Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell t...Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.展开更多
This paper studies a finite-time adaptive fractionalorder fault-tolerant control(FTC)scheme for the slave position tracking of the teleoperating cyber physical system(TCPS)with external disturbances and actuator fault...This paper studies a finite-time adaptive fractionalorder fault-tolerant control(FTC)scheme for the slave position tracking of the teleoperating cyber physical system(TCPS)with external disturbances and actuator faults.Based on the fractional Lyapunov stability theory and the finite-time stability theory,a fractional-order nonsingular fast terminal sliding mode(FONFTSM)control law is proposed to promote the tracking and fault tolerance performance of the considered system.Meanwhile,the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external disturbances.Furthermore,the finite-time stability of the closed-loop system is proved.Finally,comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.展开更多
Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CP...Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.展开更多
A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physic...A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.展开更多
Information security and quality management are often considered two different fields. However, organizations must be mindful of how software security may affect quality control. This paper examines and promotes metho...Information security and quality management are often considered two different fields. However, organizations must be mindful of how software security may affect quality control. This paper examines and promotes methods through which secure software development processes can be integrated into the Systems Software Development Life-cycle (SDLC) to improve system quality. Cyber-security and quality assurance are both involved in reducing risk. Software security teams work to reduce security risks, whereas quality assurance teams work to decrease risks to quality. There is a need for clear standards, frameworks, processes, and procedures to be followed by organizations to ensure high-level quality while reducing security risks. This research uses a survey of industry professionals to help identify best practices for developing software with fewer defects from the early stages of the SDLC to improve both the quality and security of software. Results show that there is a need for better security awareness among all members of software development teams.展开更多
An intelligent manufacturing system is a composite intelligent system comprising humans,cyber systems,and physical systems with the aim of achieving specific manufacturing goals at an optimized level.This kind of inte...An intelligent manufacturing system is a composite intelligent system comprising humans,cyber systems,and physical systems with the aim of achieving specific manufacturing goals at an optimized level.This kind of intelligent system is called a human-cyber-physical system(HCPS).In terms of technology,HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing.It can be concluded that the essence of intelligent manufacturing is to design,construct,and apply HCPSs in various cases and at different levels.With advances in information technology,intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing,and is evolving toward new-generation intelligent manufacturing(NGIM).NGIM is characterized by the in-depth integration of new-generation artificial intelligence(AI)technology(i.e.,enabling technology)with advanced manufacturing technology(i.e.,root technology);it is the core driving force of the new industrial revolution.In this study,the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs,and the implications,characteristics,technical frame,and key technologies of HCPSs for NGIM are then discussed in depth.Finally,an outlook of the major challenges of HCPSs for NGIM is proposed.展开更多
State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important ...State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.展开更多
A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over Internet.It plays a key role in modern industry since it connects phy...A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over Internet.It plays a key role in modern industry since it connects physical and cyber worlds.In order to meet ever-changing industrial requirements,its structures and functions are constantly improved.Meanwhile,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and practitioners.This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems.First,as typical system models are employed to study these systems,time-driven and event-driven systems are reviewed.Then,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are discussed.In particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders.Namely,both attack and defense strategies are discussed based on different system models.Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.展开更多
Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufactur...Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.展开更多
