As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to r...As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.展开更多
In order to solve the model update problem in mean-shift based tracker, a novel mechanism is proposed. Kalman filter is employed to update object model by filtering object kernel-histogram using previous model and cur...In order to solve the model update problem in mean-shift based tracker, a novel mechanism is proposed. Kalman filter is employed to update object model by filtering object kernel-histogram using previous model and current candidate. A self-tuning method is used for adaptively adjust all the parameters of the filters under the analysis of the filtering residuals. In addition, hypothesis testing servers as the criterion for determining whether to accept filtering result. Therefore, the tracker has the ability to handle occlusion so as to avoid over-update. The experimental results show that our method can not only keep up with the object appearance and scale changes but also be robust to occlusion.展开更多
The structure defects such as stray grains during unidirectional solidification can severely reduce the performance of single crystal turbine blades. A dendrite envelope tracking model is developed for predicting the ...The structure defects such as stray grains during unidirectional solidification can severely reduce the performance of single crystal turbine blades. A dendrite envelope tracking model is developed for predicting the structure defects of unidirectional solidification turbine blade. The normal vector of dendrite envelope is estimated by the gradient of dendrite volume fraction, and the growth velocity of the dendrite envelope (dendrite tips) is calculated with considering the anisotropy of grain growth. The solute redistribution at dendrite envelope is calculated by introducing an effective solute partition coefficient. Simulation tests show that the solute-build-up due to the rejection at envelope greatly affects grain competition and consequently solidification structure. The model is applied to predict the structure defects (e.g. stray grain) of single crystal turbine blade during unidirectional solidification. The results show that the developed model is reliable and has the following abilities: reproduce the growth competition among the different-preferential-direction grains; predict the stray grain formation; simulate the structure evolution (single crystal or dendrite grains).展开更多
Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling a...Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.展开更多
A modified Monte Carlo model of speckle tracking of shear wave propagation in scattering media is proposed. The established Monte Carlo model mainly concerns the variations of optical electric field and speckle. The t...A modified Monte Carlo model of speckle tracking of shear wave propagation in scattering media is proposed. The established Monte Carlo model mainly concerns the variations of optical electric field and speckle. The two- dimensional intensity distribution and the time evolution of speckles in different probe locations are obtained. The fluctuation of speckle intensity tracks the acoustic-radiation-force shear wave propagation, and especially the reduction of speckle intensity implies attenuation of shear wave. Then, the shear wave velocity is estimated quantitatively on the basis of the time-to-peak algorithm and linear regression processing. The results reveal that a smaller sampling interval yields higher estimation precision and the shear wave velocity is estimated more efficiently by using speckle intensity difference than by using speckle contrast difference according to the estimation error. Hence, the shear wave velocity is estimated to be 2.25 m/s with relatively high accuracy for the estimation error reaches the minimum （0.071）.展开更多
In order to achieve the complex dynamic analysis of the self-propelled seafloor pilot miner moving on the seafloor of extremely cohesive soft soil and further to make it possible to integrate the miner system with som...In order to achieve the complex dynamic analysis of the self-propelled seafloor pilot miner moving on the seafloor of extremely cohesive soft soil and further to make it possible to integrate the miner system with some subsystems to form the complete integrated deep ocean mining pilot system and perform dynamic analysis, a new method for the dynamic modeling and analysis of the miner is proposed and developed in this paper, resulting in a simplified 3D single-body vehicle model with three translational and three rotational degrees of freedom, while the track-terrain interaction model is built by partitioning the track-terrain interface into discrete elements with parameterized force dements built on the theory of terramechanics acting on each discrete dement. To evaluate and verify the correctness and effectiveness of this new modeling and analysis method, typical comparative studies with regard to computational efficiency and solution accuracy are carried out between the traditional modeling method of building the tracked vehicle as a multi-body model and the new modeling method. In full consideration of the particMar structure design of the pilot miner, the special characteristics of the seafioor soil and the hydrodynamic force of near-seafloor currnt, the dynamic simulation analysis of the miner is performed and discussed, which can provide useful guidance and reference for the practical miner system in design and operation. This new method can not only realize the rapid dynamic simulation analysis of the miner but also make possible the integration and rapid dynamic analysis of the complete integrated deep ocean mining pilot system in further researches.展开更多
