A convolutional neural network based method for event classification in event-driven multi-sensor network
A convolutional neural network based method for event classification in event-driven multi-sensor network
复制标题
事件驱动多传感器网络中基于卷积神经网络的事件分类方法
DOI：
10.1016/j.compeleceng.2017.01.005
复制
发表时间：
2017
影响因子：
4.3
通讯作者：
Zhu Fumin
中科院分区：
文献类型：
--
作者：
Tong Chao;Li Jun;Zhu Fumin
一个多传感器网络通常会产生大量的数据，其中一些代表特定的有意义的事件。对于事件驱动的多传感器网络，事件分类是后续高层决策和控制的基础。然而，分类精度的提高一直是一个挑战。最近，深度学习方法在许多传统领域取得了巨大的成功，最流行的深度架构之一是卷积神经网络（CNN），它充分利用了输入图像的部分特征。在本文中，我们做了一些类比之间的图像和传感器数据，然后提出了一种基于CNN的方法来提高事件分类的准确性同质多传感器网络。AlexNet的一个变体已经被设计和建立，用于通过声学信号对事件进行分类。结果表明，这种基于CNN的分类器优于thankNearest Neighbor（kNN）和支持向量机（SVM）方法在我们的数据集上具有更高的准确性。
A multi-sensor network usually produces a large scale of data, some of which represent specific meaningful events. For event-driven multi-sensor networks, event classification is the basis of subsequent high-level decisions and controls. However, the accuracy improvement of classification is always a challenge. Recently the deep learning methods have achieved vast success in many conventional fields, and one of the most popular deep architectures is convolutional neural network (CNN) which sufficiently utilizes partial features of the input images. In this paper, we make some analogy between an image and sensor data, then propose a CNN-based method to improve the event classification accuracy for homogenous multi-sensor networks. An variant of AlexNet has been designed and established for classifying the event by acoustic signals. The results indicate that this CNN-based classifier outperforms thankNearest Neighbor (kNN) and Support Vector Machine (SVM) methods on our data set with a higher accuracy.