Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the pro...Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the production of thoracic implants with complex geometries,offering more versatile performance.In this study,we investigated a design based on a spring-like geometry manufactured by laser powder bed fusion(LPBF),as proposed in earlier research.The biomechanical behavior of this design was analyzed using various isolated semi-ring-rib models at different levels of the rib cage.This approach enabled a comprehensive examination,leading to the proposal of several implant configurations that were incorporated into a 3D rib cage model with chest wall defects,to simulate different chest wall reconstruction scenarios.The results revealed that the implant design was too rigid for the second rib level,which therefore was excluded from the proposed implant configurations.In chest wall reconstruction simulations,the maximum stresses observed in all prostheses did not exceed 38%of the implant material's yield stress in the most unfavorable case.Additionally,all the implants showed flexibility compatible with the physiological movements of the human thorax.展开更多
Computational simulation is a very powerful tool to analyze industrial processes to reduce operating risks and improve profits from equipment. The present work describes the development of some computational algorithm...Computational simulation is a very powerful tool to analyze industrial processes to reduce operating risks and improve profits from equipment. The present work describes the development of some computational algorithms based on the numerical method to create a simulator for the continuous casting process, which is the most popular method to produce steel products for metallurgical industries. The kinematics of industrial processing was computationally reproduced using subroutines logically programmed. The cast steel by each strand was calculated using an iterative method nested in the main loop. The process was repeated at each time step （？t） to calculate the casting time, simultaneously, the steel billets produced were counted and stored. The subroutines were used for creating a computational representation of a continuous casting plant （CCP） and displaying the simulation of the steel displacement through the CCP. These algorithms have been developed to create a simulator using the programming language C＋＋. Algorithms for computer animation of the continuous casting process were created using a graphical user interface （GUI）. Finally, the simulator functionality was shown and validated by comparing with the industrial information of the steel production of three casters.展开更多
Computational fluid dynamics （CFD） plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and eng...Computational fluid dynamics （CFD） plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and engineers in the ship industry. In this paper, the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool. CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters, automatic generation of mesh, automatic analysis of fluid flow to calculate the required objective/cost function, and finally an optimization tool to evaluate the cost for optimization. In this paper, integration of a genetic algorithm program, written in MATLAB, was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT. Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters. These design variables were optimized to achieve a minimum cost function of ＂total resistance＂. Integration of a genetic algorithm with CFD tools proves to be effective for hull form ootimization.展开更多
Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple Q...Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems： optimization of grid user and grid resource provider. Grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.展开更多
This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeit...This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.展开更多
Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents...Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.展开更多
The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation ...The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.展开更多
Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycle...Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycles.However,the effects of tooth geometry parameters could manifest as the meshing cycles increase.This study investigated the effects of tooth geometry parameters on the multi-cycle meshing temperature of polyoxymethylene(POM)worm gears,aiming to control the meshing temperature elevation by tuning the tooth geometry.Firstly,a finite element(FE)model capable of separately calculating the heat generation and simulating the heat propagation was established.Moreover,an adaptive iteration algorithm was proposed within the FE framework to capture the influence of the heat generation variation from cycle to cycle.This algorithm proved to be feasible and highly efficient compared with experimental results from the literature and simulated results via the full-iteration algorithm.Multi-cycle meshing temperature analyses were conducted on a series of POM worm gears with different tooth geometry parameters.The results reveal that,within the range of 14.5°to 25°,a pressure angle of 25°is favorable for reducing the peak surface temperature and overall body temperature of POM worm gears,which influence flank wear and load-carrying capability,respectively.However,addendum modification should be weighed because it helps with load bearing but increases the risk of severe flank wear.This paper proposes an efficient iteration algorithm for multi-cycle meshing temperature analysis of polymer gears and proves the feasibility of controlling the meshing temperature elevation during multiple cycles by tuning tooth geometry.展开更多
The development of some computational algorithms based on cellular automaton was described to simulate the structures formed during the solidification of steel products.The algorithms described take results from the s...The development of some computational algorithms based on cellular automaton was described to simulate the structures formed during the solidification of steel products.The algorithms described take results from the steel thermal behavior and heat removal previously calculated using a simulator developed by present authors in a previous work.Stored time is used for displaying the steel transition from liquid to mushy and solid.And it is also used to command computational subroutines that reproduce nucleation and grain growth.These routines are logically programmed using the programming language C＋＋ and are based on a simultaneous solution of numerical methods （stochastic and deterministic） to create a graphical representation of different grain structures formed.The grain structure obtained is displayed on the computer screen using a graphical user interface （GUI）.The chaos theory and random generation numbers are included in the algorithms to simulate the heterogeneity of grain sizes and morphologies.展开更多
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource...The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.展开更多
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ...Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.展开更多
