检索结果-内蒙古大学图书馆

Latent Space Representation of Human Movement: Assessing the Effects of Fatigue

SENSORS 2024年第23期24卷 7775页

作者： Rousseau, Thomas Venture, Gentiane Hernandez, Vincent Univ Reims Fac Odontol F-51100 Reims France Univ Tokyo Dept Mech Engn Tokyo 1138654 Japan

Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through deep learning methods for dimensionality reduction. The use of adversarial autoencoders (AAEs) is explored to assess and visualize fatigue in a two-dimensional latent space, focusing on both semi-supervised and conditional approaches. By transforming complex time-series data into this latent space, the objective is to evaluate motor changes associated with fatigue within the participants' motor control by analyzing shifts in the distribution of data points and providing a visual representation of these effects. It is hypothesized that increased fatigue will cause significant changes in point distribution, which will be analyzed using clustering techniques to identify fatigue-related patterns. The data were collected using a Wii Balance Board and three Inertial Measurement Units, which were placed on the hip and both forearms (distal part, close to the wrist) to capture dynamic and kinematic information. The participants followed a fatigue-inducing protocol that involved repeating sets of 10 repetitions of four different exercises (Squat, Right Lunge, Left Lunge, and Plank Jump) until exhaustion. Our findings indicate that the AAE models are effective in reducing data dimensionality, allowing for the visualization of fatigue's impact within a 2D latent space. The latent space representation provides insights into motor control variations, revealing patterns that can be used to monitor fatigue levels and optimize training or rehabilitation programs.

关键词： fatigue human activity recognition deep learning adversarial autoencoder inertial measurement unit ground reaction force

来源：评论

学校读者我要写书评

暂无评论

A Limited-View CT Reconstruction Framework Based on Hybrid Domains and Spatial Correlation

引用

SENSORS 2022年第4期22卷 1446页

作者： Deng, Ken Sun, Chang Gong, Wuxuan Liu, Yitong Yang, Hongwen Beijing Univ Posts & Telecommun Inst Wireless Theories & Technol Lab Beijing 100876 Peoples R China

Limited-view Computed Tomography (CT) can be used to efficaciously reduce radiation dose in clinical diagnosis, it is also adopted when encountering inevitable mechanical and physical limitation in industrial inspection. Nevertheless, limited-view CT leads to severe artifacts in its imaging, which turns out to be a major issue in the low dose protocol. Thus, how to exploit the limited prior information to obtain high-quality CT images becomes a crucial issue. We notice that almost all existing methods solely focus on a single CT image while neglecting the solid fact that, the scanned objects are always highly spatially correlated. Consequently, there lies bountiful spatial information between these acquired consecutive CT images, which is still largely left to be exploited. In this paper, we propose a novel hybrid-domain structure composed of fully convolutional networks that groundbreakingly explores the three-dimensional neighborhood and works in a "coarse-to-fine" manner. We first conduct data completion in the Radon domain, and transform the obtained full-view Radon data into images through FBP. Subsequently, we employ the spatial correlation between continuous CT images to productively restore them and then refine the image texture to finally receive the ideal high-quality CT images, achieving PSNR of 40.209 and SSIM of 0.943. Besides, unlike other current limited-view CT reconstruction methods, we adopt FBP (and implement it on GPUs) instead of SART-TV to significantly accelerate the overall procedure and realize it in an end-to-end manner.

关键词： CT image reconstruction low dose protocol adversarial autoencoder deep learning hybrid domain spatial correlation inverse problems

来源：评论

学校读者我要写书评

暂无评论

A geologically-constrained deep learning algorithm for recognizing geochemical anomalies

引用

COMPUTERS & GEOSCIENCES 2022年 162卷

作者： Zhang, Chunjie Zuo, Renguang Xiong, Yihui Zhao, Xinfu Zhao, Kuidong China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 439974 Peoples R China

The effective identification of geochemical anomalies is essential in mineral exploration. Recently, data-driven deep learning algorithms have gained popularity for recognizing the geochemical patterns linked to mineralization. While purely data-driven deep learning algorithms can exploit geochemical patterns well, but the predicted and extracted results may be inconsistent with the geologic knowledge. In this study, a geologicallyconstrained deep learning algorithm was proposed to extract multivariate geochemical anomalies associated with W polymetallic mineralization in the south Jiangxi Province, China. The construction of the proposed algorithm involved two steps: (1) quantifying the spatial distribution of the known mineral deposits via fractal analysis, and (2) using prior knowledge obtained by the fractal analysis as a geological constraint to restrain an adversarial autoencoder network for delineating geochemical anomalies associated with mineralization. We conducted a comparative study of geologically-constrained and purely data-driven deep learning algorithms. We found that the former obtained more reasonable and interpretable geochemical anomalies linked to W mineralization. The results obtained by a geologically-constrained deep learning algorithm were more consistent with the regional metallogenic law. Therefore, this geological constraint can improve the generalization ability of the deep learning algorithm and enhance the interpretation of the obtained results in geosciences.

