Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on or...In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.展开更多
This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed, with IM para...This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed, with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.展开更多
An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circ...An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.展开更多
点云的处理、传输、语义分割等是3维计算机视觉领域重要的分析任务.现如今,图神经网络和图结构在点云研究方面的有效性已被证实,基于图的点云(graph-based point cloud,GPC)研究不断涌现.因此,一种统一的研究角度、框架和方法论亟待形成...点云的处理、传输、语义分割等是3维计算机视觉领域重要的分析任务.现如今,图神经网络和图结构在点云研究方面的有效性已被证实,基于图的点云(graph-based point cloud,GPC)研究不断涌现.因此,一种统一的研究角度、框架和方法论亟待形成.系统性梳理了GPC研究的各种应用场景,包括配准、降噪、压缩、表示学习、分类、分割、检测等任务,概括出GPC研究的一般性框架,提出了一条覆盖当前GPC全域研究的技术路线.具体来说,给出了GPC研究的分层概念范畴,包括底层数据处理、中层表示学习、高层识别任务;综述了各领域中的GPC模型或算法,包括静态和动态点云的处理算法、有监督和无监督的表示学习模型、传统或机器学习的GPC识别算法;总结了其中代表性的成果及其核心思想,譬如动态更新每层特征空间对应的最近邻图、分层以及参数共享的动态点聚合模块,结合图划分和图卷积提高分割精度;对比了模型性能,包括总体精度(overall accuracy,OA)、平均精度(mean accuracy,mAcc)、平均交并比(mean intersection over union,mIoU);在分析比较现有模型和方法的基础上,归纳了GPC目前面临的主要挑战,提出相应的研究问题,并展望未来的研究方向.建立的GPC研究框架具有一般性和通用性,为后续研究者从事GPC这个新型交叉领域研究提供了领域定位、技术总结及宏观视角.点云研究的出现,是探测器硬件技术长足进步后应运而生的结果;点云研究的现状表明在理论和实践之间存在一些挑战,一些关键问题还有待解决.同时,点云研究的发展将推动人工智能进入新的时代.展开更多
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金Projects(51875558,51471176)supported by the National Natural Science Foundation of ChinaProject(2017YFB1302802)supported by the National Key R&D Program of China。
文摘In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.
基金This project was supported by the National Natural Science Foundation of China (No. 69874086).
文摘This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed, with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.
基金Supported by National Natural Science Foundation (60274015) the "863" Program of P, R. China (2002AA412420)
文摘An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.
文摘点云的处理、传输、语义分割等是3维计算机视觉领域重要的分析任务.现如今,图神经网络和图结构在点云研究方面的有效性已被证实,基于图的点云(graph-based point cloud,GPC)研究不断涌现.因此,一种统一的研究角度、框架和方法论亟待形成.系统性梳理了GPC研究的各种应用场景,包括配准、降噪、压缩、表示学习、分类、分割、检测等任务,概括出GPC研究的一般性框架,提出了一条覆盖当前GPC全域研究的技术路线.具体来说,给出了GPC研究的分层概念范畴,包括底层数据处理、中层表示学习、高层识别任务;综述了各领域中的GPC模型或算法,包括静态和动态点云的处理算法、有监督和无监督的表示学习模型、传统或机器学习的GPC识别算法;总结了其中代表性的成果及其核心思想,譬如动态更新每层特征空间对应的最近邻图、分层以及参数共享的动态点聚合模块,结合图划分和图卷积提高分割精度;对比了模型性能,包括总体精度(overall accuracy,OA)、平均精度(mean accuracy,mAcc)、平均交并比(mean intersection over union,mIoU);在分析比较现有模型和方法的基础上,归纳了GPC目前面临的主要挑战,提出相应的研究问题,并展望未来的研究方向.建立的GPC研究框架具有一般性和通用性,为后续研究者从事GPC这个新型交叉领域研究提供了领域定位、技术总结及宏观视角.点云研究的出现,是探测器硬件技术长足进步后应运而生的结果;点云研究的现状表明在理论和实践之间存在一些挑战,一些关键问题还有待解决.同时,点云研究的发展将推动人工智能进入新的时代.
