Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to s...Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.展开更多
Near-space airship is a frontier and hotspot in current military research and development,and the near-space composite propeller is the key technology for its development.In order to obtain higher aerodynamic efficien...Near-space airship is a frontier and hotspot in current military research and development,and the near-space composite propeller is the key technology for its development.In order to obtain higher aerodynamic efficiency at an altitude of 22 km,a certain near-space composite propeller is designed as a long and slender aerodynamic shape with a 10 m diameter,which brings many challenges to the composite structure design.The initial design is obtained by the composite structure variable stiffness design method using based on fixed region division blending model.However,it weighs 23.142 kg,exceeding the required 20 kg.In order to meet the structural design requirements of the propeller,a variable stiffness design method using the adaptive region division blending model is proposed in this paper.Compared with the methods using the fixed region division blending model,this method optimizes region division,stacking thickness and stacking sequence in a single level,considering the coupling effect among them.Through a more refined region division,this method can provide a more optimal design for composite tapered structures.Additionally,to improve the efficiency of optimization subjected to manufacturing constraints,a hierarchical penalty function is proposed to quickly filter out the solutions that do not meet manufacturing constraints.The above methods combined with a Genetic Algorithm(GA)using specific encoding are adopted to optimize the near-space composite propeller.The optimal design of the structure weighs 18.831 kg,with all manufacturing constraints and all structural response constraints being satisfied.Compared with the initial design,the optimal design has a more refined region division,and achieves a weight reduction of 18.6%.This demonstrates that a refined region division can significantly improve the mechanical performance of the composite tapered structure.展开更多
Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fibe...Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fiber strengthened vinyl ester matrix polymeric composite with filler addition of nano-and micro-sized silicon carbide(SiC)element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear properties.The application of Artificial Neural Network(ANN)to correlate the filler addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of composites.The stuffing blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm(GA)to maximize the mechanical and wear-resistant properties.The predicted and tested ANN–GA optimal values obtained for the composite combination had a tensile,flexural,impact resilience,hardness and wear properties of 202.93 MPa,501.67 MPa,3.460 J/s,43 HV and 0.196 g,respectively,for its optimum combination of filler and reinforcement.It can be noted that the nano-sized SiC filler particle enhances most of the properties of the composite which diversifies its applications.The predicted mechanical and wear values of the developed ANN–GA model were in closer agreement with the experimental values which validate the model.展开更多
This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems.Two scheduling models were formulated as the elements of the composite schedul...This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems.Two scheduling models were formulated as the elements of the composite scheduling model,and the composite model was formulated composing these models with indispensable additional constraints.A hybrid genetic algorithm was developed to solve the composite scheduling problems.An improved representation based on random keys was developed to search permutation space.A genetic algorithm based dynamic programming approach was applied to select resource.The proposed technique and a previous technique are compared by three types of problems.All results indicate that the proposed technique is superior to the previous one.展开更多
In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-...In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange.In this paper,we introduce an improved Sparrow Search Algorithm(ISSA)to address these issues.The fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target solutions.To enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation strategies.Furthermore,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively.Finally,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities.Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods.These results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition.展开更多
The emergency communication system based on rail is an unconventional emergency communication mode,it is a complement equipment for that conventional communication system can’t work while tunnel mine accident occurs....The emergency communication system based on rail is an unconventional emergency communication mode,it is a complement equipment for that conventional communication system can’t work while tunnel mine accident occurs.Medium of transmission channel is the widely existing rail in the tunnel.In this paper we analyzed the characteristics of the rail transmission channel,verified the feasibility that information is transmitted by vibration signal in rail,we proposed the realization plan of the system.Communication protocol and processing mechanism suitable for rail transmission are designed according to the characteristics of channel bandwidth and low data transmission.Information communication with low bit rate and low bit error is realized in the communication simulation model.In the simplified model,we realized to transmit recognition speech information,and the error rate of the key text information is low to accept.The most concerned problem of personnel location in the mine disaster rescue is proposed,the composite algorithm is based on the model of signal amplitude attenuation,key node information and data frame transmission delay.Location information of hitting point can be achieved within the simplified model of the experiment.Furthermore,we discuss the characteristics of vibration signals passing through different channels.展开更多
