Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory ...Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory tracking control of the manipulator.This paper proposes the radial basis function neural network adaptive hierarchical sliding mode control(RBFNNA-HSMC)method,which combines the dynamic model of the elastic tendon-driven manipulator(ETDM)with radial basis neural network adaptive control and hierarchical sliding mode control technology.The aim is to achieve trajectory tracking control of ETDM even under conditions of model inaccuracy and disturbance.The Lyapunov stability theory demonstrates the stability of the proposed RBFNNA-HSM controller.In order to assess the effectiveness and adaptability of the proposed control method,simulations and experiments were performed on a two-DOF ETDM.The RBFNNA-HSM method shows superior tracking accuracy compared to traditional modelbased HSM control.The experiment shows that the maximum tracking error for ETDM double-joint trajectory tracking is below 2.593×10-3rad and 1.624×10-3rad,respectively.展开更多
Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled pe...Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
Subject of the halo-chaos control in beam transport networks （channels） has become a key concerned issue for many important applications of high-current proton beam since 1990＇. In this paper, the magnetic field ad...Subject of the halo-chaos control in beam transport networks （channels） has become a key concerned issue for many important applications of high-current proton beam since 1990＇. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.展开更多
Recyclable thermosets and thermoset composites with covalent adaptable networks(CANs,or dynamic covalent networks) have attracted considerable attention in recent years due to the combined merits of excellent mechanic...Recyclable thermosets and thermoset composites with covalent adaptable networks(CANs,or dynamic covalent networks) have attracted considerable attention in recent years due to the combined merits of excellent mechanical and thermal properties,and chemical stabilities of traditional thermosets and recyclable,remoldable,and reprocessable attributes of thermoplastics.In this paper,we present an overview of the current strategies for synthesizing recyclable thermosets based on CANs,which involve recyclability,reprocessability,and possible rehealability.The recent literature examples are categorized based on the underlying controlled-cleavable linkages such as transesterification,DA/retro-DA chemistry,imine bonds,disulfide metathesis,dynamic B-O bonds,hemiaminals/hexahydrotriazines,and acetal linkages.Various degradation and malleability methods and resulting mechanical properties of the recycled thermosets and thermoset composites are presented.The emerging applications of recyclable thermosets and thermoset composites,with emphasis on their usage in adhesives,biomedical materials,wearable devices,coatings,and 3D printing materials,are also illustrated.Finally,a perspective on the challenges and future perspectives is briefly summarized.展开更多
This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-l...This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC. Keywords Air-fuel ratio control - IC engine - adaptive neural networks - nonlinear programming - model predictive control Shi-Wei Wang PhD student, Liverpool John Moores University; MSc in Control Systems, University of Sheffield, 2003; BEng in Automatic Technology, Jilin University, 2000; Current research interests automotive engine control, model predictive control, sliding mode control, neural networks.Ding-Li Yu obtained B.Eng from Harbin Civil Engineering College, Harbin, China in 1981, M.Sc from Jilin University of Technology, Changchun, China in 1986 and PhD from Coventry University, U.K. in 1995, all in control engineering. He is currently a Reader in Process Control at Liverpool John Moores University, U.K. His current research interests are in process control, engine control, fault detection and adaptive neural nets. He is a member of SAFEPROCESS TC in IFAC and an associate editor of the IJMIC and the IJISS.展开更多
To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation o...To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.展开更多
The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and ...The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.展开更多
In the propagation of an epidemic in a population, individuals adaptively adjust their behavior to avoid the risk of an epidemic. Differently from existing studies where new links are established randomly, a local lin...In the propagation of an epidemic in a population, individuals adaptively adjust their behavior to avoid the risk of an epidemic. Differently from existing studies where new links are established randomly, a local link is established preferentially in this paper. We propose a new preferentially reconnecting edge strategy depending on spatial distance （PR- SD）. For the PR-SD strategy, the new link is established at random with probability p and in a shortest distance with the probability 1 p. We establish the epidemic model on an adaptive network using Cellular Automata, and demonstrate the effectiveness of the proposed model by numerical simulations. The results show that the smaller the value of parameter p, the more difficult the epidemic spread is. The PR-SD strategy breaks long-range links and establishes as many short-range links as possible, which causes the network efficiency to decrease quickly and the propagation of the epidemic is restrained effectively.展开更多
In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the ...In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.展开更多