In order to improve the adaptability of the tracked vehicle in the road and strengthen the grip of the tracked vehicle, a track surface adaptive mechanism was provided. In theory, it has been proved practically. Meanw...In order to improve the adaptability of the tracked vehicle in the road and strengthen the grip of the tracked vehicle, a track surface adaptive mechanism was provided. In theory, it has been proved practically. Meanwhile, RecurDyn, which is a multi-body kinematics software, was used to build a multi-body soft hybrid model, based on structure, elasticity, linear damping adaptive tracked vehicle;meanwhile the model was used to carry on the kinematics simulation. Through the comparison between simulated motion trail and that of traditional motion trail, this paper analyzed the deviation of the motion trail and also simulated the motion trail of the warped surface so as to test the adaptive ability of the mechanism. According to the results, the adaptive mechanism was equipped with great surface adaptability. It can also adapt to the complex warped surface, and enjoy a damping effect.展开更多
Model-set is utilized in state estimation for maneuver- ing target tracking. Two minimal symmetric model-subsets are designed and investigated by moment matching method, which include hypersphere-symmetric model-subse...Model-set is utilized in state estimation for maneuver- ing target tracking. Two minimal symmetric model-subsets are designed and investigated by moment matching method, which include hypersphere-symmetric model-subset and axis-symmetric model-subset, if system mode is a random variable and obeys certain probability distribution. They can be used as the fun- damental model-subset for multiple models estimation with fixed structure, variable structure and moving bank. The model-groups constructed by above designed subsets are given, which give the practical guidance for use of model-set in multiple models ap- proach with a variable structure. Simulation results show that the performances of two minimal model-set significantly outperform the corresponding model-sets with fixed spacing.展开更多
Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that...Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.展开更多
The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to s...The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.展开更多
Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from Jun...Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.展开更多
A 3D dendrite envelope tracking model was developed for estimating the solidification structure of unidirectionally solidified turbine blade. The normal vector of dendrite envelope was estimated by the gradient of den...A 3D dendrite envelope tracking model was developed for estimating the solidification structure of unidirectionally solidified turbine blade. The normal vector of dendrite envelope was estimated by the gradient of dendrite volume fraction, and growth velocity of the dendrite envelope (dendrite tips) was calculated with considering the anisotropy of grain growth. The solute redistribution at dendrite envelope was calculated by introducing an effective solute partition coefficient(ke). Simulation results show that the solute-build-up due to the rejection at envelope affects grain competition and consequently the solidification structure. The lower value of ke leads to more waved dendrite growth front and higher solute rejection. The model was applied to predict the structure of turbine-blade-shape samples showing good ability to reproduce the columnar and single grain structures.展开更多
Accurate simulation of tropical cyclone tracks is a prerequisite for tropical cyclone risk assessment.Against the spatial characteristics of tropical cyclone tracks in the Northwest Pacific region,stochastic simulatio...Accurate simulation of tropical cyclone tracks is a prerequisite for tropical cyclone risk assessment.Against the spatial characteristics of tropical cyclone tracks in the Northwest Pacific region,stochastic simulation method based on classification model is used to simulate tropical cyclone tracks in this region.Such simulation includes the classification method,the genesis model,the traveling model,and the lysis model.Tropical cyclone tracks in the Northwest Pacific region are classified into five categories on the basis of its movement characteristics and steering positions.In the genesis model,Gaussian kernel probability density functions with the biased cross validation method are used to simulate the annual occurrence number and genesis positions.The traveling model is established on the basis of the mean and mean square error of the historical 6 h latitude and longitude displacements.The termination probability is used as the discrimination standard in the lysis model.Then,this stochastic simulation method of tropical cyclone tracks is applied and qualitatively evaluated with different diagnostics.Results show that the tropical cyclone tracks in Northwest Pacific can be satisfactorily simulated with this classification model.展开更多
