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Journal articles on the topic 'Convolutional Auto-Encoder'

Author: Grafiati

Published: 6 September 2023

Last updated: 25 July 2025

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1

Song, Xiaona, Haichao Liu, Lijun Wang, et al. "A Semantic Segmentation Method for Road Environment Images Based on Hybrid Convolutional Auto-Encoder." Traitement du Signal 39, no. 4 (2022): 1235–45. http://dx.doi.org/10.18280/ts.390416.

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Deep convolutional neural networks (CNNs) have presented amazing performance in the task of semantic segmentation. However, the network model is complex, the training time is prolonged, the semantic segmentation accuracy is not high and the real-time performance is not good, so it is difficult to be directly used in the semantic segmentation of road environment images of autonomous vehicles. As one of the three models of deep learning, the auto-encoder (AE) has powerful data learning and feature extracting capabilities from the raw data itself. In this study, the network architecture of auto-e

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Theunissen, Carl Daniel, Steven Martin Bradshaw, Lidia Auret, and Tobias Muller Louw. "One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study." Minerals 11, no. 10 (2021): 1106. http://dx.doi.org/10.3390/min11101106.

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Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. Reconstruction-based data-driven models are trained to reconstruct the characteristic patterns of hazardous event-preceding process data with minimal residuals, facilitating effective event prediction based on reconstruction residuals. This investigation evaluated one-dimens

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Kim, Dong-Hoon, JoonWhoan Lee, and #VALUE! #VALUE! "Music Mood recognition using Convolutional Variation Auto Encoder." Journal of Korean Institute of Intelligent Systems 29, no. 5 (2019): 352–58. http://dx.doi.org/10.5391/jkiis.2019.29.5.352.

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Yasukawa, Shinsuke, Sreeraman Raghura, Yuya Nishida, and Kazuo Ishii. "Underwater image reconstruction using convolutional auto-encoder." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 262–65. http://dx.doi.org/10.5954/icarob.2021.os23-4.

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Zhao, Wei, Zuchen Jia, Xiaosong Wei, and Hai Wang. "An FPGA Implementation of a Convolutional Auto-Encoder." Applied Sciences 8, no. 4 (2018): 504. http://dx.doi.org/10.3390/app8040504.

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Li, Hongfei, Lili Meng, Jia Zhang, Yanyan Tan, Yuwei Ren, and Huaxiang Zhang. "Multiple Description Coding Based on Convolutional Auto-Encoder." IEEE Access 7 (2019): 26013–21. http://dx.doi.org/10.1109/access.2019.2900498.

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AIRCC. "AUTO ENCODER CONVOLUTIONAL NEURAL NETWORK FOR PNEUMONIA DETECTION." International Journal of Artificial Intelligence & Applications (IJAIA) 15, no. 5 (2024): 21–32. https://doi.org/10.5121/ijaia.2024.15502.

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This study presents an innovative approach utilising Autoencoder Convolutional Neural Networks (AECNNs) for pneumonia detection in paediatric chest x-rays. The research addresses the complexity ofpneumonia, considering diverse causative agents, including bacteria, viruses, and aspiration. AutoencoderConvolutional Neural Networks are employed to enhance anomaly detection by revealing hidden patternsin the data. The evaluation process involves meticulous analysis of the histogram reconstruction error,leading to the establishment of a threshold for anomaly identification. The results demonstrate

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Newlin, Dev R., and C. Seldev Christopher. "De-noising of Natural Images with Better Enhancement Using Convolutional Auto-Encoder." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (2019): 124–36. http://dx.doi.org/10.5373/jardcs/v11i12/20193221.

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Zhu, Yi, Lei Li, and Xindong Wu. "Stacked Convolutional Sparse Auto-Encoders for Representation Learning." ACM Transactions on Knowledge Discovery from Data 15, no. 2 (2021): 1–21. http://dx.doi.org/10.1145/3434767.

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Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolut

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Zhou, Jian, Xianwei Wei, Chunling Cheng, Qidong Yang, and Qun Li. "Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder." International Journal of Computational Intelligence Systems 12, no. 1 (2019): 351. http://dx.doi.org/10.2991/ijcis.2019.125905651.

