The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorit...The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.展开更多
The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sens...The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.展开更多
According to the features of movements of humanoid robot, a control system for humanoid robot walking on uneven terrain is present. Constraints of stepping over stairs are analyzed and the trajectories of feet are cal...According to the features of movements of humanoid robot, a control system for humanoid robot walking on uneven terrain is present. Constraints of stepping over stairs are analyzed and the trajectories of feet are calculated by intelligent computing methods. To overcome the shortcomings resulted from directly controlling the robot by neural network （NN） and fuzzy logic controller （FLC）, a revised particle swarm optimization （PSO） algorithm is proposed to train the weights of NN and rules of FLC. Simulations and experiments on different control methods are achieved for a detailed comparison. The results show that using the proposed methods can obtain better control effect.展开更多
Internet multi-robotics is a typical discrete-event system. In order to describe joint activities between multiple operators and multiple robots, a 4-level discrete-event model is proposed in this paper based on the c...Internet multi-robotics is a typical discrete-event system. In order to describe joint activities between multiple operators and multiple robots, a 4-level discrete-event model is proposed in this paper based on the controlled condition/event Petri nets (CCEP). On the first or mission level, the task splitting of the system is defined; on the second or multi-operator level, a precedence graph is introduced for every operator to plan his or her robotic actions; on the third or coordination level, the above precedence graphs are translated and integrated into the corresponding CCEPs in terms of specific rules; and on the last or multi-robot level, operators can select their control range by setting the corresponding control marks of the obtained CCEPs. As a consequence, a clear mechanism of operator-robot collaboration is obtained to conduct the development of the system.展开更多
To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt ...To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.展开更多
The Pathfinder paradigm has been used in generating and analyzing graph models that support clustering similar concepts and minimum-cost paths to provide an associative network structure within a domain. The co-occurr...The Pathfinder paradigm has been used in generating and analyzing graph models that support clustering similar concepts and minimum-cost paths to provide an associative network structure within a domain. The co-occurrence pathfinder network （ CPFN ） extends the traditional pathfinder paradigm so that co-occurring concepts can be calculated at each sampling time. Existing algorithms take O（n（s）） time to calculate the pathfinder network （PFN） at each sampling time for a non-completed input graph of a CPFN （r = ∞, q = n - 1）, where n is the number of nodes in the input graph, r is the Minkowski exponent and q is the maximum number of links considered in finding a minimum cost path between vertices. To reduce the complexity of calculating the CPFN, we propose a greedy based algorithm, MEC（G） algorithm, which takes shortcuts to avoid unnecessary steps in the existing algorithms, to correctly calculate a CPFN （r = ∞, q= n - 1） in O（klogk） time where k is the number of edges of the input graph. Our example demonstrates the efficiency and correctness of the proposed MEC（G） algorithm, confirming our mathematic analysis on this algorithm.展开更多
A dynamic velocity feed-forward compensation （RBF-NN） dynamic model identification was presented for control （DVFCC） approach with RBF neural network the adaptive trajectory tracking of industrial robots. The prop...A dynamic velocity feed-forward compensation （RBF-NN） dynamic model identification was presented for control （DVFCC） approach with RBF neural network the adaptive trajectory tracking of industrial robots. The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model. The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accu- racy. The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can con- vert torque signal into velocity in the standard industrial controller. Then, the need for a torque control interface was avoided in the real-time dynamic control of industrial robot. The simulations and experiments were carried out on a gas cutting manipulator. The results show that the proposed control approach can reduce steady-state error, suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space, especially under high- speed condition.展开更多
In an Internet based multi-operator and multi-robot system (IMOMR), operators have to work collaboratively to overcome the constraints of space and time. Inherently, the activities among them can be defined as a compu...In an Internet based multi-operator and multi-robot system (IMOMR), operators have to work collaboratively to overcome the constraints of space and time. Inherently, the activities among them can be defined as a computer-supported cooperative work (CSCW). As a practical application of CSCW, a collaborative task planning system (CTPS) for IMOMR is proposed in this paper on the basis of Petri nets. Its definition, components design, and concrete implementation are given in detail, respectively. As a result, a clear collaboration mechanism of multiple operators in an IMOMR is obtained to guarantee their task planning.展开更多
