The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyeffic...The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.展开更多
The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical appr...The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.展开更多
Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate des...Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.展开更多
We introduce a dual distribution of relaxation(DRT)based approach for analyzing electrochemical impedance spectroscopy(EIS)data in perovskite solar cells(PSCs),combining regression and classification with Bayesian mod...We introduce a dual distribution of relaxation(DRT)based approach for analyzing electrochemical impedance spectroscopy(EIS)data in perovskite solar cells(PSCs),combining regression and classification with Bayesian model selection and Havriliak-Negami(HN)modeling to resolve spectra into discrete,Lorentzian-like peaks.This time-domain decomposition offers a powerful alternative for identifying underlying physical processes,such as charge transfer,trap-assisted recombination,and ionic migration by directly extracting characteristic relaxation times(τ).In contrast to traditional equivalent circuit fitting or conventional DRT methods,which often yield broad and overlapping Gaussian-like peaks,our method enables sharper resolution of individual electrochemical signatures.Furthermore,we validated the framework using simulated EIS spectra for two distinct system types,determining the optimal number of peaks(Q)through statistical model selection.Applied to experimental PSC data under varying bias conditions,the approach helps to identify the voltage-dependent relaxation processes,including fast charge transfer(τ~10^(-6)s),intermediate trap-mediated recombination(τ~10^(-2)s),and slow ionic motion(τ~1 s).Lower-Q models fail to capture low-frequency features such as polarization and charge accumulation,while optimal Q yields accurate,physically meaningful representations of device behavior.This data-driven methodology highlights time-domain DRT as a rigorous and insightful tool for dissecting the complex kinetics that govern PSC performance.展开更多
Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illuminati...Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene.At this time,relying solely on the target saliency information provided by infrared images is far from sufficient.To address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named LLE-Fuse.The method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source images.The intermediate features at different scales obtained are then fused by a cross-modal attention fusion module.In addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement loss.Upon completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computational resource consumption.Finally,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual Information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.展开更多
As a major configuration of membrane elements,multi-channel porous inorganic membrane tubes were studied by means of theoretical analysis and simulation.Configuration optimization of a cylindrical 37-channel porous in...As a major configuration of membrane elements,multi-channel porous inorganic membrane tubes were studied by means of theoretical analysis and simulation.Configuration optimization of a cylindrical 37-channel porous inorganic membrane tube was studied by increasing membrane filtration area and increasing permeation efficiency of inner channels.An optimal ratio of the channel diameter to the inter-channel distance was proposed so as to increase the total membrane filtration area of the membrane tube.The three-dimensional computational fluid dynamics(CFD) simulation was conducted to study the cross-flow permeation flow of pure water in the 37-channel ceramic membrane tube.A model combining Navier–Stokes equation with Darcy's law and the porous jump boundary conditions was applied.The relationship between permeation efficiency and channel locations,and the method for increasing the permeation efficiency of inner channels were proposed.Some novel multichannel membrane configurations with more permeate side channels were put forward and evaluated.展开更多
Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device off...Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.展开更多
As a large amount of data is increasingly generated from edge devices,such as smart homes,mobile phones,and wearable devices,it becomes crucial for many applications to deploy machine learning modes across edge device...As a large amount of data is increasingly generated from edge devices,such as smart homes,mobile phones,and wearable devices,it becomes crucial for many applications to deploy machine learning modes across edge devices.The execution speed of the deployed model is a key element to ensure service quality.Considering a highly heterogeneous edge deployment scenario,deep learning compiling is a novel approach that aims to solve this problem.It defines models using certain DSLs and generates efficient code implementations on different hardware devices.However,there are still two aspects that are not yet thoroughly investigated yet.The first is the optimization of memory-intensive operations,and the second problem is the heterogeneity of the deployment target.To that end,in this work,we propose a system solution that optimizes memory-intensive operation,optimizes the subgraph distribution,and enables the compiling and deployment of DNN models on multiple targets.The evaluation results show the performance of our proposed system.展开更多