Covalent adaptable network(CAN)polymers doped with conductive nanoparticles are an ideal candidate to create reshapeable,rehealable,and fully recyclable electronics.On the other hand,3D printing as a deterministic man...Covalent adaptable network(CAN)polymers doped with conductive nanoparticles are an ideal candidate to create reshapeable,rehealable,and fully recyclable electronics.On the other hand,3D printing as a deterministic manufacturing method has a significant potential to fabricate electronics with low cost and high design freedom.In this paper,we incorporate a conductive composite consisting of polyimine CAN and multi-wall carbon nanotubes into direct-ink-writing 3D printing to create polymeric sensors with outstanding reshaping,repairing,and recycling capabilities.The developed printable ink exhibits good printability,conductivity,and recyclability.The conductivity of printed polyimine composites is investigated at different temperatures and deformation strain levels.Their shape-reforming and Joule heating-induced interfacial welding effects are demonstrated and characterized.Finally,a temperature sensor is 3D printed with defined patterns of conductive pathways,which can be easily mounted onto 3D surfaces,repaired after damage,and recycled using solvents.The sensing capability of printed sensors is maintained after the repairing and recycling.Overall,the 3D printed reshapeable,rehealable,and recyclable sensors possess complex geometry and extend service life,which assist in the development of polymer-based electronics toward broad and sustainable applications.展开更多
In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is propose...In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.展开更多
The concurrent presence of different types of traffic in multimedia applications might aggravate a burden on the underlying data network, which is bound to affect the transmission quality of the specified traffic. Rec...The concurrent presence of different types of traffic in multimedia applications might aggravate a burden on the underlying data network, which is bound to affect the transmission quality of the specified traffic. Recently, several proposals for fulfilling the quality of service(QoS) guarantees have been presented. However, they can only support coarse-grained QoS with no guarantee of throughput, jitter, delay or loss rate for different applications. To address these more challenging problems, an adaptive scheduling algorithm for Parallel data Processing with Multiple Feedback(PPMF) queues based on software defined networks(SDN) is proposed in this paper, which can guarantee the quality of service of high priority traffic in multimedia applications. PPMF combines the queue bandwidth feedback mechanism to realise the automatic adjustment of the queue bandwidth according to the priority of the packet and network conditions, which can effectively solve the problem of network congestion that has been experienced by some queues for a long time. Experimental results show PPMF significantly outperforms other existing scheduling approaches in achieving 35--80% improvement on average time delay by adjusting the bandwidth adaptively, thus ensuring the transmission quality of the specified traffic and avoiding effectively network congestion.展开更多
Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors.The t...Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors.The total mutual fund asset globally as at the end of 2016 was in excess of$40.4 trillion.Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States,Luxembourg,Ireland,France,Australia,United Kingdom,Japan,China and Brazil.We contribute to the extant literature on mutual fund performance evaluation using a collection of more sophisticated econometric models.We selected six continuous historical years that is 2010-2011,2012-2013 and 2014-2015 to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier(FANNC),and to compare our results with those from an enhanced resilient back propagation neural networks(ERBPNN)model.Our FANNC model outperformed the existing models in terms of processing time and error rate.This makes it ideal for financial application that involves large volume of data and routine updates.展开更多
The world airport network（WAN） is one of the networked infrastructures that shape today＇s economic and social activity, so its resilience against incidents affecting the WAN is an important problem. In this paper, ...The world airport network（WAN） is one of the networked infrastructures that shape today＇s economic and social activity, so its resilience against incidents affecting the WAN is an important problem. In this paper, the robustness of air route networks is extended by defining and testing several heuristics to define selection criteria to detect the critical nodes of the WAN.In addition to heuristics based on genetic algorithms and simulated annealing, custom heuristics based on node damage and node betweenness are defined. The most effective heuristic is a multiattack heuristic combining both custom heuristics. Results obtained are of importance not only for advance in the understanding of the structure of complex networks, but also for critical node detection.展开更多
Based on the adaptive network, the feedback mechanism and interplay between the network topology and the diffusive process of information are studied. The results reveal that the adaptation of network topology can dri...Based on the adaptive network, the feedback mechanism and interplay between the network topology and the diffusive process of information are studied. The results reveal that the adaptation of network topology can drive systems into the scale-free one with the assortative or disassortative degree correlations, and the hierarchical clustering. Meanwhile, the processes of the information diffusion are extremely speeded up by the adaptive changes of network topology.展开更多
