To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu...This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphorene/transition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×10^(6) °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures.展开更多
This paper deeply introduces a brand-new research method for the synchronous characteristics of DC microgrid bus voltage and an improved synchronous control strategy.This method mainly targets the problem of bus volta...This paper deeply introduces a brand-new research method for the synchronous characteristics of DC microgrid bus voltage and an improved synchronous control strategy.This method mainly targets the problem of bus voltage oscillation caused by the bifurcation behavior of DC microgrid converters.Firstly,the article elaborately establishes a mathematical model of a single distributed power source with hierarchical control.On this basis,a smallworld network model that can better adapt to the topology structure of DC microgrids is further constructed.Then,a voltage synchronization analysis method based on the main stability function is proposed,and the synchronous characteristics of DC bus voltage are deeply studied by analyzing the size of the minimum non-zero eigenvalue.In view of the situation that the line coupling strength between distributed power sources is insufficient to achieve bus voltage synchronization,this paper innovatively proposes a new improved adaptive controller to effectively control voltage synchronization.And the convergence of the designed controller is strictly proved by using Lyapunov’s stability theorem.Finally,the effectiveness and feasibility of the designed controller in this paper are fully verified through detailed simulation experiments.After comparative analysis with the traditional adaptive controller,it is found that the newly designed controller can make the bus voltages of each distributed power source achieve synchronization more quickly,and is significantly superior to the traditional adaptive controller in terms of anti-interference performance.展开更多
The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,wh...The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.展开更多
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ...Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.展开更多
A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is establis...A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.展开更多
The relative importance of each pollution factor in analytical hierarchy process (AHP) method comes from pooling expert opinions in general. Because expert opinions are based on information and judgment criteria, dete...The relative importance of each pollution factor in analytical hierarchy process (AHP) method comes from pooling expert opinions in general. Because expert opinions are based on information and judgment criteria, determining their weight may lead to uncertainty.. Therefore, an improved AHP method had been developed. The process of the improved AHP method involved four key procedures. The weights of pollution factors were completely related to the objective monitoring data through the standardization of these procedures. The environmental comprehensive quality of water and sediment of Xuanwu Lake, Nanjing, China had been evaluated. The environmental quality comprehensive indices (EQCI) of the water in 1991-1995 and 1996-2000 were 3.32 and 1.85, respectively, indicating that the water quality improved. The EQCI of the sediment in 1995 and 2000 indicates that the sediment contamination decreased from 1995 to 2000. Such results agreed with the fact that the lake had been under comprehensive control. However, with the classical AHP method, the EQCI of the sediment in the northwestern part of the lake may have indicated that sediment contamination increased from 1995 to 2000. The discrepancy may have resulted from the judgment difference of the experts. The improved AHP method can avoid arbitrariness of subjective judgment and can reflect the real influential factors of environment pollution in different periods or regions.展开更多
The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in thes...Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.展开更多
With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an import...With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.展开更多
To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can ...To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.展开更多
Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for...Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.展开更多
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ...An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.展开更多
Flying Ad hoc Network(FANET)has drawn significant consideration due to its rapid advancements and extensive use in civil applications.However,the characteristics of FANET including high mobility,limited resources,and ...Flying Ad hoc Network(FANET)has drawn significant consideration due to its rapid advancements and extensive use in civil applications.However,the characteristics of FANET including high mobility,limited resources,and distributed nature,have posed a new challenge to develop a secure and ef-ficient routing scheme for FANET.To overcome these challenges,this paper proposes a novel cluster based secure routing scheme,which aims to solve the routing and data security problem of FANET.In this scheme,the optimal cluster head selection is based on residual energy,online time,reputation,blockchain transactions,mobility,and connectivity by using Improved Artificial Bee Colony Optimization(IABC).The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities.Further,a lightweight blockchain consensus algorithm,AI-Proof of Witness Consensus Algorithm(AI-PoWCA)is proposed,which utilizes the optimal cluster head for mining.In AI-PoWCA,the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51%attack.Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90%packet delivery ratio,lowest end-to-end delay,highest throughput,resilience against security attacks,and superior in block processing time.展开更多
Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an ...Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.展开更多
The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.Th...The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox.This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction.The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information.Therefore,this method should be capable of timely diagnosis and monitoring.Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained.The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information,100%fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods.The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.展开更多
