Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive...Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.展开更多
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e...To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.展开更多
The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)...The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.展开更多
Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rollin...Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.展开更多
深入分析了城市物流竞争力的研究现状,结合深度学习相关理论,以深层自编码器(Deep Auto Encoder,DAE)标准模型与标准算法为基础,提出了基于Widrow函数的深层自编码器动量更新算法(DAE-WMA)。依据城市物流竞争力分析数据量特点,选取三种...深入分析了城市物流竞争力的研究现状,结合深度学习相关理论,以深层自编码器(Deep Auto Encoder,DAE)标准模型与标准算法为基础,提出了基于Widrow函数的深层自编码器动量更新算法(DAE-WMA)。依据城市物流竞争力分析数据量特点,选取三种UCI数据集,对基于误差函数的标准算法(DAE-ESA)、基于交叉熵的标准算法(DAE-CSA)以及DAE-WMA的模式分类能力进行仿真,仿真结果表明后者的性能优于前两者。依据物流竞争实力与竞争潜力,基于层次分析法(Analytic Hierarchy Process,AHP)通过选取7个评估维度与19个评价指标构建城市物流竞争力指标体系,利用DAE-WMA方法与社会网络分析(Social Network Analysis,SNA)方法,对我国西北五省区13个主要城市的物流竞争力进行聚类分析与实证研究,仿真结果表明DAE-WMA方法相对于SNA方法,对核心节点城市的分类结果更加合理,更有利于对问题的分析。研究结果为确定新丝绸之路经济带沿线城市物流发展策略,促进国内物流业未来的协作与发展奠定了研究基础。展开更多
为了解决联合收割机作业故障的非线性特征信号难以提取的问题,该研究提出了一种基于堆叠去噪自动编码器(Stack Denoising Auto Encoder,SDAE)和BP神经网络(Back Propagation,BP)融合的联合收割机作业故障监测及诊断的方法(SDAE-BP)。以...为了解决联合收割机作业故障的非线性特征信号难以提取的问题,该研究提出了一种基于堆叠去噪自动编码器(Stack Denoising Auto Encoder,SDAE)和BP神经网络(Back Propagation,BP)融合的联合收割机作业故障监测及诊断的方法(SDAE-BP)。以转速传感器采集联合收割机脱粒滚筒转速、籽粒搅龙转速、喂入搅龙转速、杂余搅龙转速、风机转速、输送链耙转速、割刀频率以及逐稿器振动频率,并将采集的数据集作为系统的输入。利用SDAE提取输入信号的深层次特征,并由BP神经网络辨识收割机作业状态,实现联合收割机故障监测。在SDAE-BP模型训练过程中,去噪自动编码器(Denoising Auto Encode,DAE)依次经带有不同分布中心噪声的原始数据进行训练,然后将其堆叠,并通过误差反向传播算法对模型参数进行优化,以提升模型识别故障性能和泛化能力。试验结果表明,对于2018年联合收割机田间试验数据,模型的故障诊断准确率达到99.00%,与SDAE和BP神经网络相比,分别提高了1.5和4.5个百分点。将SDAE-BP故障诊断模型用2019年的试验数据进行更新,并用2018年和2019年试验数据进行测试,结果表明,更新后的模型对2018年试验数据的故障识别准确率为99.25%,对2019年试验数据的故障识别准确率为98.74%,更新后模型在2019试验数据集上的故障识别准确率较未更新模型提高了6.52个百分点。该文所建模型能够准确识别联合收割机的故障类型,且具有较好的鲁棒性,对旋转型机械故障监测及预警具有参考价值。展开更多
针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特...针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特征输入SVDD进行通信辐射源个体开集识别实验。结果表明,在不同开放度下,该方法可以将未知通信辐射源个体和已知通信辐射源个体以高准确率区分出来,进而将开集识别转为闭集识别。同时,对已知通信辐射源个体识别有很好的识别准确率和抗噪声能力。展开更多
风速预测是风力预报中的核心与基础,采用天气研究和预报(Weather Research and Forecasting,WRF)模式进行风力预报往往存在风速预测误差较大的问题.为了提高风速预测精度,提出了一种基于深度学习和支持向量回归(Support Vector Regressi...风速预测是风力预报中的核心与基础,采用天气研究和预报(Weather Research and Forecasting,WRF)模式进行风力预报往往存在风速预测误差较大的问题.为了提高风速预测精度,提出了一种基于深度学习和支持向量回归(Support Vector Regression,SVR)相结合的风速预测模型.该模型以WRF模式预报输出的多种气象变量为基础,结合气象自动观测站传感器的实测风速,引入堆栈降噪自动编码(Stacked De-noising Auto-Encoder,SDAE)深度网络来学习样本数据中隐含的深度特征,然后将该深度网络最后一层输出的深度特征置入回归器SVR中,利用SVR良好的回归预测性能对WRF模式预报的未来1 h风速进行预测订正.结果表明:所建立的SDAE-SVR风速预测模型具有较高的风速预测精度,在对典型日的WRF模式预报未来1 h风速的预测订正中,其平均百分比误差与均方根误差仅为8.28%与0.8066m·s^-1.展开更多
针对现有恶意域名检测算法对于家族恶意域名检测精度不高和实时性不强的问题,提出一种基于BiLSTM-DAE的恶意域名检测算法。通过利用双向长短时记忆神经网络(Bi-directional Long Short Term Memory,BiLSTM)提取域名字符组合的上下文序...针对现有恶意域名检测算法对于家族恶意域名检测精度不高和实时性不强的问题,提出一种基于BiLSTM-DAE的恶意域名检测算法。通过利用双向长短时记忆神经网络(Bi-directional Long Short Term Memory,BiLSTM)提取域名字符组合的上下文序列特征,并结合深度自编码网络(Deep Auto-Encoder,DAE)逐层压缩感知提取类内有共性和类间有区分性的强字符构词特征并进行分类。实验结果表明,与当前主流恶意域名检测算法相比,该算法在保持检测开销较小的基础上,具有更高的检测精度。展开更多
文摘Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
文摘To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.