We study evolutionary prisoner＇s dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategie...We study evolutionary prisoner＇s dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategies either rewire the link between them with probability p or update their strategies with probability 1 - p depending on their payoffs. Numerical simulation shows that the final network is either split into some disconnected communities whose players share the same strategy within each community or forms a single connected network in which all nodes are in the same strategy. Interestingly, the density of cooperators in the final state can be maximised in an intermediate range of p via the competition between time scale of the network dynamics and that of the node dynamics. Finally, the mean-field analysis helps to understand the results of numerical simulation. Our results may provide some insight into understanding the emergence of cooperation in the real situation where the individuals＇ behaviour and their relationship adaptively co-evolve.展开更多
Polyimine represents a rapidly emerging class of readily accessible and affordable covalent adaptable networks(CANs)that have been extensively studied in the past few years.While being highly malleable and recyclable,...Polyimine represents a rapidly emerging class of readily accessible and affordable covalent adaptable networks(CANs)that have been extensively studied in the past few years.While being highly malleable and recyclable,the pioneering polyimine materials are relatively soft and not suitable for certain applications that require high mechanical performance.Recent studies have demonstrated the possibility of significantly improving polyimine properties by varying its monomer building blocks,but such component variations are usually not straightforward and can be potentially challenging and costly.Herein,we report an in situ oxidation polymerization strategy for preparation of mechanically strong poly(imine-amide)(PIA)hybrid CANs from simple amine and aldehyde monomers.By converting a portion of reversible imine bonds into high-strength amide linkages in situ,the obtained hybrid materials exhibit gradually improved Young’s modulus and ultimate tensile strength as the oxidation level increased.Meanwhile,the PIAs remain reprocessable and can be depolymerized into small molecules and oligomers similar as polyimine.This work demonstrates the great potential of the in situ transformation strategy as a new approach for development of various mechanically tunable CANs from the same starting building blocks.展开更多
To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership functi...To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.展开更多
Covalent adaptive networks(CANs)are capable of undergoing segment rearrangement after being heated,which endows the materials with excellent self-healing and reprocessing performance,providing an efficient solution to...Covalent adaptive networks(CANs)are capable of undergoing segment rearrangement after being heated,which endows the materials with excellent self-healing and reprocessing performance,providing an efficient solution to the environment pollution caused by the plastic wastes.The main challenge remains in developing CANs with both excellent reprocessing performance and creep-resistance property.In this study,a series of CANs containing dynamic covalent benzopyrazole-urea bonds were developed based on the addition reaction between benzopyrazole and isocyanate groups.DFT calculation confirmed that relatively low dissociation energy is obtained through undergoing a five-member ring transition state,confirming excellent dynamic property of the benzopyrazole-urea bonds.As verified by the FTIR results,this nice dynamic property can be well maintained after incorporating the benzopyrazole-urea bonds into polymer networks.Excellent self-healing and reprocessing performance is observed by the 3-ABP/PDMS elastomers owing to the dynamic benzopyrazole-urea bonds.Phase separation induced by the aggregation of the hard segments locked the benzopyrazole-urea bonds,which also makes the elastomers display excellent creep-resistance performance.This hard phase locking strategy provides an efficient approach to design CANs materials with both excellent reprocessing and creep-resistance performance.展开更多
基金Supported by Key R&D Project of Zhejiang(Grant No.2022C02052)。
文摘Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory tracking control of the manipulator.This paper proposes the radial basis function neural network adaptive hierarchical sliding mode control(RBFNNA-HSMC)method,which combines the dynamic model of the elastic tendon-driven manipulator(ETDM)with radial basis neural network adaptive control and hierarchical sliding mode control technology.The aim is to achieve trajectory tracking control of ETDM even under conditions of model inaccuracy and disturbance.The Lyapunov stability theory demonstrates the stability of the proposed RBFNNA-HSM controller.In order to assess the effectiveness and adaptability of the proposed control method,simulations and experiments were performed on a two-DOF ETDM.The RBFNNA-HSM method shows superior tracking accuracy compared to traditional modelbased HSM control.The experiment shows that the maximum tracking error for ETDM double-joint trajectory tracking is below 2.593×10-3rad and 1.624×10-3rad,respectively.