The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on th...The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on the characteristics of composite field multiplication in symmetric cipher algorithms and the realization principle of its reconfigurable architectures, this paper describes the reconfigurable composite field multiplication over GF（（2^8）k） （k=1,2,3,4） in RISC （reduced instruction set computer） processor and VLIW （very long instruction word） processor architecture, respectively. Through configuration, the architectures can realize the composite field multiplication over GF（2^8）, GF （（2^8）2）, GF（（28）3） and GF（（28）4） flexibly and efficiently. We simulated the function of circuits and synthesized the reconfigurable design based on the 0.18 μm CMOS （complementary metal oxide semiconductor） standard cell library and the comparison with other same kind designs. The result shows that the reconfigurable design proposed in the paper can provide higher efficiency under the premise of flexibility.展开更多
Metal matrix composites reinforced with graphite particles provide better machinability and tribological properties. The present study attempts to find the optimal level of machining parameters for multi-performance c...Metal matrix composites reinforced with graphite particles provide better machinability and tribological properties. The present study attempts to find the optimal level of machining parameters for multi-performance characteristics in turning of Al-SiC-Gr hybrid composites using grey-fuzzy algorithm. The hybrid composites with 5%, 7.5% and 10% combined equal mass fraction of SiC-Gr particles were used for the study and their corresponding tensile strength values are 170, 210, 204 MPa respectively. Al-10%（SiC-Gr） hybrid composite provides better machinability when compared with composites with 5% and 7.5% of SiC-Gr. Grey-fuzzy logic approach offers improved grey-fuzzy reasoning grade and has less uncertainties in the output when compared with grey relational technique. The confirmatory test reveals an increase in grey-fuzzy reasoning grade from 0.619 to 0.891, which substantiates the improvement in multi-performance characteristics at the optimal level of process parameters setting.展开更多
In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectiv...In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process,which is of great significance to the study of material performance.This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images.In the core layer area,area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm.In the transition layer area,a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result.Furthermore,a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed.It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials,and the accuracy of segmentation is significantly improved in comparison with the original method,which ensures the accuracy and robustness of the subsequent fiber feature extraction process.展开更多
All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this pap...All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.展开更多
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode...Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.展开更多
This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can...This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.展开更多
This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aim...This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.展开更多
The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner dia...The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.展开更多
An adaptive approximation-based optimization （AABO） procedure is developed for the optimum design of a composite advanced grid-stiffened （AGS） cylinder subject to post-buckling. The design taking account of post-b...An adaptive approximation-based optimization （AABO） procedure is developed for the optimum design of a composite advanced grid-stiffened （AGS） cylinder subject to post-buckling. The design taking account of post-buckling under ultimate load will be able to promote the structural efficiency compared to the conventional design in which only the linear buckling is allowed. The beam-shell offsets technique is utilized for modeling the stiffener-skin connection, and the Newton-Raphson method is employed for the post-buckling analysis. A few structural analysis efforts are carried out for establishing the Kriging model of the collapse load of the AGS cylinder for optimization to significantly increase the optimization efficiency. The multi-island genetic algorithm （MIGA） is utilized for global optimum search. An adaptive approximation framework is proposed to resolve the computational burden caused by the large domain of design variables, and it is demonstrated that much less computational expense than that of the traditional approximation-based optimization method can be achieved. The utility of making use of commercial optimization package iSIGHT in conjunction with the finite element （FE） code MSC.MARC to develop the preliminary design tool of the composite AGS cylinder is evaluated as well.展开更多
The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such ...The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.展开更多
For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We ...For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an Immune Algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.展开更多
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
文摘Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.
基金This study was co-supported by stable funding from the National Key Laboratory of Aerofoil and Grille Aerodynamics,China.