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Oh, Junghyun, and Beomhee Lee. "Condition-invariant Place Recognition Using Deep Convolutional Auto-encoder." Journal of Korea Robotics Society 14, no. 1 (2019): 8–13. http://dx.doi.org/10.7746/jkros.2019.14.1.008.

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Yang, Haoyu, Shuhe Li, Wen Chen, et al. "DeePattern: Layout Pattern Generation With Transforming Convolutional Auto-Encoder." IEEE Transactions on Semiconductor Manufacturing 35, no. 1 (2022): 67–77. http://dx.doi.org/10.1109/tsm.2021.3139354.

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Zhang Xiu, 张. 秀., 周. 巍. Zhou Wei, 段哲民 Duan Zhemin, and 魏恒璐 Wei Henglu. "Convolutional sparse auto-encoder for image super-resolution reconstruction." Infrared and Laser Engineering 48, no. 1 (2019): 126005. http://dx.doi.org/10.3788/irla201948.0126005.

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K. Oyedotun, Oyebade, and Kamil Dimililer. "Pattern Recognition: Invariance Learning in Convolutional Auto Encoder Network." International Journal of Image, Graphics and Signal Processing 8, no. 3 (2016): 19–27. http://dx.doi.org/10.5815/ijigsp.2016.03.03.

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Dzulqarnain, Muhammad Faqih, Abdul Fadlil, and Imam Riadi. "Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder." Compiler 13, no. 2 (2024): 123. https://doi.org/10.28989/compiler.v13i2.2649.

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This research investigates the development of model deep convolutional autoencoders to enhance the classification of digital batik images. The dataset used was sourced from Kaggle. The autoencoder was employed to enrich the image data prior to convolutional processing. By forcing the autoencoder to learn a lower-dimensional latent representation that captures the most salient features of the batik patterns. The performance of this enhanced model was compared against a standard convolutional neural network (CNN) without the autoencoder. Experimental results demonstrate that the incorporation of

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Lu, Yang, Shujuan Yi, Yurong Liu, and Yuling Ji. "A novel path planning method for biomimetic robot based on deep learning." Assembly Automation 36, no. 2 (2016): 186–91. http://dx.doi.org/10.1108/aa-11-2015-108.

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Purpose This paper aims to design a multi-layer convolutional neural network (CNN) to solve biomimetic robot path planning problem. Design/methodology/approach At first, the convolution kernel with different scales can be obtained by using the sparse auto encoder training algorithm; the parameter of the hidden layer is a series of convolutional kernel, and the authors use these kernels to extract first-layer features. Then, the authors get the second-layer features through the max-pooling operators, which improve the invariance of the features. Finally, the authors use fully connected layers o

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Lou, Shuting, Jiarui Deng, and Shanxiang Lyu. "Chaotic signal denoising based on simplified convolutional denoising auto-encoder." Chaos, Solitons & Fractals 161 (August 2022): 112333. http://dx.doi.org/10.1016/j.chaos.2022.112333.

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18

Wu, Pin, Siquan Gong, Kaikai Pan, Feng Qiu, Weibing Feng, and Christopher Pain. "Reduced order model using convolutional auto-encoder with self-attention." Physics of Fluids 33, no. 7 (2021): 077107. http://dx.doi.org/10.1063/5.0051155.

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19

Shi, Yanxin, Jinrong He, Zhaokui Li, and Zhigao Zeng. "Hyperspectral image classification model based on 3D convolutional auto-encoder." Journal of Image and Graphics 26, no. 8 (2021): 2021–36. http://dx.doi.org/10.11834/jig.210146.

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Zhang, Zhihong, Dongdong Chen, Zeli Wang, Heng Li, Lu Bai, and Edwin R. Hancock. "Depth-based subgraph convolutional auto-encoder for network representation learning." Pattern Recognition 90 (June 2019): 363–76. http://dx.doi.org/10.1016/j.patcog.2019.01.045.

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21

Zhou, Yuan, Yeda Zhang, Xukai Xie, and Sun-Yuan Kung. "Image super-resolution based on dense convolutional auto-encoder blocks." Neurocomputing 423 (January 2021): 98–109. http://dx.doi.org/10.1016/j.neucom.2020.09.049.

