Game theory in network security for digital twins in industry

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.

Reinforcement learning based edge computing in B5G

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

Multi-Disease Prediction Based on Deep Learning: A Survey

In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in themedical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Amongthem, the hot deep learning field has shown greater potential in applications such as disease prediction and drugresponse prediction. From the initial logistic regression model to the machine learning model, and then to thedeep learning model today, the accuracy of medical disease prediction has been continuously improved, and theperformance in all aspects has also been significantly improved. This article introduces some basic deep learningframeworks and some common diseases, and summarizes the deep learning prediction methods correspondingto different diseases. Point out a series of problems in the current disease prediction, and make a prospect for thefuture development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates thehigh correlation between deep learning and the medical field in future development. The unique feature extractionmethods of deep learning methods can still play an important role in future medical research.

Can blockchain link the future?

Recently,decentralization has been extensively explored by researchers.Blockchain,as a representation of decentralized technology,has attracted attention with its unique characteristics,such as irrevocability and security.Consequently,herein,we introduce cutting-edge blockchain technologies from four directions:blockchain system,consensus algorithms,smart contract,and scalability.Subsequently,we analyze the current lack of consensus mechanism,fault tolerance,and block capacity of the blockchain,and the integration of blockchain into 5G/6G mobile communication.Furthermore,we discuss the possible applications of blockchain in intellectual property protection,the Internet of Things,digital twins,standardization,and epidemic prevention and control.Finally,explore the impacts and solutions of blockchain on human society beyond technology.

Modeling Methods of 3D Model in Digital Twins

To understand the current application and development of 3D modeling in Digital Twins(DTs),abundant literatures on DTs and 3D modeling are investigated by means of literature review.The transition process from 3D modeling to DTs modeling is analyzed,as well as the current application of DTs modeling in various industries.The application of 3D DTs modeling in the􀀀elds of smartmanufacturing,smart ecology,smart transportation,and smart buildings in smart cities is analyzed in detail,and the current limitations are summarized.It is found that the 3D modeling technology in DTs has broad prospects for development and has a huge impact on all walks of life and even human lifestyles.At the same time,the development of DTs modeling relies on the development and support capabilities of mature technologies such as Big Data,Internet of Things,Cloud Computing,Articial Intelligence,and game technology.Therefore,although some results have been achieved,there are still limitations.This work aims to provide a good theoretical support for the further development of 3D DTs modeling.

Short‐time wind speed prediction based on Legendre multi‐wavelet neural network

As one of the most widespread renewable energy sources,wind energy is now an important part of the power system.Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation.However,due to the stochastic and un-certain nature of wind energy,more accurate forecasting is necessary for its more stable and safer utilisation.This paper proposes a Legendre multiwavelet‐based neural network model for non‐linear wind speed prediction.It combines the excellent properties of Legendre multi‐wavelets with the self‐learning capability of neural networks,which has rigorous mathematical theory support.It learns input‐output data pairs and shares weights within divided subintervals,which can greatly reduce computing costs.We explore the effectiveness of Legendre multi‐wavelets as an activation function.Mean-while,it is successfully being applied to wind speed prediction.In addition,the appli-cation of Legendre multi‐wavelet neural networks in a hybrid model in decomposition‐reconstruction mode to wind speed prediction problems is also discussed.Numerical results on real data sets show that the proposed model is able to achieve optimal per-formance and high prediction accuracy.In particular,the model shows a more stable performance in multi‐step prediction,illustrating its superiority.

Prototypical Network Based on Manhattan Distance

Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data,such as medical images,terrorist surveillance,and so on.The Metric Learning in the Few-shot Learning algorithmis classified by measuring the similarity between the classified samples and the unclassified samples.This paper improves the Prototypical Network in the Metric Learning,and changes its core metric function to Manhattan distance.The Convolutional Neural Network of the embedded module is changed,and mechanisms such as average pooling and Dropout are added.Through comparative experiments,it is found that thismodel can converge in a small number of iterations(below 15,000 episodes),and its performance exceeds algorithms such asMAML.Research shows that replacingManhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network,and mechanisms such as average pooling and Dropout can also effectively improve the model.

