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Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence

作者： Di Fan Leihua Fan Fuyan Zhao Huimin Sun Xiaolin Min School of Electronic Information Engineering Shandong University of Science and Technology Qingdao Shandong China

ISBN: (纸本)9798400711732

As online courses are more and more widely used in college courses, the analysis of online learning data has become an important means to improve teaching quality and learning effect. In this paper, an online learner clustering algorithm that integrates autoencoder and Canopy-Kmeans is proposed. The autoencoder is used to perform feature degradation and feature extraction for high-dimensional data which effectively solves the problems of data sparsity and dimensionality disaster. The Canopy algorithm is introduced to precluster the data which reduces the randomness of the K-means algorithm during initialization and improves the stability of clustering. And the improved K-means algorithm is used to perform the clustering analysis of text data. The experimental results show that the algorithm proposed behaved better in both clustering accuracy and noise resistance compared with traditional clustering algorithm, which is helpful to a better understanding of the behavioral characteristics of online learners and a better support for personalized teaching is provided based on this.

关键词： autoencoder

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MECFormer: Multiple Exposure Correction Transformer Based on autoencoder

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IEEE Access 2025年 13卷 83123-83135页

作者： Baek, Jong-Hyeon Lee, Hyo-Jun Kim, Hanul Koh, Yeong Jun Chungnam National University Department of Computer Science and Engineering Daejeon 34134 South Korea Seoul National University of Science and Technology Department of Applied Artificial Intelligence Seoul South Korea

Images captured with wrong exposure conditions inevitably produce unsatisfactory visual effects. Thus, multiple exposure correction has drawn much attention, which should correct for degraded images due to various wrong exposure conditions. However, the problem of handling the different nature of underexposed and overexposed images makes this task challenging. In this work, we introduce the novel multiple exposure correction transformer, named MECFormer, to tackle this problem. MECFormer consists of autoencoder, encoder, and dual-path aggregation decoder. First, the autoencoder extracts multi-scale exposure features representing the level of input exposure. Second, the encoder embeds input images into multi-scale image features. Third, the dual-path aggregation decoder sequentially restores exposures by effectively aggregating multi-scale exposure features and image features. MECFormer achieves the state-of-the art performance on two multi-exposure correction datasets. Also, we provide extensive ablation studies to show the effectiveness of the proposed components. © 2023 IEEE.

关键词： autoencoder Dual-path Aggregation Decoder Multiple Exposure Correction

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Physical Layer Secret Key Generation Based on Mutual Information-Driven autoencoder

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Dengke Xiong, Jun Ma, Dongtang Liu, Xiaoran Wei, Jibo National University of Defense Technology College of Electronic Science and Technology Changsha 410073 China

The reciprocity of wireless channels is a prerequisite for physical layer secret key generation (SKG). However, inherent factors, such as noise, asynchronous observations, and hardware impairments, disrupt the ideal reciprocity in the channel state information (CSI) observed by the two legitimate parties. To address this issue, we propose a mutual information-driven autoencoder (MIAE) architecture to extract reciprocal channel features from the non-ideal channel observations of legitimate parties. MIAE is constructed with an autoencoder Network (AENet) and a mutual information neural estimator (MINE). Specifically, AENet employs a structure with dual encoders and a shared decoder. The two encoders, integrated with convolutional block attention modules (CBAMs), are designed to focus on reciprocal features within CSI observations and to compensate for temporal variations caused by asynchronous observations. The shared encoder and the correspondingly designed loss function further concentrate the autoencoder’s reciprocity enhancement capability into the encoders. MINE is integrated into the proposed MIAE to estimate the mutual information between the channel features of the two parties. This estimation is then used to formulate a mutual information loss, which guides the encoders to learn channel features that closely match the optimal distribution, thereby boosting the key generation rate. Furthermore, a complete SKG scheme is designed based on the proposed channel feature extractor, MIAE. Simulation results show that our proposed MIAE can extract channel features with strong reciprocity and thus achieve excellent SKG performance based on the extracted features. The generalization performance of the proposed MIAE architecture for communication scenarios of different scales has also been examined. © 2002-2012 IEEE.

