An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-v...An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.展开更多
高压充油电缆终端的可靠运行是电缆线路稳定运行的前提,但传统充油电缆终端故障诊断模型存在效率低、可靠性差等问题。为准确判断充油电缆终端故障,提出一种最大互信息系数(maximal information coefficient,MIC)结合改进阿基米德算法(i...高压充油电缆终端的可靠运行是电缆线路稳定运行的前提,但传统充油电缆终端故障诊断模型存在效率低、可靠性差等问题。为准确判断充油电缆终端故障,提出一种最大互信息系数(maximal information coefficient,MIC)结合改进阿基米德算法(improved Archimedes optimization algorithm,IAOA)优化深度置信网络(deep belief network,DBN)的充油电缆终端故障诊断方法。首先,采用MIC理论对电缆终端用硅油中溶解气体浓度的特征量进行降维处理并提取特征量;其次,将优选的特征量作为DBN网络模型的输入,并针对DBN网络超参数选取困难的缺点,提出采用IAOA优化DBN网络模型的超参数;再者,针对AOA算法容易陷入局部最优和搜索能力差等不足,引入多种改进策略优化AOA的方法提高AOA的寻优能力。最后,通过搭建充油电缆终端故障模拟实验平台,收集充油电缆终端故障样本数据并创建类别样本标签,验证了该模型的可行性。实例表明,所提出的诊断方法可以较好地完成故障诊断,测试集的准确率为98.33%。与传统故障诊断模型相比,该方法稳定性好、识别精度高,可为保障高压充油电缆终端的可靠运行提供理论基础。展开更多
遥感图像分类是地理信息系统(geographic information system,GIS)的关键技术,对城市规划与管理起到十分重要的作用.近年来,深度学习成为机器学习领域的一个新兴研究方向.深度学习采用模拟人脑多层结构的方式,对数据从低层到高层渐进地...遥感图像分类是地理信息系统(geographic information system,GIS)的关键技术,对城市规划与管理起到十分重要的作用.近年来,深度学习成为机器学习领域的一个新兴研究方向.深度学习采用模拟人脑多层结构的方式,对数据从低层到高层渐进地进行特征提取,从而发掘数据在时间与空间上的规律,进而提高分类的准确性.深度信念网络(deep belief network,DBN)是一种得到广泛研究与应用的深度学习模型,它结合了无监督学习和有监督学习的优点,对高维数据具有较好的分类能力.提出一种基于DBN模型的遥感图像分类方法,并利用RADARSAT-2卫星6d的极化合成孔径雷达(synthetic aperture radar,SAR)图像进行了验证.实验表明,与支持向量机(SVM)及传统的神经网络(NN)方法相比,基于DBN模型的方法可以取得更好的分类效果.展开更多
基金supported by Inha University Research Grant,Korea
文摘An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.