关键词： Geologically-constrained deep learning adversarial autoencoder Fractal Geochemical exploration

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based privacy-preserving framework for synthetic trajectory generation

引用

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2022年 206卷

作者： Kim, Jong Wook Jang, Beakcheol Sanriyung Univ Dept Comp Sci Seoul South Korea Yonsei Univ Grad Sch Informat Seoul South Korea

Synthetic data generation based on state-of-the-art deep learning methods has recently emerged as a promising solution to replace the expensive and laborious collection of real data. Accordingly, several deep learning approaches have been developed to generate synthetic trajectories. These existing solutions assume that a dataset of true trajectories is available with sufficient size to train a deep learning model. However, considering that the trajectories usually contain sensitive information that individuals do not wish to disclose, this assumption is unrealistic in real world applications. We propose a novel privacy-preserving framework designed to effectively generate synthetic trajectories. In contrast to existing solutions to this problem, the proposed method exploits a differential privacy mechanism for collecting training data from individual users to protect the privacy of their locations. A deep learning model for trajectory generation is then trained using the perturbed training dataset collected under differential privacy. We present experimental results to demonstrate that the proposed framework effectively exploits a dataset of perturbed trajectories to train a deep learning model and can therefore generate synthetic trajectories with distributions similar to those of real data. Experimental results on real world datasets show that our method achieves significantly better performance than baseline approaches.

关键词： Trajectory data Location privacy Synthetic data generation Differential privacy Geo-indistinguishability adversarial autoencoder

来源：评论

学校读者我要写书评

暂无评论

DeGAN- Decomposition-based unified anomaly detection in static networks

引用

INFORMATION SCIENCES 2024年 677卷

作者： Tuzen, Ahmet Yaslan, Yusuf Aselsan Inc Ankara Turkiye Istanbul Tech Univ Istanbul Turkiye

Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem because anomalies in graphs can manifest in three different forms: anomalous nodes, anomalous edges, and anomalous subgraphs. It is crucial to detect all these anomaly types within a single framework to provide a unified solution to the graph anomaly detection task. The main objective of this study is to propose a model that is capable of detecting all static graph anomalies in a single architecture across various domains. In this paper, we introduce DeGAN (Decomposition -based unified Graph ANomaly detection), a novel framework for unified graph anomaly detection in static networks. DeGAN combines two deep learning concepts with graph decomposition to identify anomalous graph objects: a graph neural network and an adversarial autoencoder. DeGAN is featured with its capability to detect anomalies in a single process, and adopting graph decomposition has improved performance compared to the traditional adversarial learning approach. DeGAN is evaluated with six real -world datasets to demonstrate that our framework can work in multiple domains. Experimental results demonstrate that DeGAN is capable of detecting anomalous nodes, edges, and sub -graphs within a single model. Additionally, the effectiveness of the subcomponents of DeGAN has been demonstrated through experimentation. Even though DeGAN is proposed for plain graphs, it can be extended to attributed and dynamic graphs.

关键词： Graph anomaly detection Graph decomposition adversarial autoencoder Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Efficient Identification of Multiple Parameter Errors in Power Grids by Mixed-Effects Models and Generalized Least Squares

引用

FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Liang, Dong Cheng, Yawen Su, Suntao Wang, Xiaoxue Zeng, Lin Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Hebei Univ Technol Dept Elect Engn Key Lab Elctromagnt Field & Elect Apparat Reliabi Tianjin Peoples R China Cornell Univ Sch Elect & Comp Engn Ithaca NY USA

Accurate knowledge of static parameters forms the basis for nearly all applications in the energy management system. This paper proposes an efficient method for the simultaneous identification and correction of multiple network parameter errors based on a linear mixed-effects (LME) model and the generalized least squares (GLS) method. An LME model for parameter error identification is formulated using the residual equations of multiple-snapshot state estimation with equality constraints. The parameter errors are considered as the fixed effects and the measurement errors are considered as the random effects. Then, using the measurement error variances estimated by the LME model, the GLS method is used to estimate the parameter errors along with a hypothesis testing to infer whether each parameter error is zero. The semi-supervised adversarial autoencoder is used for bad data detection in the presence of erroneous parameters and limited labels such that only measurement snapshots that are free of any bad data are used. The proposed methodology is efficient in that the LME model is only used to estimate the variances of the measurement errors using a small number of measurement snapshots, therefore the huge computation burden needed to solve a large-scale LME model is avoided. In addition, the GLS only involves inversion of low-dimension matrices, which is very efficient even a large number of measurement snapshots are used. Thorough tests of the proposed methodology on a large number of scenarios are provided to show the effectiveness of the proposed methodology with promising results.