The study on artificial intelligence(AI) methods for tuning of particle accelerators has been reported in many literatures.This paper presents tuning method for agent-based control systems of transport lines in the ca...The study on artificial intelligence(AI) methods for tuning of particle accelerators has been reported in many literatures.This paper presents tuning method for agent-based control systems of transport lines in the case of sensor/actuator failures.The method uses model-based tracking concept to relax the demand on sensor data.The condition for successful operation of the stated scheme is derived,and the concept is demonstrated through simulation by applying it to the model of microtron,transport line-1 and booster of indus accelerator.The results show that this approach is very effective in transport line control during sensor/actuator failures.展开更多
Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution a...Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function.Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems,such as uncertainty of motion styles,imprecise detection of feature points and ambiguity of joint location.This paper presents a two-layer filter framework based on IMM to track human motion.First,a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points.Second,multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation.Finally,experiments using real images sequences,simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.展开更多
A multi-constrained model predictive control （ MPC ） algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computa- tion, an active steering l...A multi-constrained model predictive control （ MPC ） algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computa- tion, an active steering linear error model is applied in the MPC controller. Then, a control incre- ment constraint and a relaxing factor are taken into account in the objective function to ensure the smoothness of the trajectory, using a softening constraints technique. In addition, the controller can obtain optimal control sequences which satisfy both the actual kinematic constraints and the actuator constraints. The circular trajectory tracking performance of the proposed method is compared with that of another MPC controller. To verify the trajectory tracking capabilities of the designed control- ler at different desired speed, the simulation experiments are carried out at the speed of 3m/s, 5m/ s and 10m/s. The results demonstrate the MPC controller has a good speed adaptability.展开更多
文摘As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.
文摘In order to solve the model update problem in mean-shift based tracker, a novel mechanism is proposed. Kalman filter is employed to update object model by filtering object kernel-histogram using previous model and current candidate. A self-tuning method is used for adaptively adjust all the parameters of the filters under the analysis of the filtering residuals. In addition, hypothesis testing servers as the criterion for determining whether to accept filtering result. Therefore, the tracker has the ability to handle occlusion so as to avoid over-update. The experimental results show that our method can not only keep up with the object appearance and scale changes but also be robust to occlusion.
基金Project(ICAST No.11305054) supported by the NEDO of Japan Subproject (5133301ZT4) supported by 973 Program Project (20052176) supported by the Natural Science Foundation of Liaoning Province, China
文摘The structure defects such as stray grains during unidirectional solidification can severely reduce the performance of single crystal turbine blades. A dendrite envelope tracking model is developed for predicting the structure defects of unidirectional solidification turbine blade. The normal vector of dendrite envelope is estimated by the gradient of dendrite volume fraction, and the growth velocity of the dendrite envelope (dendrite tips) is calculated with considering the anisotropy of grain growth. The solute redistribution at dendrite envelope is calculated by introducing an effective solute partition coefficient. Simulation tests show that the solute-build-up due to the rejection at envelope greatly affects grain competition and consequently solidification structure. The model is applied to predict the structure defects (e.g. stray grain) of single crystal turbine blade during unidirectional solidification. The results show that the developed model is reliable and has the following abilities: reproduce the growth competition among the different-preferential-direction grains; predict the stray grain formation; simulate the structure evolution (single crystal or dendrite grains).
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2003AA424020), Important Scientech ProblemTackling Subject Foundation Under the State 9th 5 -Year Plan(Grant No.96 -A22 -01 -01) and Provincial Doctoral Science Foundation of LiaoningProvince, China(Grant No.2001102034).
文摘Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.
