With the development of computer science,the software technology changes with each passing day,itput forward higher and higher technical requirements for the software developers.Aim at each link in the process of deve...With the development of computer science,the software technology changes with each passing day,itput forward higher and higher technical requirements for the software developers.Aim at each link in the process of development,the present paper put forward a kind of software engineering personnel training system,the system can build a unified learning,management and evaluation system,to avoid the disadvantages of the traditional single system structure;the training process is more flexible,and it couldreduce the complexity of the artificial training and cause it to become more practical value.展开更多
The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau...The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.展开更多
In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training s...In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.展开更多
The optimal design of training sequences for channel estimation in multiple-input multiple-output （MIMO） systems under spatially correlated fading is considered. The channel is assumed to be a block-fading model wit...The optimal design of training sequences for channel estimation in multiple-input multiple-output （MIMO） systems under spatially correlated fading is considered. The channel is assumed to be a block-fading model with spatial correlation known at both the transmitter and the receiver. To minimize the channel estimation error, optimal training sequences are designed to exploit full information of the spatial correlation under the criterion of minimum mean square error （MMSE）. It is investigated that the spatial correlation is helpful to decrease the estimation error and the proposed training sequences have good performance via simulations.展开更多
The paper introduces some technology for training, simulation, restoration expert system of power grid, the structure of the system including function composition, hardware and software composition are discussed, know...The paper introduces some technology for training, simulation, restoration expert system of power grid, the structure of the system including function composition, hardware and software composition are discussed, knowledge representation and the method to establish device graphical library for expert system are given, the fault setting and diagnosis for training and simulation as well as restoration technology with deep first searching arithmetic and heuristic inference are presented. The research provides a good base for developing the training, simulation, restoration system of power companies.展开更多
Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices...Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.展开更多
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LST...In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.展开更多
针对复杂交通场景车辆检测算法自适应能力差的问题,提出了基于Co-training半监督学习方法的车辆鲁棒检测算法.首先,针对手工标记的少量样本,分别训练基于Haar-like特征的AdaBoost分类器和基于HOG(histograms of oriented gradients)特征...针对复杂交通场景车辆检测算法自适应能力差的问题,提出了基于Co-training半监督学习方法的车辆鲁棒检测算法.首先,针对手工标记的少量样本,分别训练基于Haar-like特征的AdaBoost分类器和基于HOG(histograms of oriented gradients)特征的SVM(support vector machines)分类器,使其具有一定的识别能力;然后,基于Co-training半监督学习框架,将利用2种算法进行分类得到的新样本分别加入到对方的样本库中,增加训练样本数量,再次进行分类器的训练.由于这2类特征具有冗余性,各自检测出的正负样本包含对方漏检和误检的图像.由于样本数的增加,再次训练所得到的新分类器的鲁棒性得到了很大提高,能更加准确地检测出车辆,而且由算法对未标记样本进行分类标记,不再需要人为标记,提高了车辆检测算法的自适应能力.展开更多
针对Tri-training算法利用无标记样例时会引入噪声且限制无标记样例的利用率而导致分类性能下降的缺点,提出了AR-Tri-training(Tri-training with assistant and rich strategy)算法。提出辅助学习策略,结合富信息策略设计辅助学习器,...针对Tri-training算法利用无标记样例时会引入噪声且限制无标记样例的利用率而导致分类性能下降的缺点,提出了AR-Tri-training(Tri-training with assistant and rich strategy)算法。提出辅助学习策略,结合富信息策略设计辅助学习器,并将辅助学习器应用在Tri-training训练以及说话声识别中。实验结果表明,辅助学习器在Tri-training训练的基础上不仅降低每次迭代可能产生的误标记样例数,而且能够充分地利用无标记样例以及在验证集上的错分样例信息。从实验结果可以得出,该算法能够弥补Tri-training算法的缺点,进一步提高测试率。展开更多
In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based po...In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.展开更多
Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method ca...Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.展开更多
Ventilation systems are critical for improving the cabin environment in high-speed trains,and their interest has increased significantly.However,whether air supply non-verticality deteriorates the cabin air environmen...Ventilation systems are critical for improving the cabin environment in high-speed trains,and their interest has increased significantly.However,whether air supply non-verticality deteriorates the cabin air environment,and the flow mechanism behind it and the degree of deterioration are not known.This study first analyzes the interaction between deflection angle and cabin flow field characteristics and ventilation performance.The results revealed that the interior temperature and pollutant concentration decreased slightly with increasing deflection angle,but resulted in significant deterioration of thermal comfort and air quality.This is evidenced by an increase in both draught rate and non-uniformity coefficient,an increase in the number of measurement points that do not satisfy the micro-wind speed and temperature difference requirements by about 5% and 15%,respectively,and an increase in longitudinal penetration of pollutants by a factor of about 5 and the appearance of locking regions at the ends of cabin.The results also show that changing the deflection pattern only affects the region of deterioration and does not essentially improve this deterioration.This study can provide reference and help for the ventilation design of high-speed trains.展开更多