In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems...In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems such as the bending, free vibration and buckling of nonhomogeneous long cylinders, it is difficult to obtain their solutions by the initial parameter algorithm on computer. In this paper, the substructure computational algorithm for the exact analytic method is presented through the bending of non-homogeneous long cylindrical shell. This substructure algorithm can he applied to solve the problems which can not he calculated by the initial parameter algorithm on computer. Finally, the problems can he reduced to solving a low order system of algehraic equations like the initial parameter algorithm Numerical examples are given and compared with the initial para-algorithm at the end of the paper, which confirms the correctness of the substructure computational algorithm.展开更多
Research reports show that the accuracies of many explicit friction factor models, having different levels of accuracies and complexities, have been improved using genetic algorithm (GA), a global optimization approac...Research reports show that the accuracies of many explicit friction factor models, having different levels of accuracies and complexities, have been improved using genetic algorithm (GA), a global optimization approach. However, the computational cost associated with the use of GA has yet to be discussed. In this study, the parameters of sixteen explicit models for the estimation of friction factor in the turbulent flow regime were optimized using two popular global search methods namely genetic algorithm (GA) and simulated annealing (SA). Based on 1000 interval values of Reynolds number (Re) in the range of and 100 interval values of relative roughness () in the range of , corresponding friction factor (f) data were obtained by solving Colebrook-White equation using Microsoft Excel spreadsheet. These data were then used to modify the parameters of the selected explicit models. Although both GA and SA led to either moderate or significant improvements in the accuracies of the existing friction factor models, SA outperforms the GA. Moreover, the SA requires far less computational time than the GA to complete the corresponding optimization process. It can therefore be concluded that SA is a better global optimizer than GA in the process of finding an improved explicit friction factor model as an alternative to the implicit Colebrook-White equation in the turbulent flow regime.展开更多
The present study aims at improving the accuracy of weather forecast by providing useful information on the behavior and response of a sounding temperature sensor.A hybrid approach relying on Computational Fluid Dynam...The present study aims at improving the accuracy of weather forecast by providing useful information on the behavior and response of a sounding temperature sensor.A hybrid approach relying on Computational Fluid Dynamics and a genetic algorithm(GA)is used to simulate the system represented by the bead thermistor and the surrounding air.In particular,the influence of different lead angles,sensor lead length,and lead number is considered.The results have shown that when the length of the lead wire of the bead thermistor is increased,the radiation temperature rise is reduced;when the number of lead wire is four and the angle between the lead wires is 180°,the solar radiation angle has a scarce influence on the radiation temperature rise of the sounding temperature sensor.展开更多
The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelera...The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelerate this calculation. In this paper, we perform a computational comparison in which the basis inverse is computed with five different updating schemes. Then, we propose a parallel implementation of two updating schemes on a CPU-GPU System using MATLAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is preented to establish the practical value of GPU-based implementation.展开更多
Pascal triangles are formulated for computing the coefficients of the B-spline series representation of the compactly supported spline-wavelets with minimum support and their derivatives.It is shown that with the al- ...Pascal triangles are formulated for computing the coefficients of the B-spline series representation of the compactly supported spline-wavelets with minimum support and their derivatives.It is shown that with the al- ternating signs removed,all these sequences are totally positive.On the other hand,truncations of the recipro- cal Euler-Frobenius polynomials lead to finite sequences for orthogonal wavelet decompositions.For this pur- pose,sharp estimates are given in terms of the exact reconstruction of these approximate decomposed compo- nents.展开更多
In this paper, according to the parallel environment of ELXSI computer, a parallel solving process of substructure method in static and dynamic analyses of large-scale and complex structure has been put forward, and t...In this paper, according to the parallel environment of ELXSI computer, a parallel solving process of substructure method in static and dynamic analyses of large-scale and complex structure has been put forward, and the corresponding parallel computational program has been developed.展开更多
With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper d...With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.展开更多
When the edges of a convex polygon are traversed along one direction,the interior of the convex polygon is always on the same side of the edges. Based on this characteristic of convex polygons,a new algorithm for comp...When the edges of a convex polygon are traversed along one direction,the interior of the convex polygon is always on the same side of the edges. Based on this characteristic of convex polygons,a new algorithm for computing the convex hull of a simple polygon is proposed in this paper,which is then extended to a new algorithm for computing the convex hull of a planar point set. First,the extreme points of the planar point set are found,and the subsets of point candidate for vertex of the convex hull between extreme points are obtained. Then,the ordered convex hull point sequences between extreme points are constructed separately and concatenated by removing redundant extreme points to get the convex hull. The time complexity of the new planar convex hull algorithm is O(nlogh) ,which is equal to the time complexity of the best output-sensitive planar convex hull algorithms. Compared with the algorithm having the same complexity,the new algorithm is much faster.展开更多
文摘Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the production of thoracic implants with complex geometries,offering more versatile performance.In this study,we investigated a design based on a spring-like geometry manufactured by laser powder bed fusion(LPBF),as proposed in earlier research.The biomechanical behavior of this design was analyzed using various isolated semi-ring-rib models at different levels of the rib cage.This approach enabled a comprehensive examination,leading to the proposal of several implant configurations that were incorporated into a 3D rib cage model with chest wall defects,to simulate different chest wall reconstruction scenarios.The results revealed that the implant design was too rigid for the second rib level,which therefore was excluded from the proposed implant configurations.In chest wall reconstruction simulations,the maximum stresses observed in all prostheses did not exceed 38%of the implant material's yield stress in the most unfavorable case.Additionally,all the implants showed flexibility compatible with the physiological movements of the human thorax.