基金Supported by the National Key Scientific Instrument and Equipment Development Projects of China under Grant No 81127901the National Natural Science Foundation of China under Grant Nos 61372017 and 30970828
文摘A modified Monte Carlo model of speckle tracking of shear wave propagation in scattering media is proposed. The established Monte Carlo model mainly concerns the variations of optical electric field and speckle. The two- dimensional intensity distribution and the time evolution of speckles in different probe locations are obtained. The fluctuation of speckle intensity tracks the acoustic-radiation-force shear wave propagation, and especially the reduction of speckle intensity implies attenuation of shear wave. Then, the shear wave velocity is estimated quantitatively on the basis of the time-to-peak algorithm and linear regression processing. The results reveal that a smaller sampling interval yields higher estimation precision and the shear wave velocity is estimated more efficiently by using speckle intensity difference than by using speckle contrast difference according to the estimation error. Hence, the shear wave velocity is estimated to be 2.25 m/s with relatively high accuracy for the estimation error reaches the minimum （0.071）.
基金supported by the National High Technology Research and Development Program of China(863 Program, Grant No.2006AA09Z240)the National Deep-Sea Technology Project of Development and Re-search(Grant No.DYXM-115-04-02-01)
文摘In order to achieve the complex dynamic analysis of the self-propelled seafloor pilot miner moving on the seafloor of extremely cohesive soft soil and further to make it possible to integrate the miner system with some subsystems to form the complete integrated deep ocean mining pilot system and perform dynamic analysis, a new method for the dynamic modeling and analysis of the miner is proposed and developed in this paper, resulting in a simplified 3D single-body vehicle model with three translational and three rotational degrees of freedom, while the track-terrain interaction model is built by partitioning the track-terrain interface into discrete elements with parameterized force dements built on the theory of terramechanics acting on each discrete dement. To evaluate and verify the correctness and effectiveness of this new modeling and analysis method, typical comparative studies with regard to computational efficiency and solution accuracy are carried out between the traditional modeling method of building the tracked vehicle as a multi-body model and the new modeling method. In full consideration of the particMar structure design of the pilot miner, the special characteristics of the seafioor soil and the hydrodynamic force of near-seafloor currnt, the dynamic simulation analysis of the miner is performed and discussed, which can provide useful guidance and reference for the practical miner system in design and operation. This new method can not only realize the rapid dynamic simulation analysis of the miner but also make possible the integration and rapid dynamic analysis of the complete integrated deep ocean mining pilot system in further researches.
文摘In order to improve the adaptability of the tracked vehicle in the road and strengthen the grip of the tracked vehicle, a track surface adaptive mechanism was provided. In theory, it has been proved practically. Meanwhile, RecurDyn, which is a multi-body kinematics software, was used to build a multi-body soft hybrid model, based on structure, elasticity, linear damping adaptive tracked vehicle;meanwhile the model was used to carry on the kinematics simulation. Through the comparison between simulated motion trail and that of traditional motion trail, this paper analyzed the deviation of the motion trail and also simulated the motion trail of the warped surface so as to test the adaptive ability of the mechanism. According to the results, the adaptive mechanism was equipped with great surface adaptability. It can also adapt to the complex warped surface, and enjoy a damping effect.
基金supported by Liaoning Province Innovative Team of Higher Education(2008T090)
文摘Model-set is utilized in state estimation for maneuver- ing target tracking. Two minimal symmetric model-subsets are designed and investigated by moment matching method, which include hypersphere-symmetric model-subset and axis-symmetric model-subset, if system mode is a random variable and obeys certain probability distribution. They can be used as the fun- damental model-subset for multiple models estimation with fixed structure, variable structure and moving bank. The model-groups constructed by above designed subsets are given, which give the practical guidance for use of model-set in multiple models ap- proach with a variable structure. Simulation results show that the performances of two minimal model-set significantly outperform the corresponding model-sets with fixed spacing.
文摘Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.
文摘The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.