A new efficient protocol-proving algorithm was proposed for verifying security protocols. This algorithm is based on the improved authentication tests model, which enhances the original model by formalizing the messag...A new efficient protocol-proving algorithm was proposed for verifying security protocols. This algorithm is based on the improved authentication tests model, which enhances the original model by formalizing the message reply attack. With exact causal dependency relations between messages in this model, the protocol-proving algorithm can avoid the state explosion caused by asynchronous. In order to get the straight proof of security protocols, three authentication theorems are exploited for evaluating the agreement and distinction properties. When the algorithm terminates, it outputs either the proof results or the potential flaws of the security protocol. The experiment shows that the protocol-proving algorithm can detect the type flaw attack on Neuman-Stubblebine protocol, and prove the correctness of NSL protocol by exploring only 10 states.展开更多
In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enh...In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enhancement. It is a weighted average filter integrating the average filter and the improved range filter. The weighted factors are deduced in terms of a cost function, which are adjustable to different images. To validate the proposed method, extensive tests are carried out on a developed TDI CCD imaging system. The experimental results confirm that this preprocessing method can fulfill the noise removal and edge sharpening simultaneously, which can play an important role in remote sensing field.展开更多
The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to app...The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to approximate the nonlinear functions are provided.This approach improves the precision of the approximation for the nonlinear functions,conquers the drawback of the FastSLAM1.0 by using a model ignoring the measurement data,enhances the estimation consistency of the robot pose,and reduces the degradation speed of the particle in FastSLAM algorithm.Simulation results demonstrate the excellence of the proposed algorithm and give the noise parameter influence on the proposed algorithm.展开更多
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
基金funded by the National Natural Science Foundation of China(Grant No.52375547)the Natural Science Foundation of Chongqing,China(Grant Nos.CSTB2022NSCQ-BHX0736 and CSTB2022NSCQ-MSX1523)the Chongqing Scientific Institution Incentive Performance Guiding Special Projects(Grant No.CSTB2024JXJL-YFX0034).
文摘This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphorene/transition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×10^(6) °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures.
基金supported by the National Natural Science Foundation of China(Nos.51767017 and 51867015)the Basic Research and Innovation Group Project of Gansu(No.18JR3RA13)the Major Science and Technology Project of Gansu(No.19ZD2GA003).
文摘This paper deeply introduces a brand-new research method for the synchronous characteristics of DC microgrid bus voltage and an improved synchronous control strategy.This method mainly targets the problem of bus voltage oscillation caused by the bifurcation behavior of DC microgrid converters.Firstly,the article elaborately establishes a mathematical model of a single distributed power source with hierarchical control.On this basis,a smallworld network model that can better adapt to the topology structure of DC microgrids is further constructed.Then,a voltage synchronization analysis method based on the main stability function is proposed,and the synchronous characteristics of DC bus voltage are deeply studied by analyzing the size of the minimum non-zero eigenvalue.In view of the situation that the line coupling strength between distributed power sources is insufficient to achieve bus voltage synchronization,this paper innovatively proposes a new improved adaptive controller to effectively control voltage synchronization.And the convergence of the designed controller is strictly proved by using Lyapunov’s stability theorem.Finally,the effectiveness and feasibility of the designed controller in this paper are fully verified through detailed simulation experiments.After comparative analysis with the traditional adaptive controller,it is found that the newly designed controller can make the bus voltages of each distributed power source achieve synchronization more quickly,and is significantly superior to the traditional adaptive controller in terms of anti-interference performance.
基金Supported by the EDD of China(No.80912020104)the Science and Technology Commission of Shanghai Municipality(No.22ZR1427700 and No.23692106900).
文摘The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.
基金The National Natural Science Foundation of China(No.61074147)the Natural Science Foundation of Guangdong Province(No.S2011010005059)+2 种基金the Foundation of Enterprise-University-Research Institute Cooperation from Guangdong Province and Ministry of Education of China(No.2012B091000171,2011B090400460)the Science and Technology Program of Guangdong Province(No.2012B050600028)the Science and Technology Program of Huadu District,Guangzhou(No.HD14ZD001)
文摘Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFC0805804,2017YFC0805801)
文摘A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.
基金National Natural Science Foundation of China, No.40771186.
文摘The relative importance of each pollution factor in analytical hierarchy process (AHP) method comes from pooling expert opinions in general. Because expert opinions are based on information and judgment criteria, determining their weight may lead to uncertainty.. Therefore, an improved AHP method had been developed. The process of the improved AHP method involved four key procedures. The weights of pollution factors were completely related to the objective monitoring data through the standardization of these procedures. The environmental comprehensive quality of water and sediment of Xuanwu Lake, Nanjing, China had been evaluated. The environmental quality comprehensive indices (EQCI) of the water in 1991-1995 and 1996-2000 were 3.32 and 1.85, respectively, indicating that the water quality improved. The EQCI of the sediment in 1995 and 2000 indicates that the sediment contamination decreased from 1995 to 2000. Such results agreed with the fact that the lake had been under comprehensive control. However, with the classical AHP method, the EQCI of the sediment in the northwestern part of the lake may have indicated that sediment contamination increased from 1995 to 2000. The discrepancy may have resulted from the judgment difference of the experts. The improved AHP method can avoid arbitrariness of subjective judgment and can reflect the real influential factors of environment pollution in different periods or regions.