文摘Computational simulation is a very powerful tool to analyze industrial processes to reduce operating risks and improve profits from equipment. The present work describes the development of some computational algorithms based on the numerical method to create a simulator for the continuous casting process, which is the most popular method to produce steel products for metallurgical industries. The kinematics of industrial processing was computationally reproduced using subroutines logically programmed. The cast steel by each strand was calculated using an iterative method nested in the main loop. The process was repeated at each time step （？t） to calculate the casting time, simultaneously, the steel billets produced were counted and stored. The subroutines were used for creating a computational representation of a continuous casting plant （CCP） and displaying the simulation of the steel displacement through the CCP. These algorithms have been developed to create a simulator using the programming language C＋＋. Algorithms for computer animation of the continuous casting process were created using a graphical user interface （GUI）. Finally, the simulator functionality was shown and validated by comparing with the industrial information of the steel production of three casters.
文摘Computational fluid dynamics （CFD） plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and engineers in the ship industry. In this paper, the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool. CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters, automatic generation of mesh, automatic analysis of fluid flow to calculate the required objective/cost function, and finally an optimization tool to evaluate the cost for optimization. In this paper, integration of a genetic algorithm program, written in MATLAB, was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT. Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters. These design variables were optimized to achieve a minimum cost function of ＂total resistance＂. Integration of a genetic algorithm with CFD tools proves to be effective for hull form ootimization.
基金the National Natural Science Foundation of China (60402028, 60672137) Wuhan Yonger Dawning Foundation (20045006071-15)China Specialized Research Fund for the Doctoral Program of Higher Eduction (20060497015).
文摘Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems： optimization of grid user and grid resource provider. Grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.
文摘This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.
文摘Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.
基金the Deanship of Graduate Studies and Scientific Research at Najran University for funding this work under the Easy Funding Program grant code(NU/EFP/SERC/13/166).
文摘The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.
基金Supported by National Key R&D Program of China(Grant No.2019YFE0121300)。
文摘Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycles.However,the effects of tooth geometry parameters could manifest as the meshing cycles increase.This study investigated the effects of tooth geometry parameters on the multi-cycle meshing temperature of polyoxymethylene(POM)worm gears,aiming to control the meshing temperature elevation by tuning the tooth geometry.Firstly,a finite element(FE)model capable of separately calculating the heat generation and simulating the heat propagation was established.Moreover,an adaptive iteration algorithm was proposed within the FE framework to capture the influence of the heat generation variation from cycle to cycle.This algorithm proved to be feasible and highly efficient compared with experimental results from the literature and simulated results via the full-iteration algorithm.Multi-cycle meshing temperature analyses were conducted on a series of POM worm gears with different tooth geometry parameters.The results reveal that,within the range of 14.5°to 25°,a pressure angle of 25°is favorable for reducing the peak surface temperature and overall body temperature of POM worm gears,which influence flank wear and load-carrying capability,respectively.However,addendum modification should be weighed because it helps with load bearing but increases the risk of severe flank wear.This paper proposes an efficient iteration algorithm for multi-cycle meshing temperature analysis of polymer gears and proves the feasibility of controlling the meshing temperature elevation during multiple cycles by tuning tooth geometry.
文摘The development of some computational algorithms based on cellular automaton was described to simulate the structures formed during the solidification of steel products.The algorithms described take results from the steel thermal behavior and heat removal previously calculated using a simulator developed by present authors in a previous work.Stored time is used for displaying the steel transition from liquid to mushy and solid.And it is also used to command computational subroutines that reproduce nucleation and grain growth.These routines are logically programmed using the programming language C＋＋ and are based on a simultaneous solution of numerical methods （stochastic and deterministic） to create a graphical representation of different grain structures formed.The grain structure obtained is displayed on the computer screen using a graphical user interface （GUI）.The chaos theory and random generation numbers are included in the algorithms to simulate the heterogeneity of grain sizes and morphologies.
