of Communication Systems. - : John Wiley & Sons. - 1074-5351 .- 1099-1131. ; 35:5
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 39. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Augmented Reality for Bioinformatics
- 2022
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 26:6, s. 2403-2404
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 40. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Behavioral Modeling and Prediction in Social Perception and Computing : A Survey
- 2023
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X. ; 10:4, s. 2008-2021
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Tidskriftsartikel (refereegranskat)abstract
- More data are generated through interaction between cyber space, physical space, and social space thanks to mobile network technology, giving birth to the so-called cyber–physical social intelligent ecosystem (C&P-SIE). This survey studies the development of physical social intelligence. First, it classifies and discusses the behavior modeling, learning, and adaptation applications of C&P-SIE from intelligent transportation, healthcare, public service, economy, and social networking. Then, it prospects the application of behavior modeling in the C&P-SIE from the perspectives of information security, data-driven techniques, and modeling learning under cooperative artificial intelligence technologies. The research provides a theoretical basis and new opportunities for the digital and intelligent development of smart cities and social systems. IEEE
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| 41. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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BlockNet : Beyond reliable spatial Digital Twins to Parallel Metaverse
- 2022
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Ingår i: Patterns. - : Cell Press. - 2666-3899. ; 3:5
-
Tidskriftsartikel (refereegranskat)abstract
- The development of Digital Twins has enabled them to be widely applied to various fields represented by intelligent manufacturing. A Metaverse, which is parallel to the physical world, needs mature and secure Digital Twins technology in addition to Parallel Intelligence to enable it to evolve autonomously. We propose that Blockchain combined with other areas does not simultaneously require all of the basic elements. We extract the immutable characteristics of Blockchain and propose a secure multidimensional data storage solution called BlockNet that can ensure the security of the digital mapping process of the Internet of Things, thereby improving the data reliability of Digital Twins. Additionally, to address some of the challenges faced by multiscale spatial data processing, we propose a nonmutagenic multidimensional Hash Geocoding method, allowing unique indexing of multidimensional information and avoiding information loss due to data dimensionality reduction while improving the efficiency of information retrieval and facilitating the implementation of the Metaverse through spatial Digital Twins based on these two studies. © 2022 The Author(s)
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| 42. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Deep Learning-based Smart Predictive Evaluation for Interactive Multimedia-enabled Smart Healthcare
- 2022
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Ingår i: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP). - : Association for Computing Machinery (ACM). - 1551-6857 .- 1551-6865. ; 18:1
-
Tidskriftsartikel (refereegranskat)abstract
- Two-dimensional(1) arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. This study aims to enhance the security for people's health, improve the medical level further, and increase the confidentiality of people's privacy information. Under the trend of wide application of deep learning algorithms, the convolutional neural network (CNN) is modified to build an interactive smart healthcare prediction and evaluation model (SHPE model) based on the deep learning model. The model is optimized and standardized for data processing. Then, the constructed model is simulated to analyze its performance. The results show that accuracy of the constructed system reaches 82.4%, which is at least 2.4% higher than other advanced CNN algorithms and 3.3% higher than other classical machine algorithms. It is proved based on comparison that the accuracy, precision, recall, and F1 of the constructed model are the highest. Further analysis on error shows that the constructed model shows the smallest error of 23.34 pixels. Therefore, it is proved that the built SHPE model shows higher prediction accuracy and smaller error while ensuring the safety performance, which provides an experimental reference for the prediction and evaluation of smart healthcare treatment in the later stage.
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| 43. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Deep Learning for Intelligent Human-Computer Interaction
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:22
-
Forskningsöversikt (refereegranskat)abstract
- In recent years, gesture recognition and speech recognition, as important input methods in Human-Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as "intelligent HCI", "speech recognition", "gesture recognition", and "natural language processing", nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019-2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for research. Then, the core concepts of gesture interaction are introduced and the progress of gesture recognition and speech recognition interaction is analyzed. Furthermore, the representative applications of gesture recognition and speech recognition interaction are described. Finally, the current HCI in the direction of natural language processing is investigated. The results show that the combination of intelligent HCI and deep learning is deeply applied in gesture recognition, speech recognition, emotion recognition, and intelligent robot direction. A wide variety of recognition methods were proposed in related research fields and verified by experiments. Compared with interactive methods without deep learning, high recognition accuracy was achieved. In Human-Machine Interfaces (HMIs) with voice support, context plays an important role in improving user interfaces. Whether it is voice search, mobile communication, or children's speech recognition, HCI combined with deep learning can maintain better robustness. The combination of convolutional neural networks and long short-term memory networks can greatly improve the accuracy and precision of action recognition. Therefore, in the future, the application field of HCI will involve more industries and greater prospects are expected.
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| 44. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Digital Twins : The Confluence of Virtual Reality With IoT
- 2023
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Ingår i: IEEE Consumer Electronics Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2248 .- 2162-2256. ; 12:6, s. 27-28
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Virtual reality (VR) and the Internet of Things (IoT) are two of the most important technologies to arise in the past decade or more. Taken individually, each technology represents a real sea change. VR has the potential to truly change the world in some surprising ways, while the IoT has already transformed the way we live our lives. Digital Twins is the confluence of these two developments, though, that offers the most promise and opportunity of all. VR applications promise to be the next mainstream business. They are used by companies in a wide range of industries, from product design to healthcare and employee training. At the same time, IoT platforms and related devices are much in demand in today's tech and business world. These smart devices connected to the Internet are capable of collecting, interpreting, and relaying data without human intervention or supervision.