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （50579009, 70471090） the National 10 th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02 - 02） and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [2002] 350）.
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
基金Supported by the Natural Science Foundation of Jiangsu Province (BK20211037)the Science and Technology Development Fund of Wuxi (N20201011)the Nanjing University of Information Science and Technology Wuxi Campus District graduate innovation Project。
文摘Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.
基金supported by National Natural Science Foundation of China (Grant No. 51677058)。
文摘With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.
基金Project(60874070) supported by the National Natural Science Foundation of China
文摘To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.
基金the Science and Technology Cooperation Research and Development Project of Sichuan Provincial Academy and University(Grant No.2019YFSY0024)the Key Research and Development Program in Sichuan Province of China(Grant No.2019YFG0050)the Natural Science Foundation of Guangxi Province of China(Grant No.AD19245021).
文摘Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
基金Supported by the National Natural Science Foundation of China(51175262)the Research Fund for Doctoral Program of Higher Education of China(20093218110020)+2 种基金the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)the Jiangsu Province Industry-Academy-Research Grant(BY201220116)the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
文摘An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.
基金This paper is supported in part by the National Natural Science Foundation of China(61701322)the Young and Middle-aged Science and Technology Innovation Talent Support Plan of Shenyang(RC190026)+1 种基金the Natural Science Foundation of Liaoning Province(2020-MS-237)the Liaoning Provincial Department of Education Science Foundation(JYT19052).
文摘Flying Ad hoc Network(FANET)has drawn significant consideration due to its rapid advancements and extensive use in civil applications.However,the characteristics of FANET including high mobility,limited resources,and distributed nature,have posed a new challenge to develop a secure and ef-ficient routing scheme for FANET.To overcome these challenges,this paper proposes a novel cluster based secure routing scheme,which aims to solve the routing and data security problem of FANET.In this scheme,the optimal cluster head selection is based on residual energy,online time,reputation,blockchain transactions,mobility,and connectivity by using Improved Artificial Bee Colony Optimization(IABC).The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities.Further,a lightweight blockchain consensus algorithm,AI-Proof of Witness Consensus Algorithm(AI-PoWCA)is proposed,which utilizes the optimal cluster head for mining.In AI-PoWCA,the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51%attack.Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90%packet delivery ratio,lowest end-to-end delay,highest throughput,resilience against security attacks,and superior in block processing time.
基金Supported by the National Natural Science Foundation of China(No.61273035,71471135)
文摘Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.
基金National Key R&D Program of China(2016YFd01304)Postgraduate Innovation Support Project of Shijiazhuang Tiedao University(YC20035).
文摘The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox.This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction.The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information.Therefore,this method should be capable of timely diagnosis and monitoring.Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained.The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information,100%fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods.The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.
基金The National High Technology Research and Development Program of China(863Pro-gram)(No.2005AA145110)
文摘A new efficient protocol-proving algorithm was proposed for verifying security protocols. This algorithm is based on the improved authentication tests model, which enhances the original model by formalizing the message reply attack. With exact causal dependency relations between messages in this model, the protocol-proving algorithm can avoid the state explosion caused by asynchronous. In order to get the straight proof of security protocols, three authentication theorems are exploited for evaluating the agreement and distinction properties. When the algorithm terminates, it outputs either the proof results or the potential flaws of the security protocol. The experiment shows that the protocol-proving algorithm can detect the type flaw attack on Neuman-Stubblebine protocol, and prove the correctness of NSL protocol by exploring only 10 states.
基金supported by the National Key Research and Development Project of China(No.2016YFB0501202)
文摘In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enhancement. It is a weighted average filter integrating the average filter and the improved range filter. The weighted factors are deduced in terms of a cost function, which are adjustable to different images. To validate the proposed method, extensive tests are carried out on a developed TDI CCD imaging system. The experimental results confirm that this preprocessing method can fulfill the noise removal and edge sharpening simultaneously, which can play an important role in remote sensing field.
基金National High-Tech Research and Development Program of China(No.2003AA1Z2130)Science and Technology Project of Zhejiang Province,China(No.2005C11001-02)
文摘The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to approximate the nonlinear functions are provided.This approach improves the precision of the approximation for the nonlinear functions,conquers the drawback of the FastSLAM1.0 by using a model ignoring the measurement data,enhances the estimation consistency of the robot pose,and reduces the degradation speed of the particle in FastSLAM algorithm.Simulation results demonstrate the excellence of the proposed algorithm and give the noise parameter influence on the proposed algorithm.