文摘Near-space airship is a frontier and hotspot in current military research and development,and the near-space composite propeller is the key technology for its development.In order to obtain higher aerodynamic efficiency at an altitude of 22 km,a certain near-space composite propeller is designed as a long and slender aerodynamic shape with a 10 m diameter,which brings many challenges to the composite structure design.The initial design is obtained by the composite structure variable stiffness design method using based on fixed region division blending model.However,it weighs 23.142 kg,exceeding the required 20 kg.In order to meet the structural design requirements of the propeller,a variable stiffness design method using the adaptive region division blending model is proposed in this paper.Compared with the methods using the fixed region division blending model,this method optimizes region division,stacking thickness and stacking sequence in a single level,considering the coupling effect among them.Through a more refined region division,this method can provide a more optimal design for composite tapered structures.Additionally,to improve the efficiency of optimization subjected to manufacturing constraints,a hierarchical penalty function is proposed to quickly filter out the solutions that do not meet manufacturing constraints.The above methods combined with a Genetic Algorithm(GA)using specific encoding are adopted to optimize the near-space composite propeller.The optimal design of the structure weighs 18.831 kg,with all manufacturing constraints and all structural response constraints being satisfied.Compared with the initial design,the optimal design has a more refined region division,and achieves a weight reduction of 18.6%.This demonstrates that a refined region division can significantly improve the mechanical performance of the composite tapered structure.
文摘Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fiber strengthened vinyl ester matrix polymeric composite with filler addition of nano-and micro-sized silicon carbide(SiC)element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear properties.The application of Artificial Neural Network(ANN)to correlate the filler addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of composites.The stuffing blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm(GA)to maximize the mechanical and wear-resistant properties.The predicted and tested ANN–GA optimal values obtained for the composite combination had a tensile,flexural,impact resilience,hardness and wear properties of 202.93 MPa,501.67 MPa,3.460 J/s,43 HV and 0.196 g,respectively,for its optimum combination of filler and reinforcement.It can be noted that the nano-sized SiC filler particle enhances most of the properties of the composite which diversifies its applications.The predicted mechanical and wear values of the developed ANN–GA model were in closer agreement with the experimental values which validate the model.
基金Project supported by the Grant-in-Aid for Young Scientists (B) from the Ministry of Education,Culture,Sports,Science and Technology,Japan
文摘This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems.Two scheduling models were formulated as the elements of the composite scheduling model,and the composite model was formulated composing these models with indispensable additional constraints.A hybrid genetic algorithm was developed to solve the composite scheduling problems.An improved representation based on random keys was developed to search permutation space.A genetic algorithm based dynamic programming approach was applied to select resource.The proposed technique and a previous technique are compared by three types of problems.All results indicate that the proposed technique is superior to the previous one.
基金Supported by the National Natural Science Foundation of China(62272214)。
文摘In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange.In this paper,we introduce an improved Sparrow Search Algorithm(ISSA)to address these issues.The fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target solutions.To enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation strategies.Furthermore,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively.Finally,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities.Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods.These results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition.
基金The authors would like to thank National Natural Science Foundation of China for the grant of the project(41574137)Furthermore,they would like to specially thank Prof.Guo Yong for his contributions and his support in this paper.
文摘The emergency communication system based on rail is an unconventional emergency communication mode,it is a complement equipment for that conventional communication system can’t work while tunnel mine accident occurs.Medium of transmission channel is the widely existing rail in the tunnel.In this paper we analyzed the characteristics of the rail transmission channel,verified the feasibility that information is transmitted by vibration signal in rail,we proposed the realization plan of the system.Communication protocol and processing mechanism suitable for rail transmission are designed according to the characteristics of channel bandwidth and low data transmission.Information communication with low bit rate and low bit error is realized in the communication simulation model.In the simplified model,we realized to transmit recognition speech information,and the error rate of the key text information is low to accept.The most concerned problem of personnel location in the mine disaster rescue is proposed,the composite algorithm is based on the model of signal amplitude attenuation,key node information and data frame transmission delay.Location information of hitting point can be achieved within the simplified model of the experiment.Furthermore,we discuss the characteristics of vibration signals passing through different channels.
