检索结果-内蒙古大学图书馆

Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach

引用

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2020年第1期40卷 440-453页

作者： Raja, P. M. Siva Rani, Antony Viswasa Amrita Coll Engn & Technol Comp Sci & Engn Erachakulam Tamil Nadu India

In medical image processing, brain tumor detection and segmentation is a challenging and time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues from the brain. In the brain image, the size of a tumor can be varied for different patients along with the minute details of the tumor. It is a difficult task to diagnose and classify the tumor from numerous images for the radiologists. This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach. Initially, the pre-processing stage is performed using the non-local mean filter for denoising purposes. Then the BFC (Bayesian fuzzy clustering) approach is utilized for the segmentation of brain tumors. After segmentation, robust features such as, informationtheoretic measures, scattering transform (ST) and wavelet packet Tsallis entropy (WPTE) methods are used for the feature extraction process. Finally, a hybrid scheme of the DAE (deep autoencoder) based JOA (Jaya optimization algorithm) with a softmax regression technique is utilized to classify the tumor part for the brain tumor classification process. The proposed scheme is implemented in a MATLAB environment. The simulation results are conducted by the BRATS 2015 database which proved that the proposed approach obtained the high classification accuracy (98.5 %) when compared to other state-of-art methods. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Brain tumor segmentation Brain tumor classification deep autoencoder Bayesian fuzzy clustering MRI image

来源：评论

学校读者我要写书评

暂无评论

Arrhythmia recognition by a deep spectral autoencoder 10

Arrhythmia recognition by a deep spectral autoencoder

引用

10th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Mujica-Vargas, Dante Quezada-Prospero, Enrique Luna-Alvarez, Antonio TecNM Comp Sci Dept CENIDET Cuernavaca Mexico

ISBN: (纸本)9798350359374

In this study, a scheme for the detection of cardiac arrhythmias as well as normal heartbeats is presented. The proposal is based on a deep autoencoder trained with different spectral representations, specifically: Autoregressive Burg, Covariance, Yule-Walker, Fast Fourier Transform, Periodogram and Welch. The experiment was developed taking into account the K-fold cross-validation technique in order to have an objective frame of reference considering metrics from the literature. The quantitative results obtained suggest better recognition stability when the classifier was trained with the Autoregressive Burg spectral feature.

关键词： Cardiac arrhythmia recognition deep autoencoder Spectral representations

来源：评论

学校读者我要写书评

暂无评论

A novel deep autoencoder and hyperparametric adaptive learning for imbalance intelligent fault diagnosis of rotating machinery

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 102卷 104279-104279页

作者： Li, Wanxiang Shang, Zhiwu Gao, Maosheng Qian, Shiqi Zhang, Baoren Zhang, Jie Tianjin Modern Electromech Equipment Technol Key 399 Binshui West Rd Tianjin Peoples R China Tiangong Univ Sch Mech Engn 399 Binshui West Rd Tianjin Peoples R China

In engineering applications, normal signals are often much easier to obtain than fault signals, which makes training dataset appear class imbalance to affect the training of deep autoencoder. Besides, the anti-interference and robustness of the deep autoencoder are extremely susceptible to the influence of the noise environment, resulting in insufficient state feature extraction. Moreover, the selection of hyperparameter has no theoretical guidance, and needs a lot of experiments and manual experience to determine the appropriate parameters. To address the aforementioned shortcomings, this paper proposes a novel deep autoencoder and hyperparametric adaptive learning for intelligent fault diagnosis of rotating machinery. Firstly, a data augmentation strategy based on overlapping segmentation, rotation and jitter, called ORJ data augmentation strategy, is proposed to increase the data capacity of training samples and reduce the influence of class imbalance on deep autoencoder training. Then, a lossless constraint term and a weight nonnegative constraint term modification loss function are proposed to construct a deep sparse autoencoder with lossless and nonnegative constraints (SAE-LNC), which improves the robustness of the model and makes the extracted state features have higher quality. Finally, the artificial bee colony algorithm (ABC) is used to adaptively learn the hyperparameters of deep SAE-LNC to improve the intelligence and accuracy of the diagnostic model. The experimental results of vibration signals collected from bearing test-bed show that, compared with other diagnostic methods, the proposed method can effectively capture high-quality state features, enhance the robustness of feature extraction process and obtain higher diagnostic accuracy.

