Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHIS...Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHISAO)is proposed.In this paper,a snow blowing strategy was introduced during the initialization phase of the population to improve population diversity.Secondly,the dual-population iterative strategy of SAO has been replaced by a multi-population iterative strategy,which is supplemented with a position update formula for the water evaporation phase.Additionally,Cauchy mutation perturbation has been introduced in the snow melting phase.This set of improvements better balances the exploration and exploitation phases of the algorithm,enhancing its ability to pursue excellence.Finally,a fluid activation strategy is added to activate the potential of the algorithm when its update iterations enter stagnation,helping the algorithm to escape from the local optimum.Comparison experiments between PHISAO and six metaheuristics were conducted on the CEC(Congress on Evolutionary Computation)-2017 and CEC-2022 benchmark suites.The experimental results demonstrate that the PHISAO algorithm exhibits excellent performance and robustness.In addition,the PHISAO is applied into the unmanned aerial vehicle trajectory planning problem together with particle swarm optimization,beluga whale optimization,sand cat swarm optimization,and SAO.The simulation results show that the proposed PHISAO can plan the optimal trajectory in all two different maps.The proposed PHISAO objective function values were reduced by an average of 29.49%(map 1),and 18.34%(map 2)compared to SAO.展开更多
Tailoring multiple degrees-of-freedom(DoFs)to achieve high-dimensional laser field is crucial for advancing optical technologies.While recent advancements have demonstrated the ability to manipulate a limited number o...Tailoring multiple degrees-of-freedom(DoFs)to achieve high-dimensional laser field is crucial for advancing optical technologies.While recent advancements have demonstrated the ability to manipulate a limited number of DoFs,most existing methods rely on bulky optical components or intricate systems that employ time-consuming iterative methods and,most critically,the on-demand tailoring of multiple DoFs simultaneously through a compact,single element—remains underexplored.In this study,we propose an intelligent hybrid strategy that enables the simultaneous and customizable manipulation of six DoFs:wave vector,initial phase,spatial mode,amplitude,orbital angular momentum(OAM)and spin angular momentum(SAM).Our approach advances in phase-only property,which facilitates tailoring strategy experimentally demonstrated on a compact metasurface.A fabricated sample is tailored to realize arbitrary manipulation across six DoFs,constructing a 288-dimensional space.Notably,since the OAM eigenstates constitute an infinite dimensional Hilbert space,this proposal can be further extended to even higher dimensions.Proof-of-principle experiments confirm the effectiveness in manipulation capability and dimensionality.We envision that this powerful tailoring ability offers immense potential for multifunctional photonic devices across both classical and quantum scenarios and such compactness extending the dimensional capabilities for integration on-chip requirements.展开更多
This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system...This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.展开更多
In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user pro...In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.展开更多
Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimoda...Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.展开更多
A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy ...A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.展开更多
A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal...A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.展开更多
In order to take precaution and cure against intemet of vehicles （IOV） worm propagation in expressway, the IOV worm propagation and its corresponding anti-worm strategy were studied in expressway interchange termina...In order to take precaution and cure against intemet of vehicles （IOV） worm propagation in expressway, the IOV worm propagation and its corresponding anti-worm strategy were studied in expressway interchange terminal. According to omnirange driving in expressway interchange terminal and vehicular mobile communication environment, an IOV worm propagation model is constructed; and then according to the dynamic propagation law and destructiveness of IOV worm in this environment, a novel hybrid anti-worm strategy for confrontation is designed. This worm propagation model can factually simulates the IOV worm propagation in this interchange terminal environment; and this hybrid anti-worm strategy can effectively control IOV worm propagation in the environment, moreover, it can reduce the influence on network resource overhead.展开更多
A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson corr...A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.展开更多
