检索结果-内蒙古大学图书馆

6th International Symposium on Computer Science and Intelligent Control (ISCSIC)

作者： Jiang, Pengxu Zou, Cairong Southeast Univ Nanjing Peoples R China

ISBN: (纸本)9781665454889

Speech emotion recognition (ASC) is an important part of speech recognition. The convolutional neural network (CNN) has shown excellent performance in ASC. However, the Traditional CNN models usually use the last fully connected layer as the output of the model. Lack of timefrequency related information research results in an inadequate depth of emotional information mining. Moreover, CNN only focuses on the convolution region in each convolution and cannot focus on the global features. To better explore the recognition effect of speech features in CNN, a CNN model with multiple pooling strategies is proposed. Mel-spectrum is the input to the model. CNN learns emotional details. In addition, three global average pooling layers (GAP) connect different pooling layers to reduce the dimensions of different tensors. Then, an Average Pyramid Pooling (APP) layer is used to fuse all features. Finally, a SoftMax classifier is used to classify different emotions. Experiments prove that the improved CNN model has a better recognition effect than the traditional model.

关键词： convolution neural network Multiple pooling strategies global average pooling average pyramid pooling

来源：评论

学校读者我要写书评

暂无评论

convolution neural network-based Spectrum Sensing for Cognitive Radio Systems using USRP with GNU Radio 10

Convolution Neural Network-based Spectrum Sensing for Cognit...

引用

10th International Conference on Ubiquitous and Future networks (ICUFN)

作者： Lee, Gyu-Hyung Lee, Young-Doo Koo, In-Soo Univ Ulsan Dept Elect Engn Ulsan South Korea

ISBN: (纸本)9781538646465

Spectrum sensing is the core technology in cognitive radio systems to find the available channel. In spectrum sensing, the energy detection has a disadvantage that it is difficult to detect the signal of the primary user in the low SNR. In this paper, we use a convolution neural network to enhance the performance in low SNR. For the practical test, the proposed scheme is implemented with Universal Software Radio Peripheral National Instruments 2900 devices. The experimental results of the proposed scheme are compared with the energy detection using accuracy metric according to SNR. With simulation results, we demonstrate that the proposed scheme shows much better performance in low SNR.

关键词： Cognitive radio networks Spectrum sensing convolution neural network USRP GNU Radio

来源：评论

学校读者我要写书评

暂无评论

convolution neural network for identification of obstructive sleep apnea

Convolution neural network for identification of obstructive...

引用

Medical Technologies Congress (TIPTEKNO)

作者： Al-Ratrout, Serein Hossen, Abdulnasir Michigan Technol Univ Dept Comp Sci Houghton MI 49931 USA Sultan Qaboos Univ Elect & Comp Engn Dept Muscat Oman

ISBN: (纸本)9781665454322

Identification of patients with obstructive sleep apnea from normal subjects is essential for most of hospitals. Artificial intelligence techniques are encouraged for simplicity and being less costly and also for more accurate performances compared to traditional identification methods in hospitals A convolutional neural network is used in this work for feature matching process, while the continuous wavelet transform is used for feature extraction. 40 obstructive sleep apnea subjects plus 20 normal subjects RRI data are used in this work. The data is obtained from the MIT databases. The data is divided into 80% for training and 20% for validation. A compromise between the data size and the efficiency of identification is studied. The data is divided into different lengths segments for this purposes. The results are shown in terms of subject identification and also in terms of segment identification. Voting process is included to identify subjects based on segments identification results. The best subject identification result obtained is 93.8% for trial group and 83.3% for validation group. The best segment identification result obtained is 88.45 for trial group and 82.5% for validation group. By using voting among segments a 100% identification of both trial and validation groups can be obtained

关键词： identification sleep apnea continuous wavelet transform convolution neural network voting

来源：评论

学校读者我要写书评

暂无评论

convolution neural network for Structural Failure Detection of Wind Turbine Blade: A Review 6th

Convolution Neural Network for Structural Failure Detection ...