Building the metaverse using digital twins at all scales,states,and relations

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the Metaverse.Metaverse objects can be at the micro-,meso-,or macroscale.The Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain states.Additionally,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social relations.Therefore,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

Pheromone based alternative route planning

In this work, we propose an improved alternative route calculation based on alternative figures, which is suitable for practical environments. The improvement is based on the fact that the main traffic route is the road network skeleton in a city. Our approach using nodes may generate a higher possibility of overlapping. We employ a bidirectional Dijkstra algorithm to search the route. To measure the quality of an Alternative Figures （AG）, three quotas are proposed. The experiment results indicate that the im- proved algorithm proposed in this paper is more effective than others.

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

Protecting the trust and credibility of data by tracking forgery trace based on GANs

With the advent of the 5G Internet of Things era,communication and social interaction in our daily life have changed a lot,and a large amount of social data is transmitted to the Internet.At the same time,with the rapid development of deep forgery technology,a new generation of social data trust crisis has also followed.Therefore,how to ensure the trust and credibility of social data in the 5G Internet of Things era is an urgent problem to be solved.This paper proposes a new method for forgery detection based on GANs.We first discover the hidden gradient information in the grayscale image of the forged image and use this gradient information to guide the generation of forged traces.In the classifier,we replace the traditional binary loss with the focal loss that can focus on difficult-to-classify samples,which can achieve accurate classification when the real and fake samples are unbalanced.Experimental results show that the proposed method can achieve high accuracy on the DeeperForensics dataset and with the highest accuracy is 98%.

Virtual Reality Neurorehabilitation

The present work investigates the application of virtual reality(VR)technology to neurorehabilitation.By consulting a wealth of data,the advantages of VR in neurorehabilitation are introduced,followed by the application status of VR in the rehabilitation of stroke patients,Parkinson’s patients,mental and psychological diseases.Besides,many research experiments on the application of VR technology in rehabilitation medicine at the present stage are investigated.The results indicate that compared with traditional balance training,the VR-based neurological rehabilitation training method can more effectively ease the tilt degree and strengthen the trunk control ability and balance function of patients with post-stroke tilt syndrome.When the effect of traditional rehabilitation training on the gait and balance of Parkinson’s patients is not good enough,VR-based rehabilitation training can at least be used as an alternative therapy.Moreover,VR games have made great breakthroughs in promoting limb rehabilitation and brain injury rehabilitation,which is of incredible benefit to those with motor and activity disorders.It is also beneficial to the treatment and recovery of mental disorders of patients with nerve injury.Although VR still has limitations such as high cost and technical breakthrough bottleneck,it has great advantages in relieving pain,enhancing interest,and recovering patients’mental health in neurological rehabilitation training.

A spatiotemporal intelligent framework and experimental platform for urban digital twins

Backgrounds This work emphasizes the current research status of the urban Digital Twins to establish an intelligent spatiotemporal framework.A Geospatial Artificial Intelligent(GeoAI)system is developed based on the Geographic Information System and Artificial Intelligence.It integrates multi-video technology and Virtual City in urban Digital Twins.Methods Besides,an improved small object detection model is proposed:YOLOv5-Pyramid,and Siamese network video tracking models,namely MPSiam and FSSiamese,are established.Finally,an experimental platform is built to verify the georeferencing correction scheme of video images.Result The MultiplyAccumulate value of MPSiam is 0.5B,and that of ResNet50-Siam is 4.5B.Besides,the model is compressed by 4.8times.The inference speed has increased by 3.3 times,reaching 83 Frames Per Second.3%of the Average Expectation Overlap is lost.Therefore,the urban Digital Twins-oriented GeoAI framework established here has excellent performance for video georeferencing and target detection problems.

Intelligent fire information system based on 3D GIS

Background This work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things(IoT)and achieve an actual intelligent fire rescue.A smart fire protection information system was designed based on the IoT.A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue.Methods The intelligent fire visualization platform based on the three-dimensional(3D)Geographic Information Science(GIS)covers project overview,equipment status,equipment classification,equipment alarm information,alarm classification,alarm statistics,equipment account information,and other modules.The live video accessed through the visual interface can clearly identify the stage of the fire,which facilitates the arrangement of rescue equipment and personnel.The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization:emergency rescue time and the number of vehicles.In addition,an evacuation path optimization method based on the Improved Ant Colony(IAC)algorithm was designed to realize the dynamic optimization of building fire evacuation paths.Results The experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t=17s than the initial value.In addition,the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation,demonstrating that this model could detect fire.Conclusions The IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route.Therefore,the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.

