检索结果-内蒙古大学图书馆

Development of a tool wear online monitoring system for dry gear hobbing machine based on new experimental approach and DAE-BPNN-integrated mathematic structure

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第1-2期116卷 685-698页

作者： Zou, Zheng Cao, Rupeng Chen, Wei Lei, Sicong Gao, Xu Yang, Yong Chongqing Univ Technol Chongqing 40054 Peoples R China Chongqing Machine Tool Grp Co Ltd Chongqing Peoples R China

The hob wear and tear of hob adversely affect the stability of dry gear hobbing accuracy. Quick and accurate online recognition of current hob wear state is crucial to curb this type of machining error during the continuous gear hobbing process. For most of CNC machine tools, an in-depth analysis of the spindle power consumption signal is the common way to monitor the tool wear condition. However, the variation of the spindle current signal is not only related to tool wear state but also the thermal-induced error and the different machining allowances of workpieces to be cut. Therefore, it is barely possible to find one or two individual signal features to precisely characterize the change of hob wear state. In this study, to find the features with strong characterization abilities, we proposed a new tool wear experiment strategy to collect the power consumption data and the thermal-induced error in the entire thermal deformation developing period as well as the representative value of different machining allowances of workpieces. Based on collected spindle power consumption data, we indiscriminately extracted both the time-domain and the frequency-domain features and formed a high-dimensional 47*100 feature dataset. Subsequently, some extracted features were excluded by conducting a correlation analysis with the help of collected thermal error data and machining allowance data, and the rest was further compressed to nine new synthetic features with a dimension-reduced treatment by using the deep autoencoder algorithm. Finally, a mapping model between the synthetic features and different hob wear states was developed by adopting the back propagation neural network classification algorithm. To prove the practicability of this mapping model, another gear cutting experiment was conducted and the results showed the accuracy of this hob wear state online monitoring approach can reached to over 90%.

关键词： Tool wear condition monitoring Dry gear hobbing machine deep autoencoder Spindle power consumption

来源：评论

学校读者我要写书评

暂无评论

Multimodal and multicontrast image fusion via deep generative models

引用

INFORMATION FUSION 2022年第0期88卷 146-160页

作者： Dimitri, Giovanna Maria Spasov, Simeon Duggento, Andrea Passamonti, Luca Lio, Pietro Toschi, Nicola Univ Cambridge Dept Comp Sci & Technol William Gates Bldg15 JJ Thomson Ave Cambridge CB3 0FD England Univ Roma Tor Vergata Dept Biomed & Prevent Via Montpellier 1 I-00133 Rome RM Italy Univ Cambridge Dept Clin Neurosci Herschel Smith BldgForvie SiteRobinson Way Cambridge CB2 0SZ England Massachusetts Gen Hosp AA Martinos Ctr Biomed Imaging Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA Univ Siena Dipartimento Ingn Informaz & Sci Matemat DIISM Siena Italy CNR Ist Bioimmagini & Fisiol Mol IBFM Via Flli Cervi I-20090 Milan 93 Italy

Recently, it has become progressively more evident that classic diagnostic labels are unable to accurately and reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses such as depression and anxiety disorders or behavioural phenotypes such as aggression and antisocial personality. Patient heterogeneity can be better described and conceptualized by grouping individuals into novel categories, which are based on empirically-derived sections of intersecting continua that span both across and beyond traditional categorical borders. In this context, neuroimaging data (i.e. the set of images which result from functional/metabolic (e.g. functional magnetic resonance imaging, functional near-infrared spectroscopy, or positron emission tomography) and structural (e.g. computed tomography, T1-, T2- PD- or diffusion weighted magnetic resonance imaging) carry a wealth of spatiotemporally resolved information about each patient's brain. However, they are usually heavily collapsed a priori through procedures which are not learned as part of model training, and consequently not optimized for the downstream prediction task. This is due to the fact that every individual participant usually comes with multiple whole-brain 3D imaging modalities often accompanied by a deep genotypic and phenotypic characterization, hence posing formidable computational challenges. In this paper we design and validate a deep learning architecture based on generative models rooted in a modular approach and separable convolutional blocks (which result in a 20-fold decrease in parameter utilization) in order to a) fuse multiple 3D neuroimaging modalities on a voxel-wise level, b) efficiently convert them into informative latent embeddings through heavy dimensionality reduction, c) maintain good generalizability and minimal information loss. As proof of concept, we test our architecture on the well characterized