基金supported by financial support from Universiti Sains Malaysia(USM)under FRGS Grant Number FRGS/1/2020/TK03/USM/02/1the School of Computer Sciences USM for their support.
文摘The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51704138)
文摘Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.
文摘为了解决联合收割机作业故障的非线性特征信号难以提取的问题,该研究提出了一种基于堆叠去噪自动编码器(Stack Denoising Auto Encoder,SDAE)和BP神经网络(Back Propagation,BP)融合的联合收割机作业故障监测及诊断的方法(SDAE-BP)。以转速传感器采集联合收割机脱粒滚筒转速、籽粒搅龙转速、喂入搅龙转速、杂余搅龙转速、风机转速、输送链耙转速、割刀频率以及逐稿器振动频率,并将采集的数据集作为系统的输入。利用SDAE提取输入信号的深层次特征,并由BP神经网络辨识收割机作业状态,实现联合收割机故障监测。在SDAE-BP模型训练过程中,去噪自动编码器(Denoising Auto Encode,DAE)依次经带有不同分布中心噪声的原始数据进行训练,然后将其堆叠,并通过误差反向传播算法对模型参数进行优化,以提升模型识别故障性能和泛化能力。试验结果表明,对于2018年联合收割机田间试验数据,模型的故障诊断准确率达到99.00%,与SDAE和BP神经网络相比,分别提高了1.5和4.5个百分点。将SDAE-BP故障诊断模型用2019年的试验数据进行更新,并用2018年和2019年试验数据进行测试,结果表明,更新后的模型对2018年试验数据的故障识别准确率为99.25%,对2019年试验数据的故障识别准确率为98.74%,更新后模型在2019试验数据集上的故障识别准确率较未更新模型提高了6.52个百分点。该文所建模型能够准确识别联合收割机的故障类型,且具有较好的鲁棒性,对旋转型机械故障监测及预警具有参考价值。
文摘针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特征输入SVDD进行通信辐射源个体开集识别实验。结果表明,在不同开放度下,该方法可以将未知通信辐射源个体和已知通信辐射源个体以高准确率区分出来,进而将开集识别转为闭集识别。同时,对已知通信辐射源个体识别有很好的识别准确率和抗噪声能力。
文摘风速预测是风力预报中的核心与基础,采用天气研究和预报(Weather Research and Forecasting,WRF)模式进行风力预报往往存在风速预测误差较大的问题.为了提高风速预测精度,提出了一种基于深度学习和支持向量回归(Support Vector Regression,SVR)相结合的风速预测模型.该模型以WRF模式预报输出的多种气象变量为基础,结合气象自动观测站传感器的实测风速,引入堆栈降噪自动编码(Stacked De-noising Auto-Encoder,SDAE)深度网络来学习样本数据中隐含的深度特征,然后将该深度网络最后一层输出的深度特征置入回归器SVR中,利用SVR良好的回归预测性能对WRF模式预报的未来1 h风速进行预测订正.结果表明:所建立的SDAE-SVR风速预测模型具有较高的风速预测精度,在对典型日的WRF模式预报未来1 h风速的预测订正中,其平均百分比误差与均方根误差仅为8.28%与0.8066m·s^-1.
文摘针对现有恶意域名检测算法对于家族恶意域名检测精度不高和实时性不强的问题,提出一种基于BiLSTM-DAE的恶意域名检测算法。通过利用双向长短时记忆神经网络(Bi-directional Long Short Term Memory,BiLSTM)提取域名字符组合的上下文序列特征,并结合深度自编码网络(Deep Auto-Encoder,DAE)逐层压缩感知提取类内有共性和类间有区分性的强字符构词特征并进行分类。实验结果表明,与当前主流恶意域名检测算法相比,该算法在保持检测开销较小的基础上,具有更高的检测精度。