To realize effective utilization of renewable energy sources,a novel polymorphic topology with hybrid control strategy based LLC resonant converter was analyzed and designed in this paper.By combining the merits of a ...To realize effective utilization of renewable energy sources,a novel polymorphic topology with hybrid control strategy based LLC resonant converter was analyzed and designed in this paper.By combining the merits of a full bridge LLC resonant converter,three-level half bridge LLC resonant converter,and variable frequency control mode,the converter realizes an intelligent estimation of input voltage by automatically changing its internal cir-cuit topology.Under this control strategy,different input voltages determine different operation modes.This is achieved in full bridge LLC mode when the input voltage is low.If the input voltage rises to a certain level,it operates in three-level half bridge LLC mode.These switches are digital and entirely carried out by the DSP(Digi-tal Signal Processor),which means that an auxiliary circuit is unnecessary,where a simple strategy of software modification can be utilized.Experimental results of a 500W prototype with 100V~600V input voltage and full load efficiency of up to 92%are developed to verify feasibility and practicability.This type of converter is suitable for applications with an ultra-wide input voltage range,such as wind turbines,photovoltaic generators,bioenergy,and other renewable energy sources.展开更多
In this paper, a hybrid control strategy for a matrix converter fed wind energy conversion system is presented. Since the wind speed may vary, output parameters like power, frequency and voltage may fluctuate. Hence i...In this paper, a hybrid control strategy for a matrix converter fed wind energy conversion system is presented. Since the wind speed may vary, output parameters like power, frequency and voltage may fluctuate. Hence it is necessary to design a system that regulates output parameters, such as voltage and frequency, and thereby provides a constant voltage and frequency output from the wind energy conversion system. Matrix converter is used in the proposed solution as the main power conditioner as a more efficient alternative when compared to traditional back-back converter structure. To control the output voltage, a vector modulation based refined control structure is used. A power tracker is included to maximize the mechanical output power of the turbine. Over current protection and clamp circuit input protection have been introduced to protect the system from over current. It reduces the spikes generated at the output of the converter. The designed system is capable of supplying an output voltage of constant frequency and amplitude within the expected ranges of input during the operation. The matrix converter control using direct modulation method, modified Venturini modulation method and vector modulation method was simulated, the results were compared and it was inferred that vector modulation method was superior to the other two methods. With the proposed technique, voltage transfer ratio and harmonic profile have been improved compared to the other two modulation techniques. The behaviour of the system is corroborated by MATLAB Simulink, and hardware is realized using an FPGA controller. Experimental results are found to be matching with the simulation results.展开更多
Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been prop...Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.展开更多
Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault sampl...Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
Energy optimization management can make fuel cell truck(FCT)power system more efficient,so as to improve vehicle fuel economy.When the structure of power source system and the torque distribution strategy are determin...Energy optimization management can make fuel cell truck(FCT)power system more efficient,so as to improve vehicle fuel economy.When the structure of power source system and the torque distribution strategy are determined,the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources.The paper simulates the assistance of the intelligent transport system(ITS)and carries out the eco-velocity planning using the traffic signal light.On this basis,in order to further improve the energy efficiency of FCT,a model predictive control(MPC)-based energy source optimization management strategy is innovatively developed,which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption.Under the scenarios of signalized intersections,based on the planned eco-velocity,the off-line simulation results show that the proposed MPC-based energy source management strategy(ESMS)can reduce hydrogen consumption of fuel cell up to 7%compared with the existing rule-based ESMS.Finally,the Hardware-in-the-Loop(HiL)simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.展开更多
Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based...Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.展开更多
