检索结果-内蒙古大学图书馆

Permutation polynomials x~(2k+1+3)+ax2k+2+bx over F_(22k) and their differential uniformity

Science China(Information Sciences) 2020年第10期63卷 255-257页

作者： Jie PENG Lijing ZHENG Chunsheng WU Haibin KAN Mathematics and Science College Shanghai Normal University School of Mathematics and Physics University of South China Department of Mathematics Lianyungang Normal University Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Blockchain Engineering Research Center Shanghai Institute for Advanced Communication and Data Science Shanghai Institute of Intelligent Electronics & Systems

Dear editor,A polynomial f（x）∈Fq[x] is called a permutation polynomial （PP） over the finite field Fq if the associated mapping f:c （?） f（c） from Fq to itself is bijective.A PP f is called a complete permutation polynomial if f（x）+x is a *** over finite fields of even characteristic have wide applications,including cryptography,coding theory,and communication *** many block ciphers with substitution-permutation network structure,the substitution box is usually a PP over F_(22k)for some positive integer *** resist the differential attacks,the differential uniformity of this

关键词： Finite fields Permutation polynomial Binomial Trinomial Differential uniformity

来源：评论

学校读者我要写书评

暂无评论

Real-Time Fault Detection and Diagnosis Method for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection and Diagnosis Method for Industria...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Institut Superieur des Sciences Appliques et de Technologie de Kasserine Kairouan University Tunisia The Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria The Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.

关键词： Accuracy Embedded systems Fault detection Neural networks Electrical fault detection Real-time systems Robustness Power system reliability Information technology Chemicals

来源：评论

学校读者我要写书评

暂无评论

Multimodal unrolled robust pca for background foreground separation

arXiv

引用

arXiv 2021年

作者： Markowitz, Spencer Snyder, Corey Eldar, Yonina C. Do, Minh N. Electrical and Computer Engineering Department University of Illinois Urbana Champaign United States Department of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel Signal Processing and Data Science Department of Electrical and Computer Engineering

Background foreground separation (BFS) is a popular computer vision problem where dynamic foreground objects are separated from the static background of a scene. Typically, this is performed using consumer cameras because of their low cost, human interpretability, and high resolution. Yet, cameras and the BFS algorithms that process their data have common failure modes due to lighting changes, highly reflective surfaces, and occlusion. One solution is to incorporate an additional sensor modality that provides robustness to such failure modes. In this paper, we explore the ability of a cost-effective radar system to augment the popular Robust PCA technique for BFS. We apply the emerging technique of algorithm unrolling to yield real-time computation, feedforward inference, and strong generalization in comparison with traditional deep learning methods. We benchmark on the RaDICaL dataset to demonstrate both quantitative improvements of incorporating radar data and qualitative improvements that confirm robustness to common failure modes of image-based methods. © 2021, CC BY.

关键词： Separation

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning

arXiv

引用

arXiv 2024年

作者： Yang, Kuo Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The Kargobot of DiDi Shanghai201210 China The Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China The Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

For semi-supervised learning with imbalance classes, the long-tailed distribution of data will increase the model prediction bias toward dominant classes, undermining performance on less frequent classes. Existing methods also face challenges in ensuring the selection of sufficiently reliable pseudo-labels for model training and there is a lack of mechanisms to adjust the selection of more reliable pseudo-labels based on different training stages. To mitigate this issue, we introduce uncertainty into the modeling process for pseudo-label sampling, taking into account that the model performance on the tailed classes varies over different training stages. For example, at the early stage of model training, the limited predictive accuracy of model results in a higher rate of uncertain pseudo-labels. To counter this, we propose an Uncertainty-Aware Dynamic Threshold Selection (UDTS) approach. This approach allows the model to perceive the uncertainty of pseudo-labels at different training stages, thereby adaptively adjusting the selection thresholds for different classes. Compared to other methods such as the baseline method FixMatch, UDTS achieves an increase in accuracy of at least approximately 5.26%, 1.75%, 9.96%, and 1.28% on the natural scene image datasets CIFAR10-LT, CIFAR100-LT, STL-10-LT, and the medical image dataset TissueMNIST, respectively. The source code of UDTS is publicly available at: https://***/yangk/UDTS. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Class-Incremental Learning with Prior Knowledge

arXiv

引用

arXiv 2024年

作者： Jiang, Wenhao Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Kargobot of DiDi Shanghai201210 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: https://***/StevenJ308/LwPK. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Real-Time Fault Detection Scheme for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection Scheme for Industrial Chemical Ten...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

关键词： Fault detection Power system dynamics Power system stability Feature extraction Electrical fault detection Real-time systems Stability analysis Robustness Safety Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

A Reliable Method to Estimate the Bispectral Index Value Using a Single Frontal EEG Channel for Intra and Inter Subject Variability

A Reliable Method to Estimate the Bispectral Index Value Usi...

