This paper is concerned with the robust control synthesis of autonomous underwater vehicle （AUV） for general path following maneuvers. First, we present maneuvering kinematics and vehicle dynamics in a unified frame...This paper is concerned with the robust control synthesis of autonomous underwater vehicle （AUV） for general path following maneuvers. First, we present maneuvering kinematics and vehicle dynamics in a unified framework. Based on H∞ loop-shaping procedure, the 2-DOF autopilot controller has been presented to enhance stability and path tracking. By use of model reduction, the high-order control system is reduced to one with reasonable order, and further the scaled low-order controller has been analyzed in both the frequency and the time domains. Finally, it is shown that the autopilot control system provides robust performance and stability against prescribed levels of uncertainty.展开更多
In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that...In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.展开更多
This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous drivi...This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.展开更多
We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases...We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases. In the first phase, CDC employs the idea of density-based clustering algorithm to split the original data into a number of fragmented clusters. At the same time, CDC cuts off the noises and outliers. In the second phase, CDC employs the concept of K-means clustering algorithm to select a greater cluster to be the center. Then, the greater cluster merges some smaller clusters which satisfy some constraint rules. Due to the merged clusters around the center cluster, the clustering results show high accuracy. Moreover, CDC reduces the calculations and speeds up the clustering process. In this paper, the accuracy of CDC is evaluated and compared with those of K-means, hierarchical clustering, and the genetic clustering algorithm (GCA) proposed in 2004. Experimental results show that CDC has better performance.展开更多
The computer aided engineering is aiming at the numerical simulation-the important link in the advanced digital manufacturing.Its second development based on Ansys platform can be carried out often.In common,the Visua...The computer aided engineering is aiming at the numerical simulation-the important link in the advanced digital manufacturing.Its second development based on Ansys platform can be carried out often.In common,the Visual Basic and APDL are important development tools and are applied in the product design at the same time.In the paper,the secondary development flow and method based on Ansys is described.The parameter design and analysis process of the bridge girder erecting equipment is carried on with Ansys software and its secondary development tools—APDL and Visual Basics,including the interact between the mode of Ansys batch solving and Visual Basic.The method speeds up design and enhances the product the quality and the perfor-mance.展开更多
For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the...For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.展开更多
In digital fingerprinting, preventing piracy of images by colluders is an important and tedious issue. Each image will be embedded with a unique User IDentification （UID） code that is the fingerprint for tracking th...In digital fingerprinting, preventing piracy of images by colluders is an important and tedious issue. Each image will be embedded with a unique User IDentification （UID） code that is the fingerprint for tracking the authorized user. The proposed hiding scheme makes use of a random number generator to scramble two copies of a UID, which will then be hidden in the randomly selected medium frequency coefficients of the host image. The linear support vector machine （SVM） will be used to train classifications by calculating the normalized correlation （NC） for the 2class UID codes. The trained classifications will be the models used for identifying unreadable UID codes. Experimental results showed that the success of predicting the unreadable UID codes can be increased by applying SVM. The proposed scheme can be used to provide protections to intellectual property rights of digital images aad to keep track of users to prevent collaborative piracies.展开更多
Albeit with the notable benefits associated with Dirichlet crash frequency models and spatial ones,there is little research dedicated to exploring their combined advantages.Such ensemble approach could be a viable alt...Albeit with the notable benefits associated with Dirichlet crash frequency models and spatial ones,there is little research dedicated to exploring their combined advantages.Such ensemble approach could be a viable alternative to existing models as it accounts for the unobserved heterogeneity by relaxing the constraints of specific distribution placed on the intercept while addressing the spatial correlations among roadway entities.To fill this gap,the authors aimed to develop Dirichlet semi-parametric models over the overdispersed generalized linear model framework while also incorporating spatially structured random effects using a distance-based weight matrix.Five models were developed which include four semi-parametric with flexible intercept and one parametric base model for comparison purposes.The four semi-parametric models entailed two models with a popular specification of stick-breaking Dirichlet process(DP)and two models with an alternative approach of Dirichlet distribution(DD),which are first applied in the field of traffic safety.All four models were estimated for mixture of points(discrete)and mixture of normals(continuous).The posterior density plots for the precision parameter justified the employment of the flexible Dirichlet approach to fit the crash data and supported the assumed prior for the precision parameter.All four Dirichlet models demonstrated the presence of distinct subpopulations suggesting that the intercepts of the models were not generated from a common distribution.The DP model based on mixture of normals illustrated better performance indicating its potential superiority to fit both insample and out-of-sample crash data.This finding indicated that the approach of continuous densities,unlike discrete points,may lend more flexibility to fit the data.展开更多