基金funded by the King Salman Center For Disability Research,through Research Group No.KSRG-2024-468。
文摘Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金The project supported by the Key Projects of National Natural Science Foundation of China under Grant No. 70431002 and National Natural Science Foundation of China under Grants Nos. 70371068 and 10247005
文摘Subject of the halo-chaos control in beam transport networks （channels） has become a key concerned issue for many important applications of high-current proton beam since 1990＇. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.
文摘Recyclable thermosets and thermoset composites with covalent adaptable networks(CANs,or dynamic covalent networks) have attracted considerable attention in recent years due to the combined merits of excellent mechanical and thermal properties,and chemical stabilities of traditional thermosets and recyclable,remoldable,and reprocessable attributes of thermoplastics.In this paper,we present an overview of the current strategies for synthesizing recyclable thermosets based on CANs,which involve recyclability,reprocessability,and possible rehealability.The recent literature examples are categorized based on the underlying controlled-cleavable linkages such as transesterification,DA/retro-DA chemistry,imine bonds,disulfide metathesis,dynamic B-O bonds,hemiaminals/hexahydrotriazines,and acetal linkages.Various degradation and malleability methods and resulting mechanical properties of the recycled thermosets and thermoset composites are presented.The emerging applications of recyclable thermosets and thermoset composites,with emphasis on their usage in adhesives,biomedical materials,wearable devices,coatings,and 3D printing materials,are also illustrated.Finally,a perspective on the challenges and future perspectives is briefly summarized.
文摘This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC. Keywords Air-fuel ratio control - IC engine - adaptive neural networks - nonlinear programming - model predictive control Shi-Wei Wang PhD student, Liverpool John Moores University; MSc in Control Systems, University of Sheffield, 2003; BEng in Automatic Technology, Jilin University, 2000; Current research interests automotive engine control, model predictive control, sliding mode control, neural networks.Ding-Li Yu obtained B.Eng from Harbin Civil Engineering College, Harbin, China in 1981, M.Sc from Jilin University of Technology, Changchun, China in 1986 and PhD from Coventry University, U.K. in 1995, all in control engineering. He is currently a Reader in Process Control at Liverpool John Moores University, U.K. His current research interests are in process control, engine control, fault detection and adaptive neural nets. He is a member of SAFEPROCESS TC in IFAC and an associate editor of the IJMIC and the IJISS.
基金supported by the National Science Foundation （NSF） under Grants No.60832001,No.61271174 the National State Key Lab oratory of Integrated Service Network （ISN） under Grant No.ISN01080202
文摘To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(U23A6005 and 32171721)State Key Laboratory of Pulp and Paper Engineering(202305,2023ZD01,2023C02)+1 种基金Guangdong Province Basic and Application Basic Research Fund(2023B1515040013)the Fundamental Research Funds for the Central Universities(2023ZYGXZR045).
文摘The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.
基金Project supported by the Natural Science Foundation of Jiangsu Province, China (Grant No. BK2010526)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20103223110003)the Ministry of Education Research in the Humanities and Social Sciences Planning Fund (Grant No. 12YJAZH120)
文摘In the propagation of an epidemic in a population, individuals adaptively adjust their behavior to avoid the risk of an epidemic. Differently from existing studies where new links are established randomly, a local link is established preferentially in this paper. We propose a new preferentially reconnecting edge strategy depending on spatial distance （PR- SD）. For the PR-SD strategy, the new link is established at random with probability p and in a shortest distance with the probability 1 p. We establish the epidemic model on an adaptive network using Cellular Automata, and demonstrate the effectiveness of the proposed model by numerical simulations. The results show that the smaller the value of parameter p, the more difficult the epidemic spread is. The PR-SD strategy breaks long-range links and establishes as many short-range links as possible, which causes the network efficiency to decrease quickly and the propagation of the epidemic is restrained effectively.