引用

2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Shahbakhti, Mohammad Beiramvand, Matin Krycinska, Roza Nasiri, Erfan Chen, Wei Sole-Casals, Jordi Wierzchon, Michal Broniec-Wojcik, Anna Augustyniak, Piotr Marozas, Vaidotas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Tampere University Faculty of Information Technology and Communication Tempere Finland Jagiellonian University Medical College Doctoral School in Medical and Health Sciences Krakow Poland Jagiellonian University Collegium Medicum Department of Interdisciplinary Intensive Care Krakow Poland Allameh Tabataba'i University Faculty of Statistics Mathematics and Computer Tehran Iran Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Shanghai China University of Vic-Central University of Catalonia Data and Signal Processing Research Group Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Agh University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland

ISBN: (纸本)9781665493840

Objective: Monitoring the depth of anesthesia (DoA) plays an important role for administering the drug injection during a surgery, i.e., preventing undesired awareness and inordinate anesthetic depth. Although the bispectral index (BIS) monitor is the golden standard system for the DoA monitoring, it is still not affordable for the developing countries. Alternatively, a low-cost electroencephalogram (EEG) headband can be used. The objective of this paper is to present a new algorithm for estimating the BIS values using a single frontal EEG channel. Method: In the first step, the EEG signal is filtered for the elimination of artifacts and is split into its sub-bands. In the second step, several linear and nonlinear features are extracted from each sub-band and fed to a random forest regression model in order to estimate the BIS. The performance of the proposed algorithm is assessed using EEG data recorded from twenty-four subjects during the general anesthesia and is validated in terms of correlation coefficient (CC) and absolute error (AE) between the reference and estimated BIS values. Results: The proposed algorithm achieved the mean CC of 0.83 and AE of 6.5 for intra subject variability and mean CC of 0.87 and AE of 5.5 for inter subject variability. Significance: Given the similar results for both intra and inter subject variability, the proposed algorithm has the potential to be used in the real-world scenario. © 2023 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: A comparative recognition research on excretory organism in medical applications using neural networks

引用

Multimedia Tools and Applications 2023年 1-2页

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Sultanate of Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Chennai India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Abha Kingdom of Saudi Arabia

来源：评论

学校读者我要写书评

暂无评论

Modelling the progression of Alzheimer's disease using Neutrosophic hidden Markov models

引用

Neutrosophic Sets and Systems 2023年 56卷 31-40页

作者： Nagarajan, D. Kavikumar, J. Tom, Mary Mahmud, Mufti Broumi, S. Department of Mathematics Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Mathematics and Statistics Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Campus Pagoh84600 Malaysia College of Information and Communication Technology School of Engineering & Technology |Higher Education Division CQUniversity Brisbane Campus BrisbaneQLD4000 Australia Department of Computer Science Nottingham Trent University United Kingdom United Kingdom Laboratory of Information Processing Faculty of Science Ben M’Sik University of Hassan II Casablanca Morocco

Alzheimer's disease is the primary cause of dementia. Due to the sluggish rate of progression of Alzheimer's disease, individuals have the opportunity to start receiving therapy early through routine testing. procedures since they are pricy and difficult to find. For many slowly advancing disorders, such as Alzheimer's disease (AD), the capacity to recognise changes in disease progression is essential. Machine learning methods with a high degree of modularity were used throughout the pipeline. We propose the use of Neutrosophic hidden Markov models (NHMMs) to simulate disease progression in a more thorough manner than the clinical phases of the disease. Due to the complexity and ambiguity of reality, decision-makers find it challenging to draw conclusions from precise data. Since they cannot be computed directly, the variables are encoded using a single interval Neutrosophic set. We showed that the trained HMM can imitate sickness development more accurately than the commonly acknowledged clinical phases. © (2023). All Rights Reserved.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Prediction of Drug-Disease Associations Based on Long Short-Term Memory Network and Gaussian Interaction Profile Kernel 15th

Prediction of Drug-Disease Associations Based on Long Short-...

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Jiang, Han-Jing Wang, Yan-Bin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computer Science and Technology Zhejiang University Hangzhou315000 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Drug-disease association depicts the accessibility landscape of the association at the molecular level, thereby revealing the intermolecular reaction in the process of drug reposition. However, the scarcity of drug-disease association data often complicates the drug reposition. In this work, we present a method for predicting drug-disease association, which is called drug-disease association prediction via Gaussian interaction profile kernel and long short-term memory network (G-LSTM). G-LSTM combines a recurrent network framework with a Gaussian interaction profile kernel to learn underlying features that accurately characterize drug-disease-association data. We have verified G-LSTM models on the two benchmark datasets Fdataset and Cdataset, respectively. The experimental results show that the predictive performance was significantly improved, with AUC of 0.9527 and 0.9705, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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：