The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solutio...The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.展开更多
The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and ster...The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced.Design of robust knowledge bases is performed using a developed computational intelligence-quantum/soft computing toolkit(QC/SCOptKBTM).The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described.The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described.The general design methodology of a generalizing control unit based on the physical laws of quantum computing(quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal)is considered.The modernization of the pattern recognition system based on stereo vision technology presented.The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.The main objective of this article is to demonstrate the advantages of the approach based on quantum/soft computing.展开更多
Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A...Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.展开更多
Pseudospectral （PS） computational methods for nonlinear constrained optimal control have been applied to many industrial-strength problems,notably,the recent zero-propellant-maneuvering of the international space st...Pseudospectral （PS） computational methods for nonlinear constrained optimal control have been applied to many industrial-strength problems,notably,the recent zero-propellant-maneuvering of the international space station performed by NASA.In this paper,we prove a theorem on the rate of convergence for the optimal cost computed using a Legendre PS method.In addition to the high-order convergence rate,two theorems are proved for the existence and convergence of the approximate solutions.Relative to existing work on PS optimal control as well as some other direct computational methods,the proofs do not use necessary conditions of optimal control.Furthermore,we do not make coercivity type of assumptions.As a result,the theory does not require the local uniqueness of optimal solutions.In addition,a restrictive assumption on the cluster points of discrete solutions made in existing convergence theorems is removed.展开更多
This paper puts forward a design idea for blended wing body（BWB）.The idea is described as that cruise point,maximum lift to drag point and pitch trim point are in the same flight attitude.According to this design id...This paper puts forward a design idea for blended wing body（BWB）.The idea is described as that cruise point,maximum lift to drag point and pitch trim point are in the same flight attitude.According to this design idea,design objectives and constraints are defined.By applying low and high fidelity aerodynamic analysis tools,BWB aerodynamic design methodology is established by the combination of optimization design and inverse design methods.High lift to drag ratio,pitch trim and acceptable buffet margin can be achieved by this design methodology.For 300-passenger BWB configuration based on static stability design,as compared with initial configuration,the maximum lift to drag ratio and pitch trim are achieved at cruise condition,zero lift pitching moment is positive,and buffet characteristics is well.Fuel burn of 300-passenger BWB configuration is also significantly reduced as compared with conventional civil transports.Because aerodynamic design is carried out under the constraints of BWB design requirements,the design configuration fulfills the demands for interior layout and provides a solid foundation for continuous work.展开更多
Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy...Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.展开更多
The constrained multi-objective multi-variable optimization of fans usually needs a great deal of computational fluid dynamics(CFD)calculations and is time-consuming.In this study,a new multi-model ensemble optimizati...The constrained multi-objective multi-variable optimization of fans usually needs a great deal of computational fluid dynamics(CFD)calculations and is time-consuming.In this study,a new multi-model ensemble optimization algorithm is proposed to tackle such an expensive optimization problem.The multi-variable and multi-objective optimization are conducted with a new flexible multi-objective infill criterion.In addition,the search direction is determined by the multi-model ensemble assisted evolutionary algorithm and the feature extraction by the principal component analysis is used to reduce the dimension of optimization variables.First,the proposed algorithm and other two optimization algorithms which prevail in fan optimizations were compared by using test functions.With the same number of objective function evaluations,the proposed algorithm shows a fast convergency rate on finding the optimal objective function values.Then,this algorithm was used to optimize the rotor and stator blades of a large axial fan,with the efficiencies as the objectives at three flow rates,the high,the design and the low flow rate.Forty-two variables were included in the optimization process.The results show that compared with the prototype fan,the total pressure efficiencies of the optimized fan at the high,the design and the low flow rate were increased by 3.35%,3.07%and 2.89%,respectively,after CFD simulations for 500 fan candidates with the constraint for the design pressure.The optimization results validate the effectiveness and feasibility of the proposed algorithm.展开更多