关键词： parameter error identification linear mixed-effects model generalized least squares adversarial autoencoder transmission systems

来源：评论

学校读者我要写书评

暂无评论

Graph attention Network-Based model for multiple fault detection and identification of sensors in nuclear power plant

引用

NUCLEAR ENGINEERING AND DESIGN 2024年 419卷

作者： Liu, Shuqi Zhou, Xinzhi Yu, Junhui Wang, Yan Xu, Tao Wang, Hailin Sichuan Univ Coll Elect Informat Chengdu 610000 Peoples R China Nucl Power Inst China Natl Key Lab Sci & Technol Reactor Syst Design Tec Chengdu 610213 Peoples R China

The accuracy of sensor's measurements is critical for the normal operation of nuclear power plants. However, the harsh operating environment of nuclear power plants increases the probability of sensor failure. Therefore, it is necessary to detect and identify the fault of sensors in time. This paper proposes a fault detection model based on graph attention network, which is called ALPHA-GAT, to solve multiple fault detection and identification of sensors in the primary coolant loop of a nuclear power plant. ALPHA-GAT first uses GAT to obtain the correlation matrices of sensors, and the correlation matrices are fed into an adversarial autoencoder for reconstruction. The node features of the GAT are parallelly fed into a long short -term memory network to predict the ground truth of sensors. Subsequently, the anomaly score which is used for fault detection and identification, is calculated based on reconstruction error and prediction error. A strategy based on fault decoupling to identify multi -sensor faults has also been proposed, which replaces the data of fault sensor with the prediction of the model. This strategy effectively alleviates the negative impact of fault data on subsequent diagnosis. Finally, the data from a simulation model of a nuclear power plant is used to verify the efficiency of the proposed model. The results of experiments indicate that ALPHA-GAT effectively improves the accuracy and timeliness of multi -sensor fault detection and identification.

关键词： Nuclear power plants Multi-fault Graph attention network Long short-term memory network adversarial autoencoder

来源：评论

学校读者我要写书评

暂无评论

adversarial deep evolutionary learning for drug design

引用

BIOSYSTEMS 2022年 222卷 104790页

作者： Abouchekeir, Sheriff Vu, Andrew Mukaidaisi, Muhetaer Grantham, Karl Tchagang, Alain Li, Yifeng Brock Univ Dept Comp Sci 1812 Sir Isaac Brock Way St Catharines ON L2S 3A1 Canada Natl Res Council Canada Digital Technol Res Ctr 1200 Montreal Rd Ottawa ON K1A 0R6 Canada

The design of a new therapeutic agent is a time-consuming and expensive process. The rise of machine intelligence provides a grand opportunity of expeditiously discovering novel drug candidates through smart search in the vast molecular structural space. In this paper, we propose a new approach called adversarial deep evolutionary learning (ADEL) to search for novel molecules in the latent space of an adversarial generative model and keep improving the latent representation space. In ADEL, a custom-made adversarial autoencoder (AAE) model is developed and trained under a deep evolutionary learning (DEL) process. This involves an initial training of the AAE model, followed by an integration of multi-objective evolutionary optimization in the continuous latent representation space of the AAE rather than the discrete structural space of molecules. By using the AAE, an arbitrary distribution can be provided to the training of AAE such that the latent representation space is set to that distribution. This allows for a starting latent space from which new samples can be produced. Throughout the process of learning, new samples of high quality are generated after each iteration of training and then added back into the full dataset, therefore, allowing for a more comprehensive procedure of understanding the data structure. This combination of evolving data and continuous learning not only enables improvement in the generative model, but the data as well. By comparing ADEL to the previous work in DEL, we see that ADEL can obtain better property distributions. We show that ADEL is able to design high-quality molecular structures which can be further used for virtual and experimental screenings.

关键词： Drug design adversarial autoencoder Multi-objective optimization Deep evolutionary learning adversarial deep evolutionary learning Virtual screening

来源：评论

学校读者我要写书评

暂无评论

Sketch-aae: A Seq2Seq Model to Generate Sketch Drawings 2019

Sketch-aae: A Seq2Seq Model to Generate Sketch Drawings

引用

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

作者： Jia Lu Xueming Li Xianlin Zhang School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450376259

Sketch plays an important role in human nonverbal communication, which is a superior way to describe specific objects visually. Generating human free-hand sketches has become topical in computer graphics and vision, inspired by various applications related to sketches such as sketch object recognition. Existing methods on sketch generation failed to utilize stroke sequence information of human free-hand sketches. Especially, a recent study proposed an end-to-end variational autoencoder (VAE) model called sketch-rnn which learned to sketch with human input. However, the performance of sketch-rnn is affected by the original input seriously hence decreased its robustness. In this paper, we proposed a sequence-to-sequence model called sketch-aae to generate multiple categories of humanlike sketches of higher quality than sketch-rnn. We achieve this by introducing an adversarial autoencoder (AAE) model, which uses generative adversarial networks (GAN) to improve the robustness of VAE. To our best knowledge, for the first time, the AAE model is used to synthesize sketches. A VGGNet classification model is then formulated to prove the similarity between our generated sketches and human free-hand sketches. Extensive experiments both qualitatively and quantitatively demonstrate that the proposed model is superiority over the state-of-the-art for sketch generation and multi-class sketch classification.

关键词： GAN adversarial autoencoder VAE Sketch Generation

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：