Studies the design of distributed virtual environments （DVEs） for tele-multi-robotics. The proposed design, incorporating two models （ distributlon-supported model and VE-supported model）, attempts to represent co...Studies the design of distributed virtual environments （DVEs） for tele-multi-robotics. The proposed design, incorporating two models （ distributlon-supported model and VE-supported model）, attempts to represent common functionality, communication issues, and requirements found in multi-operator DVEs. The distribution-supported model concentrates on the introduction of computer-supported collaborative work （CSCW） to realize the coordination of multi-operators, while the VE-supported model concentrates on the utilization of an object-oriented approach to strengthen the expandability and robustness of the system. Finally, the configuration anti running environments of the system are given.展开更多
Expert system plays an important role in port machine diagnosis, which aims at automatic equipment test for higher availability and efficiency of port operations. In this study, a port machine diagnosis expert system ...Expert system plays an important role in port machine diagnosis, which aims at automatic equipment test for higher availability and efficiency of port operations. In this study, a port machine diagnosis expert system is proposed based on multi-reasoning mechanism. Relying on the knowledge acquired from the experienced experts in the port machine engineering, the system builds a library of relative experience and a set of rules of reasoning and estimating. Multi-reasoning mechanism that simulates the decision-making process of domain experts is employed to achieve reliable diagnosis results. The reasoning machine integrates artificial neural network, uncertain decision making and decision tree, which complements each other by sustainable growing voting mechanism. The effect of this multi-reasoning mechanism is evaluated and validated by means of Matthew＇s Correlation Coefficient （MCC）. The system incorporating the mechanism is successfully designed, implemented and applied in Shanghai Port.展开更多
To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integr...To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.展开更多
Improving customer experience has become a more and more important role in enhancing customer service in fashion retailing business. In this study, a kind of intelligent garment coordination and try-on system for fash...Improving customer experience has become a more and more important role in enhancing customer service in fashion retailing business. In this study, a kind of intelligent garment coordination and try-on system for fashion retailing was proposed. Radio Frequency Identification （RFID） technology was used to identify customer and garment item automatically. The intelligent procedure for garment coordination recommendation using Artificial Neural Network （ANN） was developed to imitate fashion designers＇ decision-making on garment coordination. Virtual try-on algorithm based on the customer＇s 2D/mago was accomplished using imagewarping technique. The system architecture and the software framework were also described. The results show that the ＇system is a practical and useful application for fashion retailers.展开更多
This study integrates previous experimental data and employs machine learning(ML)methods,including Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),and eXtreme Gradient Boosting(XGBoost),to...This study integrates previous experimental data and employs machine learning(ML)methods,including Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),and eXtreme Gradient Boosting(XGBoost),to predict the compressive strength(CS)and tensile strength(TS)of engineered cementitious composites(ECC).XGBoost emerged as the superior model among the four ML models,providing an interpretable and highly accurate predictive framework.To optimize the model performance,hyperparameter tuning using a fivefold cross-validation approach with the data divided into 80%training and 20%testing subsets.The Shapley Additive Explanations(SHAP)algorithm was also employed to reveal the impact of important features,such as the water/binder ratio,fly ash content,and water reducer dosage,on the model’s predictions and their interrelationships.The XGBoost demonstrates the most exemplary performance,as reflected in the R^(2)values of 0.92 and 0.97 for CS and TS testing,respectively.The SHAP analysis provided insights into the impact of individual features on CS and TS,shedding light on how specific characteristics influence the predictive accuracy of these properties.This highly accurate prediction model uncovers insights into correlated features,aids in creating new mix designs of ECC,and supports global efforts toward a low-carbon future in the construction industry by reducing carbon emissions.展开更多
A neural network Smith predictive control strategy is proposed to deal with inpu t and feedback time delays in telerobot systems. The delay time is assumed to b e invariant and unknown. The proposed control structure...A neural network Smith predictive control strategy is proposed to deal with inpu t and feedback time delays in telerobot systems. The delay time is assumed to b e invariant and unknown. The proposed control structure consists of a slave syst em and a master controller. In the slave system, a recurrent neural network (RNN ) with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant, which is used to linearize the slave s ystem. The master controller is a Smith predictor for the linearized slave syste m, which provides prediction and maintains the desirable tracking performance. S tability propriety is guaranteed based on the Lyapunov method. A simulation of a two-link robotic manipulator is provided to illustrate the effectiveness of th e proposed control strategy.展开更多