The Number Theory comes back as the heart of unified Science, in a Computing Cosmos using the bases 2;3;5;7 whose two symmetric combinations explain the main lepton mass ratios. The corresponding Holic Principle induc...The Number Theory comes back as the heart of unified Science, in a Computing Cosmos using the bases 2;3;5;7 whose two symmetric combinations explain the main lepton mass ratios. The corresponding Holic Principle induces a symmetry between the Newton and Planck constants which confirm the Permanent Sweeping Holography Bang Cosmology, with invariant baryon density 3/10, the dark baryons being dephased matter-antimatter oscillation. This implies the DNA bi-codon mean isotopic mass, confirming to 0.1 ppm the electron-based Topological Axis, whose terminal boson is the base 2 c-observable Universe in the base 3 Cosmos. The physical parameters involve the Euler idoneal numbers and the special Fermat primes of Wieferich (bases 2) and Mirimanoff (base 3). The prime numbers and crystallographic symmetries are related to the 4-fold structure of the DNA bi-codon. The forgotten Eddington’s proton-tau symmetry is rehabilitated, renewing the supersymmetry quest. This excludes the concepts of Multiverse, Continuum, Infinity, Locality and Zero-mass Particle, leading to stringent predictions in Cosmology, Particle Physics and Biology.展开更多
The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicabili...The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicability and reliability of upward recursive formula in principle is amended.An optimal scheme of upward-and downward-joint recursions has been developed for the sequential F(z) computations.No additional accuracy is needed with the fundamental term of recursion because the absolute error of Fn(z) always decreases with the recursive approach.The scheme can be employed in modifying any of existent subprograms for Fn<z> computations.In the case of p-d-f-and g-type Gaussians,combining this method with Schaad's formulas can reduce,at least,the additive operations by a factor 40%;the multiplicative and exponential operations by a factor 60%.展开更多
Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living(AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applic...Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living(AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applications in cloud data centers(CDCs). This paper focuses on modeling and analysis of multi-tier AAL applications, and aims to optimize resource provisioning while meeting requests' response time constraint. This paper models a multi-tier AAL application as a hybrid multi-tier queueing model consisting of an M/M/c queueing model and multiple M/M/1 queueing models. Then, virtual machine(VM) allocation is formulated as a constrained optimization problem in a CDC, and is further solved with the proposed heuristic VM allocation algorithm(HVMA). The results demonstrate that the proposed model and algorithm can effectively achieve dynamic resource provisioning while meeting the performance constraint.展开更多
Structure modulation at multiscale is crucialfor optimizing the electromagnetic wave absorption (EWA)properties of fiber-reinforced composites. Here we selectedtwo types of wave-absorbing SiC fibers as reinforcements....Structure modulation at multiscale is crucialfor optimizing the electromagnetic wave absorption (EWA)properties of fiber-reinforced composites. Here we selectedtwo types of wave-absorbing SiC fibers as reinforcements. TheL-fiber had a relatively low resistivity of ~3 Ω·cm and the Hfiberhad a high resistivity of ~7×10^(5)Ω·cm. To adjust the impedance,BN single coating and SiO_(2)/BN dual-coating wereprepared respectively on the L-fibers. Unidirectional prepregswith different fibers were stacked in different rules to obtainthe final composites. It showed that both the fiber coatings andstacking structure significantly influence the EWA performanceof the composites. Guided by computational optimization,the stacked composites exhibited superior reflectionloss (RL) lower than −10 dB across the whole X(8.2–12.4 GHz) and Ku (12.4–18.0 GHz) bands. It is interestingto find that the introduction of the surface coatings on theL-fibers significantly widens the available thickness range ofthe stacked composite for possessing excellent performance. Inparticular, dual-coating perform better in terms of broadeningthe available thickness range of the stacked composites.展开更多
In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolut...In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution(OODE). The proposed algorithm is named IOODE with ‘I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution(DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.展开更多
基金funded by the deanship of scientific research(DSR),King Abdukaziz University,Jeddah,under grant No.(G-1436-611-225)。
文摘The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.
文摘The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.
