The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
The concept of Industrial Biosystems Engineering (IBsE) was suggested as a new engineering branch to be developed for meeting the needs for science, technology and professionals by the upcoming bioeconomy. With emphas...The concept of Industrial Biosystems Engineering (IBsE) was suggested as a new engineering branch to be developed for meeting the needs for science, technology and professionals by the upcoming bioeconomy. With emphasis on systems, IBsE builds upon the interfaces between systems biology, bioprocessing, and systems engineering. This paper discussed the background, the suggested definition, the theoretical framework and methodologies of this new discipline as well as its challenges and future development.展开更多
With the anticipated growth in air traffic complexity in the coming years,future civil aviation transportation system(CATS)is transforming into a complex cyber–physical–social system,surpassing all previous experien...With the anticipated growth in air traffic complexity in the coming years,future civil aviation transportation system(CATS)is transforming into a complex cyber–physical–social system,surpassing all previous experiences in the history of civil aviation safety management.Therefore,a new safety concept based on a system-of-systems(SoS)perspective is proposed for the next-generation aviation.This article begins by elucidating the complexity of existing aviation risks and emphasizing the necessity for an updated safety concept.It then presents the challenges of current safety management and potential solutions from the new SoS perspective.To address future risks,the concept of SoS safety is introduced with the inspiration of the human immune system in terms of capability,logic,and architecture,which can serve as a guiding framework and methodology for safety engineering in complex large-scale CATS.This concept indicates the transition from“process and outcome-oriented”to“capability-oriented”intelligent safety management.Our research highlights the development directions and potential technological areas that need to be addressed at different stages of SoS safety.The integration of SoS design and operation through rapid iterations enabled by artificial intelligence(AI)will ultimately achieve endogenous SoS safety.展开更多
The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficultie...The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.展开更多
This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-sta...This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path(state-event sequence with time information)π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed.Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
With the development of cyber-physical systems,system security faces more risks from cyber-attacks.In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resourc...With the development of cyber-physical systems,system security faces more risks from cyber-attacks.In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resource constraints(the total resource consumption of the attacks is not greater than a given initial resource of the attacker)to mislead a discrete event system under supervisory control to reach unsafe states.We consider that the attacker can implement two types of attacks:One by modifying the sensor readings observed by a supervisor and the other by enabling the actuator commands disabled by the supervisor.Each attack has its corresponding resource consumption and remains covert.To solve this problem,we first introduce a notion of combined-attackability to determine whether a closedloop system may reach an unsafe state after receiving attacks with resource constraints.We develop an algorithm to construct a corrupted supervisor under attacks,provide a verification method for combined-attackability in polynomial time based on a plant,a corrupted supervisor,and an attacker's initial resource,and propose a corresponding attack synthesis algorithm.The effectiveness of the proposed method is illustrated by an example.展开更多
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal de...The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.展开更多
This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as ru...This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.展开更多
Solid propellants are essential energy sources for rockets and other aerospace vehicles,and improvements in their performance have significant implications for the aerospace industry.The application of additive manufa...Solid propellants are essential energy sources for rockets and other aerospace vehicles,and improvements in their performance have significant implications for the aerospace industry.The application of additive manufacturing(AM)in the production of solid propellants promises a substantial leap in the design and fabrication of solid propellant grains.This review summarizes recent research on AM techniques for solid propellant manufacturing,evaluates current applications,and explores development trends.This review highlights that AM technology for solid propellants offers unparalleled advantages in terms of propellant design flexibility and functional gradient loading compared with traditional processes.This study presents a new perspective for the future manufacturing of intelligent and controllable solid propulsion systems.展开更多
As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly e...As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.展开更多