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| 45. |
- Lv, Zhihan, Dr. 1984-
(författare)
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Digital Twins in Industry 5.0
- 2023
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Ingår i: RESEARCH. - : American Association for the Advancement of Science (AAAS). - 2096-5168 .- 2639-5274. ; 6
-
Forskningsöversikt (refereegranskat)abstract
- This work aims to explore the impact of Digital Twins Technology on industrial manufacturing in the context of Industry 5.0. A computer is used to search the Web of Science database to summarize the Digital Twins in Industry 5.0. First, the background and system architecture of Industry 5.0 are introduced. Then, the potential applications and key modeling technologies in Industry 5.0 are discussd. It is found that equipment is the infrastructure of industrial scenarios, and the embedded intelligent upgrade for equipment is a Digital Twins primary condition. At the same time, Digital Twins can provide automated real-time process analysis between connected machines and data sources, speeding up error detection and correction. In addition, Digital Twins can bring obvious efficiency improvements and cost reductions to industrial manufacturing. Digital Twins reflects its potential application value and subsequent potential value in Industry 5.0 through the prospect. It is hoped that this relatively systematic overview can provide technical reference for the intelligent development of industrial manufacturing and the improvement of the efficiency of the entire business process in the Industrial X.0 era.
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| 46. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Editorial : 5G for Augmented Reality
- 2022
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Ingår i: Mobile Networks and Applications. - : Springer. - 1383-469X .- 1572-8153.
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Tidskriftsartikel (refereegranskat)
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| 47. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Editorial : Digital twins of plant and forest
- 2022
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Ingår i: Frontiers in Plant Science. - : Frontiers Media S.A.. - 1664-462X. ; 13
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 48. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Evaluation Standards of Intelligent Technology based on Financial Alternative Data
- 2022
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Ingår i: Journal of Innovation and Knowledge. - : Elsevier. - 2530-7614 .- 2444-569X. ; 7:4
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Tidskriftsartikel (refereegranskat)abstract
- After the visions of Industry 5.0 and Society 5.0 were presented, a proliferation of artificial intelligence technologies have been applied to the financial field because AI develops fast, especially intelligent analysis methods for alternative financial data. However, the organic integration of the financial industry and the Internet of Things lacks relevant standards, and there is no appropriate work summary to coordinate the formulation of these standards. This work aims to effectively improve the reliability of information acquisition and the accuracy of data processing in the financial industry. In addition, this work also investigates papers and standards related to financial intelligence technology in recent years and statistically analyzes the evaluation indicators of AI research papers. Then, a standard evaluation framework is proposed for financial intelligence technology, which is evaluated for performance verification. The comparative experiments demonstrate that the prediction accuracy of the financial intelligence standard model reaches 95.44%, and the prediction error is substantially smaller than that of the other model algorithms. The financial intelligence standard model can make accurate predictions on alternative financial data and has high reliability and validity. The model can provide an experimental reference for intellectual development in the financial field and enable participants to improve work efficiency and standards throughout the process of developing intelligent financial technology. © 2022 The Author(s)
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| 49. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Impact of Digital Twins and Metaverse on Cities : History, Current Situation, and Application Perspectives
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:24
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Forskningsöversikt (refereegranskat)abstract
- To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a detailed review of the impact of DTs and digitalization on cities is made to assess the progression of cities and standardization of their management mode. Combined with the technical elements of DTs, the coupling effect of DTs technology and urban construction and the internal logic of DTs technology embedded in urban construction are discussed. Relevant literature covering the full range of DTs technologies and their applications is collected, evaluated, and collated, relevant studies are concatenated, and relevant accepted conclusions are summarized by modules. First, the historical process and construction content of a Digital City (DC) under modern demand are analyzed, and the main ideas of a DC design and construction are discussed in combination with the key technology of DTs. Then, the metaverse is the product of the combination of various technologies in different scenes. It is a key component to promote the integration of the real world and the digital world and can provide more advanced technical support in the construction of the DC. DTs urban technology architecture is composed of an infrastructure terminal information center terminal and application server end. Urban intelligent management is realized through physical urban data collection, transmission, processing, and digital urban visualization. The construction of DTs urban platform can improve the city's perception and decision-making ability and bring a broader vision for future planning and progression. The interactive experience of the virtual world covered by the metaverse can effectively support and promote the integration of the virtual and real, and will also greatly promote the construction of SCs. In summary, this work is of important reference value for the overall development and practical adoption of DTs cities, which improves the overall operation efficiency and the governance level of cities.
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| 50. |
- Lv, Zhihan, Dr. 1984-
(författare)
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Metaverse Age : Scheduling Strategies for Digital Resource Management
- 2023
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Ingår i: IEEE Consumer Electronics Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2248 .- 2162-2256. ; 12:4, s. 4-6
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Tidskriftsartikel (refereegranskat)abstract
- The metaverse is a new Internet application and social form: virtual and real integration of various new technologies. This article explores optimal strategies for digital resource management (DRM) and multiobjective task scheduling in the metaverse era. To this end, it explores the correlation between the metaverse and the Internet of Things. The metaverse's operation and control, and the DRM and schedules are further elaborated. Finally, the next-generation interaction between humans and the metaverse is outlooked. Thus, it provides a reference for the potential development of DRM and scheduling in the metaverse era.
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