Virtual-reality and intelligent hardware in Digital Twins

Several new models and formats for the digital transformation of the manufacturing industry appear because of the rapid integration of information technology and the real economy,as well as the increasingly obvious evolution trend of industrial digitalization,networking,and intelligence.Among them,digital twins have increasingly become a research hotspot in all sectors of the industry and have broad prospects.It maps physical objects in virtual space in a digital way and simulates their behavioral characteristics in real environments.It makes the gap between virtuality and reality disappear based on their closed-loop interaction.Digital twins are undoubtedly an important and strategic technology in response to familiar products,production,and services.It can also speculate some indicators that cannot be directly measured by machine learning through collecting the direct data of limited physical sensor indicators.This can realize an assessment of the current state,a diagnosis of past problems,and a prediction of future trends,and simulate possibilities to provide more comprehensive decision support.

Virtual-reality and intelligent hardware in digital twins

Several new models and formats for the digital transformation of the manufacturing industry appear because of the rapid integration of information technology and the real economy,as well as the increasingly obvious evolution trend of industrial digitalization,networking,and intelligence.Among them,digital twins have increasingly become a research hotspot in all sectors of the industry and have broad prospects.It maps physical objects in virtual space in a digital way and simulates their behavioral characteristics in real environments.It makes the gap between virtuality and reality disappear based on their closed-loop interaction.Digital twins are undoubtedly an important and strategic technology in response to familiar products,production,and services.It can also speculate some indicators that cannot be directly measured by machine learning through collecting the direct data of limited physical sensor indicators.This can realize an assessment of the current state,a diagnosis of past problems,and a prediction of future trends,and simulate possibilities to provide more comprehensive decision support.

Artificial intelligence in the digital twins:State of the art,challenges,and future research topics[version 1;peer review:1 approved,1 approved with reservations]

Advanced computer technologies such as big data,Artificial Intelligence(AI),cloud computing,digital twins,and edge computing have been applied in various fields as digitalization has progressed.To study the status of the application of digital twins in the combination with AI,this paper classifies the applications and prospects of AI in digital twins by studying the research results of the current published literature.We discuss the application status of digital twins in the four areas of aerospace,intelligent manufacturing in production workshops,unmanned vehicles,and smart city transportation,and we review the current challenges and topics that need to be looked forward to in the future.It was found that the integration of digital twins and AI has significant effects in aerospace flight detection simulation,failure warning,aircraft assembly,and even unmanned flight.In the virtual simulation test of automobile autonomous driving,it can save 80%of the time and cost,and the same road conditions reduce the parameter scale of the actual vehicle dynamics model and greatly improve the test accuracy.In the intelligent manufacturing of production workshops,the establishment of a virtual workplace environment can provide timely fault warning,extend the service life of the equipment,and ensure the overall workshop operational safety.In smart city traffic,the real road environment is simulated,and traffic accidents are restored,so that the traffic situation is clear and efficient,and urban traffic management can be carried out quickly and accurately.Finally,we looked forward to the future of digital twins and AI,hoping to provide a reference for future research in related fields.

Digital Twins in Industry5.0

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.O era.

An event-driven dynamic updating method for 3D geo-databases

With the rapid development of 3D Digital City, the focus of research has shifted from 3D city modeling and geo-database construction to 3D geo-database service and maintenance. The frequent modifications on geometry, texture, attribute, and topology present a great challenge to the 3D geo-database updating.This article proposes an event-driven spatiotemporal database model (ESDM) that combines the historical and present 3D city models with the semantic classification and state expression, triggered by changing events predefined. In addition, a corresponding dynamic updating method based on adaptive matching algorithm is presented to perform the dynamic updating operation for the complex 3D city models automatically, according to the compound matching of semantics, attributes, and spatial locations. finally, the validity and feasibility of the proposed ESDM and its updating method are demonstrated through a 3D geo-database with more than 1.5 million 3D city models.