A single-phase anti-perovskite medium-entropy alloy nitride foams(MEANFs),as innovative materials for electromagnetic wave(EMW)absorption,have been successfully synthesized through the lattice expansion induced by nit...A single-phase anti-perovskite medium-entropy alloy nitride foams(MEANFs),as innovative materials for electromagnetic wave(EMW)absorption,have been successfully synthesized through the lattice expansion induced by nitrogen doping.This achievement notably overcomes the inherent constraints of conventional metal-based absorbers,including low resonance frequency,high conductivity,and elevated density,for the synergistic advantages provided by multimetallic alloys and foams.Microstructural analysis with comprehensive theoretical calculations provides in-depth insights into the formation mechanism,electronic structure,and magnetic moment of MEANFs.Furthermore,deliberate component design along with the foam structure proves to be an effective strategy for enhancing impedance matching and absorption.The results show that the MEANFs exhibit a minimum reflection loss(RL_(min))value of-60.32 dB and a maximum effective absorption bandwidth(EAB_(max))of 5.28 GHz at 1.69 mm.This augmentation of energy dissipation in EMW is predominantly attributed to factors such as porous structure,interfacial polarization,defect-induced polarization,and magnetic resonance.This study demonstrates a facile and efficient approach for synthesizing single-phase medium-entropy alloys,emphasizing their potential as materials for electromagnetic wave absorption due to their adjustable magnetic-dielectric properties.展开更多
1 Introduction In recent years,the rapid development of industrial big data and artificial intelligence(AI)technologies has revolutionized the industrial landscape.Industrial systems,such as manufacturing,energy,trans...1 Introduction In recent years,the rapid development of industrial big data and artificial intelligence(AI)technologies has revolutionized the industrial landscape.Industrial systems,such as manufacturing,energy,transportation,and logistics,have become increasingly complex,generating vast amounts of data[1–3].These big data encompass a wide range of data sources,including sensor data,production logs,and maintenance records,which hold valuable insights[4–6].Moreover,machine learning-based AI techniques can be applied to extract meaningful insights from this big data[7].展开更多
This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent s...This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.展开更多
Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces ...Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.展开更多
Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling...Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R^(2) value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach.展开更多
Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural n...Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.展开更多
Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and requir...Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
文摘The concept of Industrial Biosystems Engineering (IBsE) was suggested as a new engineering branch to be developed for meeting the needs for science, technology and professionals by the upcoming bioeconomy. With emphasis on systems, IBsE builds upon the interfaces between systems biology, bioprocessing, and systems engineering. This paper discussed the background, the suggested definition, the theoretical framework and methodologies of this new discipline as well as its challenges and future development.
基金supported by the National Natural Science Foundation of China(72225012)the National Key Research and Development Program of China(2023YFB4302901)+1 种基金the National Natural Science Foundation of China(72288101,71822101,and 62201577)the Safety Capability Building Fund of the Civil Aviation Administration of China(ASSA2023/19).
文摘With the anticipated growth in air traffic complexity in the coming years,future civil aviation transportation system(CATS)is transforming into a complex cyber–physical–social system,surpassing all previous experiences in the history of civil aviation safety management.Therefore,a new safety concept based on a system-of-systems(SoS)perspective is proposed for the next-generation aviation.This article begins by elucidating the complexity of existing aviation risks and emphasizing the necessity for an updated safety concept.It then presents the challenges of current safety management and potential solutions from the new SoS perspective.To address future risks,the concept of SoS safety is introduced with the inspiration of the human immune system in terms of capability,logic,and architecture,which can serve as a guiding framework and methodology for safety engineering in complex large-scale CATS.This concept indicates the transition from“process and outcome-oriented”to“capability-oriented”intelligent safety management.Our research highlights the development directions and potential technological areas that need to be addressed at different stages of SoS safety.The integration of SoS design and operation through rapid iterations enabled by artificial intelligence(AI)will ultimately achieve endogenous SoS safety.
文摘The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.
基金supported by the Special Fund for Scientific and Technological Innovation Strategy of Guangdong Province(2022A0505030025)the Science and Technology Fund,FDCT,Macao SAR(0064/2021/A2)
文摘This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path(state-event sequence with time information)π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed.Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
基金partially supported by the Science Technology Development Fund,Macao Special Administrative Region(0029/2023/RIA1)the National Research Foundation Singapore under its AI Singapore Programme(AISG2-GC-2023-007)
文摘With the development of cyber-physical systems,system security faces more risks from cyber-attacks.In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resource constraints(the total resource consumption of the attacks is not greater than a given initial resource of the attacker)to mislead a discrete event system under supervisory control to reach unsafe states.We consider that the attacker can implement two types of attacks:One by modifying the sensor readings observed by a supervisor and the other by enabling the actuator commands disabled by the supervisor.Each attack has its corresponding resource consumption and remains covert.To solve this problem,we first introduce a notion of combined-attackability to determine whether a closedloop system may reach an unsafe state after receiving attacks with resource constraints.We develop an algorithm to construct a corrupted supervisor under attacks,provide a verification method for combined-attackability in polynomial time based on a plant,a corrupted supervisor,and an attacker's initial resource,and propose a corresponding attack synthesis algorithm.The effectiveness of the proposed method is illustrated by an example.
