2021 3rd International Symposium on Signal Processing Systems (SSPS)最新文献

Research on fault diagnosis method of walking gearbox of combine harvester based on DA-KELM 基于DA-KELM的联合收割机行走齿轮箱故障诊断方法研究

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481124

Zhi Sun, Xinzhong Wang, You Wu

{"title":"Research on fault diagnosis method of walking gearbox of combine harvester based on DA-KELM","authors":"Zhi Sun, Xinzhong Wang, You Wu","doi":"10.1145/3481113.3481124","DOIUrl":"https://doi.org/10.1145/3481113.3481124","url":null,"abstract":"Aiming at the problem of the low recognition rate of the rolling bearing fault of the walking gearbox of the combine harvester, a gearbox rolling bearing fault diagnosis method based on the dragonfly optimization algorithm kernel extreme learning machine is proposed. The Variational Mode Decomposition(VMD) algorithm optimized by the particle swarm optimization algorithm is used to decompose the experimentally extracted vibration signals of the gearbox in different working states, and the sample entropy value is extracted from the Intrinsic Mode components obtained by the decomposition as the fault characteristic value, and the The time-domain and frequency-domain characteristics of the vibration signal together constitute the fault feature set. The DA-KELM algorithm is used to identify the fault in the feature set of the vibration signal in various states. Through pattern recognition of four states: normal, roller pitting, outer raceway pitting, and inner raceway pitting of the rolling bearing in the traveling gearbox of the combine harvester, The best classification accuracy is 95.625%. At the same time, this method was compared with the common classification algorithm, and the experimental results show that this method has advantages in the accuracy of fault identification.","PeriodicalId":112570,"journal":{"name":"2021 3rd International Symposium on Signal Processing Systems (SSPS)","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116589012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of Instrument Amplifier Based on Crane 基于起重机的仪表放大器设计

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481118

Haoran Sun

引用次数: 0

Direction of Arrival Estimation Using One-dimensional Convolutional Neural Network and Gated Recurrent Unit 基于一维卷积神经网络和门控循环单元的到达方向估计

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481116

Mingyue Li, Yougen Xu, Zhiwen Liu

{"title":"Direction of Arrival Estimation Using One-dimensional Convolutional Neural Network and Gated Recurrent Unit","authors":"Mingyue Li, Yougen Xu, Zhiwen Liu","doi":"10.1145/3481113.3481116","DOIUrl":"https://doi.org/10.1145/3481113.3481116","url":null,"abstract":"This paper introduces a deep learning (DL) framework to address direction of arrival (DOA) estimation problem. Traditional signal processing methods such as multiple signal classification (MUSIC) highly rely on signal model and array geometry. However, DL methods, being data-driven, make analytical process of signal or array less important. In this paper, a neural network architecture combining one-dimensional convolutional neural network (1D CNN) and gated recurrent unit (GRU) is proposed to estimate DOA of multiple signals. The multi-signal DOA estimation is treated as a multi-class multi-label classification issue. First a dataset using the covariance matrix of target signals received by a circular antenna array is generated. The proposed 1D CNN-GRU model then learns the relationship between covariance matrix elements and DOAs through training. Experimental results show that our proposed method has higher accuracy than MUSIC and is able to deal with multi-path DOA estimation. Besides, 1D CNN-GRU is proved to have lower root mean squared error (RMSE) than other DL methods, because features over small local areas and time-sequence are both learnt by 1D CNN layers and GRU layers. In addition, 1D CNN-GRU exhibits effectiveness in experiments using real-world data.","PeriodicalId":112570,"journal":{"name":"2021 3rd International Symposium on Signal Processing Systems (SSPS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114819777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Closed-Form Target Localization Method for FDA-MIMO Based on root-MUSIC and ESPRIT Algorithm 基于root-MUSIC和ESPRIT算法的FDA-MIMO封闭目标定位方法

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481115

Licheng Wang, Yougen Xu, Zhiwen Liu

引用次数: 0

Decrease the time for classification of the incoming signals from BCI 减少了BCI输入信号的分类时间

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481126

G. Dimitrov, G. Panayotova, E. Kovatcheva, Pavel Petrov, Magdalena Garvanova, Snejana Petrova, I. Dimitrova, Olexiy Bychkov

引用次数: 0

Multi-domain learning target tracking algorithm based on objective regression optimization 基于目标回归优化的多领域学习目标跟踪算法

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481122

Xi Yue

引用次数: 0

Analysis on Approaches and Structures of Image Semantic Segmentation 图像语义分割的方法和结构分析

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481123

Haozheng Ji

引用次数: 0

An Overview of Deep Learning in MRI and CT Medical Image Processing 深度学习在MRI和CT医学图像处理中的应用综述

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481125

Ahliddin Shomirov, Jing Zhang

{"title":"An Overview of Deep Learning in MRI and CT Medical Image Processing","authors":"Ahliddin Shomirov, Jing Zhang","doi":"10.1145/3481113.3481125","DOIUrl":"https://doi.org/10.1145/3481113.3481125","url":null,"abstract":"The medical image is a set of all organizations, institutions, and resources whose primary goal is to improve health. The extensive growth of medical data increases the utility of machine learning and deep learning in the healthcare domains. Nowadays, the use of in-depth training to process medical images has received particular attention. In recent years, medical instruments have developed rapidly with the help of artificial intelligence and are widely used to process medical images. Artificial intelligence is numerous sources of medical imaging processing such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). CT and MRI image processing tasks with a high computation time requirement and computation speed. Nowadays, one of the most critical trends in the development of computer technology in neuroscience is the processing of medical images and digital images, which are used to improve image quality, restore damaged images, identify individual elements and diagnose various diseases. In this paper, we briefly review the progress and challenges associated with in-deep learning in the processing of CT and MRI medical images.","PeriodicalId":112570,"journal":{"name":"2021 3rd International Symposium on Signal Processing Systems (SSPS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123356943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Computational seismic analysis of optical instrument structures: Computational analysis of seismic response of wide-field optical spectrograph structures in large telescopes 光学仪器结构的计算地震分析:大型望远镜宽视场光谱仪结构地震响应的计算分析

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481121

Aman Shrestha, Daxu Zhang, Lingyu Zheng

引用次数: 0

Spectral representations of digital signals using non-binary Galois fields. 使用非二进制伽罗瓦场的数字信号的频谱表示。

2021 3rd International Symposium on Signal Processing Systems (SSPS) Pub Date : 2021-03-26 DOI: 10.1145/3481113.3481120

I. Suleimenov, D. Matrassulova, I. Moldakhan

引用次数: 1