共查询到20条相似文献，搜索用时 15 毫秒
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Suliman Aladhadh Hidayat Ur Rehman Ali Mustafa Qamar Rehan Ullah Khan 《计算机、材料和连续体（英文）》2021,69(3):3399-3411
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目的 为实时监测快递分拣过程中粗暴对待包裹的行为,设计一款基于树莓派+EdgeTPU的快递暴力分拣人体行为视觉识别系统.方法 基于TensorFlow深度学习框架,使用PoseNet模型实时采集人体姿态数据,通过LSTM+Attention模型实现人体动作识别,结合MobileSSD进行场景识别,最终实现暴力分拣人体行为视觉识别.结果 实验证明,文中提出的视觉识别方法可以实现暴力分拣5种动作的快速、准确识别,LSTM+Attention人体动作分类模型的测试准确率达到了80％.结论 基于该方法构建的嵌入式暴力分拣行为识别系统,可以实时监测快递分拣中粗暴对待包裹的行为,并实时地告警. 相似文献
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As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the influence of time series features on classification results. Simultaneously, it is trained and tested by the MIT-BIH arrhythmia database. Besides, Generative Adversarial Networks (GAN) is adopted as a method of data equalization for solving data imbalance problem. The simulation results show that for the inter-patient arrhythmia classification, the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy, of which the accuracy can reach 94.05%. Especially, it has a better advantage for the classification effect of supraventricular ectopic beats (class S) and fusion beats (class F). 相似文献
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从结构响应信号中挖掘敏感损伤特征是基于模式分类的损伤识别方法的关键.为此,将深度信念网络和长短期记忆网络进行混合组网,通过混合学习机制有机结合了两种网络在高阶抽象特征提取和考虑数据序列相关性上的优点.将响应信号传递比值输入深度信念网络,实现初步数据压缩和特征提取,以减少响应中的冗余信息;将特征序列依次输入长短期记忆网络,以考虑响应间的相关性并获取敏感损伤特征;利用Softmax分类层对长短期记忆网络输出的特征进行分类,实现对不同结构损伤模式的识别.三维试验钢框架的损伤识别结果表明:混合学习机制能更好地训练网络参数,整体微调后更有利于后续的损伤特征分类;混合组网方式在包含数值或实测噪声的情况下仍可以有效进行数据压缩、特征提取和分类,准确识别了试验框架的多种损伤工况. 相似文献
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Fu-Kwun Wang Chang-Yi Huang Tadele Mamo Xiao-Bin Cheng 《Quality and Reliability Engineering International》2021,37(1):34-46
Proton exchange membrane fuel cell (PEMFC) stacks are widely used in mobile and portable applications due to their clean and efficient model of operation. We propose an ensemble model based on a stacked long short-term memory model that combines three machine-learning models, including long short-term memory with attention mechanism, support vector regression, and random forest regression, to improve the degradation prediction of a PEMFC stack. The prediction intervals can be derived using the dropout technique. The proposed model is compared with some existing models using two PEMFC stacks. The results show that the proposed model outperforms the other models in terms of mean absolute percentage error and root mean square error. Regarding the remaining useful life prediction, the proposed model with the sliding window approach can provide better results. 相似文献
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目的 天气和随机因素会改变误差的统计特征;因此考虑对影响风电功率的多种气候因素进行特征提取;为优化功率时序特征提取;提出基于多特征提取(multimodal feature extraction;MFE)-卷积神经网络(convolutional neural network;CNN)-长短期记忆(long-short term memory;LSTM)网络的风电功率预测方法。 方法 首先;对数值天气预报(numerical weather prediction;NWP)数据提取11种统计性特征;通过提取基本特征和统计性特征对原始数据进行聚类;并根据类别分别建立预测模型;以提高预测模型的适应性;其次;在网络架构上对LSTM进行改进;通过CNN的特征提取能力和LSTM的非线性序列预测能力;实现对风电功率历史信息和NWP数据的充分挖掘。最后;利用我国新疆某风电场数据;通过MFE消融实验、CNN消融实验;验证了所提短期风电功率预测方法的有效性和优越性。 结果 相比于自回归移动平均(autoregressive integrated moving average;ARIMA)、全连接循环神经网络(fully recurrent neural network;FRNN)模型和MFE-LSTM、CNN-LSTM模型;MFE-CNN-LSTM预测方法的均方根误差与平均绝对误差均有所下降。 结论 MFE-CNN-LSTM预测方法可有效提取特征;并且MFE与CNN有效提升了预测准确性。 相似文献
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风力发电过程具有较强的随机性,导致风力发电功率的预测准确度不高。针对上述问题,提出了一种融合深度学习算法的风力发电功率预测方法。以历史风力发电功率数据作为输入,建立风力发电功率预测模型,实现对未来一个时间刻度的风力发电功率预测。算例结果表明,与传统时序预测方法相比,基于长短期记忆神经网络的风力发电功率预测结果在各项指标中误差更小,验证了上述方法在风力发电功率预测中的可行性和有效性,提升了风力发电功率预测的准确性。 相似文献
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为了解决人们在强噪声环境下,通过空气途径传递的语音信号会严重失真的问题,提出了一种基于深层双向长短期记忆-深度卷积神经网络(Deep Bidirectional Long and Short Term Memory-Deep Convolutional Neural Network,DBLSTM-DCNN)的骨导语音转... 相似文献
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无人机产业近年来发展迅猛,在军用和民用方面都拥有广泛的应用前景。无人机的航迹记录在其航行过程中发挥着重要作用,无人机的航迹预测也成为当前世界研究的热点,使用神经网络进行航迹预测更可以充分发挥其优势。首先对国内外学者关于航迹预测的文献进行了梳理,根据航迹预测的原理对目前飞行器航迹预测算法进行了总结和分类,针对利用神经网络模型预测无人机航迹并逐步改进模型以提高预测精度的问题进行了研究。接着对于传统神经网络模型预测精度不够高的问题,提出一种带误差修正的嵌套长短期记忆 (ENLSTM) 神经网络预测模型。ENLSTM 在嵌套长短期记忆网络模型的基础上引入了误差修正项,从而使得预测精度更高。最后使用 BP、RNN、LSTM 和 ENLSTM 四种神经网络模型分别对无人机的真实航迹数据和模拟航迹数据进行仿真实验,得出结论：循环神经网络相对 BP 神经网络在无人机航迹的预测上更具有优势,基于基础循环神经网络的逐步改进提升了模型的预测能力,ENLSTM 模型对于无人机的航迹预测具有更好的效果。 相似文献
