The interception problem of Hypersonic Gliding Vehicles(HGVs)has been an important aspect of missile defense systems.In order to provide interceptors with accurate information of target trajectory,a model based on an ...The interception problem of Hypersonic Gliding Vehicles(HGVs)has been an important aspect of missile defense systems.In order to provide interceptors with accurate information of target trajectory,a model based on an improved Long Short-Time Memory(LSTM)network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target.Firstly,for trajectory prediction required by intercepting guidance laws,the altitude,velocity and velocity direction of the target are formulated in the form of analytic functions,consisting of linear decay terms and amplitude decay sinusoidal terms.Then,the dynamic characteristics of the model parameters are analyzed,and the target trajectory prediction pipeline is proposed with the prediction error considered.Finally,an improved LSTM network is designed to estimate parameters in a dynamically-updated manner,and estimation results are used for the calculation of the final trajectory prediction pipeline.The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account.Simulation is conducted and the results show the high accuracy of the algorithm in HGVs’trajectory prediction which is conducive to increasing the interception success rate.展开更多
In daily life,people use their hands in various ways for most daily activities.There are many applications based on the position,direction,and joints of the hand,including gesture recognition,gesture prediction,roboti...In daily life,people use their hands in various ways for most daily activities.There are many applications based on the position,direction,and joints of the hand,including gesture recognition,gesture prediction,robotics and so on.This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures.The model is applied to the NAO robot to verify the effectiveness of the proposed method.First of all,in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion,the Kalman filter is applied to the original data.Then some new feature descriptors are introduced.The length feature,angle feature and angular velocity feature are extracted from the filtered data.These features are fed into the long-short time memory recurrent neural network(LSTM-RNN)with different combinations.Experimental results show that the combination of coordinate,length and angle features achieves the highest accuracy of 99.31%,and it can also run in real time.Finally,the trained model is applied to the NAO robot to play the finger-guessing game.Based on the predicted gesture,the NAO robot can respond in advance.展开更多
This paper proposes a using Cellular-Based Vehicle Probe(CVP)at road-section(RS)method to detect and setup a model for traffic flow information(info)collection and monitor.There are multiple traffic collection devices...This paper proposes a using Cellular-Based Vehicle Probe(CVP)at road-section(RS)method to detect and setup a model for traffic flow information(info)collection and monitor.There are multiple traffic collection devices including CVP,ETC-Based Vehicle Probe(EVP),Vehicle Detector(VD),and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem,monitor and control.The main project has been applied at Tai#2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of 2017-2018.This paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory(LTSM)from recurrent neural network(RNN)model.We also provide a model verification and validation methodology with RNN for cross verification of system performance.展开更多
为了克服卷积神经网络(Convolutional Neural Network,CNN)轴承故障诊断方法特征提取过程困难以及难以捕获时间序列数据之间的长期依赖关系的问题,提出一种改进的卷积-长短时记忆网络(Convolutional Neural Network-Long and Short Term...为了克服卷积神经网络(Convolutional Neural Network,CNN)轴承故障诊断方法特征提取过程困难以及难以捕获时间序列数据之间的长期依赖关系的问题,提出一种改进的卷积-长短时记忆网络(Convolutional Neural Network-Long and Short Term Memory,CNN-LSTM)滚动轴承故障诊断方法。将二维轴承加速度振动信号输入CNN提取局部特征,再将轴承特征信息加载到LSTM长期记忆单元中,引入遗忘机制提取时序数据的全局特征。利用轴承振动信号的局部深层特征和全局时序特征,学习不同区间长度的序列特征,从而提高故障诊断精度。实验结果表明,该方法可用于轴承故障诊断,且具有较高的分类精度和较强的稳定性。展开更多
基金co-supported by the National Natural Science Foundation of China(No.61427809).
文摘The interception problem of Hypersonic Gliding Vehicles(HGVs)has been an important aspect of missile defense systems.In order to provide interceptors with accurate information of target trajectory,a model based on an improved Long Short-Time Memory(LSTM)network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target.Firstly,for trajectory prediction required by intercepting guidance laws,the altitude,velocity and velocity direction of the target are formulated in the form of analytic functions,consisting of linear decay terms and amplitude decay sinusoidal terms.Then,the dynamic characteristics of the model parameters are analyzed,and the target trajectory prediction pipeline is proposed with the prediction error considered.Finally,an improved LSTM network is designed to estimate parameters in a dynamically-updated manner,and estimation results are used for the calculation of the final trajectory prediction pipeline.The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account.Simulation is conducted and the results show the high accuracy of the algorithm in HGVs’trajectory prediction which is conducive to increasing the interception success rate.
基金supported in part by National Nature Science Foundation of China(NSFC)(U20A20200,61861136009)in part by Guangdong Basic and Applied Basic Research Foundation(2019B1515120076,2020B1515120054)in part by Industrial Key Technologies R&D Program of Foshan(2020001006308)。
文摘In daily life,people use their hands in various ways for most daily activities.There are many applications based on the position,direction,and joints of the hand,including gesture recognition,gesture prediction,robotics and so on.This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures.The model is applied to the NAO robot to verify the effectiveness of the proposed method.First of all,in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion,the Kalman filter is applied to the original data.Then some new feature descriptors are introduced.The length feature,angle feature and angular velocity feature are extracted from the filtered data.These features are fed into the long-short time memory recurrent neural network(LSTM-RNN)with different combinations.Experimental results show that the combination of coordinate,length and angle features achieves the highest accuracy of 99.31%,and it can also run in real time.Finally,the trained model is applied to the NAO robot to play the finger-guessing game.Based on the predicted gesture,the NAO robot can respond in advance.
文摘This paper proposes a using Cellular-Based Vehicle Probe(CVP)at road-section(RS)method to detect and setup a model for traffic flow information(info)collection and monitor.There are multiple traffic collection devices including CVP,ETC-Based Vehicle Probe(EVP),Vehicle Detector(VD),and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem,monitor and control.The main project has been applied at Tai#2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of 2017-2018.This paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory(LTSM)from recurrent neural network(RNN)model.We also provide a model verification and validation methodology with RNN for cross verification of system performance.
文摘为了克服卷积神经网络(Convolutional Neural Network,CNN)轴承故障诊断方法特征提取过程困难以及难以捕获时间序列数据之间的长期依赖关系的问题,提出一种改进的卷积-长短时记忆网络(Convolutional Neural Network-Long and Short Term Memory,CNN-LSTM)滚动轴承故障诊断方法。将二维轴承加速度振动信号输入CNN提取局部特征,再将轴承特征信息加载到LSTM长期记忆单元中,引入遗忘机制提取时序数据的全局特征。利用轴承振动信号的局部深层特征和全局时序特征,学习不同区间长度的序列特征,从而提高故障诊断精度。实验结果表明,该方法可用于轴承故障诊断,且具有较高的分类精度和较强的稳定性。
