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Acoustics recognition of excavation equipment based on MF-PLPCC features and RELM 36

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Acoustics recognition of excavation equipment based on MF-PL...

第36届中国控制会议

作者： Min cao Jianzhong Wang jiuwen cao Huanqiang Zeng Key Lab for IOT and Information Fusion Technology of Zhejiang Hangzhou Dianzi University School of Information Science and Engineering Huaqiao University

ISBN: (纸本)9781538629185

With the development of urbanization,the underground pipelines play an important role in ensuring the basic conditions of citizen ***,construction activities are continuously increasing in the mainland of China nowadays,which lead to serious damages and threatens to the underground pipelines by excavation *** this end,we focus on developing an automatically monitoring system for underground pipeline network protection in this *** acoustic characteristics of excavation equipment are first ***,a recognition framework combining with the feature extraction algorithm of Mel-Frequency Linear Prediction Cepstral Coefficients（MF-PLPCC） and the classification algorithm of Regularized Extreme Learning Machine（RELM） is developed for excavation *** MF-PLPCC processing includes the use of Mel filter bank that simulates the acoustic perception of human ear to different frequency,and the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in *** results demonstrate that the method of this paper provides substantial improvements in recognition accuracy comparing to other state-of-the-art methods.

关键词： MF-PLPCC RELM underground pipeline recognition method power function

A descriptor system approach to robust stabilityof uncertain degenerate systems with discreteand distribute delays

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控制理论与应用（英文版） 2007年第4期5卷 357-364页

作者： jiuwen cao Shouming ZHONG Yuanyuan HU College of Applied Mathematics University of Electronic Science and Technology of China Chengdu Sichuan 610054 China

The robust stability of uncertain linear degenerate systems with discrete and distributed delays is studied in this paper. The uncertainties under consideration are norm bounded, and possibly time varying. A novel robust stability criterion of the system is derived by constructing Lyapunov functions. The degenerate systems are transformed to a descriptor system and the stability criteria are formulated in the form of a linear matrix inequality （LMI）. Therefore, it is easy to check the robust stability of the degenerate systems by using this method. Numerical examples are also worked out to illustrate the obtained results.

关键词： Degenerate system Discrete delay Distributed delay Linear matrix inequality (LMI)

Robust stability for uncertain neutral systems with mixed time-varying delays

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控制理论与应用（英文版） 2009年第2期7卷 192-196页

作者： Jinzhong CUI jiuwen cao Shouming ZHONG Yuanyuan HU College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan 610054 China College of Applied Mathematics University of Electronic Science and Technology of China Chengdu Sichuan 610054 China

The robust stability of uncertain neutral systems with mixed time-varying delays is investigated in this paper. The uncertainties under consideration are norm-bounded and time-varying. Based on the Lyapunov stability theory, a delay-dependent stability criterion is derived and given in the form of a linear matrix inequality （LMI）. Finally, a numerical example is given to illustrate significant improvement over some existing results.

关键词： Robust stability Neutral systems Lyapunov-Krasovskii functionals LMI control tool-box

Vibration-based hypervelocity impact identification and localization

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Frontiers of Information Technology & Electronic Engineering 2022年第4期23卷 515-529页

作者： Jiao BAO Lifu LIU jiuwen cao Department of Computer Engineering Chengdu Technological UniversityChengdu611730China Machine Learning and I-health International Cooperation Base of Zhejiang Province Hangzhou Dianzi UniversityHangzhou310018China Artificial Intelligence Institute Hangzhou Dianzi UniversityHangzhou310018China

Hypervelocity impact(HVI)vibration source identification and localization have found wide applications in many fields,such as manned spacecraft protection and machine tool collision damage detection and *** this paper,we study the synchrosqueezed transform(SST)algorithm and the texture color distribution(TCD)based HVI source identification and localization using impact *** extracted SST and TCD image features are fused for HVI image *** achieve more accurate detection and localization,the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the *** conventional classification and regression models are merged by voting and stacking to achieve the final detection and *** demonstrate the effectiveness of the proposed algorithm,the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for *** experimental results show that the proposed HVI identification and localization algorithm is more accurate than other ***,based on sensor distribution,an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.

关键词： Ensemble learning Synchrosqueezied transform Gray-level co-occurrence matrix Image entropy Distance estimation

Fusion of Shallow and Deep features for Gait Recognition Through Multiple Walking Views

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Fusion of Shallow and Deep features for Gait Recognition Thr...

第40届中国控制会议

作者： Xuedong Yu Muqing Deng Peng Lin Jianzhong Wang jiuwen cao School of Automation Hangzhou Dianzi University School of Automation Guangdong University of Technology

Gait recognition has attracted widespread attention in recent years, as the gait of humans has a strong discriminative ability even under low-quality image sequences captured at a distance. This paper describes a gait recognition algorithm by combining shallow and deep features through multiple walking views. For each walking sequence, the binary silhouettes are characterized with width features as shallow parameters, including arm silhouette widths and leg silhouette widths, which can implicitly reflect the temporal changes of silhouette shape. In addition, using deep transfer learning algorithm, deep gait features can be represented as the spatial variation of pixel points in the human silhouette, which can represent the spatial changes among silhouette images. Both shallow and deep features can be used separately for gait recognition using Support Vector Machine classifier（SVM）. They are fused on the decision level to improve the gait recognition performance. The fusion of two different kinds of features provides a comprehensive characterization of gait dynamics, which is not sensitive to the walking views variation. CASIA-B dataset is used to perform the proposed method. A better performance of this method compared to the related work methods has been shown after experimental result in terms of rate accuracy of identification and classification.