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22

Qiang, Zhenping, Libo He, Fei Dai, Qinghui Zhang, and Junqiu Li. "Image Inpainting Based on Improved Deep Convolutional Auto-encoder Network." Chinese Journal of Electronics 29, no. 6 (2020): 1074–84. http://dx.doi.org/10.1049/cje.2020.09.008.

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Subramaniam, Sudha, K. B. Jayanthi, C. Rajasekaran, and C. Sunder. "Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks." Journal of Medical Imaging and Health Informatics 11, no. 10 (2021): 2546–57. http://dx.doi.org/10.1166/jmihi.2021.3841.

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Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size of the kernel as well as the

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Zhong, Zhidan, Hao Liu, Wentao Mao, Xinghui Xie, and Yunhao Cui. "Rolling Bearing Fault Diagnosis across Operating Conditions Based on Unsupervised Domain Adaptation." Lubricants 11, no. 9 (2023): 383. http://dx.doi.org/10.3390/lubricants11090383.

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In practical industrial scenarios, mechanical equipment frequently operates within dynamic working conditions. To address the challenge posed by the incongruent data distribution between source and target domains amidst varying operational contexts, particularly in the absence of labels within the target domain, this study presents a solution involving deep feature construction and an unsupervised domain adaptation strategy for rolling bearing fault diagnosis across varying working conditions. The proposed methodology commences by subjecting the original vibration signal of the bearing to a fa

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Shi, Han, Haozheng Fan, and James T. Kwok. "Effective Decoding in Graph Auto-Encoder Using Triadic Closure." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 906–13. http://dx.doi.org/10.1609/aaai.v34i01.5437.

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The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph convolutional network, the decoder reconstructs the graph structure by only considering two nodes at a time, thus ignoring possible interactions among edges. On the other hand, structured prediction, which considers the whole graph simultaneously, is computationally expensive. In this paper, we utilize the well-known triadic closure property which is exhibited in many real-world networks. We propose the triad decoder, w

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26

Swapna.C. "Conv2D-LSTM-AE-GAN: Convolutional 2D LSTM Auto Encoder Generative Adversarial Network." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 792–806. https://doi.org/10.52783/jisem.v10i14s.2396.

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Surveillance video refers to video footage captured by cameras for the purpose of monitoring and recording activities in specific environments. These videos are commonly used for security purposes in places such as airports, shopping malls, streets, industrial facilities, hospitals, and other public or private spaces. The primary objective of surveillance video systems is to maintain safety, detect suspicious activities, and collect evidence for investigation. Anomaly detection in Surveillance video is an important and evolving field with applications across various industries. It involves ana

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Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 25–31. http://dx.doi.org/10.5194/isprsannals-iii-7-25-2016.

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Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window

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Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 25–31. http://dx.doi.org/10.5194/isprs-annals-iii-7-25-2016.

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Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window

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Kollias, Georgios, Vasileios Kalantzis, Tsuyoshi Ide, Aurélie Lozano, and Naoki Abe. "Directed Graph Auto-Encoders." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7211–19. http://dx.doi.org/10.1609/aaai.v36i7.20682.

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We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns pairs of interpretable latent representations for the nodes of directed graphs, and uses parameterized graph convolutional network (GCN) layers for its encoder and an asymmetric inner product decoder. Parameters in the encoder control the weighting of representations exchanged between neighboring nodes. We demonstrate the ability of the proposed model to learn meaningful latent embeddings and achieve superior perform

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Archana, D. "Brain Tumor Detection Using Convolution Neural Networks." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46974.

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ABSTRACT Early diagnosis of brain tumors is important for improving patient prognoses; however, traditional diagnostic methods like biopsies require invasive surgical procedures. In this paper, we introduce two deep learning-based methods—a new two-dimensional Convolutional Neural Network (CNN) and a convolutional auto-encoder network—that enable the accurate classification of brain tumors from magnetic resonance imaging (MRI). A data set of 7,000 T1-weighted contrast-enhanced MRI images was utilized, including glioma, meningioma, pituitary gland tumor, and normal brain samples. Preprocessing

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Li, Bingyu, Lei Wang, Qiaoyong Jiang, Wei Li, and Rong Huang. "Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network." Applied Sciences 13, no. 13 (2023): 7839. http://dx.doi.org/10.3390/app13137839.