Deforestation in the Brazilian Amazon,with approximately 17%ofthe biome lost,remains a critical global issue.This study analyzes deforestation dynamics using various quantitative approaches,including linear regression...Deforestation in the Brazilian Amazon,with approximately 17%ofthe biome lost,remains a critical global issue.This study analyzes deforestation dynamics using various quantitative approaches,including linear regression,univariate time series models,and multivariate dynamic time series models.While traditional models likeOLS and ARIMA provided valuable insights,they were limited incapturing the complex temporal dynamics of deforestation.In contrast,the BVAR model demonstrated superior performance by effectively modeling lagged effects and feedback loops amongvariables.Granger causality tests confirmed that cattle ranchingsignificantly precedes changes in deforestation rates.Impulse Response Function(IRF)analysis revealed that shocks in timber extraction,cattle ranching,and soybean production lead to significant increase deforestation,with effects emerging in the 1st,4th,and 8th years,respectively.Variance decomposition showed thatcattle ranching dominates deforestation in the short term,whiletimber extraction becomes the primary driver in the long term.These findings underscore the effectiveness of BVAR in capturing the complexities of land-use change dynamics in the Amazon.展开更多
The enormous advantages of active transportation lead the transportation research focus towards enhancing the walking and biking trips.The present study explored the influential factors to the walking and biking trave...The enormous advantages of active transportation lead the transportation research focus towards enhancing the walking and biking trips.The present study explored the influential factors to the walking and biking travel frequency based on data obtained from the National Household Travel Survey California add-on survey.The study features some highlights.First,bivariate models were used to account for the common unobserved heterogeneity shared at both household and personal levels.Second,endogeneity was explicitly considered.Third,both variable importance ranking and correlation analysis are employed to determine the different features to be fed into each of the joint models.The results illustrated that the models developed with endogeneity performed better than the models without endogeneity.Four influential variables which includes mode to work by bicycle,public transit usage,count of household members,and multiple race responses,tend to have statistically significant impacts on walking and biking trips.展开更多
基金a part of the project titled "Development of Key Marine Equipments for Enhancement of Ocean Industry-Development of Underwater Manipulator and Thrusting System Driven by Electric Motor" funded by the Ministry of Land, Transport and Maritime Affairs, Korea
文摘This paper is concerned with the robust control synthesis of autonomous underwater vehicle （AUV） for general path following maneuvers. First, we present maneuvering kinematics and vehicle dynamics in a unified framework. Based on H∞ loop-shaping procedure, the 2-DOF autopilot controller has been presented to enhance stability and path tracking. By use of model reduction, the high-order control system is reduced to one with reasonable order, and further the scaled low-order controller has been analyzed in both the frequency and the time domains. Finally, it is shown that the autopilot control system provides robust performance and stability against prescribed levels of uncertainty.
文摘In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.
基金funded by Chongqing Science and Technology Bureau (No.cstc2021jsyj-yzysbAX0008)Chongqing University of Arts and Sciences (No.P2021JG13)2021 Humanities and Social Sciences Program of Chongqing Education Commission (No.21SKGH227).
文摘This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.
文摘We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases. In the first phase, CDC employs the idea of density-based clustering algorithm to split the original data into a number of fragmented clusters. At the same time, CDC cuts off the noises and outliers. In the second phase, CDC employs the concept of K-means clustering algorithm to select a greater cluster to be the center. Then, the greater cluster merges some smaller clusters which satisfy some constraint rules. Due to the merged clusters around the center cluster, the clustering results show high accuracy. Moreover, CDC reduces the calculations and speeds up the clustering process. In this paper, the accuracy of CDC is evaluated and compared with those of K-means, hierarchical clustering, and the genetic clustering algorithm (GCA) proposed in 2004. Experimental results show that CDC has better performance.
文摘The computer aided engineering is aiming at the numerical simulation-the important link in the advanced digital manufacturing.Its second development based on Ansys platform can be carried out often.In common,the Visual Basic and APDL are important development tools and are applied in the product design at the same time.In the paper,the secondary development flow and method based on Ansys is described.The parameter design and analysis process of the bridge girder erecting equipment is carried on with Ansys software and its secondary development tools—APDL and Visual Basics,including the interact between the mode of Ansys batch solving and Visual Basic.The method speeds up design and enhances the product the quality and the perfor-mance.