基金This work was supported in part by the Natural Science Foundation of the Education Department of Henan Province(Grant 22A520025)the National Natural Science Foundation of China(Grant 61975053)the National Key Research and Development of Quality Information Control Technology for Multi-Modal Grain Transportation Efficient Connection(2022YFD2100202).
文摘Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.
基金the ORSP of Pandit Deendayal Energy University and DST SERB(IPA/2021/96)for the financial supportthe Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP 2/345/45。
文摘We introduce a dual distribution of relaxation(DRT)based approach for analyzing electrochemical impedance spectroscopy(EIS)data in perovskite solar cells(PSCs),combining regression and classification with Bayesian model selection and Havriliak-Negami(HN)modeling to resolve spectra into discrete,Lorentzian-like peaks.This time-domain decomposition offers a powerful alternative for identifying underlying physical processes,such as charge transfer,trap-assisted recombination,and ionic migration by directly extracting characteristic relaxation times(τ).In contrast to traditional equivalent circuit fitting or conventional DRT methods,which often yield broad and overlapping Gaussian-like peaks,our method enables sharper resolution of individual electrochemical signatures.Furthermore,we validated the framework using simulated EIS spectra for two distinct system types,determining the optimal number of peaks(Q)through statistical model selection.Applied to experimental PSC data under varying bias conditions,the approach helps to identify the voltage-dependent relaxation processes,including fast charge transfer(τ~10^(-6)s),intermediate trap-mediated recombination(τ~10^(-2)s),and slow ionic motion(τ~1 s).Lower-Q models fail to capture low-frequency features such as polarization and charge accumulation,while optimal Q yields accurate,physically meaningful representations of device behavior.This data-driven methodology highlights time-domain DRT as a rigorous and insightful tool for dissecting the complex kinetics that govern PSC performance.
基金This researchwas Sponsored by Xinjiang Uygur Autonomous Region Tianshan Talent Programme Project(2023TCLJ02)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01C349).
文摘Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene.At this time,relying solely on the target saliency information provided by infrared images is far from sufficient.To address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named LLE-Fuse.The method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source images.The intermediate features at different scales obtained are then fused by a cross-modal attention fusion module.In addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement loss.Upon completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computational resource consumption.Finally,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual Information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.
基金Supported by the National Basic Research Program of China(2012CB224806)the National Natural Science Foundation of China(21490584,21476236)the National High Technology Research and Development Program of China(2012AA03A606)
文摘As a major configuration of membrane elements,multi-channel porous inorganic membrane tubes were studied by means of theoretical analysis and simulation.Configuration optimization of a cylindrical 37-channel porous inorganic membrane tube was studied by increasing membrane filtration area and increasing permeation efficiency of inner channels.An optimal ratio of the channel diameter to the inter-channel distance was proposed so as to increase the total membrane filtration area of the membrane tube.The three-dimensional computational fluid dynamics(CFD) simulation was conducted to study the cross-flow permeation flow of pure water in the 37-channel ceramic membrane tube.A model combining Navier–Stokes equation with Darcy's law and the porous jump boundary conditions was applied.The relationship between permeation efficiency and channel locations,and the method for increasing the permeation efficiency of inner channels were proposed.Some novel multichannel membrane configurations with more permeate side channels were put forward and evaluated.
基金supported by National Natural Science Foundation of China (Grant No.61261017, No.61571143 and No.61561014)Guangxi Natural Science Foundation (2013GXNSFAA019334 and 2014GXNSFAA118387)+3 种基金Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (No.CRKL150112)Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (GXKL0614202, GXKL0614101 and GXKL061501)Sci.and Tech.on Info.Transmission and Dissemination in Communication Networks Lab (No.ITD-U14008/KX142600015)Graduate Student Research Innovation Project of Guilin University of Electronic Technology (YJCXS201523)
文摘Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.