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In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes, this paper proposes an operational risk prediction model based on an automatic encoder and convolutional neural networks. First, we use an automatic encoder to extract features of motion risk factors and obtain feature components that can highly represent risk. Secondly, based on the causal relationship between sports risk and risk characteristics,

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Lu, Yingjing. "The Level Weighted Structural Similarity Loss: A Step Away from MSE." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9989–90. http://dx.doi.org/10.1609/aaai.v33i01.33019989.

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The Mean Square Error (MSE) has shown its strength when applied in deep generative models such as Auto-Encoders to model reconstruction loss. However, in image domain especially, the limitation of MSE is obvious: it assumes pixel independence and ignores spatial relationships of samples. This contradicts most architectures of Auto-Encoders which use convolutional layers to extract spatial dependent features. We base on the structural similarity metric (SSIM) and propose a novel level weighted structural similarity (LWSSIM) loss for convolutional Auto-Encoders. Experiments on common datasets on

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Xingkang, ZHOU, and YU Jianbo. "Gearbox Fault Diagnosis Based on One-dimension Residual Convolutional Auto-encoder." Journal of Mechanical Engineering 56, no. 7 (2020): 96. http://dx.doi.org/10.3901/jme.2020.07.096.

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Gunjali, Drakshaveni, and Prasad Naik Hansavath. "Improvised convolutional auto encoder for thyroid nodule image enhancement and segmentation." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 342. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp342-351.

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Thyroid <span>ultrasonography and thermography are a widely used clinical technique for nodule diagnosis in thyroid regions. However, it remains difficult to detect and recognize the nodules due to low contrast, high noise, and diverse appearance of nodules. To alleviate doctors’ tremendous labor in the diagnosis procedure, we advocate a machine learning approach to the detection and recognition tasks in this paper. Moreover, this research mainly focuses on segmenting the image and finding the probable region. In this research work an improvised convolutional auto encoder (ICAE) is intro

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KISHIMOTO, Takuya, Nobutada FUJII, Ruriko WATANABE, et al. "Disease Strain Detection Method of Farm Products Using Convolutional Auto Encoder." Proceedings of Design & Systems Conference 2021.31 (2021): 3406. http://dx.doi.org/10.1299/jsmedsd.2021.31.3406.

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Memarzadeh, Milad, Bryan Matthews, and Ilya Avrekh. "Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder." Aerospace 7, no. 8 (2020): 115. http://dx.doi.org/10.3390/aerospace7080115.

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The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This is in part due the airlines, manufacturers, FAA, and research institutions all continually working to improve the safety of the operations. However, the current approach for identifying vulnerabilities in NAS operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition.

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Yan, Wenjie, Dong Wang, Mengjing Cao, and Jing Liu. "Deep Auto Encoder Model With Convolutional Text Networks for Video Recommendation." IEEE Access 7 (2019): 40333–46. http://dx.doi.org/10.1109/access.2019.2905534.

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Alsubai, Shtwai, Muhammad Umer, Nisreen Innab, Stavros Shiaeles, and Michele Nappi. "Multi-scale convolutional auto encoder for anomaly detection in 6G environment." Computers & Industrial Engineering 194 (August 2024): 110396. http://dx.doi.org/10.1016/j.cie.2024.110396.

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Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "Spatial-Spectral Unsupervised Convolutional Sparse Auto-Encoder Classifier for Hyperspectral Imagery." Photogrammetric Engineering & Remote Sensing 83, no. 3 (2017): 195–206. http://dx.doi.org/10.14358/pers.83.3.195.

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Cai, Xi, Suyuan Li, Xinyue Liu, and Guang Han. "Vision-Based Fall Detection With Multi-Task Hourglass Convolutional Auto-Encoder." IEEE Access 8 (2020): 44493–502. http://dx.doi.org/10.1109/access.2020.2978249.

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Wang, Jun, Jian Zhou, Liangding Li, Jiapeng Chi, Feiling Yang, and Dezhi Han. "Deep Feature Based on Convolutional Auto-Encoder for Compact Semantic Hashing." Journal of Physics: Conference Series 1229 (May 2019): 012032. http://dx.doi.org/10.1088/1742-6596/1229/1/012032.

