Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the rea...Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.展开更多
This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi...This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi analytical solutions of temperature increment and displacement of multi-layered composite structures are obtained by using the Laplace transform method,upon which the effects of thermal resistance coefficient,partition coefficient,thermal conductivity ratio and heat capacity ratio on the responses are studied.The results show that the generalized imperfect thermal contact model can realistically describe the imperfect thermal contact problem.Accordingly,it may degenerate into other thermal contact models by adjusting the thermal resistance coefficient and partition coefficient.展开更多
PGM(pragmatic general multicast)是一种在IP协议中广泛应用的可靠的组播传输协议.但PGM标准本身没有拥塞控制方案,不能实时响应网络需求,及时地调节源端发送速率.针对这个问题,在保证PGM协议可扩展性的基础上,在发送方与CLR(current l...PGM(pragmatic general multicast)是一种在IP协议中广泛应用的可靠的组播传输协议.但PGM标准本身没有拥塞控制方案,不能实时响应网络需求,及时地调节源端发送速率.针对这个问题,在保证PGM协议可扩展性的基础上,在发送方与CLR(current limiting receiver)之间采用了一种新的闭环控制器来实时地调节源端的发送速率,使其逐渐趋于稳定,并具有较快的响应速度.而且在网络拓扑结构动态变化的情况下,对所提出的拥塞控制方案进行了仿真实验.仿真结果表明,所提出的算法具有较好的可扩展性、稳定性和较快的响应速度,控制方案使网络性能表现良好. PGM(pragmatic general multicast)是一种在IP协议中广泛应用的可靠的组播传输协议.但PGM标准本身没有拥塞控制方案,不能实时响应网络需求,及时地调节源端发送速率.针对这个问题,在保证PGM协议可扩展性的基础上,在发送方与CLR(current limiting receiver)之间采用了一种新的闭环控制器来实时地调节源端的发送速率,使其逐渐趋于稳定,并具有较快的响应速度.而且在网络拓扑结构动态变化的情况下,对所提出的拥塞控制方案进行了仿真实验.仿真结果表明,所提出的算法具有较好的可扩展性、稳定性和较快的响应速度,控制方案使网络性能表现良好.展开更多
为了实现大规模、多区域的可燃气体的统一监测预警,使用无线传感器网络自组网技术、通用分组无线业务(general packet radio service,GPRS)无线传输技术和服务器数据库设计了可燃气体报警器采集节点、可燃气体报警器汇聚传输节点并完成...为了实现大规模、多区域的可燃气体的统一监测预警,使用无线传感器网络自组网技术、通用分组无线业务(general packet radio service,GPRS)无线传输技术和服务器数据库设计了可燃气体报警器采集节点、可燃气体报警器汇聚传输节点并完成服务器云管理平台的搭建.温湿度补偿算法提高了可燃气体报警器的采集精度,多级报警策略使可燃气体预警更智能,数据传输加密算法使数据传输更安全.系统可以应用在居民小区环境对可燃气体泄露智能预警.相关管理部门根据服务器云管理平台对预警信息及时处理并长期对监测数据进行统计分析.最后对搭建的远程可燃气体监测预警系统进行应用测试.结果表明:系统丢包率和用户收到预警信息时间间隔都满足可燃气体预警的实际要求.展开更多
将粒子群优化(PSO)算法与BP神经网络相结合,应用在传感器静态非线性特性的校正中.用PSO算法所得到的全局最优值作为BP神经网络的初始权值,训练BP神经网络,训练结束后的神经网络作为传感器的静态特性校正器.应用结果表明,该方法可以提高B...将粒子群优化(PSO)算法与BP神经网络相结合,应用在传感器静态非线性特性的校正中.用PSO算法所得到的全局最优值作为BP神经网络的初始权值,训练BP神经网络,训练结束后的神经网络作为传感器的静态特性校正器.应用结果表明,该方法可以提高BP神经网络的精度,并且该神经网络具有良好的泛化能力.
Abstract：
A static nonlinear errors method for correcting the sensors based on BP neural network using particle swarm optimization (PSO) is described. The global best values of particle swarm are used as initial weights of BP neural network to train BP neural network. Then the trained neural network is regarded as the sensor's corrector. The application results show that this method can improve the precision of the BP neural network, and the generalization capability of the neural network is good.展开更多
提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神...提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.
Abstract：
A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method.展开更多
基金supported by the National Natural Science Foundation of China(62101575)the Research Project of NUDT(ZK22-57)the Self-directed Project of State Key Laboratory of High Performance Computing(202101-16).