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
文摘The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.
文摘This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.
基金supported by National Key Research and Development Program of China(Grant.No.2022YFB4603102)Insight Action(Grant.No.AA5F41D0).
文摘Solid propellants are essential energy sources for rockets and other aerospace vehicles,and improvements in their performance have significant implications for the aerospace industry.The application of additive manufacturing(AM)in the production of solid propellants promises a substantial leap in the design and fabrication of solid propellant grains.This review summarizes recent research on AM techniques for solid propellant manufacturing,evaluates current applications,and explores development trends.This review highlights that AM technology for solid propellants offers unparalleled advantages in terms of propellant design flexibility and functional gradient loading compared with traditional processes.This study presents a new perspective for the future manufacturing of intelligent and controllable solid propulsion systems.
基金support for this research from the Natural Science Foundation of Henan Province(252300421005).
文摘As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.52071294)the National Key Research and Development Program(Grant No.2022YFE0109800)the Natural Science Foundation of Zhejiang Province(Grant No.LY20E020015).
文摘A single-phase anti-perovskite medium-entropy alloy nitride foams(MEANFs),as innovative materials for electromagnetic wave(EMW)absorption,have been successfully synthesized through the lattice expansion induced by nitrogen doping.This achievement notably overcomes the inherent constraints of conventional metal-based absorbers,including low resonance frequency,high conductivity,and elevated density,for the synergistic advantages provided by multimetallic alloys and foams.Microstructural analysis with comprehensive theoretical calculations provides in-depth insights into the formation mechanism,electronic structure,and magnetic moment of MEANFs.Furthermore,deliberate component design along with the foam structure proves to be an effective strategy for enhancing impedance matching and absorption.The results show that the MEANFs exhibit a minimum reflection loss(RL_(min))value of-60.32 dB and a maximum effective absorption bandwidth(EAB_(max))of 5.28 GHz at 1.69 mm.This augmentation of energy dissipation in EMW is predominantly attributed to factors such as porous structure,interfacial polarization,defect-induced polarization,and magnetic resonance.This study demonstrates a facile and efficient approach for synthesizing single-phase medium-entropy alloys,emphasizing their potential as materials for electromagnetic wave absorption due to their adjustable magnetic-dielectric properties.
基金supported by the Science and Technology Innovation Program of Hunan Province(No.2023RC3097)in part the National Natural Science Foundation of China(No.52105108)in part Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘1 Introduction In recent years,the rapid development of industrial big data and artificial intelligence(AI)technologies has revolutionized the industrial landscape.Industrial systems,such as manufacturing,energy,transportation,and logistics,have become increasingly complex,generating vast amounts of data[1–3].These big data encompass a wide range of data sources,including sensor data,production logs,and maintenance records,which hold valuable insights[4–6].Moreover,machine learning-based AI techniques can be applied to extract meaningful insights from this big data[7].
基金supported in part by the National Key Research and Development Program of China(2021YFE0206100)the National Natural Science Foundation of China(62425310,62073321)+2 种基金the National Defense Basic Scientific Research Program(JCKY2019203C029,JCKY2020130C025)the Science and Technology Development FundMacao SAR(FDCT-22-009-MISE,0060/2021/A2,0015/2020/AMJ)
文摘This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.
文摘Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.
基金the financial support provided by the Special Foundation for State Major Basic Research Program of China(2021YFD2101005)National Natural Science Foundation of China(22478057,22178045).
文摘Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R^(2) value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach.
文摘Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.
基金funded by Fundación CajaCanarias and Fundación Bancaria“la Caixa”,grant number 2023DIG11.
文摘Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.