关键词： Fault diagnosis Rotating machinery deep autoencoder Artificial bee colony algorithm Adaptively learn Class imbalance

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection of Water Level Using deep autoencoder

引用

SENSORS 2021年第19期21卷 6679页

作者： Nicholaus, Isack Thomas Park, Jun Ryeol Jung, Kyuil Lee, Jun Seoung Kang, Dae-Ki Dongseo Univ Dept Comp Engn Busan 47011 South Korea Buzzni AI Lab Seoul 08788 South Korea JCMEEK Seoul 07591 South Korea Infran R&D Ctr Seoul 07994 South Korea

Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of the water levels and report the analysis and time/point(s) of abnormality. This research's motivation is the level difficulty and time-consuming managing facilities responsible for controlling water levels due to the rare occurrence of abnormal patterns. Consequently, we employed deep autoencoder, one of the types of artificial neural network architectures, to learn different patterns from the given sequences of data points and reconstruct them. Then we use the reconstructed patterns from the deep autoencoder together with a threshold to report which patterns are abnormal from the normal ones. We used a stream of time-series data collected from sensors to train the model and then evaluate it, ready for deployment as the anomaly detection system framework. We run extensive experiments on sensor data from water tanks. Our analysis shows why we conclude vanilla deep autoencoder as the most effective solution in this scenario.

关键词： anomaly detection deep autoencoder time-series

来源：评论

学校读者我要写书评

暂无评论

Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder

引用

MEASUREMENT 2021年 179卷 109452-109452页

作者： Pei, Xinglong Zheng, Xiaoyang Wu, Jinliang Chongqing Univ Technol Sch Artificial Intelligence Chongqing 400054 Peoples R China

Accurate fault diagnosis is essential to ensure the stability and reliability of machine systems. deep learning (DL) techniques have demonstrated potential in realizing intelligent fault diagnosis. However, the obtained bearing signals in the actual environment involve various interferences and are consistently nonstationary. Moreover, it is difficult to simultaneously acquire a large amount of balanced data to ensure suitable training. Consequently, traditional DL-based methods cannot achieve a satisfactory diagnostic accuracy. To overcome this problem and enhance the diagnostic performance in complex situations, this paper proposes a method to realize effective intelligent bearing fault diagnosis. A novel index is established to evaluate the quality of the demodulation spectra, along with a novel compressed sensing deep autoencoder based on compressed sensing. Additionally, a multiscale neural network architecture incorporating a multiscale coarse-grained procedure is adopted. Experimental results and comparative analysis with existing intelligent diagnosis methods demonstrate the effectiveness and robustness of the proposed method.

关键词： Bearing fault diagnosis Teager energy operator deep autoencoder Compressed sensing Multiscale feature extraction

来源：评论

学校读者我要写书评

暂无评论

Reduction of Trajectory Encoding Data Using a deep autoencoder Network: Robotic Throwing 28th

Reduction of Trajectory Encoding Data Using a Deep Autoencod...

引用

28th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Loncarevic, Zvezdan Pahic, Rok Simonic, Mihael Ude, Ales Gams, Andrej Jozef Stefan Inst Jamova Cesta 39 Ljubljana 1000 Slovenia

ISBN: (纸本)9783030196486;9783030196479

Autonomous learning and adaptation of robotic trajectories by complex robots in unstructured environments, for example with the use of reinforcement learning, very quickly encounters problems where the dimensionality of the search space is beyond the range of practical use. Different methods of reducing the dimensionality have been proposed in the literature. In this paper we explore the use of deep autoencoders, where the dimensionality of autoencoder latent space is low. However, a database of actions is required to train a deep autoencoder network. The paper presents a study on the number of required database samples in order to achieve dimensionality reduction without much loss of information.