Hybrid catalysts based on iron phthalocyanine(FePc)have raised much attention due to their promising applications in electrocatalytic oxygen reduction reaction(ORR).Various hybridization strategies have been developed...Hybrid catalysts based on iron phthalocyanine(FePc)have raised much attention due to their promising applications in electrocatalytic oxygen reduction reaction(ORR).Various hybridization strategies have been developed for improving their activity and durability.However,the influence of different hybridization strategies on their catalytic performance remains unclear.In this study,Fe Pc was effectively distributed on molybdenum disulfide(MoS_(2))forming Fe Pc-based hybrid catalysts,namely Fe Pc-MoS_(2),Fe Pc*-MoS_(2),and Fe Pc-Py-MoS_(2),respectively,to disclose the related influence.Through direct hybridization,the stacked and highly dispersed Fe Pc on MoS_(2)resulted in Fe Pc-MoS_(2),and Fe Pc*-MoS_(2),respectively,in which the substrate and Fe Pc are mainly bound through van der Waals interactions.Through covalent hybridization strategy using pyridyl(Py)as a linker,Fe Pc-Py-MoS_(2)hybrid catalyst was prepared.Experimental and theoretical results disclosed that the linker hybridization of Fe Pc and MoS_(2)facilitated the exposure of Fe-N4 sites,maintained the intrinsic activity of Fe Pc by forming a more dispersed phase and increased the durability via Fe-N bonding,rendering the Fe Pc-Py-MoS_(2)an excellent ORR hybrid catalyst.Compared with van der Waals hybridized Fe Pc-MoS_(2)and Fe Pc*-MoS_(2)in alkaline media,the linker hybridized Fe Pc-Py-MoS_(2)showed an obviously enhanced ORR activity with a half-wave potential(E_(1/2))of 0.88 V vs RHE and an ultralow Tafel slope of 26 m V dec-1.Besides,the Fe Pc-Py-MoS_(2)exhibited a negligible decay of E_(1/2) after 50,000 CV cycles for ORR,showing its superior durability.This work gives us more insight into the influence of different hybrid strategies on Fe Pc catalysts and provides further guidance for the development of highly efficient and durable ORR catalysts.展开更多
In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter re...In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is verified.Through chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then formed.In the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements,the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.展开更多
Numerical simulation is dominant in solving partial differential equations(PDEs),but balancing fine-grained grids with low computational costs is challenging.Recently,solving PDEs with neural networks(NNs)has gained i...Numerical simulation is dominant in solving partial differential equations(PDEs),but balancing fine-grained grids with low computational costs is challenging.Recently,solving PDEs with neural networks(NNs)has gained interest,yet cost-effectiveness and high accuracy remain a challenge.This work introduces a novel paradigm for solving PDEs,called multi-scale neural computing(MSNC),considering spectral bias of NNs and local approximation properties in the finite difference method(FDM).The MSNC decomposes the solution with a NN for efficient capture of global scale and the FDM for detailed description of local scale,aiming to balance costs and accuracy.Demonstrated advantages include higher accuracy(10 times for 1D PDEs,20 times for 2D PDEs)and lower costs(4 times for 1D PDEs,16 times for 2D PDEs)than the standard FDM.The MSNC also exhibits stable convergence and rigorous boundary condition satisfaction,showcasing the potential for hybrid of NN and numerical method.展开更多
Middle meningeal artery embolization(MMAE)has revolutionized chronic subdural hematoma management,yet procedural risks persist due to anatomical variability.We analyze a case report by Zhao et al describing transient ...Middle meningeal artery embolization(MMAE)has revolutionized chronic subdural hematoma management,yet procedural risks persist due to anatomical variability.We analyze a case report by Zhao et al describing transient diplopia caused by inadvertent embolization of the lacrimal artery via a dynamic middle meningeal–ophthalmic anastomosis.This correspondence advances three critical innovations in MMAE safety.First,intraoperative anastomotic unmasking—exposing occult middle meningeal-ophthalmic collaterals during particle injection—reveals dynamic vascular behavior missed by preoperative angiography,underscoring the need for adaptive imaging protocols.Second,hybrid embolization(liquid agents for proximal occlusion+particles for distal control)balances precision and safety,reducing reflux risks compared to monotherapy.Third,a 108-day follow-up establishes a benchmark for functional recovery,challenging assumptions about irreversible cranial nerve injuries and emphasizing structured postprocedural care.Collectively,these findings advocate for procedural agility,multimodal embolic strategies,and sustained rehabilitation to optimize MMAE outcomes while minimizing iatrogenic harm.展开更多
基金supported by the Key Research and Development Program of Henan Province under grant Nos.241111222900 and 241111210400the Natural Science Foundation of Henan under grant No.222300420583+3 种基金the Key Research Projects of Henan Higher Education Institutions under grant No.22A590003the National Natural Science Foundation of China under Grant 62103379the Key Science and Technology Program of Henan Province under grant No.232102220067the Maker Space Incubation Project under Grant No.2023ZCKJ102.
