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
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
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
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
为分析安徽省土地利用时空演化特征,以1980年、1995年、2000年、2005年、2010年、2015年、2020年土地利用现状图为基础,利用Sankey图、土地利用动态度等方法分析了安徽省近40年土地利用演变特征;结合Getis-Ord General G聚类方法和多距...为分析安徽省土地利用时空演化特征,以1980年、1995年、2000年、2005年、2010年、2015年、2020年土地利用现状图为基础,利用Sankey图、土地利用动态度等方法分析了安徽省近40年土地利用演变特征;结合Getis-Ord General G聚类方法和多距离空间聚类(Ripleys K函数)方法对安徽省土地利用变化累积量进行了时空模式分析;并基于地理探测器模型分析了多种驱动因子对土地利用变化的单一与交互解释程度。结果表明:①1980—2020年安徽省建设用地、草地、水域面积分别增加35.04%、2.44%和0.75%,耕地、林地分别减少4.63%和0.98%;安徽省综合土地利用动态度逐渐增加,建设用地和耕地变化较快,单一动态度最高分别达到3.15%和-0.39%;林地、草地、水域变化较为稳定。②1980—2020年安徽省土地利用的剧烈变化具有显著的聚集性,通过K-means聚类可将不同程度的变化较好地分类;土地利用剧烈变化区域聚集特征受观测尺度变化的影响小于平缓变化区域。③地理探测结果表明:与人类活动强度密切联系的社会因子(夜间灯光数据、GDP、人口、到城市和主要道路距离等)和地形因子(高程、坡度)以及各因子间交互作用是土地利用变化的重要推动力。展开更多
为了能更有效地处理含有噪音数据的数据集,提出了一个基于GDT(general distribution table)的对FOIL系统的改进方法,该方法利用GDT的思想、规则强度等概念,考虑到数据由噪音引起的不确定性.通过对FOIL系统算法的改进,能够很好地解决FOI...为了能更有效地处理含有噪音数据的数据集,提出了一个基于GDT(general distribution table)的对FOIL系统的改进方法,该方法利用GDT的思想、规则强度等概念,考虑到数据由噪音引起的不确定性.通过对FOIL系统算法的改进,能够很好地解决FOIL系统对含有噪音训练例集的学习能力,提高FOIL的学习精度.同时,通过例子阐述了该方法的实施过程,分析表明:该算法是一种新的有效地处理含有噪音数据的一阶谓词学习系统.展开更多
KD(D&K) (Synthesized Knowledge Discovery System based on Database and KnowledgeBase Cooperating Mechanism) is first advanced in this paper on the basis of KDD (KnowledgeDiscovery System based on Database) and KDK ...KD(D&K) (Synthesized Knowledge Discovery System based on Database and KnowledgeBase Cooperating Mechanism) is first advanced in this paper on the basis of KDD (KnowledgeDiscovery System based on Database) and KDK (Knowledge Discovery System based on KnowledgeBase). KD (D&K) is not simple addition of KDD and KDK but a new system with absolutelynew character and essential development, which is distinct from KDD and KDK and includes them.Not only the generalized structural frame of control rules KD (D&K) acquiring method is proposed,but also the theoretical basis of its key technical problem--don ble-bases cooperating mechanism isdiscussed according to the original academic idea to restrict KDD by basic knowledge base.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.
文摘为分析安徽省土地利用时空演化特征,以1980年、1995年、2000年、2005年、2010年、2015年、2020年土地利用现状图为基础,利用Sankey图、土地利用动态度等方法分析了安徽省近40年土地利用演变特征;结合Getis-Ord General G聚类方法和多距离空间聚类(Ripleys K函数)方法对安徽省土地利用变化累积量进行了时空模式分析;并基于地理探测器模型分析了多种驱动因子对土地利用变化的单一与交互解释程度。结果表明:①1980—2020年安徽省建设用地、草地、水域面积分别增加35.04%、2.44%和0.75%,耕地、林地分别减少4.63%和0.98%;安徽省综合土地利用动态度逐渐增加,建设用地和耕地变化较快,单一动态度最高分别达到3.15%和-0.39%;林地、草地、水域变化较为稳定。②1980—2020年安徽省土地利用的剧烈变化具有显著的聚集性,通过K-means聚类可将不同程度的变化较好地分类;土地利用剧烈变化区域聚集特征受观测尺度变化的影响小于平缓变化区域。③地理探测结果表明:与人类活动强度密切联系的社会因子(夜间灯光数据、GDP、人口、到城市和主要道路距离等)和地形因子(高程、坡度)以及各因子间交互作用是土地利用变化的重要推动力。
文摘为了能更有效地处理含有噪音数据的数据集,提出了一个基于GDT(general distribution table)的对FOIL系统的改进方法,该方法利用GDT的思想、规则强度等概念,考虑到数据由噪音引起的不确定性.通过对FOIL系统算法的改进,能够很好地解决FOIL系统对含有噪音训练例集的学习能力,提高FOIL的学习精度.同时,通过例子阐述了该方法的实施过程,分析表明:该算法是一种新的有效地处理含有噪音数据的一阶谓词学习系统.
文摘KD(D&K) (Synthesized Knowledge Discovery System based on Database and KnowledgeBase Cooperating Mechanism) is first advanced in this paper on the basis of KDD (KnowledgeDiscovery System based on Database) and KDK (Knowledge Discovery System based on KnowledgeBase). KD (D&K) is not simple addition of KDD and KDK but a new system with absolutelynew character and essential development, which is distinct from KDD and KDK and includes them.Not only the generalized structural frame of control rules KD (D&K) acquiring method is proposed,but also the theoretical basis of its key technical problem--don ble-bases cooperating mechanism isdiscussed according to the original academic idea to restrict KDD by basic knowledge base.