基金funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 2012-2040supported by the BK21 project of the Korean government
文摘Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.
基金Project(ICAST11305054) supported by NEDO of Japan Project(5133301ZT4) supported by the National Basic Research Program of China Project(20052176) supported by the Natural Science Foundation of Liaoning Province, China
文摘A 3D dendrite envelope tracking model was developed for estimating the solidification structure of unidirectionally solidified turbine blade. The normal vector of dendrite envelope was estimated by the gradient of dendrite volume fraction, and growth velocity of the dendrite envelope (dendrite tips) was calculated with considering the anisotropy of grain growth. The solute redistribution at dendrite envelope was calculated by introducing an effective solute partition coefficient(ke). Simulation results show that the solute-build-up due to the rejection at envelope affects grain competition and consequently the solidification structure. The lower value of ke leads to more waved dendrite growth front and higher solute rejection. The model was applied to predict the structure of turbine-blade-shape samples showing good ability to reproduce the columnar and single grain structures.
基金National Natural Science Foundation of China(51408174)Provincial Undergraduate Innovation and Entrepreneurship Training Program of Hefei University of Technology(S201910359302)
文摘Accurate simulation of tropical cyclone tracks is a prerequisite for tropical cyclone risk assessment.Against the spatial characteristics of tropical cyclone tracks in the Northwest Pacific region,stochastic simulation method based on classification model is used to simulate tropical cyclone tracks in this region.Such simulation includes the classification method,the genesis model,the traveling model,and the lysis model.Tropical cyclone tracks in the Northwest Pacific region are classified into five categories on the basis of its movement characteristics and steering positions.In the genesis model,Gaussian kernel probability density functions with the biased cross validation method are used to simulate the annual occurrence number and genesis positions.The traveling model is established on the basis of the mean and mean square error of the historical 6 h latitude and longitude displacements.The termination probability is used as the discrimination standard in the lysis model.Then,this stochastic simulation method of tropical cyclone tracks is applied and qualitatively evaluated with different diagnostics.Results show that the tropical cyclone tracks in Northwest Pacific can be satisfactorily simulated with this classification model.
文摘The study on artificial intelligence(AI) methods for tuning of particle accelerators has been reported in many literatures.This paper presents tuning method for agent-based control systems of transport lines in the case of sensor/actuator failures.The method uses model-based tracking concept to relax the demand on sensor data.The condition for successful operation of the stated scheme is derived,and the concept is demonstrated through simulation by applying it to the model of microtron,transport line-1 and booster of indus accelerator.The results show that this approach is very effective in transport line control during sensor/actuator failures.
基金the Research Fund for the Young Teacher of Shanghai(No.Z-2009-12)the New Teacher Fund of Shanghai University of Electric Power (No.K-2010-16)
文摘Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function.Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems,such as uncertainty of motion styles,imprecise detection of feature points and ambiguity of joint location.This paper presents a two-layer filter framework based on IMM to track human motion.First,a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points.Second,multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation.Finally,experiments using real images sequences,simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.
基金Supported by the National Natural Science Foundation of China(51275041,61304194)the Doctoral Fund of Ministry of Education of China(20121101120015)the Fundamental Research Funds from Beijing Institute of Technology(20120342011)
文摘A multi-constrained model predictive control （ MPC ） algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computa- tion, an active steering linear error model is applied in the MPC controller. Then, a control incre- ment constraint and a relaxing factor are taken into account in the objective function to ensure the smoothness of the trajectory, using a softening constraints technique. In addition, the controller can obtain optimal control sequences which satisfy both the actual kinematic constraints and the actuator constraints. The circular trajectory tracking performance of the proposed method is compared with that of another MPC controller. To verify the trajectory tracking capabilities of the designed control- ler at different desired speed, the simulation experiments are carried out at the speed of 3m/s, 5m/ s and 10m/s. The results demonstrate the MPC controller has a good speed adaptability.
