To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development...To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.展开更多
The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and e...The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.展开更多
In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuz...In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.展开更多
Based on rational behavior model of three layers, a tracking control system is designed for straight line tracking which is commonly used in underwater survey missions. An intelligent PID control law implemented as pl...Based on rational behavior model of three layers, a tracking control system is designed for straight line tracking which is commonly used in underwater survey missions. An intelligent PID control law implemented as planning level during the control system using transverse deviation is came up with. Continuous tracking of path expressed by a point sequence can be realized by the law. Firstly, a path tracking control system based on rational behavior model of three layers is designed, mainly satisfying the needs of underactuated AUV. Since there is no need to perform spatially coupled maneuvers, the 3D path tracking control is decoupled into planar 2D path tracking and depth or height tracking separately. Secondly, planar path tracking controller is introduced. For the reason that more attention is paid to comparing with vertical position control, transverse deviation in analytical form is derived. According to the Lyapunov direct theory, control law is designed using discrete PID algorithm whose parameters obey adaptive fuzzy adjustment. Reference heading angle is given as an output of the guidance controller conducted by lateral deviation together with its derivative. For the purpose of improving control quality and facilitating parameter modifying, data normalize modules based on Sigmoid function are applied to input-output data manipulation. Lastly, a sequence of experiments was carried out successfully, including tests in Longfeng lake and at the Yellow sea. In most challenging sea conditions, tracking errors of straight line are below 2 m in general. The results show that AUV is able to compensate the disturbance brought by sea current. The provided test results demonstrate that the designed guidance controller guarantees stably and accurately straight route tracking. Besides, the proposed control system is accessible for continuous comb-shaped path tracking in region searching.展开更多
An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line p...An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.展开更多
During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this...During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this paper establishes a relationship between the degree of compaction K and strain ε. The relationship between the compaction degree K and the shear strength of soil(cohesion c and frictional angle φ) was clearly established through indoor experiments. The subroutine UMAT in ABAQUS finite element numerical software was developed to realize an accurate calculation of the subgrade soil compaction quality. This value was compared and analyzed against the assumed compaction value of the model, thereby verifying the accuracy of the intelligent compaction calculation results for subgrade soil. On this basis, orthogonal tests of the influential factors(frequency, amplitude, and quality) for the degree of compaction and sensitivity analysis were carried out. Finally, the ‘acceleration intelligent compaction value’, which is based on the acceleration signal, is proposed for a compaction meter value that indicates poor accuracy. The research results can provide guidance and basis for further research into the accurate control of compaction quality for roadbeds and pavements.展开更多
The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circu...The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.展开更多
In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested...In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested. The IDSS possesses selflearning and adaptive properties, and has been used for managing and analyzing the optimal operational conditions since June 1992. Electric energy consumption has been reduced remarkably and the coefficient of recovery of cobalt and nickel has been increased.展开更多
This paper describes the structure and function of the intelligent decision support system (IDSS) on the process of nickel matte smelter. The knowledge and model base system based on fuzzy-decision rules ale specially...This paper describes the structure and function of the intelligent decision support system (IDSS) on the process of nickel matte smelter. The knowledge and model base system based on fuzzy-decision rules ale specially suggested. The IDSS possesses the self-learning and adaptive properties, andhas been used for managing and analyzing the production information, optimizing the composition of the charge mixture, and deciding the optimal operational conditions. Electric energy consumption has been reduced remarkably and the yield of nickel has been increased.展开更多
文摘To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.
基金Project(41574123)supported by the National Natural Science Foundation of ChinaProject(2015zzts250)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2013FY110800)supported by the National Basic Research Scientific Program of China
文摘The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.
文摘In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.