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Nishio, Mizuho, Chihiro Nagashima, Saori Hirabayashi, et al. "Convolutional auto-encoder for image denoising of ultra-low-dose CT." Heliyon 3, no. 8 (2017): e00393. http://dx.doi.org/10.1016/j.heliyon.2017.e00393.

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Wang, Fei, Qiming Ma, Wenhan Liu, et al. "A novel ECG signal compression method using spindle convolutional auto-encoder." Computer Methods and Programs in Biomedicine 175 (July 2019): 139–50. http://dx.doi.org/10.1016/j.cmpb.2019.03.019.

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Zhang, Han, Jiadong Hua, Tong Tong, Tian Zhang, and Jing Lin. "Dispersion compensation of Lamb waves based on a convolutional auto-encoder." Mechanical Systems and Signal Processing 198 (September 2023): 110432. http://dx.doi.org/10.1016/j.ymssp.2023.110432.

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Hao, Cui, Wang Kesheng, Li Yu, Yang Binyuan, and miao Qiang. "A data enlargement strategy for fault classification through a convolutional auto-encoder." MATEC Web of Conferences 255 (2019): 05001. http://dx.doi.org/10.1051/matecconf/201925505001.

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The amount of data is of crucial to the accuracy of fault classification through machine learning techniques. In wind energy harvest industry, due to the shortage of faulty data obtained in real practice, together with ever changing operational conditions, fault detection and evaluation of wind turbine blade problems become intractable through conventional machine learning methods. In this paper, a modified unsupervised learning method, namely a convolutional auto-encoder based data enlargement strategy (ABE) is proposed for wind turbine blade fault classification. Limited simulation results f

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J, Vishwesh, and Raviraj P. "Improved Differential Evolution with Stacked Auto Encoder for EEG Motor Imagery Classification." Indian Journal of Science and Technology 16, no. 6 (2023): 391–400. https://doi.org/10.17485/IJST/v16i6.2076.

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ABSTRACT <strong>Objectives:</strong> To develop an improved version of Differential Evolution (DE) algorithm to overcome the complexity in extracting the features from the Electroencephalogram (EEG) based Brain-Computer Interfaces (BCI) systems; To develop a Stacked Auto Encoder (SAE) for classifying motor imagery signals into left, right, feet and tongue movements, respectively. <strong>Methods:</strong> Improved Differential Evolution Optimization Algorithm (IDEOA) is proposed for the selection of features which is extracted by the hybrid CSP-CNN feature extraction model. Ext

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Tsui, Benjamin, William A. P. Smith, and Gavin Kearney. "Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach." Applied Sciences 10, no. 17 (2020): 5764. http://dx.doi.org/10.3390/app10175764.

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Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high fr

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Gong, Xuejiao, Bo Tang, Ruijin Zhu, Wenlong Liao, and Like Song. "Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder." Energies 13, no. 17 (2020): 4291. http://dx.doi.org/10.3390/en13174291.

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Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvoluti

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Liu, Jie, Qiu Tang, Wei Qiu, Jun Ma, Yuhong Qin, and Biao Sun. "Automatic Power Quality Disturbance Diagnosis Based on Residual Denoising Convolutional Auto-Encoder." Applied Sciences 11, no. 16 (2021): 7637. http://dx.doi.org/10.3390/app11167637.

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With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time–frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated P

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Ayaluri, Mallikarjuna Reddy, Sudheer Reddy K., Srinivasa Reddy Konda, and Sudharshan Reddy Chidirala. "Efficient steganalysis using convolutional auto encoder network to ensure original image quality." PeerJ Computer Science 7 (February 16, 2021): e356. http://dx.doi.org/10.7717/peerj-cs.356.

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Steganalysis is the process of analyzing and predicting the presence of hidden information in images. Steganalysis would be most useful to predict whether the received images contain useful information. However, it is more difficult to predict the hidden information in images which is computationally difficult. In the existing research method, this is resolved by introducing the deep learning approach which attempts to perform steganalysis tasks in effectively. However, this research method does not concentrate the noises present in the images. It might increase the computational overhead wher

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