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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhang, Deft Wang, Kangli Gao, Jianfeng Che, Xiuming Tianjin Univ Technol Maritime Coll Tianjin Peoples R China Northern Nav Serv Ctr Tianjin AtoN Div Tianjin Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

In order to improve the intelligent energy efficiency management of ships, evaluate the fuel utilization efficiency of marine diesel engine. In this paper, a fuel consumption model of marine diesel engine based on autoencoder and deep neural network is established, and the autoencoder is used to perform nonlinear dimensionality reduction on the data to obtain more valuable data features, thereby improving the accuracy of the model. The model is verified and compared using the sailing parameters, environmental parameters and fuel consumption of the actual ship during normal sailing. The accuracy rate of the model established in this paper reaches 95.19%, and the results show that the model in this paper can meet the prediction and evaluation analysis of the energy consumption of the marine diesel engine.

关键词： Ship Energy Efficiency Deep Neural Network autoencoder

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autoencoder-based Data Compression Model Experiment for Semantic Communication 38

Autoencoder-based Data Compression Model Experiment for Sema...

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38th International Conference on Information Networking (ICOIN)

作者： Oh, Jinyoung Choi, Yunkyung Oh, Chanyoung Na, Woongsoo Kongju Natl Univ Dept Software Cheonan South Korea

ISBN: (纸本)9798350330946;9798350330953

In recent years, the convergence of neural network architectures and semantic communication has led to innovative strides in representation learning. This paper explores the application of autoencoders, a subset of neural networks designed for unsupervised learning, in encoding and decoding data to capture semantic nuances. Additionally, we discuss future research directions, proposing a semantic communication model learned from time series data and presenting experimental results. Our experiments involve encoding and decoding time series data using autoencoders, evaluating the feasibility of integrating autoencoder technology into semantic communication. Following the experiment, our proposed model exhibited a loss rate of approximately 0.15-0.2% for the time series data. This outcome represents a notable result, especially when compared to the compression rate of transmission data. It hints at the potential for a future autoencoder-based semantic communication model.

关键词： Semantic communication autoencoder encoding/decoding data compression

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autoencoder-based outlier detection for sparse, high dimensional data 8

Autoencoder-based outlier detection for sparse, high dimensi...

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8th IEEE International Conference on Big Data (Big Data)

作者： Chen, Wanghu Li, Huijun Li, Jing Arshad, Ali Northwest Normal Univ Inst Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9781728162515

Outlier detection is essential in many data mining tasks. For high-dimensional data, its outlier detection often faces two challenges caused by sparse spatial distribution of data and big difficulties to get enough class labels. Therefore, it is valuable to explore a simpler and more effective approach to unsupervised outlier detection. In this paper, focusing on high-dimensional sparse data, an unsupervised outlier detection approach based on autoencoders and Robust PCA is proposed. Because Robust PAC has greater advantages in feature extraction of high-dimensional data and autoencoder has powerful capabilities in the reconstruction of normal data, the proposed approach can effectively address the two problems concerned above. The proposed approach is compared with some representative approaches, including ABOD, KNN, LOF, SOS and SOD, on eight well-known public datasets. The experiments show that compared with them, the proposed approach has advantages in both precision and recall rate, and can more accurately distinguish between normal data and outliers.

关键词： high-dimensional data outlier detection autoencoder Robust PCA

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Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第2期5卷 025006页

作者： Massegur, David Da Ronch, Andrea Univ Southampton Fac Engn & Phys Sci Southampton SO16 7QF England

Calculating aerodynamic loads around an aircraft using computational fluid dynamics is a user's and computer-intensive task. An attractive alternative is to leverage neural networks (NNs) bypassing the need of solving the governing fluid equations at all flight conditions of interest. NNs have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph NNs which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The test case is for the NASA common research model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal having used an existing database.