引用

6th International Conference on Advanced Production and Industrial Engineering, ICAPIE 2021

作者： Gajbhiye, Ameya Warudkar, Vilas Department of Mechanical Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9789811696121

Wind turbine farms are located in remote locations such as sea, mountain top and grassland. It is not economical to regularly visit wind turbines for maintenance, hence, condition monitoring is applied to monitor the health of components. Structural defects such as cracks and breakage are identified by investigating the images from drone surveillance or vibration spectrum. This paper reviews the potential of convolution neural network (CNN) for condition monitoring of wind turbine blades which will eliminate human intervention for fault detection (breakage). Reducing human intervention can directly reduce the maintenance cost of wind turbines. We have focused on vibration monitoring and surveillance through drones of wind turbine blades which are an integral part of condition monitoring. Literature review indicates convolution neural network is most widely used for crack detection;we will investigate the possibility of breakage detection due to fatigue. We conclude that convolution neural network can be used for crack and breakage detection under give conditions. Considering the architecture of CNN, it can also be used for crack detection using frequency time spectrum. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Breakage Detection Condition Monitoring convolution neural network Crack Detection Vibration Monitoring Vibration Spectrum Wind Turbine Blade

来源：评论

学校读者我要写书评

暂无评论

Deep convolution neural network for damage identifications based on time-domain PZT impedance technique

引用

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2021年第5期35卷 1809-1819页

作者： Alazzawi, Osama Wang, Dansheng Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China Univ Wasit Coll Engn Dept Civil Engn Kut Iraq

Recently, Intelligence-based structural health monitoring (SHM) methods have investigated widely. Most of these methods are for detecting and classifying different structural damages by the means of features extraction from the structural responses signals, for instance different back propagation artificial neural networks SHM based methods. However, automatic features extraction, that eliminates the need for expertise and performing visual inspection to evaluate structures status is still a big challenge. In this study, therefore, a novel convolution neural network-based algorithm along with a hybrid training method has been proposed to detect, quantify and localize structural damage. The proposed method has been evaluated experimentally, many damaged and undamaged structural conditions have been conducted, acquiring samples of time-domain PZT impedance response signals from a beam. As the results show that, the method obtained a significant execution on damage detection, damage size evaluation and damage location recognition with high accuracy and reliability.

关键词： convolution neural network Deep learning Damage identification Electromechanical impedance PZT sensor

来源：评论

学校读者我要写书评

暂无评论

A motor imagery EEG signal classification algorithm based on recurrence plot convolution neural network

引用

PATTERN RECOGNITION LETTERS 2021年 146卷 134-141页

作者： Meng, XianJia Qiu, Shi Wan, Shaohua Cheng, Keyang Cui, Lei Northwest Univ Sch Informat Sci & Technol Xian 710027 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol CAS Xian 710119 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Peoples R China Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212000 Jiangsu Peoples R China

With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm based on recurrence plot convolution neural network is proposed. Firstly, EEG signals are preprocessed to enhance the signal intensity in the exercise interval. Secondly, time-domain and frequency-domain features are extracted respectively to construct the feature mode of recurrence plot. Finally, a new neural network is established to realize the accurate recognition of left and right movements. This research can also be transferred to other research fields. ? 2021 Elsevier B.V. All rights reserved.

关键词： EEG signal Recurrence plot convolution neural network Classification Motor imagery

来源：评论

学校读者我要写书评

暂无评论

Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network

引用

Petroleum Science 2024年第2期21卷 752-764页

作者： Kai-Ming Su Jun-Gang Lu Jian Yu Zi-Xing Lu Shi-Jia Chen Hubei Key Laboratory of Petroleum Geochemistry and Environment Wuhan430100HubeiChina College of Resources and Environment Yangtze UniversityWuhan430100HubeiChina School of Geoscience and Technology Southwest Petroleum UniversityChengdu610500SichuanChina Changqing Oilfield Company PetroChinaXi'an710021ShaanxiChina

Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic *** is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity ***,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this *** overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,*** this way,the mass chromatogram can be automatically *** research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(*** and PMI)that represent the organic matter thermal maturity and parent material type,***,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,*** significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.