A methodology is presented whereby a neural network is used to learn the inverse kinematic relationships of the position and orientation of a six joint manipulator. The arm solution for the orientation of a manipulato...A methodology is presented whereby a neural network is used to learn the inverse kinematic relationships of the position and orientation of a six joint manipulator. The arm solution for the orientation of a manipulator using a self organizing neural net is studied in this paper. A new training model of the self organizing neural network is proposed by thoroughly studying Martinetz, Ritter and Schulten′s self organizing neural network based on Kohonen′s self organizing mapping algorithm using a Widrow Hoff type error correction rule and closely combining the characters of the inverse kinematic relationship for a robot arm. The computer simulation results for a PUMA 560 robot show that the proposed method has a significant improvement over other methods documented in the references in self organizing capability and precision by training process.展开更多
Wavelet network, a class of neural network consisting of wavelets, is proposed to solve the inverse kinematics problem in robotic manipulator. A wavelet network suitable for dealing with multi-input and multi-output s...Wavelet network, a class of neural network consisting of wavelets, is proposed to solve the inverse kinematics problem in robotic manipulator. A wavelet network suitable for dealing with multi-input and multi-output system is constructed. The network is optimized by reducing the number of wavelets handling large dimension problem according to the sample data. The algorithms for sparseness analysis of input data and fitting wavelets to the output data with orthogonal method are introduced. Then Levenberg-Marquardt algorithm is used to train the network. Simulation results showed that this method is capable of solving the inverse kinematics problem for PUMA560.展开更多
In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive conve...In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.展开更多
Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector mach...Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.展开更多
The ultra-low specific speed centrifugal blower is widely used in energy industries due to its features such as low flow rate,high pressure and low manufacturing cost. However,the width-to-diameter ratio of the above ...The ultra-low specific speed centrifugal blower is widely used in energy industries due to its features such as low flow rate,high pressure and low manufacturing cost. However,the width-to-diameter ratio of the above blower becomes relatively small to satisfy the needed operation condition and its performances are considerably degraded as a result of relatively high leakage,disc friction and passage friction loss consequently. The purpose of this paper is to improve its performance through the optimization design of the blade’s profile properly. Based on artificial neural networks (ANN) and hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs),the optimization design approach is established. By conjoining Bezier parameterization and FINE/TURBO solver,the optimized blade is designed by adjusting the profile gradually. An industrial ultra-low specific speed centrifugal blower with parallel hub and shroud has been selected as a reference case for optimization design. The performance investigations of the centrifugal blowers with different types of blades are conducted. The conclusions of the performance improvement of the optimized blade provide positive evidences in the application of the optimization design of the above blower blade.展开更多
文摘The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.
基金Y. Wang was supported in part by the US National Science Foundation (NSF) under Grant Nos.CNS-0721666,CNS-0915331,and CNS-1050398Y. Liu was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61074092+1 种基金by the Shandong Provincial Natural Science Foundation,China under Grant No.Q2008E01Z. Guo was partially supported by the NSFC under Grant Nos. 61170258 and 6093301
文摘The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.
基金This material is based upon work funded by State Key Laboratory of Robotics and System （HIT） Foundation of China under Grant No. SKLRS-2012-MS-06, China Postdoctoral Science Foundation under Grant No. 2013M531022, Research project of laboratory work in universities of Zhejiang Province under Grant No. ZD201504, Educational technology research program of Zhejiang Province under Grant No. JA027.
文摘According to the features of movements of humanoid robot, a control system for humanoid robot walking on uneven terrain is present. Constraints of stepping over stairs are analyzed and the trajectories of feet are calculated by intelligent computing methods. To overcome the shortcomings resulted from directly controlling the robot by neural network （NN） and fuzzy logic controller （FLC）, a revised particle swarm optimization （PSO） algorithm is proposed to train the weights of NN and rules of FLC. Simulations and experiments on different control methods are achieved for a detailed comparison. The results show that using the proposed methods can obtain better control effect.
文摘Internet multi-robotics is a typical discrete-event system. In order to describe joint activities between multiple operators and multiple robots, a 4-level discrete-event model is proposed in this paper based on the controlled condition/event Petri nets (CCEP). On the first or mission level, the task splitting of the system is defined; on the second or multi-operator level, a precedence graph is introduced for every operator to plan his or her robotic actions; on the third or coordination level, the above precedence graphs are translated and integrated into the corresponding CCEPs in terms of specific rules; and on the last or multi-robot level, operators can select their control range by setting the corresponding control marks of the obtained CCEPs. As a consequence, a clear mechanism of operator-robot collaboration is obtained to conduct the development of the system.
文摘To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.