关键词： decision fusion shallow features deep features transfer learning

1D-Convolutional Neural Network based Vibration Signal Fault Detection

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1D-Convolutional Neural Network based Vibration Signal Fault...

第32届中国过程控制会议(CPCC2021)

作者： Jixiang Wei Peng Lin Shicheng Yu jiuwen cao Xiaoping Lai Machine Learning and I-health International Cooperation Base of Zhejiang Province and with the Artificial Intelligence InstituteHangzhou Dianzi University

Convolutional neural networks(CNNs) are very good at picking up on patterns in the input image and have become widely used in computer *** the help of the excellent structured data processing ability of CNNs,a one-dimensional convolutional neural network(1 D-CNN) is constructed for fault detection and classification of time series data from vibration system of rotating machinery under various operating ***,a compact 1 D-CNN model with a wide convolution kernel is proposed in the first layer,and Dropout technology is used in the convolution layer to simulate training interference to enhance the anti-noise ***,a double 1 D-CNN model is proposed for cross-condition fault ***,the effectiveness of the method is verified by simulation of compressor instability data.

关键词： Fault detection One-Dimensional Convolutional Neural Network Domain adaptation Rotating machinery

Classification of myocardial infarction based on ECG signals and multi-network stacking model

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Classification of myocardial infarction based on ECG signals...

第40届中国控制会议

作者： Tianqi Zhao Muqing Deng Peng Lin Jianzhong Wang jiuwen cao School of Automation School of Artificial Intelligence Hangzhou Dianzi University School of Automation Guangdong University of Technology

Myocardial infarction is one of the common cardiovascular diseases, and it also is a major cause of death and disability worldwide every year. Therefore, early detection of myocardial infarction is vital for patients. Deep learning algorithms have been used successfully for the recognition of myocardial infarction in recent years, but most of them are based on the segmentation of heartbeat signals and the single deep learning model. In this paper, we proposed a new myocardial infarction classification method based on an improved ECG feature extraction scheme and the multi-network stacking model. Firstly, the mean amplitude spectrum（MAS） is used in feature extraction to elaborate the frequency distribution difference of each wave among ECG signals. Secondly, due to the lack of time-domain features in the MAS map, we proposed a novel time-frequency feature fusion method. Finally, the time-frequency feature is fed to the multi-network stacking model for the classification of myocardial infarction. To improve the diversity of feature learning, this paper researches to stack different networks for learning the characteristic of the time-frequency feature. Meanwhile, we used the improved training methods to solve the problem of data imbalance. Experiments using the PTB database show that this feature extraction scheme and multi-network stacking model are effective for the classification of myocardial infarction.

关键词： ECG Myocardial infarction Feature extraction Deep learning

正则化超限学习机的多分块松弛交替方向乘子法

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模式识别与人工智能 2019年第12期32卷 1107-1115页

作者：张立佳赖晓平曹九稳杭州电子科技大学人工智能研究院

针对超限学习机在大数据环境下计算负担过重的问题,文中提出正则化超限学习机的多分块松弛交替方向乘子法及N-等分和N/2-等分情形的标量化实现.模型分块使算法具有高度的并行结构,与松弛技术结合提高算法的收敛速度.通过分析,建立算法... 详细信息

针对超限学习机在大数据环境下计算负担过重的问题,文中提出正则化超限学习机的多分块松弛交替方向乘子法及N-等分和N/2-等分情形的标量化实现.模型分块使算法具有高度的并行结构,与松弛技术结合提高算法的收敛速度.通过分析,建立算法收敛的充要条件,给出最优收敛率及最优参数.在基准数据集上仿真计算收敛率随分块数的变化关系,对比不同算法的收敛速率和GPU加速比.实验表明,文中算法具有较低的计算复杂度和较高的并行性.

关键词：机器学习并行优化超限学习机交替方向乘子法大数据

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儿童癫痫综合征智能分析:综述与展望

模式识别与人工智能 2024年第2期37卷 144-161页

作者：郑润泽冯袁盟胡丁寒蒋铁甲高峰曹九稳杭州电子科技大学自动化学院杭州310018 杭州电子科技大学浙江省机器学习与智慧健康国际合作基地杭州310018 浙江大学医学院附属儿童医院神经内科杭州310052 浙江大学医学院附属儿童医院国家儿童健康与疾病临床医学研究中心杭州310003

儿童癫痫综合征智能分析是指通过统计分析、机器学习等数据驱动方法,挖掘临床有效生物标志物,构建相应的专家系统,以解决临床和预后管理问题的研究.文中首先简述儿童癫痫综合征的定义、发作类型和分类等临床基础知识.然后,回顾基于脑电... 详细信息

儿童癫痫综合征智能分析是指通过统计分析、机器学习等数据驱动方法,挖掘临床有效生物标志物,构建相应的专家系统,以解决临床和预后管理问题的研究.文中首先简述儿童癫痫综合征的定义、发作类型和分类等临床基础知识.然后,回顾基于脑电信号的儿童癫痫综合征智能分析框架和各组成部分典型方法存在的优缺点,包括数据收集及预处理、特征提取、决策器系统和专家系统.其中,将专家系统分为特定波形检测系统、诊断分类系统、发作检测系统、发作预测系统和量化评估系统,并进行全面概括与理论解释.最后,结合儿童癫痫综合征智能分析领域现有研究的局限性和挑战,展望未来研究方向,以推动儿童癫痫综合征智能分析系统的研究进展,减轻该病带来的负面影响.

关键词：儿童癫痫综合征生物标志物脑电信号智能分析专家系统