The prevalence of human papilloma virus (HPV)-16 in patients with cervical cancer,the physical status of HPV-16 in patients with cervical lesions,and the role of HPV-16 integration in cervi-cal carcinogenesis were inv...The prevalence of human papilloma virus (HPV)-16 in patients with cervical cancer,the physical status of HPV-16 in patients with cervical lesions,and the role of HPV-16 integration in cervi-cal carcinogenesis were investigated.HPV genotyping was performed by using PCR approach with the primer GP5+/GP6+ and type-specific primer on biopsy specimens taken operatively from 198 women.Multiple PCR was done to detect physical status of HPV-16 in a series of cervical liquid-based cytology samples and biopsy specimens obtained from different cervical lesions with HPV-16 infection,includ-ing 112 specimens with cervical cancer,151 specimens with CINⅠ,246 specimens with CINⅡ and 120 specimens with CINⅢ.The results showed that there were 112 cervical cancer samples (56.57% of total cervical cancer patients) with HPV-16 infection.The frequency of HPV-16 pure integration was 65.18% (73/112),56.57% (47/120),23.58% (58/246) and 7.95% (12/151) in cervical cancer,CINⅢ,CINⅡand CINⅠ patients respectively.In situ hybridization was performed on some paraffin-embedded sections of CINⅡ,CINⅢ and cervical cancer to verify the physical status of HPV-16 infection.Sig-nificant difference was observed between cervical cancer and CINⅠ,CINⅡ,CINⅢ in the frequency of HPV-16 integration (P<0.01).It is suggested that HPV-16 is the most prevalent type and is associated with cervical cancer.In the case of HPV-16 infection there are close associations between the severity of cervical lesions and the frequency of HPV-16 integration.The application of testing HPV genotyping and physical status based on detection of HC-Ⅱ HPV DNA would be in favor of predicting the progno-sis of cervical precancerosis and enhancing the screening accuracy of cervical cancer.展开更多
As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this pa...As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this paper proposes a cyber-physical-social based security architecture (IPM) to deal with Information, Physical, and Management security perspectives, and presents how the architectural abstractions support U2IoT model. In particular, 1) an information security model is established to describe the mapping relations among U2IoT, security layer, and security requirement, in which social layer and additional intelligence and compatibility properties are infused into IPM;2) physical security referring to the external context and inherent infrastructure are inspired by artificial immune algorithms;3) recommended security strategies are suggested for social management control. The proposed IPM combining the cyber world, physical world and human social provides constructive proposal towards the future IoT security and privacy protection.展开更多
This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomne...This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources.Therefore,it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system.This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs.The goal is to minimize the expected operation cost of MGs.The uncertainties are modeled based on autoregressive moving average(ARMA) model to expose the effects of physical world on cyber world.Through the comparison of the simulation results with deterministic method,it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.展开更多
Conventional power systems are being developed into grid cyber physical systems(GCPS) with widespread application of communication, computer, and control technologies. In this article, we propose a quantitative analys...Conventional power systems are being developed into grid cyber physical systems(GCPS) with widespread application of communication, computer, and control technologies. In this article, we propose a quantitative analysis method for a GCPS. Based on this, we discuss the relationship between cyberspace and physical space, especially the computational similarity within the GCPS both in undirected and directed bipartite networks. We then propose a model for evaluating the fusion of the three most important factors: information, communication, and security. We then present the concept of the fusion evaluation cubic for the GCPS quantitative analysis model. Through these models, we can determine whether a more realistic state of the GCPS can be found by enhancing the fusion between cyberspace and physical space. Finally, we conclude that the degree of fusion between the two spaces is very important, not only considering the performance of the whole business process, but also considering security.展开更多
Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid i...Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality.展开更多
Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve servi...Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve service self-organization with its nodes autonomously working in both physical and cyber environments.To solve the problem,the social nature of nodes in CPS is firstly addressed,and then a formal social semantic descriptions is presented for physical environment,node service and task in order to make the nodes communicate automatically and physical environment sensibly.Further,the Horn clause is introduced to represent the reasoning rules of service organizing.Based on the match function,which is defined for measurement between semantics,the semantic aware measurement is presented to evaluate whether environment around a node can satisfy the task requirement or not.Moreover,the service capacity evaluation method for nodes is addressed to find out the competent service from both cyber and physical features of nodes.According to aforementioned two measurements,the task semantic decomposition algorithm and the organizing matrix are defined and the service self-organizing mechanism for CPS is proposed.Finally,examinations are given to further verify the efficiency and feasibility of the proposed mechanism.展开更多