文摘The Pathfinder paradigm has been used in generating and analyzing graph models that support clustering similar concepts and minimum-cost paths to provide an associative network structure within a domain. The co-occurrence pathfinder network （ CPFN ） extends the traditional pathfinder paradigm so that co-occurring concepts can be calculated at each sampling time. Existing algorithms take O（n（s）） time to calculate the pathfinder network （PFN） at each sampling time for a non-completed input graph of a CPFN （r = ∞, q = n - 1）, where n is the number of nodes in the input graph, r is the Minkowski exponent and q is the maximum number of links considered in finding a minimum cost path between vertices. To reduce the complexity of calculating the CPFN, we propose a greedy based algorithm, MEC（G） algorithm, which takes shortcuts to avoid unnecessary steps in the existing algorithms, to correctly calculate a CPFN （r = ∞, q= n - 1） in O（klogk） time where k is the number of edges of the input graph. Our example demonstrates the efficiency and correctness of the proposed MEC（G） algorithm, confirming our mathematic analysis on this algorithm.
文摘A dynamic velocity feed-forward compensation （RBF-NN） dynamic model identification was presented for control （DVFCC） approach with RBF neural network the adaptive trajectory tracking of industrial robots. The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model. The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accu- racy. The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can con- vert torque signal into velocity in the standard industrial controller. Then, the need for a torque control interface was avoided in the real-time dynamic control of industrial robot. The simulations and experiments were carried out on a gas cutting manipulator. The results show that the proposed control approach can reduce steady-state error, suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space, especially under high- speed condition.
文摘In an Internet based multi-operator and multi-robot system (IMOMR), operators have to work collaboratively to overcome the constraints of space and time. Inherently, the activities among them can be defined as a computer-supported cooperative work (CSCW). As a practical application of CSCW, a collaborative task planning system (CTPS) for IMOMR is proposed in this paper on the basis of Petri nets. Its definition, components design, and concrete implementation are given in detail, respectively. As a result, a clear collaboration mechanism of multiple operators in an IMOMR is obtained to guarantee their task planning.
文摘Studies the design of distributed virtual environments （DVEs） for tele-multi-robotics. The proposed design, incorporating two models （ distributlon-supported model and VE-supported model）, attempts to represent common functionality, communication issues, and requirements found in multi-operator DVEs. The distribution-supported model concentrates on the introduction of computer-supported collaborative work （CSCW） to realize the coordination of multi-operators, while the VE-supported model concentrates on the utilization of an object-oriented approach to strengthen the expandability and robustness of the system. Finally, the configuration anti running environments of the system are given.
文摘Expert system plays an important role in port machine diagnosis, which aims at automatic equipment test for higher availability and efficiency of port operations. In this study, a port machine diagnosis expert system is proposed based on multi-reasoning mechanism. Relying on the knowledge acquired from the experienced experts in the port machine engineering, the system builds a library of relative experience and a set of rules of reasoning and estimating. Multi-reasoning mechanism that simulates the decision-making process of domain experts is employed to achieve reliable diagnosis results. The reasoning machine integrates artificial neural network, uncertain decision making and decision tree, which complements each other by sustainable growing voting mechanism. The effect of this multi-reasoning mechanism is evaluated and validated by means of Matthew＇s Correlation Coefficient （MCC）. The system incorporating the mechanism is successfully designed, implemented and applied in Shanghai Port.
基金Projects(51275138,51475025)supported by the National Natural Science Foundation of ChinaProject(12531109)supported by the Science Foundation of Heilongjiang Provincial Department of Education,China+1 种基金Projects(XJ2015002,G-YZ90)supported by Hong Kong Scholars Program,ChinaProject(2015M580037)supported by Postdoctoral Science Foundation of China
文摘To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.
基金National Nature Science Foundations of China (No.60975059, No.60775052)Specialized Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China (No.20090075110002)Projects of Shanghai Committee of Science and Technology, China (No.09JC1400900, No.08JC1400100, No.10DZ0506500)
文摘Improving customer experience has become a more and more important role in enhancing customer service in fashion retailing business. In this study, a kind of intelligent garment coordination and try-on system for fashion retailing was proposed. Radio Frequency Identification （RFID） technology was used to identify customer and garment item automatically. The intelligent procedure for garment coordination recommendation using Artificial Neural Network （ANN） was developed to imitate fashion designers＇ decision-making on garment coordination. Virtual try-on algorithm based on the customer＇s 2D/mago was accomplished using imagewarping technique. The system architecture and the software framework were also described. The results show that the ＇system is a practical and useful application for fashion retailers.