关键词： autoencoder channel feature extraction mutual information physical layer security secret key generation

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A convolutional autoencoder framework for ECG signal analysis

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Heliyon 2025年第2期11卷 e41517页

作者： Lomoio, Ugo Vizza, Patrizia Giancotti, Raffaele Petrolo, Salvatore Flesca, Sergio Boccuto, Fabiola Guzzi, Pietro Hiram Veltri, Pierangelo Tradigo, Giuseppe Department of Surgical and Medical Sciences University of Catanzaro Catanzaro Italy DIMES University of Calabria Rende Italy SMARTEST Laboratory E-Campus University Novedrate Italy Division of Cardiology Department of Surgical and Medical Sciences University of Catanzaro Catanzaro Italy

Electrocardiographic (ECG) signals are used to evaluate heart activity and to identify disease-related anomalies. Reliable support systems are useful for analyzing ECG signals, for instance, in long-term data acquisition and evaluation (e.g., 24-hour holter recording) or to support physicians in reading ECGs. Analysis of time varying signals may be done by using autoencoders (AEs) deep neural networks. AE specialized for signal data, named Convolutional autoencoder (CAE), showed the best performances in the analysis of ECG signals. This paper presents a CAE-based framework for ECG signal analysis and anomaly identification. The trained phase is performed on synthetic data signals. The trained neural network obtained is used for the detection of anomalies in ECG signals. The trained framework has been tested on 12 lead ECG signals on a benchmark dataset and applied in scenarios where anomalies are related to cardiological risks and pathologies. The results show interesting results in automatic anomaly detection to support physicians in the decision process. The results show that the CAE-based framework is able to identify anomalies in ECG signals with a ROC AUC of 97.82% on simulated test set and a ROC AUC of 99.75% using a real test set. Finally, the proposed method has been enriched by means of reconstruction error based explainability modules and time-windows based preprocessing modules. Explainability results have been validated using abnormalities annotated by a cardiologist as ground truth and compared with explainations results. System with both code and data, is available at https://***/UgoLomoio/ECG_DSS_CAE. © 2024 The Author(s)

关键词： Anomaly detection autoencoder Decision support systems ECG Signal annotation

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autoencoder Quasi-Recurrent Neural Networks for Remaining Useful Life Prediction of Engineering Systems

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第2期27卷 1081-1092页

作者： Cheng, Yiwei Hu, Kui Wu, Jun Zhu, Haiping Shao, Xinyu Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan 430074 Peoples R China

Remaining useful life (RUL) prediction is a key solution to improve the reliability, availability, and maintainability of engineering systems. Long short-term memory (LSTM) and convolution neural networks (CNN) are the current hotspots in the field of RUL prediction. However, the LSTM-based prognostic approach has a slow loop step to process large-scale time-series data since the dependence of the data processing process at each time on the output of the previous time limits parallelism, and the CNN-based prognostic approach is not fit for time-series data although it can process the data in parallel. In this article, a new autoencoder quasi-recurrent neural networks (AEQRNN) based prognostic approach is proposed for RUL prediction of the engineering systems. The AEQRNN contains convolution components that can process input data in parallel, and pooling components which has two LSTM-like gate structures to process time-series data. In addition, the AEQRNN can automatically extract hidden features from monitoring signals without manual feature design. The effectiveness of the proposed prognostic approach is validated by three prognostic benchmarking datasets, including a turbofan engine dataset, a rolling bearing dataset, and a machining tool dataset. Experimental results demonstrate that this approach has both superior prognostic performance and training speed in comparison with other kinds of recurrent-neural-network-based approaches and various state-of-the-art approaches in the recent literature.

关键词： Feature extraction Predictive models Convolution Training Engines Monitoring Logic gates autoencoder deep learning (DL) multisensor data quasi-recurrent neural networks (QRNN) remaining useful life (RUL)

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autoencoder-based self-supervised hashing for cross-modal retrieval

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第11期80卷 17257-17274页

作者： Li, Yifan Wang, Xuan Cui, Lei Zhang, Jiajia Huang, Chengkai Luo, Xuan Qi, Shuhan Harbin Inst Technol Shenzhen Comp Sci & Technol Shenzhen Peoples R China

Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal retrieval are twofold: the heterogeneous gap in different modalities and the semantic gap among similar data with various modalities. To address these issues, we adopt self-supervised fashion to bridge the heterogeneous gap by generating the cohesive features of different instances. To mitigate the semantic gap, we use triplet sampling to optimize the semantic loss in inter-modal and intra-modal, which increase the discriminability of our approach. Experimental on two benchmark datasets show the efficiency and robustness of our method, and the extended experiments show the scalability.