基金Projects(51179035,51279221) supported by the National Natural Science Foundation of ChinaProject(2014M561333) supported by Postdoctoral Science Foundation of China
文摘Based on rational behavior model of three layers, a tracking control system is designed for straight line tracking which is commonly used in underwater survey missions. An intelligent PID control law implemented as planning level during the control system using transverse deviation is came up with. Continuous tracking of path expressed by a point sequence can be realized by the law. Firstly, a path tracking control system based on rational behavior model of three layers is designed, mainly satisfying the needs of underactuated AUV. Since there is no need to perform spatially coupled maneuvers, the 3D path tracking control is decoupled into planar 2D path tracking and depth or height tracking separately. Secondly, planar path tracking controller is introduced. For the reason that more attention is paid to comparing with vertical position control, transverse deviation in analytical form is derived. According to the Lyapunov direct theory, control law is designed using discrete PID algorithm whose parameters obey adaptive fuzzy adjustment. Reference heading angle is given as an output of the guidance controller conducted by lateral deviation together with its derivative. For the purpose of improving control quality and facilitating parameter modifying, data normalize modules based on Sigmoid function are applied to input-output data manipulation. Lastly, a sequence of experiments was carried out successfully, including tests in Longfeng lake and at the Yellow sea. In most challenging sea conditions, tracking errors of straight line are below 2 m in general. The results show that AUV is able to compensate the disturbance brought by sea current. The provided test results demonstrate that the designed guidance controller guarantees stably and accurately straight route tracking. Besides, the proposed control system is accessible for continuous comb-shaped path tracking in region searching.
基金Project (50275150) supported by the National Natural Science Foundation of ChinaProject (RL200002) supported by the Foundation of the Robotics Laboratory, Chinese Academy of Sciences
文摘An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.
基金Project(51878164) supported by the National Natural Science Foundation of ChinaProjects(BK20161421, BK20140109) supported by the Natural Science Foundation of Jiangsu Province, China+4 种基金Project(141076) supported by the Huoyingdong Foundation of the Ministry of Education of ChinaProject(BZ2017011) supported by the Science and Technology Support Project of Jiangsu Province, ChinaProject(2242015R30027) supported by the Fundamental Research Funds for the Central Universities, ChinaProject(grant number KFJ170106) supported by the Changsha University of Science & Technology via Open Fund of National Engineering Laboratory of Highway Maintenance Technology, ChinaProject(2018B51) supported by the Science and Technology Support Project of Qilu Transportation Development Group, China。
文摘During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this paper establishes a relationship between the degree of compaction K and strain ε. The relationship between the compaction degree K and the shear strength of soil(cohesion c and frictional angle φ) was clearly established through indoor experiments. The subroutine UMAT in ABAQUS finite element numerical software was developed to realize an accurate calculation of the subgrade soil compaction quality. This value was compared and analyzed against the assumed compaction value of the model, thereby verifying the accuracy of the intelligent compaction calculation results for subgrade soil. On this basis, orthogonal tests of the influential factors(frequency, amplitude, and quality) for the degree of compaction and sensitivity analysis were carried out. Finally, the ‘acceleration intelligent compaction value’, which is based on the acceleration signal, is proposed for a compaction meter value that indicates poor accuracy. The research results can provide guidance and basis for further research into the accurate control of compaction quality for roadbeds and pavements.
基金Project(2018YFB1600600)supported by the National Key Research and Development Program,ChinaProject(20YJAZH083)supported by the Ministry of Education,China+1 种基金Project(20YJAZH083)supported by the Humanities and Social Sciences,ChinaProject(51878161)supported by the National Natural Science Foundation of China。
文摘The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.
文摘In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested. The IDSS possesses selflearning and adaptive properties, and has been used for managing and analyzing the optimal operational conditions since June 1992. Electric energy consumption has been reduced remarkably and the coefficient of recovery of cobalt and nickel has been increased.
文摘This paper describes the structure and function of the intelligent decision support system (IDSS) on the process of nickel matte smelter. The knowledge and model base system based on fuzzy-decision rules ale specially suggested. The IDSS possesses the self-learning and adaptive properties, andhas been used for managing and analyzing the production information, optimizing the composition of the charge mixture, and deciding the optimal operational conditions. Electric energy consumption has been reduced remarkably and the yield of nickel has been increased.
