[1] |
邓立军, 尚文天, 刘剑, 等. 风门开闭对矿井风流影响规律模拟实验研究[J/OL]. 安全与环境学报.[2022-07-24]. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=AQHJ20220507000.
|
|
DENG Lijun, SHANG Wentian, LIU Jian, et al. Simulation experimental study on the effect of air door opening and closing on the wind flow law of mine[J/OL]. Journal of Safety and Environment.[2022-07-24]. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=AQHJ20220507000.
|
[2] |
刘剑. 矿井智能通风关键科学技术问题综述[J]. 煤矿安全, 2020, 51(10):108-111,117.
|
|
LIU Jian. Overview on key scientific and technical issues of mine intelligent ventilation[J]. Safety in Coal Mines, 2020, 51(10): 108-111,117.
|
[3] |
熊兴隆, 崔雅峰, 杨立香, 等. 一种机场环境光纤预警系统的信号识别新算法[J]. 光电子·激光, 2017, 28(9):985-991.
|
|
XIONG Xinglong, CUI Yafeng, YANG Lixiang, et al. A new method for signal recognition of the fiber-optic alarm system round airport[J]. Journal of Optoelectronics Laser, 2017, 28(9):985-991.
|
[4] |
SINDI H, NOUR M, RAWA M, et al. A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification[J]. Expert Systems with Applications, 2021, 174: DOI: 10.1016/j.eswa.2021.114785.
doi: 10.1016/j.eswa.2021.114785
|
[5] |
瞿合祚, 刘恒, 李晓明, 等. 一种电能质量多扰动分类中特征组合优化方法[J]. 电力自动化设备, 2017, 37(3):146-152.
|
|
QU Hezuo, LIU Heng, LI Xiaoming, et al. Feature combination optimization for multi-disturbance classification of power quality[J]. Electric Power Automation Equipment, 2017, 37(3):146-152.
|
[6] |
王慧慧, 王萍, 刘涛, 等. 基于生长-修剪优化RBF神经网络的电能质量扰动分类[J]. 电网技术, 2018, 42(8):2408-2415.
|
|
WANG Huihui, WANG Ping, LIU Tao, et al. Power quality disturbance classification based on growing and pruning optimal RBF neural network[J]. Power System Technology, 2018, 42(8):2408-2415.
|
[7] |
屈相帅, 段斌, 尹桥宣, 等. 基于稀疏自动编码器深度神经网络的电能质量扰动分类方法[J]. 电力自动化设备, 2019, 39(5):157-162.
|
|
QU Xiangshuai, DUAN Bin, YI Qiaoxuan, et al. Classification method of power quality disturbances based on deep neural network of sparse auto-encoder[J]. Electric Power Automation Equipment, 2019, 39(5):157-162.
|
[8] |
李建闽, 林海军, 梁成斌, 等. 基于双分辨率S变换和学习向量量化神经网络的电能质量扰动检测方法[J]. 电工技术学报, 2019, 34(16):3453-3463.
|
|
LI Jianmin, LIN Haijun, LIANG Chengbin, et al. Detection method of power quality disturbances based on double resolution s transform and learning vector quantization neural network[J]. Transactions of China Electrotechnical Society, 2019, 34(16):3453-3463.
|
[9] |
李盼, 娄钊瑜, 马康, 等. 一种自适应S变换在电能质量特征提取中的应用[J]. 中国电机工程学报, 2021, 41(22):7660-7668.
|
|
LI Pan, LOU Zhaoyu, MA Kang, et al. Application of adaptive S-transform in power quality feature extraction[J]. Proceedings of the CSEE. 2021, 41(22):7660-7668.
|
[10] |
吴建章, 梅飞, 郑建勇, 等. 基于改进经验小波变换和XGBoost的电能质量复合扰动分类[J]. 电工技术学报, 2022, 37(1):232-243,253.
|
|
WU Jianzhang, MEI Fei, ZHENG Jianyong, et al. Recognition of multiple power quality disturbances based on modified empirical wavelet transform and XGBoost[J]. Transactions of China Electrotechnical Society, 2022, 37(1):232-243,253.
|
[11] |
黄鸿铿, 李应. 用Bark频谱投影识别低信噪比动物声音[J]. 智能系统学报, 2018, 13(4):610-618.
|
|
HUANG Hongkeng, LI Ying. Identifying low-SNR animal sounds based on Bark spectral projection[J]. CAAI Transactions on Intelligent Systems, 2018, 13(4):610-618.
|
[12] |
王若平, 李仁仁, 陈达亮, 等. 基于改进小波包去噪与梅尔倒谱系数的低信噪比交通环境声音识别[J]. 科学技术与工程, 2019, 19(36):290-295.