关键词： deep autoencoder Phenotype stratification Latent embeddings Precision medicine Separable Convolutions Multimodal neuroimaging

来源：评论

学校读者我要写书评

暂无评论

Adversarially regularized joint structured clustering network

引用

INFORMATION SCIENCES 2022年 615卷 136-151页

作者： Yang, Yachao Ju, Fujiao Sun, Yanfeng Gao, Junbin Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Univ Sydney Business Sch Discipline Business Analyt Sydney NSW 2006 Australia

deep clustering has achieved great success as its powerful ability to learn effective repre-sentation. Especially, graph network clustering has attracted more and more attention. Considering the great success of Graph autoencoder (GAE) in encoding the graph structure and deep autoencoder (DAE) in extracting valuable representations from the data itself, in this paper, we construct an Adversarially regularized Joint Structured Clustering Network (AJSCN) by integrating GAE and DAE. The framework links GAE and DAE together by trans-ferring the representation learned by DAE to the corresponding layer of GAE to alleviate the over-smoothing problem. Furthermore, the latent representation learned by GAE is enforced to match a prior distribution via an adversarial training scheme to avoid the free of any structure of latent space. We design a joint supervision mechanism to improve the clustering performance consisting of self-supervision and mutual supervision. The self -supervision is to learn more compact representations, and mutual-supervision makes dif-ferent representations more consistent. Experiment results demonstrate the superiority of the proposed model against the state-of-the-art algorithms and achieve significant improvement on six benchmark datasets. (c) 2022 Elsevier Inc. All rights reserved.

关键词： deep clustering deep autoencoder Graph autoencoder Adversarial training Self-supervision

来源：评论

学校读者我要写书评

暂无评论

Non-intrusive quality assessment of noise-suppressed speech using unsupervised deep features

引用

SPEECH COMMUNICATION 2021年 130卷 27-44页

作者： Soni, Meet H. Patil, Hemant A. Tata Consultancy Serv Mumbai Maharashtra India Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

Objective quality assessment aims towards evaluating the perceptual quality of a signal using a machine-based algorithm. Due to different challenges involved in the subjective evaluation of speech quality, it is necessary to develop objective measures. The goal of any non-intrusive quality assessment metric for noise-suppressed speech is to assess the quality of a noise-suppressed signal in the absence of any clean reference signal. As per the ITU-T P.835 recommendations, the quality assessment of noise-suppressed speech involves predicting three quality scores, namely, signal quality, background quality, and overall quality score, and hence, considered in this study. In recent literature, the non-intrusive quality assessment problem is presented as a regression problem, in which the mapping between a set of acoustic features and corresponding quality scores is found using a perceptual model. Recently, we proposed the use of deep autoencoder (DAE) features and Subband autoencoder (SBAE) features for acoustic representation and an Artificial Neural Network (ANN) as a regression model. DAE and SBAE are variants of autoencoder architecture that have bottleneck structure in the hidden layers. Such architecture represents the class of generalized nonlinear Principal Component Analysis (PCA) that guarantees reconstruction of the input features with arbitrary accuracy. Both the features (DAE and SBAE) are extracted using unsupervised deep learning architectures, and they demonstrated better performance than the state-of-the-art spectral feature set, namely, Mel Filterbank Energies (FBEs). In this paper, we present more detailed analysis of previously proposed features, i.e., DAE and SBAE features, and analyze the usefulness of these features in predicting signal as well as background quality scores in addition to the overall quality score. We compare the performance of all the three features with each other as well as with current ITU-T P.563 metric for non-intrusive speec

关键词： Non-intrusive quality assessment deep autoencoder Subband autoencoder Regression Artificial neural network

来源：评论

学校读者我要写书评

暂无评论

A deep Learning-Based Compression Algorithm for 9-DOF Inertial Measurement Unit Signals Along With an Error Compensating Mechanism