文摘Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHISAO)is proposed.In this paper,a snow blowing strategy was introduced during the initialization phase of the population to improve population diversity.Secondly,the dual-population iterative strategy of SAO has been replaced by a multi-population iterative strategy,which is supplemented with a position update formula for the water evaporation phase.Additionally,Cauchy mutation perturbation has been introduced in the snow melting phase.This set of improvements better balances the exploration and exploitation phases of the algorithm,enhancing its ability to pursue excellence.Finally,a fluid activation strategy is added to activate the potential of the algorithm when its update iterations enter stagnation,helping the algorithm to escape from the local optimum.Comparison experiments between PHISAO and six metaheuristics were conducted on the CEC(Congress on Evolutionary Computation)-2017 and CEC-2022 benchmark suites.The experimental results demonstrate that the PHISAO algorithm exhibits excellent performance and robustness.In addition,the PHISAO is applied into the unmanned aerial vehicle trajectory planning problem together with particle swarm optimization,beluga whale optimization,sand cat swarm optimization,and SAO.The simulation results show that the proposed PHISAO can plan the optimal trajectory in all two different maps.The proposed PHISAO objective function values were reduced by an average of 29.49%(map 1),and 18.34%(map 2)compared to SAO.
基金supported by the National Key Research and Development Program of China(2022YFB3607700)National Natural Science Foundation of China(62350011,62375014)+1 种基金Beijing Natural Science Foundation(1232031)Special Fund for Basic Scientific Research of Central Universities of China(2024CX11002).
文摘Tailoring multiple degrees-of-freedom(DoFs)to achieve high-dimensional laser field is crucial for advancing optical technologies.While recent advancements have demonstrated the ability to manipulate a limited number of DoFs,most existing methods rely on bulky optical components or intricate systems that employ time-consuming iterative methods and,most critically,the on-demand tailoring of multiple DoFs simultaneously through a compact,single element—remains underexplored.In this study,we propose an intelligent hybrid strategy that enables the simultaneous and customizable manipulation of six DoFs:wave vector,initial phase,spatial mode,amplitude,orbital angular momentum(OAM)and spin angular momentum(SAM).Our approach advances in phase-only property,which facilitates tailoring strategy experimentally demonstrated on a compact metasurface.A fabricated sample is tailored to realize arbitrary manipulation across six DoFs,constructing a 288-dimensional space.Notably,since the OAM eigenstates constitute an infinite dimensional Hilbert space,this proposal can be further extended to even higher dimensions.Proof-of-principle experiments confirm the effectiveness in manipulation capability and dimensionality.We envision that this powerful tailoring ability offers immense potential for multifunctional photonic devices across both classical and quantum scenarios and such compactness extending the dimensional capabilities for integration on-chip requirements.
基金Sponsored by the Natural Science Foundation of Hunan ProvinceChina(Grant No.13JJ3049)the Fundamental Research Funds for the Central Universities(Grant No.2012AA01A301-1)
文摘This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.
文摘In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.
基金funded by the National Natural Science Foundation of China (Grant No.61872126,No.62273290)supported by the Key project of Natural Science Foundation of Shandong Province (Grant No.ZR2020KF019).
文摘Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.
基金Shanghai Municipal Science and Technology Commission, China (No. 033012017).
文摘A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.
基金Supported by the National Science and Technology Support Program(2013BAG12B01)Foundational and Advanced Research Program General Project of Chongqing City(cstc2013jcyjjq60002)
文摘A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.
基金Project(61005008) supported by the National Natural Science Foundation of ChinaProject(JI300D004) supported by the COSTIND Application Foundation of China
文摘In order to take precaution and cure against intemet of vehicles （IOV） worm propagation in expressway, the IOV worm propagation and its corresponding anti-worm strategy were studied in expressway interchange terminal. According to omnirange driving in expressway interchange terminal and vehicular mobile communication environment, an IOV worm propagation model is constructed; and then according to the dynamic propagation law and destructiveness of IOV worm in this environment, a novel hybrid anti-worm strategy for confrontation is designed. This worm propagation model can factually simulates the IOV worm propagation in this interchange terminal environment; and this hybrid anti-worm strategy can effectively control IOV worm propagation in the environment, moreover, it can reduce the influence on network resource overhead.
基金Projects(61374140,61673173)supported by the National Natural Science Foundation of ChinaProjects(222201717006,222201714031)supported by the Fundamental Research Funds for the Central Universities,China
文摘A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.
