检索结果-内蒙古大学图书馆

Encoder-X: Solving Unknown Coefficients Automatically in Polynomial Fitting by Using an autoencoder

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第8期33卷 3264-3276页

作者： Wang, Guojun Li, Weijun Zhang, Liping Sun, Linjun Chen, Peng Yu, Lina Ning, Xin Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Microelect Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Beijing Key Lab Semicond Neural Network Intellige Beijing 100083 Peoples R China Shenzhen DAPU Microelect Co Ltd Shenzhen 518100 Peoples R China

Modeling, prediction, and recognition tasks depend on the proper representation of the objective curves and surfaces. Polynomial functions have been proved to be a powerful tool for representing curves and surfaces. Until now, various methods have been used for polynomial fitting. With a recent boom in neural networks, researchers have attempted to solve polynomial fitting by using this end-to-end model, which has a powerful fitting ability. However, the current neural network-based methods are poor in stability and slow in convergence speed. In this article, we develop a novel neural network-based method, called Encoder-X, for polynomial fitting, which can solve not only the explicit polynomial fitting but also the implicit polynomial fitting. The method regards polynomial coefficients as the feature value of raw data in a polynomial space expression and therefore polynomial fitting can be achieved by a special autoencoder. The entire model consists of an encoder defined by a neural network and a decoder defined by a polynomial mathematical expression. We input sampling points into an encoder to obtain polynomial coefficients and then input them into a decoder to output the predicted function value. The error between the predicted function value and the true function value can update parameters in the encoder. The results prove that this method is better than the compared methods in terms of stability, convergence, and accuracy. In addition, Encoder-X can be used for solving other mathematical modeling tasks.

关键词： Mathematical model Task analysis Convergence Taylor series Stability criteria Fitting Decoding autoencoder neural network polynomial fitting

来源：评论

学校读者我要写书评

暂无评论

Regional spatio-temporal forecasting of particulate matter using autoencoder based generative adversarial network

引用

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第5期36卷 1255-1276页

作者： Abirami, S. Chitra, P. Thiagarajar Coll Engn Dept Comp Sci Engn Madurai 625015 Tamil Nadu India

Accurate forecasting of air pollutant PM2.5(particulate matter with diameter less than 2.5 mu m) is beneficial to society. However, the non-linear spatio-temporal correlations, multi-feasible forecast values and incomplete training data due to stochasticity make it challenging for discriminative deep learning approaches to forecasting PM2.5 data. In this paper, a generative modeling approach is proposed to overcome the challenges in forecasting PM2.5 data by considering it as an ill-posed inverse problem. To strengthen its applicability, the proposed approach is theoretically validated. Furthermore, based on the proposed generative modeling, an autoencoder-based generative adversarial network (GAN) named Air-GAN is developed. Air-GAN combines a convolutional neural network- long short-term memory (CNN-LSTM) based Encoder with a conditional Wasserstein GAN (WGAN) to capture non-linear correlations in the data distribution via inverse mapping from the forecast distribution. The condition vector to conditional WGAN is the novelty in Air-GAN, which employs this inverse learning and allows the WGAN's Generator to generate accurate forecast estimates from noise distribution. The condition vector is composed of two elements: (1) the category label of the best correlated meteorological parameter with the PM2.5 data, assigned using an efficient classifier and (2) the output of the CNN-LSTM-based Encoder which is the latent representation of the forecast. The extensive evaluation of Air-GAN for predicting the real-time PM2.5 data of Delhi demonstrates its superior performance with an average inference error of 5.3 mu g/m(3), which achieves 31.7% improvement over the baseline approaches. The improved performance of Air-GAN demonstrates its efficiency to forecast stochastic PM2.5 data by generalizing to out-of-distribution data.

关键词： Spatio-temporal forecasting Particulate matter autoencoder Generative adversarial network Inverse problem CNN-LSTM

来源：评论

学校读者我要写书评

暂无评论

Exploring the Effect of autoencoder Based Feature Learning for a Deep Reinforcement Learning Policy for Providing Proactive Help 24th

Exploring the Effect of Autoencoder Based Feature Learning f...

引用

24th International Conference on Artificial Intelligence in Education (AIED)

作者： Alam, Nazia Mostafavi, Behrooz Chi, Min Barnes, Tiffany North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9783031363351;9783031363368

Providing timely assistance to students in intelligent tutoring systems is a challenging research problem. In this study, we aim to address this problem by determining when to provide proactive help with autoencoder based feature learning and a deep reinforcement learning (DRL) model. To increase generalizability, we only use domain-independent features for the policy. The proposed pedagogical policy provides next-step proactive hints based on the prediction of the DRL model. We conduct a study to examine the effectiveness of the new policy in an intelligent logic tutor. Our findings provide insight into the use of DRL policies utilizing autoencoder based feature learning to determine when to provide proactive help to students.

关键词： Intelligent Tutoring Systems Deep Reinforcement Learning autoencoder

来源：评论

学校读者我要写书评

暂无评论

Improved Image Compression using autoencoder and Discrete Cosine Transformation 5

Improved Image Compression using Autoencoder and Discrete Co...