Considered as a top priority of industrial devel- opment, Industry 4.0 (or Industrie 4.0 as the German ver- sion) has being highlighted as the pursuit of both academy and practice in companies. In this paper, based ...Considered as a top priority of industrial devel- opment, Industry 4.0 (or Industrie 4.0 as the German ver- sion) has being highlighted as the pursuit of both academy and practice in companies. In this paper, based on the review of state of art and also the state of practice in dif- ferent countries, shortcomings have been revealed as the lacking of applicable framework for the implementation of Industrie 4.0. Therefore, in order to shed some light on the knowledge of the details, a reference architecture is developed, where four perspectives namely manufacturing process, devices, software and engineering have been highlighted. Moreover, with a view on the importance of Cyber-Physical systems, the structure of Cyber-Physical System are established for the in-depth analysis. Further cases with the usage of Cyber-Physical System are also arranged, which attempts to provide some implications to match the theoretical findings together with the experience of companies. In general, results of this paper could be useful for the extending on the theoretical understanding of Industrie 4.0. Additionally, applied framework and proto- types based on the usage of Cyber-Physical Systems are also potential to help companies to design the layout of sensor nets, to achieve coordination and controlling of smart machines, to realize synchronous production with systematic structure, and to extend the usage of information and communication technologies to the maintenance scheduling.展开更多
As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the...As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.展开更多
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number 223202.
文摘Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.
基金supported by the National Natural Science Foundation of China(61973331,61973257)the National Key Research and Development Plan Programs of China(2018YFB0106101).
文摘This paper studies a finite-time adaptive fractionalorder fault-tolerant control(FTC)scheme for the slave position tracking of the teleoperating cyber physical system(TCPS)with external disturbances and actuator faults.Based on the fractional Lyapunov stability theory and the finite-time stability theory,a fractional-order nonsingular fast terminal sliding mode(FONFTSM)control law is proposed to promote the tracking and fault tolerance performance of the considered system.Meanwhile,the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external disturbances.Furthermore,the finite-time stability of the closed-loop system is proved.Finally,comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.
文摘Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.
基金supported by the Researchers Supporting Program(TUMA-Project-2021-27)Almaarefa University,Riyadh,Saudi ArabiaTaif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia。
文摘A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.
文摘Information security and quality management are often considered two different fields. However, organizations must be mindful of how software security may affect quality control. This paper examines and promotes methods through which secure software development processes can be integrated into the Systems Software Development Life-cycle (SDLC) to improve system quality. Cyber-security and quality assurance are both involved in reducing risk. Software security teams work to reduce security risks, whereas quality assurance teams work to decrease risks to quality. There is a need for clear standards, frameworks, processes, and procedures to be followed by organizations to ensure high-level quality while reducing security risks. This research uses a survey of industry professionals to help identify best practices for developing software with fewer defects from the early stages of the SDLC to improve both the quality and security of software. Results show that there is a need for better security awareness among all members of software development teams.
文摘An intelligent manufacturing system is a composite intelligent system comprising humans,cyber systems,and physical systems with the aim of achieving specific manufacturing goals at an optimized level.This kind of intelligent system is called a human-cyber-physical system(HCPS).In terms of technology,HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing.It can be concluded that the essence of intelligent manufacturing is to design,construct,and apply HCPSs in various cases and at different levels.With advances in information technology,intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing,and is evolving toward new-generation intelligent manufacturing(NGIM).NGIM is characterized by the in-depth integration of new-generation artificial intelligence(AI)technology(i.e.,enabling technology)with advanced manufacturing technology(i.e.,root technology);it is the core driving force of the new industrial revolution.In this study,the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs,and the implications,characteristics,technical frame,and key technologies of HCPSs for NGIM are then discussed in depth.Finally,an outlook of the major challenges of HCPSs for NGIM is proposed.
基金This work is financially supported by the National Key Research and Development Program of China(2016YFB1101700)the National Natural Science Foundation of China(51875030)the Academic Excellence Foundation of BUAA for PhD Students.