关键词： Organic geochemistry Biomarker Mass chromatographic analysis Automated interpretation convolution neural network Machine learning

来源：评论

学校读者我要写书评

暂无评论

Real-time object tracking system based on field-programmable gate array and convolution neural network

引用

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2017年第1期14卷

作者： Lyu, Congyi Chen, Haoyao Jiang, Xin Li, Peng Liu, Yunhui Beijing Inst Technol Sch Mechatron Engn Intelligent Robot Inst Beijing Peoples R China Harbin Inst Technol Shenzhen Grad Sch Sch Mech Engn & Automat Shenzhen Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China

Vision-based object tracking has lots of applications in robotics, like surveillance, navigation, motion capturing, and so on. However, the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms. The problem can prevent current systems from being used in many robotic applications which have limitations of payload and power, for example, micro air vehicles. In these applications, the central processing unit-or graphics processing unit-based computers are not good choices due to the high weight and power consumption. To address the problem, this article proposed a real-time object tracking system based on field-programmable gate array, convolution neural network, and visual servo technology. The time-consuming image processing algorithms, such as distortion correction, color space convertor, and Sobel edge, Harris corner features detector, and convolution neural network were redesigned using the programmable gates in field-programmable gate array. Based on the field-programmable gate array-based image processing, an image-based visual servo controller was designed to drive a two degree of freedom manipulator to track the target in real time. Finally, experiments on the proposed system were performed to illustrate the effectiveness of the real-time object tracking system.

关键词： Visual tracking FPGA convolution neural network visual servoing robot vision

来源：评论

学校读者我要写书评

暂无评论

A convolution neural network based semi-parametric dynamic model for industrial robot

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第7期236卷 3683-3700页

作者： Zhuang, Chungang Yao, Yihui Shen, Yichao Xiong, Zhenhua Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

Robot dynamic model is widely applied to control, collision detection and motion planning. Accurate dynamic model can achieve better performance for the above applications. Traditional dynamic models have several limitations, such as the complex hypotheses for friction model and the requirement of additional joint torque sensors. This article constructs a convolution neural network (CNN) based semi-parametric dynamic (SPD) model by only using the motor encoder signals and motor currents. The SPD model not only contains the physically feasible parameters but also compensates the dynamic model by CNN. The parametric and non-parametric parts constitute the SPD model. A lightweight CNN is proposed to simultaneously ensure the accuracy and computational efficiency. To effectively train the CNN model, a dataset generation method, which expands the excitation trajectory and only uses a continuous trajectory to record data, is proposed. The CNN-based SPD model is verified on a 6-DoF laboratory-developed industrial robot only with the proprioceptive sensors. Compared with the traditional rigid body dynamics (RBD) model, the average error of the CNN-based SPD model is reduced by 9.23% in terms of the experimental results. Meanwhile, the proposed CNN-based method achieves better performance than other supervised methods.

关键词： Robot dynamics rigid body dynamics semi-parametric dynamic model convolution neural network industrial robot

来源：评论

学校读者我要写书评

暂无评论

Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network

引用

SPECTROSCOPY LETTERS 2021年第9期54卷 645-664页

作者： Tang, Xingjia Zhang, Penchang Du, Jian Xu, Zongben Xi An Jiao Tong Univ Sch Math & Stat Xian Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China

It is of great social value and cultural and technological innovation demonstration value to carry out the research on the scientific identification method of painting and calligraphy works of art, and it is of great effect to the trade, collection, and protection of painting and calligraphy works of art. Spectral imaging, as an information acquisition method of attribute and visual synchronous perception, can be used for painting and calligraphy identification. In particular, through hyperspectral imaging and data analyses, we can identify the pigment ink used in painting, judge the printing characteristics, and find the painting information invisible to human eyes, to comprehensively judge the authenticity and abnormality of painting. However, due to its lack of matching painting and calligraphy identification model and algorithm, as well as special painting and calligraphy atlas database support, it is difficult to be competent for rapid and accurate identification in practice. Because of the above problems, in this paper, it is simulated the expert identification process for artificial intelligence analysis and modeling, adopts the idea of combining hyperspectral imaging and Atlas intelligent learning, proposes a method of atlas feature extraction for calligraphy and painting identification, and designs and studies convolution neural network(CNN) based on atlas feature, traditional image feature, and the mixed feature of atlas and image, to judge the authenticity of calligraphy and painting, the author and so on. The actual test results show that the convolution neural network based on the atlas features is the best, the author classification accuracy and authenticity identification rate in the test sample set are more than 96.5%, and it can also be seen that in the pseudo color image data, adding multivariate spectral features can greatly improve the accuracy significantly.

关键词： Painting and calligraphy identification spectral imaging atlas features analysis convolution neural network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：