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目的 过对不同预测方法的误差对比研究,选取预测生鲜农产品物流需求量更精准方法,为疫情情况下山东省生鲜农产品市场进行科学性、合理化决策提供参考。方法 公路货物周转量、互联网普及率、GDP、人口数量、第一产业增加值等十大影响因素作为自变量,以生鲜农产品的需求量作为因变量,分别将小波神经网络、人工神经网络(BP)、遗传算法优化神经网络(GA−BP)、粒子群优化神经网络(PSO−BP)、长短时记忆网络(LSTM)等5种方法的数据预测进行对比分析。结果 波神经网络和BP神经网络的预测值明显低于真实值,且平均相对误差接近20%,而优化后的GA−BP、PSO−BP、LSTM算法误差均小于5%,分别为4.06%、1.162%、0.45%,因此,LSTM预测精度最高,效果最好。结论 来山东省的生鲜农产品需求量将持续增长,LSTM算法以其精确度更高,学习能力更强的优点,将会被更多地应用到物流领域研究中。 相似文献
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针对风电齿轮箱状态监测数据的多变量动态时空关联性特点,提出了一种基于长短期记忆(long shortterm memory,LSTM)网络的齿轮箱故障预测方法,主要包括离线建模和在线监测两个阶段。首先,以齿轮箱油温为目标预测变量,充分考虑其与其它相关输入变量之间在时空维度上的重要关联信息,对历史监测数据进行训练学习,建立齿轮箱正常运行时的油温监测LSTM模型,通过对预测残差进行评估计算设定相应的检测阈值;然后,将训练好的油温监测LSTM模型用于在线测试,通过模型残差分析和阈值比较实现齿轮箱故障状态的检测和预测;最后,通过风电场测试数据对所提出的方法进行验证。结果表明,相比于其它传统方法,该方法表现出更好的预测性能,能够较早预测故障的发生。 相似文献
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The processing of sound signals is significantly improved recently. Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques. This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music. For analysis and process part, we represented normalized waveforms for each of input data via frequency domain signals. Then we looked into shorted segment to see the difference wave pattern for different singers. Fourier transform is applied to get spectrogram of the music signals. Filterbank is applied to represent the spectrogram based on the human ear instead of the distance on the frequency dimension, and the final spectrogram has been plotted by Mel scale. For generating part, we created two-dimensional tabular data for data manipulation. With the 2D data, any kind of analysis can be done since it has digit values for the music signals. Then, we generated a new music by applying LSTM toward the song audience preferred more. As the result, it has been proved that the created music showed the similar waveforms with the original music. This study made a step forward for music signal processing. If this study expands further, it can find the pattern that listeners like so music can be generated within favorite singer’s voice in the way that the listener prefers. 相似文献
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提出了一种采用δ规则作为学习算法的扩展双向联想记忆神经网络模型，并从理论上证明了其稳定性。该模型克服了现有采用Ｈｅｂｂ规则作为学习算法的联想记忆神经网络对记忆模式有正交性要求和所模式吸引域小的不足。实验结果表明，其联想记忆能力优于目前现有的联想记忆网络。 相似文献
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碳足迹测量与估计是低碳供应链评估的重要指标,目前缺乏统一的碳足迹衡量标准与界限,同时传统的碳足迹测量方法需要大量的计算成本.因此,提出一种先核算后预测的两阶段全生命周期碳足迹估算方法.在第 1阶段,电网物资供应链被划分为 5个阶段,并构建相应的测算模型,实现对碳足迹的定量描述与评估;在第 2阶段,以电缆产品作为碳源,构建基于长短时记忆神经网络(long short-term memory neural network,LSTM)的供应链全生命周期碳排放量预测模型.基于 2020～2023年电网供应链的碳足迹管理数据进行了数值实验,预测准确率为99.3%.通过与BP神经网络和GABP神经网络构建的模型对比,证明模型的准确性与优越性,实现对碳足迹的有效核算与预测. 相似文献
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Computer Assisted Diagnosis (CAD) is an effective method to detect lung cancer from computed tomography (CT) scans. The development of artificial neural network makes CAD more accurate in detecting pathological changes. Due to the complexity of the lung environment, the existing neural network training still requires large datasets, excessive time, and memory space. To meet the challenge, we analysis 3D volumes as serialized 2D slices and present a new neural network structure lightweight convolutional neural network (CNN)-long short-term memory (LSTM) for lung nodule classification. Our network contains two main components: (a) optimized lightweight CNN layers with tiny parameter space for extracting visual features of serialized 2D images, and (b) LSTM network for learning relevant information among 2D images. In all experiments, we compared the training results of several models and our model achieved an accuracy of 91.78% for lung nodule classification with an AUC of 93%. We used fewer samples and memory space to train the model, and we achieved faster convergence. Finally, we analyzed and discussed the feasibility of migrating this framework to mobile devices. The framework can also be applied to cope with the small amount of training data and the development of mobile health device in future. 相似文献