检索结果-内蒙古大学图书馆

LSTMED: An uneven dynamic process monitoring method based on LSTM and autoencoder neural network

NEURAL NETWORKS 2023年 158卷 30-41页

作者： Deng, Wenfeng Li, Yuxuan Huang, Keke Wu, Dehao Yang, Chunhua Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

Due to the complicated production mechanism in multivariate industrial processes, different dynamic features of variables raise challenges to traditional data-driven process monitoring methods which assume the process data is static or dynamically consistent. To tackle this issue, this paper proposes a novel process monitoring method based on the long short-term memory (LSTM) and autoencoder neu-ral network (called LSTMED) for multivariate process monitoring with uneven dynamic features. First, the LSTM units are arranged in the encoder-decoder form to construct an end-to-end model. Then, the constructed model is trained in an unsupervised manner to capture long-term time dependency within variables and dominant representation of high dimensional process data. Afterward, the kernel density estimation (KDE) method is performed to determine the control limit only based on the reconstruction error from historical normal data. Finally, effective online monitoring for uneven dynamic process can be achieved. The performance and advantage of the process monitoring method proposed are explained through typical cases, including the numerical simulation and Tennessee Eastman (TE) benchmark process, and comparative experimental analysis with state-of-the-art methods.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Uneven dynamic process Long and short-term memory neural network autoencoder Process monitoring

来源：评论

学校读者我要写书评

暂无评论

Person identification using autoencoder-CNN approach with multitask-based EEG biometric

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第35期83卷 83205页

作者： Bandana Das, Banee Kumar Ram, Saswat Sathya Babu, Korra Mohapatra, Ramesh Kumar Mohanty, Saraju P. SRM Univ Comp Sci & Engn Amravati Andhra Pradesh India SRM Univ Dept Elect & Commun Engn Amravati Andhra Pradesh India IIITDM Comp Sci Engn Kurnool India Natl Inst Technol Rourkela Comp Sci & Engn Rourkela India Univ North Texas Comp Sci & Engn Denton TX USA

In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognize, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject's motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states. The experiments have been conducted on the standard publicly available motor imagery EEG dataset with 109 subjects. The highest recognition rate of 87.60% for task-based identification and 99.89% recognition rate for resting-state has been recorded using the autoencoder-CNN model. The outcomes imply that the overall performance of our proposed framework is similar or advanced to that of the state-of-the-art method. The shape is a realistic technique to control the full-size extent of EEG data and to pick out the individual based totally on their specific task.

关键词： Electroencephalography (EEG) Biometric authentication Person identification autoencoder Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Distributed denial of service attack detection using autoencoder and deep neural networks

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 3969-3979页

作者： Catak, Ferhat Ozgur Mustacoglu, Ahmet Fatih TUBITAK BILGEM Cyber Secur Inst TR-41400 Kocaeli Turkey Istanbul Sehir Univ Cyber Secur Engn Istanbul Turkey

Today, many companies are faced with the huge network traffics mainly consisting of the various type of network attacks due to the increased usage of the botnet, fuzzier, shellcode or network related vulnerabilities. These types of attacks are having a negative impact on the organization because they block the day-to-day operations. By using the classification models, the attacks could be identified and separated earlier. The Distributed Denial of Service Attacks (DDoS) primarily focus on preventing or reducing the availability of a service to innocent users. In this research, we focused primarily on the classification of network traffics based on the deep learning methods and technologies for network flow models. In order to increase the classification performance of a model that is based on the deep neural networks has been used. The model used in this research for the classification of network traffics evaluated and the related metrics showing the classification performance have been depicted in the figures and tables. As the results indicate, the proposed model can perform well enough for detecting DDoS attacks through deep learning technologies.

关键词： cyber security ddos deep learning autoencoder

来源：评论

学校读者我要写书评

暂无评论

autoencoder Image Denoising to Increase Optical Character Recognition Performance in Text Conversion

Autoencoder Image Denoising to Increase Optical Character Re...