文摘State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.
基金supported by Institutional Fund Projects(IFPNC-001-135-2020)technical and financial support from the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia。
文摘A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over Internet.It plays a key role in modern industry since it connects physical and cyber worlds.In order to meet ever-changing industrial requirements,its structures and functions are constantly improved.Meanwhile,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and practitioners.This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems.First,as typical system models are employed to study these systems,time-driven and event-driven systems are reviewed.Then,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are discussed.In particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders.Namely,both attack and defense strategies are discussed based on different system models.Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.
基金Supported by National Natural Science Foundation of China(Grant No.61272428)PhD Programs Foundation of Ministry of Education of China(Grant No.20120002110067)
文摘Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.
基金supported by grants from National Natura Science Foundation of China (No. 81001151,No. 30973205)Natural Science Foundation of Hubei Province (No2010CDB09503)
文摘The prevalence of human papilloma virus (HPV)-16 in patients with cervical cancer,the physical status of HPV-16 in patients with cervical lesions,and the role of HPV-16 integration in cervi-cal carcinogenesis were investigated.HPV genotyping was performed by using PCR approach with the primer GP5+/GP6+ and type-specific primer on biopsy specimens taken operatively from 198 women.Multiple PCR was done to detect physical status of HPV-16 in a series of cervical liquid-based cytology samples and biopsy specimens obtained from different cervical lesions with HPV-16 infection,includ-ing 112 specimens with cervical cancer,151 specimens with CINⅠ,246 specimens with CINⅡ and 120 specimens with CINⅢ.The results showed that there were 112 cervical cancer samples (56.57% of total cervical cancer patients) with HPV-16 infection.The frequency of HPV-16 pure integration was 65.18% (73/112),56.57% (47/120),23.58% (58/246) and 7.95% (12/151) in cervical cancer,CINⅢ,CINⅡand CINⅠ patients respectively.In situ hybridization was performed on some paraffin-embedded sections of CINⅡ,CINⅢ and cervical cancer to verify the physical status of HPV-16 infection.Sig-nificant difference was observed between cervical cancer and CINⅠ,CINⅡ,CINⅢ in the frequency of HPV-16 integration (P<0.01).It is suggested that HPV-16 is the most prevalent type and is associated with cervical cancer.In the case of HPV-16 infection there are close associations between the severity of cervical lesions and the frequency of HPV-16 integration.The application of testing HPV genotyping and physical status based on detection of HC-Ⅱ HPV DNA would be in favor of predicting the progno-sis of cervical precancerosis and enhancing the screening accuracy of cervical cancer.
文摘As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this paper proposes a cyber-physical-social based security architecture (IPM) to deal with Information, Physical, and Management security perspectives, and presents how the architectural abstractions support U2IoT model. In particular, 1) an information security model is established to describe the mapping relations among U2IoT, security layer, and security requirement, in which social layer and additional intelligence and compatibility properties are infused into IPM;2) physical security referring to the external context and inherent infrastructure are inspired by artificial immune algorithms;3) recommended security strategies are suggested for social management control. The proposed IPM combining the cyber world, physical world and human social provides constructive proposal towards the future IoT security and privacy protection.
基金supported by National Natural Science Foundation of China(61100159,61233007)National High Technology Research and Development Program of China(863 Program)(2011AA040103)+2 种基金Foundation of Chinese Academy of Sciences(KGCX2-EW-104)Financial Support of the Strategic Priority Research Program of Chinese Academy of Sciences(XDA06021100)the Cross-disciplinary Collaborative Teams Program for Science,Technology and Innovation,of Chinese Academy of Sciences-Network and System Technologies for Security Monitoring and Information Interaction in Smart Grid Energy Management System for Micro-smart Grid
文摘This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources.Therefore,it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system.This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs.The goal is to minimize the expected operation cost of MGs.The uncertainties are modeled based on autoregressive moving average(ARMA) model to expose the effects of physical world on cyber world.Through the comparison of the simulation results with deterministic method,it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.