文摘To realize effective utilization of renewable energy sources,a novel polymorphic topology with hybrid control strategy based LLC resonant converter was analyzed and designed in this paper.By combining the merits of a full bridge LLC resonant converter,three-level half bridge LLC resonant converter,and variable frequency control mode,the converter realizes an intelligent estimation of input voltage by automatically changing its internal cir-cuit topology.Under this control strategy,different input voltages determine different operation modes.This is achieved in full bridge LLC mode when the input voltage is low.If the input voltage rises to a certain level,it operates in three-level half bridge LLC mode.These switches are digital and entirely carried out by the DSP(Digi-tal Signal Processor),which means that an auxiliary circuit is unnecessary,where a simple strategy of software modification can be utilized.Experimental results of a 500W prototype with 100V~600V input voltage and full load efficiency of up to 92%are developed to verify feasibility and practicability.This type of converter is suitable for applications with an ultra-wide input voltage range,such as wind turbines,photovoltaic generators,bioenergy,and other renewable energy sources.
文摘In this paper, a hybrid control strategy for a matrix converter fed wind energy conversion system is presented. Since the wind speed may vary, output parameters like power, frequency and voltage may fluctuate. Hence it is necessary to design a system that regulates output parameters, such as voltage and frequency, and thereby provides a constant voltage and frequency output from the wind energy conversion system. Matrix converter is used in the proposed solution as the main power conditioner as a more efficient alternative when compared to traditional back-back converter structure. To control the output voltage, a vector modulation based refined control structure is used. A power tracker is included to maximize the mechanical output power of the turbine. Over current protection and clamp circuit input protection have been introduced to protect the system from over current. It reduces the spikes generated at the output of the converter. The designed system is capable of supplying an output voltage of constant frequency and amplitude within the expected ranges of input during the operation. The matrix converter control using direct modulation method, modified Venturini modulation method and vector modulation method was simulated, the results were compared and it was inferred that vector modulation method was superior to the other two methods. With the proposed technique, voltage transfer ratio and harmonic profile have been improved compared to the other two modulation techniques. The behaviour of the system is corroborated by MATLAB Simulink, and hardware is realized using an FPGA controller. Experimental results are found to be matching with the simulation results.
基金supported by the National Natural Science Foundation of China (52275480)the Guizhou Provincial Science and Technology Program of Qiankehe Zhongdi Guiding ([2023]02)+1 种基金the Guizhou Provincial Science and Technology Program of Qiankehe Platform Talent Project (GCC[2023]001)the Guizhou Provincial Science and Technology Project of Qiankehe Platform Project (KXJZ[2024]002).
文摘Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.
文摘Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
基金the National Natural Science Foundation of China(No.U1564208).
文摘Energy optimization management can make fuel cell truck(FCT)power system more efficient,so as to improve vehicle fuel economy.When the structure of power source system and the torque distribution strategy are determined,the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources.The paper simulates the assistance of the intelligent transport system(ITS)and carries out the eco-velocity planning using the traffic signal light.On this basis,in order to further improve the energy efficiency of FCT,a model predictive control(MPC)-based energy source optimization management strategy is innovatively developed,which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption.Under the scenarios of signalized intersections,based on the planned eco-velocity,the off-line simulation results show that the proposed MPC-based energy source management strategy(ESMS)can reduce hydrogen consumption of fuel cell up to 7%compared with the existing rule-based ESMS.Finally,the Hardware-in-the-Loop(HiL)simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.
基金jointly supported by the National Social Science Foundation of China(Grant Nos.:08ATQ003 and 10&ZD134)
文摘Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.