引用

International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) - Blockchain Technology, Intelligent Systems, and the Applications for Human Life

作者： Alamsyah, Nur Fauzan, Mohamad Nurkamal Putrada, Aji Gautama Pane, Syafrial Fachri Telkom Univ Adv & Creat Networks Res Ctr Bandung Indonesia

ISBN: (纸本)9798350334449

Document digitization has an important role in helping the company's activities be more efficient, such as detecting text in invoice document images using optical character recognition (OCR). However, writing in images has many problems, especially tediously saved documents that can cause noise or interference in the picture, resulting in difficultly recognized writing. Our research aims to build an autoencoder for denoising text images and evaluate the OCR's performance in converting the denoised image into text. The first step in the research is to test the OCR characteristics on the original text image and the text image given Gaussian noise. The next step is to build the optimal autoencoder model for denoising by studying the effect of dataset size and optimizer type. The last step is to test the OCR performance on the denoised text image produced by the optimum autoencoder model. The test results show that datasetsize affects denoising performance and OCR performance. From several autoencoder models compared, the autoencoder with dataset size = 40 has the optimum performance, where the MSE values of the model for train and validation are 1277 and 1385, respectively. With images denoised from the optimum model, the OCR performance in converting images into text is 100%.

关键词： optical character recognition Gaussian noise denoising autoencoder text image

来源：评论

学校读者我要写书评

暂无评论

User-Centric Online Gossip Training for autoencoder-Based CSI Feedback

引用

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 559-572页

作者： Guo, Jiajia Zuo, Yiping Wen, Chao-Kai Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

Recently, the autoencoder framework has shown great potential in reducing the feedback overhead of the downlink channel state information (CSI). In this work, we further find that the user equipment in practical systems occasionally moves in a relatively stable area for a long time, and the corresponding communication environment is relatively stable. A user-centric online training strategy is proposed to further improve CSI feedback performance using the above characteristics. The key idea of the proposed method is to train a new encoder for a specific area without changes to the decoder at the base station. Given that the CSI training samples are insufficient, two data augmentation strategies, including random erasing and random phase shift, are introduced to improve the neural network generalization. In addition, the proposed user-centric online training framework is extended to the multi-user scenario for considerable performance improvement via gossip learning, which is a fully decentralized distributed learning framework and can use crowd intelligence. The simulation results show that the proposed user-centric online gossip training offers a more substantial increase in the feedback accuracy and can considerably improve autoencoder generalization.

关键词： Training Artificial neural networks Downlink Decoding Correlation Computer architecture 5G mobile communication autoencoder CSI feedback gossip learning user-centric online training

来源：评论

学校读者我要写书评

暂无评论

Optimized Deep autoencoder Model for Internet of Things Intruder Detection

引用

IEEE ACCESS 2022年 10卷 8434-8448页

作者： Lahasan, Badr Samma, Hussein Univ Shabwah Fac Comp & Informat Technol Shabwah Yemen Univ Teknol Malaysia UTM Fac Engn Sch Comp Johor Baharu 81310 Johor Malaysia

The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To achieve this objective, an efficient two-layer optimizer is used to evolve a lightweight deep autoencoder model by performing simultaneous selection for the input features, the training instances, and the number of hidden neurons. The optimized deep model is constructed guided by both the accuracy of a K-nearest neighbor (KNN) classifier and the complexity of the autoencoder model. To evaluate the performance of the proposed optimized model, it has been applied for the N-baiot intrusion detection dataset. Reported results showed that the proposed model achieved anomaly detection accuracy of 99% with a lightweight autoencoder model with on average input features around 30 and output hidden neurons of 2 only. In addition, the proposed two-layers optimizer was able to outperform several optimizers such as Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

关键词： Convolutional neural networks Floods Deep learning Data models Optimization Botnet Anomaly detection Deep learning autoencoder IoT anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Novel Deep Learning-Enabled LSTM autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第7期22卷 4507-4518页

作者： Ashraf, Javed Bakhshi, Asim D. Moustafa, Nour Khurshid, Hasnat Javed, Abdullah Beheshti, Amin Natl Sci & Technol NUST Dept Comp Software Engn Islamabad 44000 Pakistan Univ New South Wales ADFA Sch Engn & Informat Technol Canberra ACT 2612 Australia Sir Syed Ctr Adv Studies Engn CASE Inst Technol Islamabad 44000 Pakistan Macquarie Univ Dept Comp Sydney NSW 2109 Australia

Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniques, thus barring AVs from the effective use in our routine lives. Once manual vehicles are connected to the Internet, called the Internet of Vehicles (IoVs), it would be exploited by cyber-attacks, like denial of service, sniffing, distributed denial of service, spoofing and replay attacks. In this article, we present a deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles (V2V) communications and vehicles to infrastructure (V2I) networks. A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated using two benchmark datasets, i.e., the car hacking dataset for in-vehicle communications and the UNSW-NB15 dataset for external network communications. The experimental results demonstrated that our proposed system achieved over a 99% accuracy for detecting all types of attacks on the car hacking dataset and a 98% accuracy on the UNSW-NB15 dataset, outperforming other eight intrusion detection techniques.