基金Supported by the National Natural Science Foundation of China(61202492,61309022,61309008)the Natural Science Foundation for Young of Shaanxi Province(2013JQ8013)
文摘The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on the characteristics of composite field multiplication in symmetric cipher algorithms and the realization principle of its reconfigurable architectures, this paper describes the reconfigurable composite field multiplication over GF（（2^8）k） （k=1,2,3,4） in RISC （reduced instruction set computer） processor and VLIW （very long instruction word） processor architecture, respectively. Through configuration, the architectures can realize the composite field multiplication over GF（2^8）, GF （（2^8）2）, GF（（28）3） and GF（（28）4） flexibly and efficiently. We simulated the function of circuits and synthesized the reconfigurable design based on the 0.18 μm CMOS （complementary metal oxide semiconductor） standard cell library and the comparison with other same kind designs. The result shows that the reconfigurable design proposed in the paper can provide higher efficiency under the premise of flexibility.
文摘Metal matrix composites reinforced with graphite particles provide better machinability and tribological properties. The present study attempts to find the optimal level of machining parameters for multi-performance characteristics in turning of Al-SiC-Gr hybrid composites using grey-fuzzy algorithm. The hybrid composites with 5%, 7.5% and 10% combined equal mass fraction of SiC-Gr particles were used for the study and their corresponding tensile strength values are 170, 210, 204 MPa respectively. Al-10%（SiC-Gr） hybrid composite provides better machinability when compared with composites with 5% and 7.5% of SiC-Gr. Grey-fuzzy logic approach offers improved grey-fuzzy reasoning grade and has less uncertainties in the output when compared with grey relational technique. The confirmatory test reveals an increase in grey-fuzzy reasoning grade from 0.619 to 0.891, which substantiates the improvement in multi-performance characteristics at the optimal level of process parameters setting.
文摘In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process,which is of great significance to the study of material performance.This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images.In the core layer area,area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm.In the transition layer area,a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result.Furthermore,a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed.It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials,and the accuracy of segmentation is significantly improved in comparison with the original method,which ensures the accuracy and robustness of the subsequent fiber feature extraction process.
基金Supported by the National Natural Science Foundation of China （No. 60302020）.
文摘All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.
文摘Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.
文摘This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.
基金the Shandong Province Key Research and Development Program under Grant No.2021SFGC0601.
文摘This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.
基金supported by the National Natural Science Foundation of China(Grant No.52271277)the Natural Science Foundation of Jiangsu Province(Grant.No.BK20211343)+1 种基金the State Key Laboratory of Ocean Engineering(Shanghai Jiao Tong University)(Grant.No.GKZD010081)Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant.No.SJCX22_1906).
文摘The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.
基金National Basic Research Program of China (070022)Ph.D.Innovation Foundation of Beijing University of Aeronautics and Astronautics
文摘An adaptive approximation-based optimization （AABO） procedure is developed for the optimum design of a composite advanced grid-stiffened （AGS） cylinder subject to post-buckling. The design taking account of post-buckling under ultimate load will be able to promote the structural efficiency compared to the conventional design in which only the linear buckling is allowed. The beam-shell offsets technique is utilized for modeling the stiffener-skin connection, and the Newton-Raphson method is employed for the post-buckling analysis. A few structural analysis efforts are carried out for establishing the Kriging model of the collapse load of the AGS cylinder for optimization to significantly increase the optimization efficiency. The multi-island genetic algorithm （MIGA） is utilized for global optimum search. An adaptive approximation framework is proposed to resolve the computational burden caused by the large domain of design variables, and it is demonstrated that much less computational expense than that of the traditional approximation-based optimization method can be achieved. The utility of making use of commercial optimization package iSIGHT in conjunction with the finite element （FE） code MSC.MARC to develop the preliminary design tool of the composite AGS cylinder is evaluated as well.
文摘The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.
基金Supported by the National Key Technologies Re-search and Development Programinthe 10th Five-Year Plan of China(2004BA721A05)
文摘For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an Immune Algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.