|
|
WANG Ruoping, LI Renren, CHEN Daliang, et al. Low signal to noise ratio traffic environment acoustic recognition based on improved wavelet packet denoising and mel cepstrum coefficient[J]. Science Technology and Engineering, 2019, 19(36):290-295.
|
[13] |
陈秋菊, 徐建国. 优化正交匹配追踪和短时谱估计用于声音识别[J]. 计算机工程与应用, 2020, 56(7):162-169.
doi: 10.3778/j.issn.1002-8331.1812-0126
|
|
CHEN Qiuju, XU Jianguo. Sound event recognition based on optimized orthogonal matching pursuit[J]. Journal of Electronics & Information Technology, 2020, 56(7):162-169.
|
[14] |
苏映新. 自适应粒子群优化匹配追踪声音事件识别算法[J]. 激光与光电子学进展, 2020, 57(10):293-299.
|
|
SU Yingxin. Sound event recognition based on adaptive particle swarm optimized matching tracking[J]. Laser & Optoelectronics Progress, 2020, 57(10):293-299.
|
[15] |
牛晓可, 黄伊鑫, 徐华兴, 等. 基于听皮层神经元感受野的强噪声环境下说话人识别[J]. 计算机应用, 2020, 40(10):3034-3040.
doi: 10.11772/j.issn.1001-9081.2020020272
|
|
NIU Xiaoke, HUANG Yixin, XU Huaxing, et al. Speaker recognition in strong noise environment based on auditory cortical neuronal receptive field[J]. Journal of Computer Applications, 2020, 40(10):3034-3040.
doi: 10.11772/j.issn.1001-9081.2020020272
|
[16] |
张一杨, 姚明林. 基于自适应稀疏分解的声音识别算法[J]. 计算机应用与软件, 2021, 38(6):161-165.
|
|
ZHANG Yiyang, YAO Minglin. Sound recognition algorithm based on adaptive sparse decomposition[J]. Computer Applications and Software, 2021, 38(6):161-165.
|
[17] |
XIE Jie, HU Kai, ZHU Mingying, et al. Bioacoustic signal classification in continuous recordings: syllable-segmentation vs sliding-window[J]. Expert Systems with Applications, 2020, 152: DOI : 10.1016/j.eswa.2020.113390.
doi: 10.1016/j.eswa.2020.113390
|
[18] |
方磊. 长大公路隧道通风物理模型试验研究[D]. 西安: 长安大学, 2005.
|
|
FANG Lei. The physical model experiment research of long highway thunnel ventilation[D]. Xi'an: Chang'an University, 2005.
|
[19] |
王亚琼, 谢永利, 刘洪洲, 等. 海底隧道半横向通风孔物理模型试验[J]. 中国公路学报, 2010, 23(3):76-82.
|
|
WANG Yaqiong, XIE Yongli, LIU Hongzhou, et al. Physical model experiment on semi transverse ventilation air inlet and outlet of subsea tunnel[J]. China Journal of Highway and Transport, 2010, 23(3):76-82.
|
[20] |
王亚琼, 李林峰, 来凯, 等. 隧道通风阻力格栅局部阻力试验[J]. 中国公路学报, 2020, 33(3):152-159.
doi: 10.19721/j.cnki.1001-7372.2020.03.013
|
|
WANG Yaqiong, LI Linfeng, LAI Kai, et al. Experiment on local resistant of grid in tunnel ventilation model test[J]. China Journal of Highway and Transport, 2020, 33(3):152-159.
doi: 10.19721/j.cnki.1001-7372.2020.03.013
|
[21] |
刘继川, 桂蕾. 城市公共安全风险评估与控制对策研究:以武汉市为例[J]. 中国安全科学学报, 2022, 32(1):164-171.
doi: 10.16265/j.cnki.issn1003-3033.2022.01.022
|
|
LIU Jichuan, GUI lei. Urban public safety risk assessment and control measures: a case study on Wuhan city[J]. China Safety Science Journal, 2022, 32(1):164-171.
doi: 10.16265/j.cnki.issn1003-3033.2022.01.022
|
[22] |
杨黎霞, 许茂增, 陈仁祥. 数据样本有限的交通恐怖袭击行为识别[J]. 中国安全科学学报, 2021, 31(8):30-37.
doi: 10.16265/j.cnki.issn1003-3033.2021.08.005
|
|
YANG Lixia, XU Maozeng, CHEN Renxiang. Study on recognition of traffic terrorist attacks with limited data samples[J]. China Safety Science Journal, 2021, 31(8):30-37.
doi: 10.16265/j.cnki.issn1003-3033.2021.08.005
|