文摘This study integrates previous experimental data and employs machine learning(ML)methods,including Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),and eXtreme Gradient Boosting(XGBoost),to predict the compressive strength(CS)and tensile strength(TS)of engineered cementitious composites(ECC).XGBoost emerged as the superior model among the four ML models,providing an interpretable and highly accurate predictive framework.To optimize the model performance,hyperparameter tuning using a fivefold cross-validation approach with the data divided into 80%training and 20%testing subsets.The Shapley Additive Explanations(SHAP)algorithm was also employed to reveal the impact of important features,such as the water/binder ratio,fly ash content,and water reducer dosage,on the model’s predictions and their interrelationships.The XGBoost demonstrates the most exemplary performance,as reflected in the R^(2)values of 0.92 and 0.97 for CS and TS testing,respectively.The SHAP analysis provided insights into the impact of individual features on CS and TS,shedding light on how specific characteristics influence the predictive accuracy of these properties.This highly accurate prediction model uncovers insights into correlated features,aids in creating new mix designs of ECC,and supports global efforts toward a low-carbon future in the construction industry by reducing carbon emissions.
文摘A neural network Smith predictive control strategy is proposed to deal with inpu t and feedback time delays in telerobot systems. The delay time is assumed to b e invariant and unknown. The proposed control structure consists of a slave syst em and a master controller. In the slave system, a recurrent neural network (RNN ) with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant, which is used to linearize the slave s ystem. The master controller is a Smith predictor for the linearized slave syste m, which provides prediction and maintains the desirable tracking performance. S tability propriety is guaranteed based on the Lyapunov method. A simulation of a two-link robotic manipulator is provided to illustrate the effectiveness of th e proposed control strategy.
文摘A methodology is presented whereby a neural network is used to learn the inverse kinematic relationships of the position and orientation of a six joint manipulator. The arm solution for the orientation of a manipulator using a self organizing neural net is studied in this paper. A new training model of the self organizing neural network is proposed by thoroughly studying Martinetz, Ritter and Schulten′s self organizing neural network based on Kohonen′s self organizing mapping algorithm using a Widrow Hoff type error correction rule and closely combining the characters of the inverse kinematic relationship for a robot arm. The computer simulation results for a PUMA 560 robot show that the proposed method has a significant improvement over other methods documented in the references in self organizing capability and precision by training process.
文摘Wavelet network, a class of neural network consisting of wavelets, is proposed to solve the inverse kinematics problem in robotic manipulator. A wavelet network suitable for dealing with multi-input and multi-output system is constructed. The network is optimized by reducing the number of wavelets handling large dimension problem according to the sample data. The algorithms for sparseness analysis of input data and fitting wavelets to the output data with orthogonal method are introduced. Then Levenberg-Marquardt algorithm is used to train the network. Simulation results showed that this method is capable of solving the inverse kinematics problem for PUMA560.
基金The National Key R&D Program of China(No.2018YFB1500800)the Specialized Development Foundation for the Achievement Transformation of Jiangsu Province(No.BA2019025)+1 种基金Pre-Research Fund of Science and Technology on Near-Surface Detection Laboratory(No.6142414190405)the Open Project of the Key Laboratory of Wireless Sensor Network&Communication of Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences(No.20190907).
文摘In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.
基金National Science Foundation and Technology Innovation Fund of P.R.China(No.70371040and02LJ-14-05-01)
文摘Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.
基金supported by the National Natural Science Foundation of China (Grant No.50776056)the National High Technology Research and Development Program of China ("863" Program) (Grant No.2009AA05Z201)
文摘The ultra-low specific speed centrifugal blower is widely used in energy industries due to its features such as low flow rate,high pressure and low manufacturing cost. However,the width-to-diameter ratio of the above blower becomes relatively small to satisfy the needed operation condition and its performances are considerably degraded as a result of relatively high leakage,disc friction and passage friction loss consequently. The purpose of this paper is to improve its performance through the optimization design of the blade’s profile properly. Based on artificial neural networks (ANN) and hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs),the optimization design approach is established. By conjoining Bezier parameterization and FINE/TURBO solver,the optimized blade is designed by adjusting the profile gradually. An industrial ultra-low specific speed centrifugal blower with parallel hub and shroud has been selected as a reference case for optimization design. The performance investigations of the centrifugal blowers with different types of blades are conducted. The conclusions of the performance improvement of the optimized blade provide positive evidences in the application of the optimization design of the above blower blade.
