深度学习及其在煤矿安全领域的应用

杨小彬; 周世禄; 李娜; 王逍遥

深度学习及其在煤矿安全领域的应用

计量
- 文章访问数: 185
- HTML全文浏览量: 2
- PDF下载量: 5
出版历程
- 发布日期: 2019-01-19

Deep Learning and Its Application in Coal Mine Safety

摘要

摘要: 深度学习作为最近兴起的多层神经网络学习算法，凭借其优异的表现，迅速成为各个领域研究的热点话题。为引起更多从事煤矿安全领域的研究者对深度学习进行探索和讨论，并推动深度学习在煤矿安全领域的应用，详细阐述了近几年深度学习在图像识别和声音识别等方面所取得的进展及其应用领域，分析了煤矿中矿工的不安全行为和状态监测以及机械设备的故障检测两方面存在的问题，并针对相应问题分别利用图像识别和声音识别2种方法提出了模型的训练流程。
- 深度学习 /
- 煤矿安全管理 /
- 图像识别 /
- 声音识别 /
- 智能监控
Abstract: As a recently rising multi-layer neural network learning algorithm, deep learning has rapidly become a hot topic in various fields for its excellent performance. In order to attract more researchers in the field of coal mine safety to explore and discuss the deep learning and to promote the application of deep learning in the field of coal mine safety, this paper describes the progress and application of deep learning in the image recognition and voice recognition in recent years. This paper also analyzes the problems of the miners’ unsafe behavior and status monitoring and the fault detection of mechanical equipment in coal mines, and the training process of the model is proposed by using image recognition and sound recognition separately for corresponding problems.
- deep learning /
- coal mine safety management /
- image recognition /
- sound recognition /
- intelligent monitor

HTML全文

参考文献(24)

[1]	Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[2]	Silver D, Huang A, Maddison C, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587); 484-489.
[3]	张建明,詹智财,成科扬,等.深度学习的研究与发展[J].江苏大学学报(自然科学版),2015,36(2):191.
[4]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [C]//Proc of the 26th Annual Conf on Neural Information Processing Systems, Cambridge, MA; MIT Press, 2012;1097-1105.
[5]	Yosinski J, Clune J, Bengio Y, et al. How transferable features in deep neural networks [C]//Proc of the 28th Annual Conf on Neural Information Processing Systems, Cambridge, MA; MIT Press, 2014: 3320-3328.
[6]	Zeiler M D, Fergus R, Visualizing and understanding convolutional networks[C]//Proc of the 16th European Conf on Computer Vision, Berlin; Springer, 2014: 297.
[7]	周宇杰.深度学习在图像识别领域的应用现状与优势[J].中国安防,2016(7):75-78.
[8]	刘涵,贺霖,李军.深度学习进展及其在图像处理领域的应用[J].中兴通讯技术,2017,23(4):36-40.
[9]	张军阳,王慧丽,郭阳,等.深度学习相关研究综述[J].计算机应用研究,2018(7):1-12.
[10]	Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013, 35(8): 1798-1828.
[11]	杨俊安,王一,刘辉,等.深度学习理论及其在语音识别领域的应用[J].通信对抗,2014,33(3):1-5.
[12]	余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804.
[13]	于大海,孙建民.浅谈语音识别技术的应用和发展[J].科技传播,2009(9):22-23.
[14]	郭利刚,赵凡.声音匹配识别算法的研究与实践[J].中国传媒大学学报(自然科学版),2007,14(1):20.
[15]	王炳锡,屈丹,彭煊.实用语音识别基础[M].北京:国防工业出版社,2005.
[16]	高晓旭,董丁稳,杨日丽.BP神经网络在煤矿本质安全评价模型中的应用[J].西安科技大学学报,2011, 31(6):780-785.
[17]	张兴凯.我国“十二五”期间生产安全死亡事故直接经济损失估算[J].中国安全生产科学技术,2016,12(6):5-8.
[18]	Chen H, Qi H, Feng Q. Characteristics of direct causes and human factors in major gas explosion accidents in Chinese coal mines: Case study spanning the years 1980-2010[J]. Journal of Loss Prevention in the Process Industries, 2013, 26(1): 38-44.
[19]	曾宇航.我国煤矿安全监控系统研究综述[J].软件导刊,2010,9(7):140-142.
[20]	叶旭庆.基于3D卷积神经网络的人体行为识别[D].西安:西安电子科技大学,2015.
[21]	孟令声.煤矿机电设备安全现状、存在问题及对策[J].中小企业管理与科技(中旬刊),2016(1):287.
[22]	郑孝东.煤矿机电设备安全现状、存在问题及对策[J].能源技术与管理,2015,40(2):122-124.
[23]	宗春英.基于人工智能的故障监测和诊断系统的研究[J].制造业自动化,2012,34(7):52-54.
[24]	Roland Helm The application advantages of MEMS microphones acceleration Replace the traditional electret microphone [J]. Compotech China,2008(3): 26.

施引文献

资源附件(0)

计量

文章访问数: 185
HTML全文浏览量: 2
PDF下载量: 5
被引次数: 0

深度学习及其在煤矿安全领域的应用

计量

出版历程

Deep Learning and Its Application in Coal Mine Safety

计量

出版历程

目录