关键词： Intelligent transport systems CAN bus internet of vehicles autonomous vehicles intrusion detection system deep learning LSTM autoencoder

来源：评论

学校读者我要写书评

暂无评论

Automated eye blink artefact removal from EEG using support vector machine and autoencoder

引用

IET SIGNAL PROCESSING 2019年第2期13卷 141-148页

作者： Ghosh, Rajdeep Sinha, Nidul Biswas, Saroj Kumar NIT Silchar Dept Comp Sci & Engn Silchar Assam India NIT Silchar Dept Elect Engn Silchar Assam India

Electroencephalogram (EEG) is a highly sensitive instrument and is frequently corrupted with eye blinks. Methods based on adaptive noise cancellation (ANC) and discrete wavelet transform (DWT) have been used as a standard technique for removal of eye blink artefacts. However, these methods often require visual inspection and appropriate thresholding for identifying and removing artefactual components from the EEG signal. The proposed work describes an automated windowed method with a window size of 0.45 s that is slid forward and fed to a support vector machine (SVM) classifier for identification of artefacts, after the identification of artefacts, it is fed to an autoencoder for correction of artefacts. The proposed method is evaluated on the data collected from the project entitled 'Analysis of Brain Waves and Development of Intelligent Model for Silent Speech Recognition'. From the results it is observed that the proposed method performs better in identifying and removing artefactual components from EEG data than existing wavelet and ANC based methods. The proposed method does not require the application of independent component analysis (ICA) before processing and can be applied to multiple channels in parallel.

关键词： electroencephalography discrete wavelet transforms medical signal processing support vector machines signal denoising eye signal classification visual inspection automated windowed method autoencoder automated eye blink artefact removal support vector machine highly sensitive instrument discrete wavelet transform adaptive noise cancellation EEG signal electroencephalogram EEG data

来源：评论

学校读者我要写书评

暂无评论

Impact of autoencoder based compact representation on emotion detection from audio

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第2期13卷 867-885页

作者： Patel, Nivedita Patel, Shireen Mankad, Sapan H. Nirma Univ Inst Technol CSE Dept Ahmadabad Gujarat India

Emotion recognition from speech has its fair share of applications and consequently extensive research has been done over the past few years in this interesting field. However, many of the existing solutions aren't yet ready for real time applications. In this work, we propose a compact representation of audio using conventional autoencoders for dimensionality reduction, and test the approach on two benchmark publicly available datasets. Such compact and simple classification systems where the computing cost is low and memory is managed efficiently may be more useful for real time application. System is evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS). Three classifiers, namely, support vector machines (SVM), decision tree classifier, and convolutional neural networks (CNN) have been implemented to judge the impact of the approach. The results obtained by attempting classification with Alexnet and Resnet50 are also reported. Observations proved that this introduction of autoencoders indeed can improve the classification accuracy of the emotion in the input audio files. It can be concluded that in emotion recognition from speech, the choice and application of dimensionality reduction of audio features impacts the results that are achieved and therefore, by working on this aspect of the general speech emotion recognition model, it may be possible to make great improvements in the future.

关键词： Audio Emotion RAVDESS TESS autoencoder

来源：评论

学校读者我要写书评

暂无评论

DSVD-autoencoder: A scalable distributed privacy-preserving method for one-class classification

引用

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期36卷 177-199页

作者： Fontenla-Romero, Oscar Perez-Sanchez, Beatriz Guijarro-Berdinas, Bertha Univ A Coruna CITIC Fac Informat Dept Comp Sci & Informat Technol Campus Elvina S-N La Coruna 15071 Spain

One-class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. autoencoder is the type of neural network that has been widely applied in these one-class problems. In the Big Data era, new challenges have arisen, mainly related with the data volume. Another main concern derives from Privacy issues when data is distributed and cannot be shared among locations. These two conditions make many of the classic and brilliant methods not applicable. In this paper, we present distributed singular value decomposition (DSVD-autoencoder), a method for autoencoders that allows learning in distributed scenarios without sharing raw data. Additionally, to guarantee privacy, it is noniterative and hyperparameter-free, two interesting characteristics when dealing with Big Data. In comparison with the state of the art, results demonstrate that DSVD-autoencoder provides a highly competitive solution to deal with very large data sets by reducing training from several hours to seconds while maintaining good accuracy.

关键词： autoencoder big data distributed learning neural network one-class classification privacy-preserving singular value decomposition

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：