基金supported by the National Natural Science Foundation of China(U21A20519)。
文摘As a large amount of data is increasingly generated from edge devices,such as smart homes,mobile phones,and wearable devices,it becomes crucial for many applications to deploy machine learning modes across edge devices.The execution speed of the deployed model is a key element to ensure service quality.Considering a highly heterogeneous edge deployment scenario,deep learning compiling is a novel approach that aims to solve this problem.It defines models using certain DSLs and generates efficient code implementations on different hardware devices.However,there are still two aspects that are not yet thoroughly investigated yet.The first is the optimization of memory-intensive operations,and the second problem is the heterogeneity of the deployment target.To that end,in this work,we propose a system solution that optimizes memory-intensive operation,optimizes the subgraph distribution,and enables the compiling and deployment of DNN models on multiple targets.The evaluation results show the performance of our proposed system.
文摘The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
文摘The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced.Design of robust knowledge bases is performed using a developed computational intelligence-quantum/soft computing toolkit(QC/SCOptKBTM).The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described.The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described.The general design methodology of a generalizing control unit based on the physical laws of quantum computing(quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal)is considered.The modernization of the pattern recognition system based on stereo vision technology presented.The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.The main objective of this article is to demonstrate the advantages of the approach based on quantum/soft computing.
基金supported by the National Natural Science Foundation of China(61173017)the National High Technology Research and Development Program(863 Program)(2014AA01A701)
文摘Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.
基金supported by the Air Force Office of Scientific Research(No.F1ATA0-90-4-3G001)and Air Force Research Laboratory
文摘Pseudospectral （PS） computational methods for nonlinear constrained optimal control have been applied to many industrial-strength problems,notably,the recent zero-propellant-maneuvering of the international space station performed by NASA.In this paper,we prove a theorem on the rate of convergence for the optimal cost computed using a Legendre PS method.In addition to the high-order convergence rate,two theorems are proved for the existence and convergence of the approximate solutions.Relative to existing work on PS optimal control as well as some other direct computational methods,the proofs do not use necessary conditions of optimal control.Furthermore,we do not make coercivity type of assumptions.As a result,the theory does not require the local uniqueness of optimal solutions.In addition,a restrictive assumption on the cluster points of discrete solutions made in existing convergence theorems is removed.
文摘This paper puts forward a design idea for blended wing body（BWB）.The idea is described as that cruise point,maximum lift to drag point and pitch trim point are in the same flight attitude.According to this design idea,design objectives and constraints are defined.By applying low and high fidelity aerodynamic analysis tools,BWB aerodynamic design methodology is established by the combination of optimization design and inverse design methods.High lift to drag ratio,pitch trim and acceptable buffet margin can be achieved by this design methodology.For 300-passenger BWB configuration based on static stability design,as compared with initial configuration,the maximum lift to drag ratio and pitch trim are achieved at cruise condition,zero lift pitching moment is positive,and buffet characteristics is well.Fuel burn of 300-passenger BWB configuration is also significantly reduced as compared with conventional civil transports.Because aerodynamic design is carried out under the constraints of BWB design requirements,the design configuration fulfills the demands for interior layout and provides a solid foundation for continuous work.
基金National Natural Science Foundation of China under Grant Nos.51639006 and 51725901
文摘Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.
基金support of National Science and Technology Major Project(2017-11-0007-0021)。
文摘The constrained multi-objective multi-variable optimization of fans usually needs a great deal of computational fluid dynamics(CFD)calculations and is time-consuming.In this study,a new multi-model ensemble optimization algorithm is proposed to tackle such an expensive optimization problem.The multi-variable and multi-objective optimization are conducted with a new flexible multi-objective infill criterion.In addition,the search direction is determined by the multi-model ensemble assisted evolutionary algorithm and the feature extraction by the principal component analysis is used to reduce the dimension of optimization variables.First,the proposed algorithm and other two optimization algorithms which prevail in fan optimizations were compared by using test functions.With the same number of objective function evaluations,the proposed algorithm shows a fast convergency rate on finding the optimal objective function values.Then,this algorithm was used to optimize the rotor and stator blades of a large axial fan,with the efficiencies as the objectives at three flow rates,the high,the design and the low flow rate.Forty-two variables were included in the optimization process.The results show that compared with the prototype fan,the total pressure efficiencies of the optimized fan at the high,the design and the low flow rate were increased by 3.35%,3.07%and 2.89%,respectively,after CFD simulations for 500 fan candidates with the constraint for the design pressure.The optimization results validate the effectiveness and feasibility of the proposed algorithm.