引用

IEEE SENSORS JOURNAL 2019年第2期19卷 632-640页

作者： Sepahvand, Majid Abdali-Mohammadi, Fardin Razi Univ Dept Comp Engn & Informat Technol Kermanshah *** Iran

sensors with microelectromechanical systems technology are an integral part of many modern electronic devices such as wearable medical products, which are inherently subject to memory, bandwidth, and energy constraints due to their size and purpose. One of the important challenges for the progress in this area is the storage, transmission, and processing of large quantities of inertial sensors signal. To address this issue, this paper presents a method for near-lossless compression of multi-axis inertial signals. To improve the inertial signal compression capability, the proposed compression method employs the independent component analysis method with a principal component analysis preprocessing step to extract independent components from the signals. A deep autoencoder is used to compress the independent components and later to estimate them in the reconstruction phase. The reconstruction error is also quantized and coded using arithmetic coding and transmitted alongside the compressed components. This paper also proposes a new approach for improving the quality of the reconstructed signals. In this approach, on the receiver side, the reconstruction error is fed to the Madgwick filter as an external noise and is compensated using this filter. The experimental results demonstrate the high compression rate and low reconstruction error of the proposed method compared to the state-of-the-art methods.

关键词： deep autoencoder estimator independent component analysis inertial signal near-lossless compression wearable computing

来源：评论

学校读者我要写书评

暂无评论

A deep learning approach for collaborative prediction of Web service QoS

引用

SERVICE ORIENTED COMPUTING AND APPLICATIONS 2021年第1期15卷 5-20页

作者： Smahi, Mohammed Ismail Hadjila, Fethallah Tibermacine, Chouki Benamar, Abdelkrim Univ Tlemcen Comp Sci Dept LRIT Tilimsen Algeria Univ Montpellier LIRMM Montpellier France CNRS Montpellier France

Web services are a cornerstone of many crucial domains, including cloud computing and the Internet of Things. In this context, QoS prediction for Web services is a highly important and challenging issue that facilitates the building of value-added processes such as compositions and workflows of services. Current QoS prediction approaches, like collaborative filtering methods, mainly suffer from obstacles related to data sparsity and the cold-start problem. Moreover, previous studies have not conducted in-depth explorations of the impact of the geographical characteristics of services/users and QoS ratings on the prediction problem. To address these difficulties, we propose a deep-learning-based approach for QoS prediction. The main idea consists of combining a matrix factorization model based on a deep autoencoder (DAE) with a clustering technique based on geographical characteristics to improve the effectiveness of prediction. The overall method proceeds as follows: first, we cluster the input QoS data using a self-organizing map that incorporates knowledge of geographical neighborhoods;by doing so, we can reduce the data sparsity while preserving the topology of the input data. In addition, the clustering step effectively overcomes the cold-start problem. Next, we train a DAE that minimizes the squared loss between the ground-truth QoS and the predicted one, for each cluster. Finally, the unknown QoS of a new service is predicted using the trained DAE related to the closest cluster. To evaluate the effectiveness and robustness of our approach, we conducted a comprehensive set of experiments based on a real-world Web service QoS dataset. The experimental results show that our method achieves better prediction performance than several state-of-the-art methods.

关键词： Web service QoS prediction deep autoencoder Self-organizing map

来源：评论

学校读者我要写书评

暂无评论

Intelligent Channel Learning Exploiting Practical Energy Harvesting for Wireless MISO Systems

引用

IEEE SYSTEMS JOURNAL 2022年第4期16卷 5879-5882页

作者： Bhattacharya, Pritha Agarwal, Anirudh Verma, Monica LNM Inst Informat Technol Dept Elect & Commun Engn Jaipur 302031 Rajasthan India

This article proposes a novel practical energy harvesting (EH) model-assisted deep learning framework for intelligent channel tracking. Specifically, a multiantenna wireless system is considered for energy beamforming in a nonlinear model-based EH scenario. deep autoencoder technique is utilized for learning the channel characteristics due to nonconvexity of the channel estimation optimization problem. The performance evaluation is validated in low signal-to-noise ratio regimes, thereby providing key optimal design insights. Numerical results depict an overall performance enhancement as compared with existing benchmarks.