引用

5th International Conference on Innovative Trends in Information Technology (ICITIIT)

作者： Jindal, Sarthak Paka, Indradhar Agarwal, Esha Mishra, Atul BML Munjal Univ Gurgaon India

ISBN: (纸本)9798350386813;9798350386820

This study proposes a method for improved image compression using a combination of discrete cosine transformation (DCT) and autoencoder. Images typically contain large amounts of data that require significant storage space, making them difficult to store and transmit. Compressing images is a practical solution to this issue as it reduces memory usage and enables faster transmission to the receiver. In this approach, we use DCT as a preprocessing step before training an autoencoder model to compress the image while retaining all essential information. The proposed method involves a convolutional neural network (CNN) that performs down-sampling and up-sampling operations on the input data processed by DCT. The performance of the proposed method is evaluated and compared with traditional image compression techniques such as JPEG, JPEG 2000, and BPG. Experimental results demonstrate that the proposed approach outperforms the traditional techniques in terms of compression ratio and image quality.

关键词： Compression autoencoder Neural network UX Convolutional

来源：评论

学校读者我要写书评

暂无评论

Robust Seizure Prediction Based on Riemannian Manifold Enhanced Denoising Adversarial autoencoder

Robust Seizure Prediction Based on Riemannian Manifold Enhan...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Peng, Peizhen Wu, Yansheng Yang, Wanqi Wei, Wenxin Yang, Ming School of Computer and Electronic Information The School of Artificial Intelligence Nanjing Normal University Nanjing China

ISBN: (纸本)9798350368741

The seizure early warning devices based on multichannel EEG signals is one of the most used assisted-living strategies for drug-resistant epileptic patients. One of the challenges in the development of these devices is that existing algorithms cannot avoid the effects of electrode loosening. To alleviate such problem, a seizure prediction model robust to corrupted EEG recordings is proposed in this paper. This method intends to learn a stable feature space adapted to different corruption versions via constructing a jointly-optimized autoencoder. Robust information is captured during the reconstruction process by developing a corruption module, while an adversarial training procedure circumvents overfitting using variational inference. Moreover, a regularization term is designed to minimize the distance among various corruption versions in the embedding space with the Riemannian manifold-based distribution alignment. Both raw EEG data and corrupted EEG data are utilized to evaluate prediction performance. Experimental results indicate that this method can predict seizures effectively and stably under the condition of electrode looseness. © 2025 IEEE.

关键词： autoencoder Electroencephalography(EEG) Riemannian manifold seizure prediction time series classification

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

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

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

Comparing autoencoder Variants for Real-Time Denoising of Hyperspectral X-Ray

引用

IEEE SENSORS JOURNAL 2022年第18期22卷 17997-18007页

作者： Bonettini, Nicolo Gonano, Carlo Andrea Bestagini, Paolo Marcon, Marco Garavelli, Bruno Tubaro, Stefano Politecn Milan Comp Sci & Engn Milan Italy Politecn Milan Elect Engn Milan Italy Politecn Milan Informat Technol Milan Italy Informat & Commun Technol ICT Trieste Italy

Hyperspectral X ray analysis is used in many industrial pipelines, from quality control to detection of low-density contaminants in food. Unfortunately, the signal acquired by X-ray sensors is often affected by a great amount of noise. This hinders the performance of most of the applications building on top of these acquisitions (e.g., detection of food contaminants). Therefore, a good denoising pipeline is necessary. This article proposes a comparison between three different autoencoder variants: the Variational autoencoder, the Augmented autoencoder, and a plain vanilla autoencoder. All the networks are trained in an unsupervised fashion to denoise a given noisy spectrum. Focusing on the specific application of recognizing possible food contaminants, we force the latent space of the networks to have just two parameters, as suggested by the physical law of Lambert- Beer. We validate our experiments on a synthetic dataset composed of roughly 15 million spectra. Results suggest that the Augmented autoencoder is the best network configuration for this task, showing excellent performance without suffering from the nondeterministic behavior of the Variational autoencoder.

关键词： autoencoder denoising hyperspectral X-ray

来源：评论

学校读者我要写书评

暂无评论

Residual-recursive autoencoder for accelerated evolution in savonius wind turbines optimization

引用

NEUROCOMPUTING 2022年第0期500卷 909-920页

作者： Zhou, Qianwei Li, Baoqing Tao, Peng Xu, Zhang Zhou, Chen Wu, Yanzhuang Hu, Haigen Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Key Lab Visual Media Intelligent Proc Technol Zhej Hangzhou Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China

Recent studies verified that a genetic algorithm can discover efficient and innovative wind turbines by using image encoding and decoding techniques. To accelerate the optimization, in this work, ResidualRecursion autoencoder (RRAE) is proposed to extract low-dimensional latent codes from rotors' crosssection images while maintaining reconstruction accuracy as high as possible. As a kind of neural network framework, the advantages of using RRAE are threefold: 1) RRAE can wrap over different kinds of autoencoders and improve their performance;2) RRAE is compatible with different kinds of loss functions and works well with very low-dimensional latent codes;3) RRAE is easy to use and efficient in decoding latent codes which is important to the rapid convergence of the genetic algorithm. The experiment results has shown that the reconstruction loss has decreased by 30.56% on a recursive autoencoder, 11.40% to 29.34% on different feedforward autoencoders. Two RRAE-accelerated optimizations have been carried out in this work. One has used only 14% of the calculation required by the baseline method without any deterioration in rotor performance. The other one has used 52.33% and increased the rotor performance by 7.59%.

关键词： Recursive neural network autoencoder Deep learning Geometry optimization Savonius rotor

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：