关键词： geometric deep learning autoencoder CFD aerodynamics graph convolutional network multi-mesh scheme

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Face Aging With Boundary Equilibrium Conditional autoencoder

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IEEE ACCESS 2018年 6卷 54834-54843页

作者： Chen, Longxiang Hu, Xiaolong Zhang, Zuping Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

Since generative adversarial networks (GANs) were proposed in 2014, mode collapse has been a problem that affects many researchers when training GANs. With the reconstruction loss of an autoencoder, conditional adversarial autoencoder (CAAE) is free from mode collapse. However, its reconstruction loss will bring a saturation problem, in which the encoder maps every input image into just one latent variable. Combining the CAAE with a boundary equilibrium generative adversarial network, we propose a boundary equilibrium conditional autoencoder (BECAE) focusing on the face aging task. Our model is the first GANs that renders images through a discriminator. We also introduce some statistics to measure the level of the saturation problem. The results show that the BECAE has successfully solved the saturation problem and can generate face images of the same quality as the images generated by the CAAE.

关键词： Face aging generative adversarial networks boundary equilibrium autoencoder non-convergence saturation problem

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Adversarially Approximated autoencoder for Image Generation and Manipulation

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IEEE TRANSACTIONS ON MULTIMEDIA 2019年第9期21卷 2387-2396页

作者： Xu, Wenju Keshmiri, Shawn Wang, Guanghui Univ Kansas Dept Aerosp Engn Lawrence KS 66045 USA Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA

Regularized autoencoders learn the latent codes, a structure with the regularization under the distribution, which enables them the capability to infer the latent codes given observations and generate new samples given the codes. However, they are sometimes ambiguous as they tend to produce reconstructions that are not necessarily a faithful reproduction of the inputs. The main reason is to enforce the learned latent code distribution to match a prior distribution while the true distribution remains unknown. To improve the reconstruction quality and learn the latent space a manifold structure, this paper presents a novel approach using the adversarially approximated autoencoder (AAAE) to investigate the latent codes with adversarial approximation. Instead of regularizing the latent codes by penalizing on the distance between the distributions of the model and the target, AAAE learns the autoencoder flexibly and approximates the latent space with a simpler generator. The ratio is estimated using a generative adversarial network to enforce the similarity of the distributions. In addition, the image space is regularized with an additional adversarial regularizer. The proposed approach unifies two deep generative models for both latent space inference and diverse generation. The learning scheme is realized without regularization on the latent codes, which also encourages faithful reconstruction. Extensive validation experiments on four real-world datasets demonstrate the superior performance of AAAE. In comparison to the state-of-the-art approaches, AAAE generates samples with better quality and shares the properties of a regularized autoencoder with a nice latent manifold structure.

关键词： autoencoder generative adversarial network adversarial approximation faithful reconstruction latent manifold structure

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Innovations autoencoder and its Application in One-class Anomalous Sequence Detection

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-27页

作者： Wang, Xinyi Tong, Lang Cornell Univ Dept Elect & Comp Engn Ithaca NY 14850 USA

An innovations sequence of a time series is a sequence of independent and identically distributed random variables with which the original time series has a causal representation. The innovation at a time is statistically independent of the history of the time series. As such, it represents the new information contained at present but not in the past. Because of its simple probability structure, the innovations sequence is the most efficient signature of the original. Unlike the principle or independent component representations, an innovations sequence preserves not only the complete statistical properties but also the temporal order of the original time series. An long-standing open problem is to find a computationally tractable way to extract an innovations sequence of non-Gaussian processes. This paper presents a deep learning approach, referred to as Innovations autoencoder (IAE), that extracts innovations sequences using a causal convolutional neural network. An application of IAE to the one-class anomalous sequence detection problem with unknown anomaly and anomaly-free models is also presented.

关键词： Innovations sequence Generative adversary networks autoencoder Out-of-distribution detection Non-parametric anomaly detection

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Multimodal Imputation-Based Multimodal autoencoder Framework for AQI Classification and Prediction of Indian Cities

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IEEE ACCESS 2024年 12卷 108350-108363页

作者： Rao, Routhu Srinivasa Kalabarige, Lakshmana Rao Holla, M. Raviraja Sahu, Aditya Kumar Gandhi Inst Technol & Management Dept Comp Sci & Engn Visakhapatnam 530045 Andhra Pradesh India GMRIT Dept Comp Sci & Engn AI Res Lab Rajam 532127 India Manipal Acad Higher Educ MAHE Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Amrita Vishwa Vidyapeetham Amrita Sch Comp Amaravati 522503 Andhra Pradesh India