关键词： Decoding Channel estimation Training Wireless communication Signal to noise ratio MISO communication deep learning Channel estimation (CE) deep autoencoder mean square error (mse) practical energy harvesting (EH) wireless power transfer (WPT)

来源：评论

学校读者我要写书评

暂无评论

deep auto encoder based on a transient search capsule network for student performance prediction

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期82卷 23427-23451页

作者： Rahul Katarya, Rahul Delhi Technol Univ Dept Comp Sci & Engn Big Data Analyt & Web Intelligence Lab New Delhi India

Prediction of Student performance through a machine predicts a student's future success. It can be considered an essential procedure to determine the students' academic excellence and identify them at high risk for academic performance. Prediction of student performance also provides universities with a high reputation and ranking. The evaluation of 'What students can do with their learning' is still a tedious task. There are many challenging factors to solve this problem, mainly owing to the enormous amount of data collected from students. Most of the research works have focused on developing new methodologies for student performance prediction. But all the existing work has some performance limitations. Here, a new model called transient search capsule network based on the deep autoencoder (TSCNDE) is introduced to detect student performance. The TSCNDE method is implemented with the help of the PYTHON tool. The performance prediction process has been completed with the help of the OULA dataset. The obtained results are assessed on accuracy (99.2%), precision, (99.8%), specificity (98.7%), and sensitivity (98.9%) parameters. The results obtained showed that the TSCNDE method is about 99.2% more accurate than the other related method. Also, the obtained results are compared with some existing deep learning and machine learning methods.

关键词： deep learning Student performance prediction OULA dataset Classification deep autoencoder Capsule network

来源：评论

学校读者我要写书评

暂无评论

A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes

引用

JOURNAL OF SUPERCOMPUTING 2022年第2期78卷 2873-2908页

作者： Bilal, Muhammad Maqsood, Muazzam Yasmin, Sadaf Ul Hasan, Najam Rho, Seungmin COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock Pakistan Dhofar Univ Coll Engn Dept Elect & Comp Engn Salalah Oman Chung Ang Univ Dept Ind Secur Seoul 06974 South Korea

deep learning base solutions for computer vision made life easier for humans. Video data contain a lot of hidden information and patterns, that can be used for Human Action Recognition (HAR). HAR can apply to many areas, such as behavior analysis, intelligent video surveillance, and robotic vision. Occlusion, viewpoint variation, and illumination are some issues that make the HAR task more difficult. Some action classes have similar actions or some overlapping parts in them. This, among many other problems, is the main reason that contributes the most to misclassification. Traditional hand-engineering and machine learning-based solutions lack the ability to handle overlapping actions. In this paper, we propose a deep learning-based spatiotemporal HAR framework for overlapping human actions in long videos. Transfer learning techniques are used for deep feature extraction. Fine-tuned pre-trained CNN models learn the spatial relationship at the frame level. An optimized deep autoencoder was used to squeeze high-dimensional deep features. An RNN with LSTM was used to learn the long-term temporal relationships. An iterative module added at the end to fine-tune the trained model on new videos that learns and adopt changes. Our proposed framework achieved state-of-the-art performance in spatiotemporal HAR for overlapping human actions in long visual data streams for non-stationary surveillance environments.

关键词： Human action recognition Overlapping actions Transfer learning Video content analysis deep autoencoder

来源：评论

学校读者我要写书评

暂无评论

A Hybrid deep Ensemble for Speech Disfluency Classification

引用

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2021年第8期40卷 3968-3995页

作者： Pravin, Sheena Christabel Palanivelan, M. Rajalakshmi Engn Coll Dept ECE Chennai Tamil Nadu India

In this paper, a novel Hybrid deep Ensemble (HDE) is proposed for automatic speech disfluency classification on a sparse speech dataset. Categorizations of speech disfluencies for diagnosis of speech disorders have so long relied on sophisticated deep learning models. Such a task can be accomplished by a straightforward approach with high accuracy by the proposed model which is an optimal combination of diverse machine learning and deep learning algorithms in a hierarchical arrangement which includes a deep autoencoder that yields the compressed latent features. The proposed model has shown considerable improvement in downgrading processing time overcoming the issues of cumbersome hyper-parameter tuning and huge data demand of the deep learning algorithms with high classification accuracy. Experimental results show that the proposed Hybrid deep Ensemble has superior performance compared to the individual base learners, and the deep neural network as well. The proposed model and the baseline models were evaluated in terms of Cohen's kappa coefficient, Hamming loss, Jaccard score, F-score and classification accuracy.

关键词： Hybrid deep Ensemble Speech disfluency classification Sparse speech dataset deep autoencoder Latent features

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：