基金supported by National Natural Science Foundationof China (No. 60674056)National Key Basic Research and Devel-opment Program of China (No. 2002CB312200)+1 种基金Outstanding YouthFunds of Liaoning Province (No. 2005219001)Educational De-partment of Liaoning Province (No. 2006R29 and No. 2007T80)
文摘In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.
基金support from the National Science Foundation(Grant CMMI-1901807)。
文摘Covalent adaptable network(CAN)polymers doped with conductive nanoparticles are an ideal candidate to create reshapeable,rehealable,and fully recyclable electronics.On the other hand,3D printing as a deterministic manufacturing method has a significant potential to fabricate electronics with low cost and high design freedom.In this paper,we incorporate a conductive composite consisting of polyimine CAN and multi-wall carbon nanotubes into direct-ink-writing 3D printing to create polymeric sensors with outstanding reshaping,repairing,and recycling capabilities.The developed printable ink exhibits good printability,conductivity,and recyclability.The conductivity of printed polyimine composites is investigated at different temperatures and deformation strain levels.Their shape-reforming and Joule heating-induced interfacial welding effects are demonstrated and characterized.Finally,a temperature sensor is 3D printed with defined patterns of conductive pathways,which can be easily mounted onto 3D surfaces,repaired after damage,and recycled using solvents.The sensing capability of printed sensors is maintained after the repairing and recycling.Overall,the 3D printed reshapeable,rehealable,and recyclable sensors possess complex geometry and extend service life,which assist in the development of polymer-based electronics toward broad and sustainable applications.
基金supported by the Science&Technology Department of Sichuan Province under Grant No.2020YJ0044。
文摘In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.
基金supported by National Key Basic Research Program of China(973 Program)under grant no.2012CB315802National Natural Science Foundation of China under grant no.61671081 and no.61132001Prospective Research on Future Networks of Jiangsu Future Networks Innovation Institute under grant no.BY2013095-4-01
文摘The concurrent presence of different types of traffic in multimedia applications might aggravate a burden on the underlying data network, which is bound to affect the transmission quality of the specified traffic. Recently, several proposals for fulfilling the quality of service(QoS) guarantees have been presented. However, they can only support coarse-grained QoS with no guarantee of throughput, jitter, delay or loss rate for different applications. To address these more challenging problems, an adaptive scheduling algorithm for Parallel data Processing with Multiple Feedback(PPMF) queues based on software defined networks(SDN) is proposed in this paper, which can guarantee the quality of service of high priority traffic in multimedia applications. PPMF combines the queue bandwidth feedback mechanism to realise the automatic adjustment of the queue bandwidth according to the priority of the packet and network conditions, which can effectively solve the problem of network congestion that has been experienced by some queues for a long time. Experimental results show PPMF significantly outperforms other existing scheduling approaches in achieving 35--80% improvement on average time delay by adjusting the bandwidth adaptively, thus ensuring the transmission quality of the specified traffic and avoiding effectively network congestion.
文摘Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors.The total mutual fund asset globally as at the end of 2016 was in excess of$40.4 trillion.Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States,Luxembourg,Ireland,France,Australia,United Kingdom,Japan,China and Brazil.We contribute to the extant literature on mutual fund performance evaluation using a collection of more sophisticated econometric models.We selected six continuous historical years that is 2010-2011,2012-2013 and 2014-2015 to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier(FANNC),and to compare our results with those from an enhanced resilient back propagation neural networks(ERBPNN)model.Our FANNC model outperformed the existing models in terms of processing time and error rate.This makes it ideal for financial application that involves large volume of data and routine updates.