基金financial support from the National Natural Science Foundation of China(51872156,22075163)the National Key Research Program(2020YFC2201103,2020YFA0210702)+1 种基金the China Postdoctoral Science Foundation funded project(2020 M670343)the Shuimu Tsinghua Scholar Program。
文摘Hybrid catalysts based on iron phthalocyanine(FePc)have raised much attention due to their promising applications in electrocatalytic oxygen reduction reaction(ORR).Various hybridization strategies have been developed for improving their activity and durability.However,the influence of different hybridization strategies on their catalytic performance remains unclear.In this study,Fe Pc was effectively distributed on molybdenum disulfide(MoS_(2))forming Fe Pc-based hybrid catalysts,namely Fe Pc-MoS_(2),Fe Pc*-MoS_(2),and Fe Pc-Py-MoS_(2),respectively,to disclose the related influence.Through direct hybridization,the stacked and highly dispersed Fe Pc on MoS_(2)resulted in Fe Pc-MoS_(2),and Fe Pc*-MoS_(2),respectively,in which the substrate and Fe Pc are mainly bound through van der Waals interactions.Through covalent hybridization strategy using pyridyl(Py)as a linker,Fe Pc-Py-MoS_(2)hybrid catalyst was prepared.Experimental and theoretical results disclosed that the linker hybridization of Fe Pc and MoS_(2)facilitated the exposure of Fe-N4 sites,maintained the intrinsic activity of Fe Pc by forming a more dispersed phase and increased the durability via Fe-N bonding,rendering the Fe Pc-Py-MoS_(2)an excellent ORR hybrid catalyst.Compared with van der Waals hybridized Fe Pc-MoS_(2)and Fe Pc*-MoS_(2)in alkaline media,the linker hybridized Fe Pc-Py-MoS_(2)showed an obviously enhanced ORR activity with a half-wave potential(E_(1/2))of 0.88 V vs RHE and an ultralow Tafel slope of 26 m V dec-1.Besides,the Fe Pc-Py-MoS_(2)exhibited a negligible decay of E_(1/2) after 50,000 CV cycles for ORR,showing its superior durability.This work gives us more insight into the influence of different hybrid strategies on Fe Pc catalysts and provides further guidance for the development of highly efficient and durable ORR catalysts.
基金This paper is supported by the National Youth Natural Science Foundation of China(61802208)the National Natural Science Foundation of China(61572261 and 61876089)+3 种基金the Natural Science Foundation of Anhui(1908085MF207,KJ2020A1215,KJ2021A1251 and KJ2021A1253)the Excellent Youth Talent Support Foundation of Anhui(gxyqZD2019097 and gxyqZD2021142)the Postdoctoral Foundation of Jiangsu(2018K009B)the Foundation of Fuyang Normal University(TDJC2021008).
文摘In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is verified.Through chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then formed.In the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements,the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.
基金supported by the National Natural Science Foundation of China(Grant No.92152301).
文摘Numerical simulation is dominant in solving partial differential equations(PDEs),but balancing fine-grained grids with low computational costs is challenging.Recently,solving PDEs with neural networks(NNs)has gained interest,yet cost-effectiveness and high accuracy remain a challenge.This work introduces a novel paradigm for solving PDEs,called multi-scale neural computing(MSNC),considering spectral bias of NNs and local approximation properties in the finite difference method(FDM).The MSNC decomposes the solution with a NN for efficient capture of global scale and the FDM for detailed description of local scale,aiming to balance costs and accuracy.Demonstrated advantages include higher accuracy(10 times for 1D PDEs,20 times for 2D PDEs)and lower costs(4 times for 1D PDEs,16 times for 2D PDEs)than the standard FDM.The MSNC also exhibits stable convergence and rigorous boundary condition satisfaction,showcasing the potential for hybrid of NN and numerical method.
文摘Middle meningeal artery embolization(MMAE)has revolutionized chronic subdural hematoma management,yet procedural risks persist due to anatomical variability.We analyze a case report by Zhao et al describing transient diplopia caused by inadvertent embolization of the lacrimal artery via a dynamic middle meningeal–ophthalmic anastomosis.This correspondence advances three critical innovations in MMAE safety.First,intraoperative anastomotic unmasking—exposing occult middle meningeal-ophthalmic collaterals during particle injection—reveals dynamic vascular behavior missed by preoperative angiography,underscoring the need for adaptive imaging protocols.Second,hybrid embolization(liquid agents for proximal occlusion+particles for distal control)balances precision and safety,reducing reflux risks compared to monotherapy.Third,a 108-day follow-up establishes a benchmark for functional recovery,challenging assumptions about irreversible cranial nerve injuries and emphasizing structured postprocedural care.Collectively,these findings advocate for procedural agility,multimodal embolic strategies,and sustained rehabilitation to optimize MMAE outcomes while minimizing iatrogenic harm.