Irregularities in the track and uneven forces acting on the train can cause shifts in the position of the superconducting magnetic levitation train relative to the track during operation.These shifts lead to asymmetri...Irregularities in the track and uneven forces acting on the train can cause shifts in the position of the superconducting magnetic levitation train relative to the track during operation.These shifts lead to asymmetries in the flow field structure on both sides of the narrow suspension gap,resulting in instability and deterioration of the train’s aerodynamic characteristics,significantly impacting its operational safety.In this study,we firstly validate the aerodynamic characteristics of the superconducting magnetic levitation system by developing a numerical simulation method based on wind tunnel test results.We then investigate the influence of lateral translation parameters on the train’s aerodynamic performance under conditions both with and without crosswinds.We aim to clarify the evolution mechanism of the flow field characteristics under the coupling effect between the train and the U-shaped track and to identify the most unfavorable operational parameters contributing to the deterioration of the train’s aerodynamic properties.The findings show that,without crosswinds,a lateral translation of 30 mm causes a synchronous resonance phenomenon at the side and bottom gaps of the train-track coupling,leading to the worst aerodynamic performance.Under crosswind conditions,a lateral translation of 40 mm maximizes peak pressure fluctuations and average turbulent kinetic energy around the train,resulting in the poorest aerodynamic performance.This research provides theoretical support for enhancing the operational stability of superconducting magnetic levitation trains.展开更多
A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a n...A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a new mitigation method is urgently expected to be explored.This study proposed a novel asymptotic linear method(ALM)for micro pressure wave(MPW)mitigation to achieve a constant gradient of initial c ompression waves(ICWs),via a study with various open ratios on hoods.The properties of ICWs and MPWs under various open ratios of hoods were analyzed.The results show that as the open ratio increases,the MPW amplitude at the tunnel exit initially decreases before rising.At the open ratio of 2.28%,the slope of the ICW curve is linearly coincident with a supposed straight line in the ALM,which further reduces the MPW amplitude by 26.9%at 20 m and 20.0%at 50 m from the exit,as compared to the unvented hood.Therefore,the proposed method effectively mitigates MPW and quickly determines the upper limit of alleviation for the MPW amplitude at a fixed train-tunnel operation condition.All achievements provide a ne w potential measure for the adaptive design of tunnel hoods.展开更多
文摘With the development of computer science,the software technology changes with each passing day,itput forward higher and higher technical requirements for the software developers.Aim at each link in the process of development,the present paper put forward a kind of software engineering personnel training system,the system can build a unified learning,management and evaluation system,to avoid the disadvantages of the traditional single system structure;the training process is more flexible,and it couldreduce the complexity of the artificial training and cause it to become more practical value.
文摘The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.
基金supported by the National Natural Science Foundation of China(62371049)。
文摘In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.
基金the National Science Foundation for Distinguished Young Scholars (60725105)the SixthProject of the Key Project of National Nature Science Foundation of China (60496316)+2 种基金the National "863" Project (2007AA012288)the National Nature Science Foundation of China (60572146)the "111" Project (B08038).
文摘The optimal design of training sequences for channel estimation in multiple-input multiple-output （MIMO） systems under spatially correlated fading is considered. The channel is assumed to be a block-fading model with spatial correlation known at both the transmitter and the receiver. To minimize the channel estimation error, optimal training sequences are designed to exploit full information of the spatial correlation under the criterion of minimum mean square error （MMSE）. It is investigated that the spatial correlation is helpful to decrease the estimation error and the proposed training sequences have good performance via simulations.
基金TheKeyProblemTacklingProjectinHunanProvince! (No .Izf 9831)
文摘The paper introduces some technology for training, simulation, restoration expert system of power grid, the structure of the system including function composition, hardware and software composition are discussed, knowledge representation and the method to establish device graphical library for expert system are given, the fault setting and diagnosis for training and simulation as well as restoration technology with deep first searching arithmetic and heuristic inference are presented. The research provides a good base for developing the training, simulation, restoration system of power companies.
基金supported by the National Natural Science Foundation of China(62171088,U19A2052,62020106011)the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China(ZYGX2021YGLH215,ZYGX2022YGRH005)。
文摘Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.
文摘In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.