文摘The world airport network（WAN） is one of the networked infrastructures that shape today＇s economic and social activity, so its resilience against incidents affecting the WAN is an important problem. In this paper, the robustness of air route networks is extended by defining and testing several heuristics to define selection criteria to detect the critical nodes of the WAN.In addition to heuristics based on genetic algorithms and simulated annealing, custom heuristics based on node damage and node betweenness are defined. The most effective heuristic is a multiattack heuristic combining both custom heuristics. Results obtained are of importance not only for advance in the understanding of the structure of complex networks, but also for critical node detection.
基金Project supported by the Key Project of Hunan Provincial Educational Department of China (Grant No 04A058)the General Project of Hunan Provincial Educational Department of China (Grant No 07C754)the National Natural Science Foundation of China (Grant No 30570432)
文摘Based on the adaptive network, the feedback mechanism and interplay between the network topology and the diffusive process of information are studied. The results reveal that the adaptation of network topology can drive systems into the scale-free one with the assortative or disassortative degree correlations, and the hierarchical clustering. Meanwhile, the processes of the information diffusion are extremely speeded up by the adaptive changes of network topology.
基金Project supported by the National Natural Science Foundation of China (Grant No. 20873130)the Graduate Innovation Fund of USTC
文摘We study evolutionary prisoner＇s dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategies either rewire the link between them with probability p or update their strategies with probability 1 - p depending on their payoffs. Numerical simulation shows that the final network is either split into some disconnected communities whose players share the same strategy within each community or forms a single connected network in which all nodes are in the same strategy. Interestingly, the density of cooperators in the final state can be maximised in an intermediate range of p via the competition between time scale of the network dynamics and that of the node dynamics. Finally, the mean-field analysis helps to understand the results of numerical simulation. Our results may provide some insight into understanding the emergence of cooperation in the real situation where the individuals＇ behaviour and their relationship adaptively co-evolve.
基金the University of Colorado Boulder and the National Science Foundation (No. 49100423C0008, Y.J.) for financial support
文摘Polyimine represents a rapidly emerging class of readily accessible and affordable covalent adaptable networks(CANs)that have been extensively studied in the past few years.While being highly malleable and recyclable,the pioneering polyimine materials are relatively soft and not suitable for certain applications that require high mechanical performance.Recent studies have demonstrated the possibility of significantly improving polyimine properties by varying its monomer building blocks,but such component variations are usually not straightforward and can be potentially challenging and costly.Herein,we report an in situ oxidation polymerization strategy for preparation of mechanically strong poly(imine-amide)(PIA)hybrid CANs from simple amine and aldehyde monomers.By converting a portion of reversible imine bonds into high-strength amide linkages in situ,the obtained hybrid materials exhibit gradually improved Young’s modulus and ultimate tensile strength as the oxidation level increased.Meanwhile,the PIAs remain reprocessable and can be depolymerized into small molecules and oligomers similar as polyimine.This work demonstrates the great potential of the in situ transformation strategy as a new approach for development of various mechanically tunable CANs from the same starting building blocks.
文摘To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.
基金supported by the National Natural Science Foundation of China(No.52173113)。
文摘Covalent adaptive networks(CANs)are capable of undergoing segment rearrangement after being heated,which endows the materials with excellent self-healing and reprocessing performance,providing an efficient solution to the environment pollution caused by the plastic wastes.The main challenge remains in developing CANs with both excellent reprocessing performance and creep-resistance property.In this study,a series of CANs containing dynamic covalent benzopyrazole-urea bonds were developed based on the addition reaction between benzopyrazole and isocyanate groups.DFT calculation confirmed that relatively low dissociation energy is obtained through undergoing a five-member ring transition state,confirming excellent dynamic property of the benzopyrazole-urea bonds.As verified by the FTIR results,this nice dynamic property can be well maintained after incorporating the benzopyrazole-urea bonds into polymer networks.Excellent self-healing and reprocessing performance is observed by the 3-ABP/PDMS elastomers owing to the dynamic benzopyrazole-urea bonds.Phase separation induced by the aggregation of the hard segments locked the benzopyrazole-urea bonds,which also makes the elastomers display excellent creep-resistance performance.This hard phase locking strategy provides an efficient approach to design CANs materials with both excellent reprocessing and creep-resistance performance.