关键词： Cross-modal retrieval Hash learning autoencoder Self-supervised

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autoencoder With Emotion Embedding for Speech Emotion Recognition

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IEEE ACCESS 2021年 9卷 51231-51241页

作者： Zhang, Chenghao Xue, Lei Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years. However, although a wide diversity of methods has been proposed in SER, these approaches still cannot improve the performance. A key issue in the low performance of the SER system is how to effectively extract emotion-oriented features. In this paper, we propose a novel algorithm, an autoencoder with emotion embedding, to extract deep emotion features. Unlike many previous works, instance normalization, which is a common technique in the style transfer field, is introduced into our model rather than batch normalization. Furthermore, the emotion embedding path in our method can lead the autoencoder to efficiently learn a priori knowledge from the label. It can enable the model to distinguish which features are most related to human emotion. We concatenate the latent representation learned by the autoencoder and acoustic features obtained by the openSMILE toolkit. Finally, the concatenated feature vector is utilized for emotion classification. To improve the generalization of our method, a simple data augmentation approach is applied. Two publicly available and highly popular databases, IEMOCAP and EMODB, are chosen to evaluate our method. Experimental results demonstrate that the proposed model achieves significant performance improvement compared to other speech emotion recognition systems.

关键词： Feature extraction Speech recognition Emotion recognition Spectrogram Noise reduction Hidden Markov models Acoustics Speech emotion recognition autoencoder emotion embedding instance normalization

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autoencoder-Combined Generative Adversarial Networks for Synthetic Image Data Generation and Detection of Jellyfish Swarm

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IEEE ACCESS 2018年 6卷 54207-54214页

作者： Kim, Kyukwang Myung, Hyun Korea Adv Inst Sci & Technol Urban Robot Lab Daejeon 34141 South Korea

Image-based sensing of jellyfish is important as they can cause great damage to the fisheries and seaside facilities and need to be properly controlled. In this paper, we present a deep-learning-based technique to generate a synthetic image of the jellyfish easily with autoencoder-combined generative adversarial networks. The proposed system can easily generate simple images with a smaller number of data sets compared with other generative networks. The generated output showed high similarity with the real-image data set. The application using a fully convolutional network and regression network to estimate the size of the jellyfish swarm was also demonstrated, and showed high accuracy during the estimation test.

关键词： autoencoder generative adversarial networks jellyfish swarm fully convolutional network regression

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autoencoder framework based on orthogonal projection constraints improves anomalies detection

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NEUROCOMPUTING 2021年 450卷 372-388页

作者： Yu, Qien Kavitha, Muthusubash Kurita, Takio Hiroshima Univ Dept Informat Engn Higashihiroshima Hiroshima 7398521 Japan Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Hiroshima 7398521 Japan Nagasaki Univ Sch Informat & Data Sci Nagasaki Japan

In this study, we propose a novel autoencoder framework based on orthogonal projection constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets. Orthogonal projection is useful to capture the null subspace that consists of noisy information for AD, which is explicitly ignored in the existing approaches. The exploration of double subspaces, called normal space (NS) and abnormal space (AS) can improve the discriminative manifold information. Therefore, in this study, autoencoder framework based on the OPC learning method is proposed that combines the orthogonal subspace score and the reconstruction error score in the target tasks for AD. To the best of our knowledge, this is the first study that introduces an autoencoder-based model with two orthogonal subspaces for AD. Through the orthogonality, the anomaly-free data and abnormalnnosiy information are projected into the NS and the AS, respectively. Thus, it potentially addresses the problem of the distribution of generative model by combining the abilities of two subspaces that can appropriately learn the features and establish a strict boundaries around the normal data. For image datasets, we propose a convolutional autoencoder based on OPC. Additionally, the generalization and adaptability of the proposed method in AD was investigated using vector datasets by implementing a fully-connected layer-based OPC in the encoder-decoder structure. The effectiveness of the proposed framework for AD was evaluated through the comparison with state-of-the-art approaches. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Orthogonal projection autoencoder Anomaly detection Subspace detection

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autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第7期48卷 1065-1079页

作者： Yang, Yimin Wu, Q. M. Jonathan Wang, Yaonan Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feed-forward neural networks, provides efficient unified learning solutions for the applications of regression and classification. Although, it provides promising performance and robustness and has been used for various applications, the single-layer architecture possibly lacks the effectiveness when applied for natural signals. In order to over come this shortcoming, the following work indicates a new architecture based on multilayer network framework. The significant contribution of this paper are as follows: 1) unlike existing multilayer ELM, in which hidden nodes are obtained randomly, in this paper all hidden layers with invertible functions are calculated by pulling the network output back and putting it into hidden layers. Thus, the feature learning is enriched by additional information, which results in better performance;2) in contrast to the existing multilayer network methods, which are usually efficient for classification applications, the proposed architecture is implemented for dimension reduction and image reconstruction;and 3) unlike other iterative learning-based deep networks (DL), the hidden layers of the proposed method are obtained via four steps. Therefore, it has much better learning efficiency than DL. Experimental results on 33 datasets indicate that, in comparison to the other existing dimension reduction techniques, the proposed method performs competitively better with fast training speeds.

关键词： autoencoder deep learning (DL) dimension reduction extreme learning machine (ELM) feature selection generalization performance

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