文摘Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.
基金Projects(42477162,52108347,52178371,52168046,52178321,52308383)supported by the National Natural Science Foundation of ChinaProjects(2023C03143,2022C01099,2024C01219,2022C03151)supported by the Zhejiang Key Research and Development Plan,China+6 种基金Project(LQ22E080010)supported by the Exploring Youth Project of Zhejiang Natural Science Foundation,ChinaProject(LR21E080005)supported by the Outstanding Youth Project of Natural Science Foundation of Zhejiang Province,ChinaProject(2022M712964)supported by the Postdoctoral Science Foundation of ChinaProject(2023AFB008)supported by the Natural Science Foundation of Hubei Province for Youth,ChinaProject(202203)supported by Engineering Research Centre of Rock-Soil Drilling&Excavation and Protection,Ministry of Education,ChinaProject(202305-2)supported by the Science and Technology Project of Zhejiang Provincial Communication Department,ChinaProject(2021K256)supported by the Construction Research Founds of Department of Housing and Urban-Rural Development of Zhejiang Province,China。
文摘This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi analytical solutions of temperature increment and displacement of multi-layered composite structures are obtained by using the Laplace transform method,upon which the effects of thermal resistance coefficient,partition coefficient,thermal conductivity ratio and heat capacity ratio on the responses are studied.The results show that the generalized imperfect thermal contact model can realistically describe the imperfect thermal contact problem.Accordingly,it may degenerate into other thermal contact models by adjusting the thermal resistance coefficient and partition coefficient.
文摘PGM(pragmatic general multicast)是一种在IP协议中广泛应用的可靠的组播传输协议.但PGM标准本身没有拥塞控制方案,不能实时响应网络需求,及时地调节源端发送速率.针对这个问题,在保证PGM协议可扩展性的基础上,在发送方与CLR(current limiting receiver)之间采用了一种新的闭环控制器来实时地调节源端的发送速率,使其逐渐趋于稳定,并具有较快的响应速度.而且在网络拓扑结构动态变化的情况下,对所提出的拥塞控制方案进行了仿真实验.仿真结果表明,所提出的算法具有较好的可扩展性、稳定性和较快的响应速度,控制方案使网络性能表现良好. PGM(pragmatic general multicast)是一种在IP协议中广泛应用的可靠的组播传输协议.但PGM标准本身没有拥塞控制方案,不能实时响应网络需求,及时地调节源端发送速率.针对这个问题,在保证PGM协议可扩展性的基础上,在发送方与CLR(current limiting receiver)之间采用了一种新的闭环控制器来实时地调节源端的发送速率,使其逐渐趋于稳定,并具有较快的响应速度.而且在网络拓扑结构动态变化的情况下,对所提出的拥塞控制方案进行了仿真实验.仿真结果表明,所提出的算法具有较好的可扩展性、稳定性和较快的响应速度,控制方案使网络性能表现良好.
文摘为了实现大规模、多区域的可燃气体的统一监测预警,使用无线传感器网络自组网技术、通用分组无线业务(general packet radio service,GPRS)无线传输技术和服务器数据库设计了可燃气体报警器采集节点、可燃气体报警器汇聚传输节点并完成服务器云管理平台的搭建.温湿度补偿算法提高了可燃气体报警器的采集精度,多级报警策略使可燃气体预警更智能,数据传输加密算法使数据传输更安全.系统可以应用在居民小区环境对可燃气体泄露智能预警.相关管理部门根据服务器云管理平台对预警信息及时处理并长期对监测数据进行统计分析.最后对搭建的远程可燃气体监测预警系统进行应用测试.结果表明:系统丢包率和用户收到预警信息时间间隔都满足可燃气体预警的实际要求.
文摘将粒子群优化(PSO)算法与BP神经网络相结合,应用在传感器静态非线性特性的校正中.用PSO算法所得到的全局最优值作为BP神经网络的初始权值,训练BP神经网络,训练结束后的神经网络作为传感器的静态特性校正器.应用结果表明,该方法可以提高BP神经网络的精度,并且该神经网络具有良好的泛化能力.
Abstract：
A static nonlinear errors method for correcting the sensors based on BP neural network using particle swarm optimization (PSO) is described. The global best values of particle swarm are used as initial weights of BP neural network to train BP neural network. Then the trained neural network is regarded as the sensor's corrector. The application results show that this method can improve the precision of the BP neural network, and the generalization capability of the neural network is good.
文摘提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.
Abstract：
A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method.