Rising urbanization necessitates robust air quality monitoring and prediction systems, particularly in developing nations like India, to mitigate adverse health impacts. Previous research primarily focused on machine learning algorithms for Air Quality Index (AQI) prediction and classification. We propose a novel MI-MMA-XGB which coupled features of multimodal imputer(MI) with the features of multi-modal autoencoder (MMA) and fed to an XGBoost(XGB) algorithm for AQI prediction and classification. Moreover, imputation approaches namely, KNN, MICE, and SVD were employed to address problems with null values and outliers. Furthermore, SMOTE is employed to balance the imputed data and then the model was trained on both balanced and unbalanced imputed data to extract predictive features. In this process, our model MI-MMA-XGB achieves significant accuracy, reaching 97.14% and 93.87% with and without SMOTE, respectively. Additionally, it attains an R-2 score of 0.9578 and an RMSE of 0.203 for AQI prediction in Indian cities. The proposed model outperforms baseline models in both classification and regression tasks across various evaluation metrics.

关键词： AQI India environmental sustainability PM2.5 PM10 imputation autoencoder

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Temporal Transaction Scraping Assisted Point of Compromise Detection With autoencoder Based Feature Engineering

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IEEE ACCESS 2021年 9卷 109536-109547页

作者： Ogme, Fuat Yavuz, A. Gokhan Guvensan, M. Amac Karsligil, M. Elif Yildiz Tech Univ Intelligent Syst Lab Dept Comp Engn TR-34220 Istanbul Turkey

Credit card fraudsters exploit various methods to capture card information. One of the common methods is to duplicate the credit cards by skimming. In this study, we introduce a new point of compromise detection method in order to trace and identify merchants where the skimming operation took place and card information has been captured by criminals. The proposed method first extracts discriminative features by using principle component analysis(PCA) and autoencoder extractors and then it clusters similar fraudulent transactions with K-Means algorithm, afterwards it highlights possible merchants that are involved in this scheme by finding matching merchants in the produced clusters with a retrospective analysis of all transactions. Our experiments showed that the proposed method could achieve promising results with zero-knowledge on the existing skimming points. The application of our proposed method on real-life card transactions enabled us to pinpoint 7 out of 9 point of compromise previously identified by the reporting bank.

关键词： Credit cards Feature extraction Patents Deep learning Business Bibliographies Unsupervised learning Financial fraud point of compromise detection credit card skimming clustering autoencoder retrospective analysis

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Convolutional variational autoencoder and multi-scale attention convolutional neural network based diagnostics on filament current sensors for mass spectrometers

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Li, Xinshuo Zhou, Wenxing Yin, Jiancheng Zhang, Zhen Huang, Gang Sheng, Yunlong Li, Pinghua Zhuang, Xuye Shandong Univ Technol Mech Engn Sch Zibo 255049 Shandong Peoples R China China Astronaut Res & Training Ctr Beijing 100094 Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn Xian 710049 Shaanxi Peoples R China

Fault detection of the filament current sensor of the spaceborne mass spectrometer is of great significance to the safe operation of the mass spectrometer. Neglecting sensor failures may affect the normal operation of the mass spectrometer, which in turn affects the health of the astronauts and the space missions. This paper proposes an enhanced intelligent diagnosis method based on filament current sensor for mass spectrometer via improved deep learning network, aiming to improve the accuracy and reliability of the filament current sensor when the fault samples are insufficient. The improved convolutional variational autoencoder (CVAE) model not only enhances the data for four typical sensor fault signals, namely bias, drift, missing and random, but also solves the problem of insufficient samples when spike, Precision degradation and stuck fault occur in sensors. Short-time Fourier transform (STFT) is utilized to transform one-dimensional data into two-dimensional spectrograms, which gives more fault characteristics to the data set. The improved multi-scale attention mechanism convolutional neural network (MSAM-CNN) model is constructed to perform fault diagnosis on the generated spectrogram, which solves the problem of low accuracy of fault identification in traditional convolutional neural network (CNN) models. The results of ablation and comparison experiments show that the samples generated by CVAE have smaller Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), the enhanced samples improve the classification and clustering of MSAM-CNN, and compared to other diagnostic models, MSAM-CNN achieves the highest accuracy, precision, recall, and F1-score of 99.2%, 99.3%, 98.8%, and 0.991.

关键词： Filament current sensor Data enhancement Convolutional variational autoencoder Fault diagnosis Multi-scale attention mechanism Convolutional neural network

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