关键词： deep autoencoder Reinforcement learning Robotic throwing

来源：评论

学校读者我要写书评

暂无评论

deep autoencoder on Personalized Facet Selection 26th

Deep Autoencoder on Personalized Facet Selection

引用

26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Chantamunee, Siripinyo Wong, Kok Wai Fung, Chun Che Murdoch Univ Coll Sci Hlth Engn & Educ Discipline Informat Technol Math & Stat Perth WA Australia

ISBN: (纸本)9783030368081;9783030368074

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep autoencoder on the prediction of facet interests. autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests.

关键词： deep autoencoder Faceted search Personalized facet selection

来源：评论

学校读者我要写书评

暂无评论

Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval

引用

PATTERN RECOGNITION LETTERS 2019年 125卷 806-812页

作者： Siddiqua, Ayesha Fan, Guoliang Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Increased accuracy and affordability of depth sensors such as Kinect has created a great depth-data source for various 3D oriented applications. Specifically, 3D model retrieval is attracting attention in the field of computer vision and pattern recognition due to its numerous applications. A cross-domain retrieval approach such as depth image based 3D model retrieval has the challenges of occlusion, noise and view variability present in both query and training data. In this paper, we propose a new supervised deep autoencoder approach followed by semantic modeling to retrieve 3D shapes based on depth images. The key novelty is the two-fold feature abstraction to cope with the incompleteness and ambiguity present in the depth images. First, we develop a supervised autoencoder to extract robust features from both real depth images and synthetic ones rendered from 3D models, which are intended to balance reconstruction and classification capabilities of mix-domain data. Then semantic modeling of the supervised autoencoder features offers the next level of abstraction to cope with the incompleteness and ambiguity of the depth data. It is interesting that unlike any other pairwise model structures, we argue that cross-domain retrieval is still possible using only one single deep network trained on real and synthetic data. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross-modal 3D model retrieval. (C) 2019 Elsevier B.V. All rights reserved.

关键词： 3D model retrieval Shape matching Cross-modal retrieval deep autoencoder

来源：评论

学校读者我要写书评

暂无评论

Health Assessment for Crane Pumps based on Vehicle Tests using deep autoencoder and Metric Learning

Health Assessment for Crane Pumps based on Vehicle Tests usi...

引用

IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Xiao, Dengyu Huang, Yixiang Qin, Chengjin Shi, Haotian Liu, Chengliang Shan, Zenghai Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Xuzhou Construct Machinery Grp Co Ltd Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781538683576

Pump is one of the key components in a crane, which once fails will severely hurt the reliability of the hydraulic system and cause great loss. Therefore, accurate, reliable and effective crane pump health assessment must be performed. However, the research about pump health assessment still stays at the stage of bench tests, which have the limited help for the realworld pump health prognosis. In this paper, to evaluate crane pump health status and avoid the issue above, the real-world vehicle tests of several cranes with different service years are performed to acquire the pump signals during the cranes' actual operations. deep autoencoder (DAE), a kind of unsupervised learning approach, which possesses the capacity to learn meaningful representations from raw signal, is used reduce the data dimension before they are sent to Mahalanobis-Taguchi System in metric learning. Mahalanobis distance (MD) is utilized to reveal the performance degradation and assess the health condition. Performances of other feature learning methods such as statistical features, EMD, MLP, CNN are tested and contrasted. Results show that the proposed approach achieves the best performance.

关键词： Health assessment crane pumps deep autoencoder metric learning

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Facial Image Occlusion Detection with deep autoencoder 11

Unsupervised Facial Image Occlusion Detection with Deep Auto...

引用

11th International Conference on Digital Image Processing (ICDIP)

作者： Wang Xu-dong Wei Hong-quan Li Shao-mei Gao Chao Huang Rui-yang Natl Digital Switching Syst Engn & Technol R&D Ct Zhengzhou 450002 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still a challenging problem. Although there are many works to solve the problem of obscuring the face, the occlusion is still a challenge in face recognition. To overcome this issue, firstly we should detect the occlusion position in the facial images. We construct a robust self-encoding machine to solve the occlusion detection problem in face images and uses synthetic occlusion data for training. We evaluated our method under various synthetic occlusion face images. Experiments show that our method can effectively detect various types of occlusion masks in an unsupervised manner and has better robustness to the occlusion categories.

关键词： Face Recognition Occlusion Detection deep autoencoder

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：