文摘针对Tri-training算法利用无标记样例时会引入噪声且限制无标记样例的利用率而导致分类性能下降的缺点,提出了AR-Tri-training(Tri-training with assistant and rich strategy)算法。提出辅助学习策略,结合富信息策略设计辅助学习器,并将辅助学习器应用在Tri-training训练以及说话声识别中。实验结果表明,辅助学习器在Tri-training训练的基础上不仅降低每次迭代可能产生的误标记样例数,而且能够充分地利用无标记样例以及在验证集上的错分样例信息。从实验结果可以得出,该算法能够弥补Tri-training算法的缺点,进一步提高测试率。
基金Project(52272339)supported by the National Natural Science Foundation of ChinaProject(2023YFB390730303)supported by the National Key Research and Development Program of China+2 种基金Project(L2023G004)supported by the Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.Project(QZKFKT2023-005)supported by the State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive,ChinaProject(2022JZZ05)supported by the Open Foundation of MOE Key Laboratory of Engineering Structures of Heavy Haul Railway(Central South University),China。
文摘In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.
基金Projects(52302447,52388102,52372369)supported by the National Natural Science Foundation of China。
文摘Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.
基金Project(12372049)supported by the National Natural Science Foundation of ChinaProject(2682023ZTPY036)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2023TPL-T06)supported by the Independent Project of State Key Laboratory of Rail Transit Vehicle System,China。
文摘Ventilation systems are critical for improving the cabin environment in high-speed trains,and their interest has increased significantly.However,whether air supply non-verticality deteriorates the cabin air environment,and the flow mechanism behind it and the degree of deterioration are not known.This study first analyzes the interaction between deflection angle and cabin flow field characteristics and ventilation performance.The results revealed that the interior temperature and pollutant concentration decreased slightly with increasing deflection angle,but resulted in significant deterioration of thermal comfort and air quality.This is evidenced by an increase in both draught rate and non-uniformity coefficient,an increase in the number of measurement points that do not satisfy the micro-wind speed and temperature difference requirements by about 5% and 15%,respectively,and an increase in longitudinal penetration of pollutants by a factor of about 5 and the appearance of locking regions at the ends of cabin.The results also show that changing the deflection pattern only affects the region of deterioration and does not essentially improve this deterioration.This study can provide reference and help for the ventilation design of high-speed trains.
基金Projects(52372369,52302447,52388102)supported by the National Natural Science Foundation of ChinaProjects(2022YFB4301201-02,2023YFB4302502-02)supported by the National Key R&D Program of China。
文摘Irregularities in the track and uneven forces acting on the train can cause shifts in the position of the superconducting magnetic levitation train relative to the track during operation.These shifts lead to asymmetries in the flow field structure on both sides of the narrow suspension gap,resulting in instability and deterioration of the train’s aerodynamic characteristics,significantly impacting its operational safety.In this study,we firstly validate the aerodynamic characteristics of the superconducting magnetic levitation system by developing a numerical simulation method based on wind tunnel test results.We then investigate the influence of lateral translation parameters on the train’s aerodynamic performance under conditions both with and without crosswinds.We aim to clarify the evolution mechanism of the flow field characteristics under the coupling effect between the train and the U-shaped track and to identify the most unfavorable operational parameters contributing to the deterioration of the train’s aerodynamic properties.The findings show that,without crosswinds,a lateral translation of 30 mm causes a synchronous resonance phenomenon at the side and bottom gaps of the train-track coupling,leading to the worst aerodynamic performance.Under crosswind conditions,a lateral translation of 40 mm maximizes peak pressure fluctuations and average turbulent kinetic energy around the train,resulting in the poorest aerodynamic performance.This research provides theoretical support for enhancing the operational stability of superconducting magnetic levitation trains.
基金Project(24A0006)supported by the Key Project of Scientific Research Fund of Hunan Provincial Department of Education,ChinaProject(2024JJ5430)supported by the Natural Science Foundation of Hunan Province,ChinaProjects(2024JK2045,2023RC3061)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a new mitigation method is urgently expected to be explored.This study proposed a novel asymptotic linear method(ALM)for micro pressure wave(MPW)mitigation to achieve a constant gradient of initial c ompression waves(ICWs),via a study with various open ratios on hoods.The properties of ICWs and MPWs under various open ratios of hoods were analyzed.The results show that as the open ratio increases,the MPW amplitude at the tunnel exit initially decreases before rising.At the open ratio of 2.28%,the slope of the ICW curve is linearly coincident with a supposed straight line in the ALM,which further reduces the MPW amplitude by 26.9%at 20 m and 20.0%at 50 m from the exit,as compared to the unvented hood.Therefore,the proposed method effectively mitigates MPW and quickly determines the upper limit of alleviation for the MPW amplitude at a fixed train-tunnel operation condition.All achievements provide a ne w potential measure for the adaptive design of tunnel hoods.