基金supported by The National Key Research and Development Program of China (Title: Basic Theories and Methods of Analysis and Control of the Cyber Physical Systems for Power Grid (Basic Research Class 2017YFB0903000))the State Grid Science and Technology Project (Title: Research on Architecture and Several Key Technologies for Grid Cyber Physical System,No.SGRIXTKJ[2016]454)
文摘Conventional power systems are being developed into grid cyber physical systems(GCPS) with widespread application of communication, computer, and control technologies. In this article, we propose a quantitative analysis method for a GCPS. Based on this, we discuss the relationship between cyberspace and physical space, especially the computational similarity within the GCPS both in undirected and directed bipartite networks. We then propose a model for evaluating the fusion of the three most important factors: information, communication, and security. We then present the concept of the fusion evaluation cubic for the GCPS quantitative analysis model. Through these models, we can determine whether a more realistic state of the GCPS can be found by enhancing the fusion between cyberspace and physical space. Finally, we conclude that the degree of fusion between the two spaces is very important, not only considering the performance of the whole business process, but also considering security.
基金support by National Natural Science Foundation of China(61202354,51507084)Nanjing University of Post and Telecommunications Science Foundation(NUPTSF)(NT214203)
文摘Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality.
基金Supported by the National Natural Science Foundation of China(61103069,71171148)the National High-Tech Research and Development Plan of China(″863″ Plan)(2012BAD35B01)+2 种基金the Innovation Program of Shanghai Municipal Education Commission(13YZ052)the Shanghai Committee of Science and Technology(11DZ1501703,11dz12106001)the Program of Shanghai Normal University(DXL125,DCL201302)
文摘Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve service self-organization with its nodes autonomously working in both physical and cyber environments.To solve the problem,the social nature of nodes in CPS is firstly addressed,and then a formal social semantic descriptions is presented for physical environment,node service and task in order to make the nodes communicate automatically and physical environment sensibly.Further,the Horn clause is introduced to represent the reasoning rules of service organizing.Based on the match function,which is defined for measurement between semantics,the semantic aware measurement is presented to evaluate whether environment around a node can satisfy the task requirement or not.Moreover,the service capacity evaluation method for nodes is addressed to find out the competent service from both cyber and physical features of nodes.According to aforementioned two measurements,the task semantic decomposition algorithm and the organizing matrix are defined and the service self-organizing mechanism for CPS is proposed.Finally,examinations are given to further verify the efficiency and feasibility of the proposed mechanism.
文摘Considered as a top priority of industrial devel- opment, Industry 4.0 (or Industrie 4.0 as the German ver- sion) has being highlighted as the pursuit of both academy and practice in companies. In this paper, based on the review of state of art and also the state of practice in dif- ferent countries, shortcomings have been revealed as the lacking of applicable framework for the implementation of Industrie 4.0. Therefore, in order to shed some light on the knowledge of the details, a reference architecture is developed, where four perspectives namely manufacturing process, devices, software and engineering have been highlighted. Moreover, with a view on the importance of Cyber-Physical systems, the structure of Cyber-Physical System are established for the in-depth analysis. Further cases with the usage of Cyber-Physical System are also arranged, which attempts to provide some implications to match the theoretical findings together with the experience of companies. In general, results of this paper could be useful for the extending on the theoretical understanding of Industrie 4.0. Additionally, applied framework and proto- types based on the usage of Cyber-Physical Systems are also potential to help companies to design the layout of sensor nets, to achieve coordination and controlling of smart machines, to realize synchronous production with systematic structure, and to extend the usage of information and communication technologies to the maintenance scheduling.
基金supported in part by the National Natural Science Foundation of China(61533019,91720000)Beijing Municipal Science and Technology Commission(Z181100008918007)the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles(pICRI-IACVq)
文摘As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.