文摘The Number Theory comes back as the heart of unified Science, in a Computing Cosmos using the bases 2;3;5;7 whose two symmetric combinations explain the main lepton mass ratios. The corresponding Holic Principle induces a symmetry between the Newton and Planck constants which confirm the Permanent Sweeping Holography Bang Cosmology, with invariant baryon density 3/10, the dark baryons being dephased matter-antimatter oscillation. This implies the DNA bi-codon mean isotopic mass, confirming to 0.1 ppm the electron-based Topological Axis, whose terminal boson is the base 2 c-observable Universe in the base 3 Cosmos. The physical parameters involve the Euler idoneal numbers and the special Fermat primes of Wieferich (bases 2) and Mirimanoff (base 3). The prime numbers and crystallographic symmetries are related to the 4-fold structure of the DNA bi-codon. The forgotten Eddington’s proton-tau symmetry is rehabilitated, renewing the supersymmetry quest. This excludes the concepts of Multiverse, Continuum, Infinity, Locality and Zero-mass Particle, leading to stringent predictions in Cosmology, Particle Physics and Biology.
文摘The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicability and reliability of upward recursive formula in principle is amended.An optimal scheme of upward-and downward-joint recursions has been developed for the sequential F(z) computations.No additional accuracy is needed with the fundamental term of recursion because the absolute error of Fn(z) always decreases with the recursive approach.The scheme can be employed in modifying any of existent subprograms for Fn<z> computations.In the case of p-d-f-and g-type Gaussians,combining this method with Schaad's formulas can reduce,at least,the additive operations by a factor 40%;the multiplicative and exponential operations by a factor 60%.
文摘Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living(AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applications in cloud data centers(CDCs). This paper focuses on modeling and analysis of multi-tier AAL applications, and aims to optimize resource provisioning while meeting requests' response time constraint. This paper models a multi-tier AAL application as a hybrid multi-tier queueing model consisting of an M/M/c queueing model and multiple M/M/1 queueing models. Then, virtual machine(VM) allocation is formulated as a constrained optimization problem in a CDC, and is further solved with the proposed heuristic VM allocation algorithm(HVMA). The results demonstrate that the proposed model and algorithm can effectively achieve dynamic resource provisioning while meeting the performance constraint.
基金supported by the Natural Science Foundation of Xiamen, China (3502Z202373011)the Fundamental Research Funds for the Central Universities (20720220066, 20720230027)the National Key Project of China (2022-JCJQ-ZD-067-11)。
文摘Structure modulation at multiscale is crucialfor optimizing the electromagnetic wave absorption (EWA)properties of fiber-reinforced composites. Here we selectedtwo types of wave-absorbing SiC fibers as reinforcements. TheL-fiber had a relatively low resistivity of ~3 Ω·cm and the Hfiberhad a high resistivity of ~7×10^(5)Ω·cm. To adjust the impedance,BN single coating and SiO_(2)/BN dual-coating wereprepared respectively on the L-fibers. Unidirectional prepregswith different fibers were stacked in different rules to obtainthe final composites. It showed that both the fiber coatings andstacking structure significantly influence the EWA performanceof the composites. Guided by computational optimization,the stacked composites exhibited superior reflectionloss (RL) lower than −10 dB across the whole X(8.2–12.4 GHz) and Ku (12.4–18.0 GHz) bands. It is interestingto find that the introduction of the surface coatings on theL-fibers significantly widens the available thickness range ofthe stacked composite for possessing excellent performance. Inparticular, dual-coating perform better in terms of broadeningthe available thickness range of the stacked composites.
基金supported by the National Natural Science Foundation of China(No.61273039)
文摘In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution(OODE). The proposed algorithm is named IOODE with ‘I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution(DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.