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
文摘The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.
基金supported by the Changzhou Science and Technology Support Project(CE20235045)Open Subject of Jiangsu Province Key Laboratory of Power Transmission and Distribution(2021JSSPD12)+1 种基金Talent Projects of Jiangsu University of Technology(KYY20018)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633).
文摘Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.
文摘In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems such as the bending, free vibration and buckling of nonhomogeneous long cylinders, it is difficult to obtain their solutions by the initial parameter algorithm on computer. In this paper, the substructure computational algorithm for the exact analytic method is presented through the bending of non-homogeneous long cylindrical shell. This substructure algorithm can he applied to solve the problems which can not he calculated by the initial parameter algorithm on computer. Finally, the problems can he reduced to solving a low order system of algehraic equations like the initial parameter algorithm Numerical examples are given and compared with the initial para-algorithm at the end of the paper, which confirms the correctness of the substructure computational algorithm.
文摘Research reports show that the accuracies of many explicit friction factor models, having different levels of accuracies and complexities, have been improved using genetic algorithm (GA), a global optimization approach. However, the computational cost associated with the use of GA has yet to be discussed. In this study, the parameters of sixteen explicit models for the estimation of friction factor in the turbulent flow regime were optimized using two popular global search methods namely genetic algorithm (GA) and simulated annealing (SA). Based on 1000 interval values of Reynolds number (Re) in the range of and 100 interval values of relative roughness () in the range of , corresponding friction factor (f) data were obtained by solving Colebrook-White equation using Microsoft Excel spreadsheet. These data were then used to modify the parameters of the selected explicit models. Although both GA and SA led to either moderate or significant improvements in the accuracies of the existing friction factor models, SA outperforms the GA. Moreover, the SA requires far less computational time than the GA to complete the corresponding optimization process. It can therefore be concluded that SA is a better global optimizer than GA in the process of finding an improved explicit friction factor model as an alternative to the implicit Colebrook-White equation in the turbulent flow regime.
文摘The present study aims at improving the accuracy of weather forecast by providing useful information on the behavior and response of a sounding temperature sensor.A hybrid approach relying on Computational Fluid Dynamics and a genetic algorithm(GA)is used to simulate the system represented by the bead thermistor and the surrounding air.In particular,the influence of different lead angles,sensor lead length,and lead number is considered.The results have shown that when the length of the lead wire of the bead thermistor is increased,the radiation temperature rise is reduced;when the number of lead wire is four and the angle between the lead wires is 180°,the solar radiation angle has a scarce influence on the radiation temperature rise of the sounding temperature sensor.
文摘The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelerate this calculation. In this paper, we perform a computational comparison in which the basis inverse is computed with five different updating schemes. Then, we propose a parallel implementation of two updating schemes on a CPU-GPU System using MATLAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is preented to establish the practical value of GPU-based implementation.
基金Research supported by NSF Grant DMS 89-0-01345 and ARO Contract No.DAAL 03-90-G-0091.
文摘Pascal triangles are formulated for computing the coefficients of the B-spline series representation of the compactly supported spline-wavelets with minimum support and their derivatives.It is shown that with the al- ternating signs removed,all these sequences are totally positive.On the other hand,truncations of the recipro- cal Euler-Frobenius polynomials lead to finite sequences for orthogonal wavelet decompositions.For this pur- pose,sharp estimates are given in terms of the exact reconstruction of these approximate decomposed compo- nents.
文摘In this paper, according to the parallel environment of ELXSI computer, a parallel solving process of substructure method in static and dynamic analyses of large-scale and complex structure has been put forward, and the corresponding parallel computational program has been developed.
基金Project supported by the National Basic Research Program of China(Grant No.2006CB7057005)the National High Technology Research and Development Program of China(Grant No.2009AA012200)the National Natural Science Foundation of China (Grant No.60672104)
文摘With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.
基金Project (No. 2004AA420100) supported by the National Hi-TechResearch and Development Program (863) of China
文摘When the edges of a convex polygon are traversed along one direction,the interior of the convex polygon is always on the same side of the edges. Based on this characteristic of convex polygons,a new algorithm for computing the convex hull of a simple polygon is proposed in this paper,which is then extended to a new algorithm for computing the convex hull of a planar point set. First,the extreme points of the planar point set are found,and the subsets of point candidate for vertex of the convex hull between extreme points are obtained. Then,the ordered convex hull point sequences between extreme points are constructed separately and concatenated by removing redundant extreme points to get the convex hull. The time complexity of the new planar convex hull algorithm is O(nlogh) ,which is equal to the time complexity of the best output-sensitive planar convex hull algorithms. Compared with the algorithm having the same complexity,the new algorithm is much faster.