基金supported by the National Natural Science Foundation of China(62033010)Qing Lan Project of Jiangsu Province(R2023Q07)。
文摘For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.
文摘In digital fingerprinting, preventing piracy of images by colluders is an important and tedious issue. Each image will be embedded with a unique User IDentification （UID） code that is the fingerprint for tracking the authorized user. The proposed hiding scheme makes use of a random number generator to scramble two copies of a UID, which will then be hidden in the randomly selected medium frequency coefficients of the host image. The linear support vector machine （SVM） will be used to train classifications by calculating the normalized correlation （NC） for the 2class UID codes. The trained classifications will be the models used for identifying unreadable UID codes. Experimental results showed that the success of predicting the unreadable UID codes can be increased by applying SVM. The proposed scheme can be used to provide protections to intellectual property rights of digital images aad to keep track of users to prevent collaborative piracies.
文摘Albeit with the notable benefits associated with Dirichlet crash frequency models and spatial ones,there is little research dedicated to exploring their combined advantages.Such ensemble approach could be a viable alternative to existing models as it accounts for the unobserved heterogeneity by relaxing the constraints of specific distribution placed on the intercept while addressing the spatial correlations among roadway entities.To fill this gap,the authors aimed to develop Dirichlet semi-parametric models over the overdispersed generalized linear model framework while also incorporating spatially structured random effects using a distance-based weight matrix.Five models were developed which include four semi-parametric with flexible intercept and one parametric base model for comparison purposes.The four semi-parametric models entailed two models with a popular specification of stick-breaking Dirichlet process(DP)and two models with an alternative approach of Dirichlet distribution(DD),which are first applied in the field of traffic safety.All four models were estimated for mixture of points(discrete)and mixture of normals(continuous).The posterior density plots for the precision parameter justified the employment of the flexible Dirichlet approach to fit the crash data and supported the assumed prior for the precision parameter.All four Dirichlet models demonstrated the presence of distinct subpopulations suggesting that the intercepts of the models were not generated from a common distribution.The DP model based on mixture of normals illustrated better performance indicating its potential superiority to fit both insample and out-of-sample crash data.This finding indicated that the approach of continuous densities,unlike discrete points,may lend more flexibility to fit the data.
基金supported by the Coordination for the Improvement of Higher Education Personnel-Brazil(CAPES)-Financing Code 001,through the Social Demand Program(Programa de Demanda Social-DS)for the doctoral scholarship.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.
文摘Deforestation in the Brazilian Amazon,with approximately 17%ofthe biome lost,remains a critical global issue.This study analyzes deforestation dynamics using various quantitative approaches,including linear regression,univariate time series models,and multivariate dynamic time series models.While traditional models likeOLS and ARIMA provided valuable insights,they were limited incapturing the complex temporal dynamics of deforestation.In contrast,the BVAR model demonstrated superior performance by effectively modeling lagged effects and feedback loops amongvariables.Granger causality tests confirmed that cattle ranchingsignificantly precedes changes in deforestation rates.Impulse Response Function(IRF)analysis revealed that shocks in timber extraction,cattle ranching,and soybean production lead to significant increase deforestation,with effects emerging in the 1st,4th,and 8th years,respectively.Variance decomposition showed thatcattle ranching dominates deforestation in the short term,whiletimber extraction becomes the primary driver in the long term.These findings underscore the effectiveness of BVAR in capturing the complexities of land-use change dynamics in the Amazon.
文摘The enormous advantages of active transportation lead the transportation research focus towards enhancing the walking and biking trips.The present study explored the influential factors to the walking and biking travel frequency based on data obtained from the National Household Travel Survey California add-on survey.The study features some highlights.First,bivariate models were used to account for the common unobserved heterogeneity shared at both household and personal levels.Second,endogeneity was explicitly considered.Third,both variable importance ranking and correlation analysis are employed to determine the different features to be fed into each of the joint models.The results illustrated that the models developed with endogeneity performed better than the models without endogeneity.Four influential variables which includes mode to work by bicycle,public transit usage,count of household members,and multiple race responses,tend to have statistically significant impacts on walking and biking trips.
