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International Joint Conference on Rough Sets (IJCRS)

作者： Mei, Qiuyu Yu, Hong Wang, Guoyin Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing Peoples R China Chongqing Univ Posts & Telecommun Key Lab Cyberspace Big Data Intelligent Secur Minist Educ Chongqing Peoples R China

ISBN: (纸本)9783031509582;9783031509599

Wind speed interval prediction is of great significance in power resource scheduling and planning. However, the complex and variable characteristics of wind speed make quality forecasting challenging. In this paper, a novel hybrid model, abbreviated as RSAE-LSTM, for wind speed interval prediction is proposed. The model employs a rough stacked autoencoder (RSAE) and long short-term memory neural network (LSTM). The RSAE initially handles uncertainties and extracts important potential features from the wind speed data. Then, the generated features are utilized as input to the LSTM network to construct the prediction intervals (PIs). Meanwhile, a new loss function is proposed for developing model to construct PIs effectively. The experimental results show that compared with the comparison methods, the proposed method could obtain high-quality PIs and achieve at least a 39% improvement in the coverage width criterion (CWC) index.

关键词： Rough set theory Prediction intervals LSTM autoencoder

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Deep text clustering using stacked autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第8期81卷 10861-10881页

作者： Hosseini, Soodeh Varzaneh, Zahra Asghari Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran Shahid Bahonar Univ Kerman Mahani Math Res Ctr Kerman Iran

Text data is a type of unstructured information, which is easily processed by a human, but it is hard for the computer to understand. Text mining techniques effectively discover meaningful information from text, which has received a great deal of attention in recent years. The aim of this study is to evaluate and analyze the comments and suggestions presented by Barez Iran Company. Barez is an unlabeled dataset. Extracting useful information from unlabeled large textual data by human to manually be very difficult and time consuming. Therefore, in this paper we analyze suggestions presented in Persian using BERTopic modeling for cluster analysis of the dataset. In BERTopic, each document belongs to a topic with a probability distribution. As a result, seven latent topics are found, covering a broad range of issues such as Installation, manufacture, correction, and device. Then we propose a novel deep text clustering based on hybrid of a stacked autoencoder and k-means clustering to organize text documents into meaningful groups for mining information from Barez data in an unsupervised method. Our data clustering has three main steps: 1) Text representation with a new pre-trained BERT model for language understanding called ParsBERT, 2) Text feature extraction based on based on a new architecture of stacked autoencoder to reduce the dimension of data to provide robust features for clustering, 3) Cluster the data by k-means clustering. We employ the Barez dataset to verify our work's effectiveness;Silhouette Score is used to evaluate the resulting clusters with the best value of 0.60 with 3 clusters grouping. Experimental evaluations demonstrate that the proposed algorithm clearly outperforms other clustering methods.

关键词： autoencoder Clustering BERT word embedding Topic modelling

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Interpolation and extrapolation between the magnetic chiral states using autoencoder

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COMPUTER PHYSICS COMMUNICATIONS 2022年 272卷 108244-108244页

作者： Yoon, H. G. Lee, C. Lee, D. B. Park, S. M. Choi, J. W. Kwon, H. Y. Won, C. Kyung Hee Univ Dept Phys Seoul 02447 South Korea Korea Inst Sci & Technol Ctr Spintron Seoul 02792 South Korea

autoencoder, an artificial neural network, was adopted to generate spin structures that interpolate and extrapolate between two distinct magnetic chiral states, the labyrinth structure and the skyrmion structure. We trained the autoencoder using two distinct magnetic chiral structures. Each input data is encoded through a deep learning process into a latent code, a new representation of the information in a reduced dimensional space. We investigated the latent space to acquire information on the structure of the latent code distribution. With the acquired information, we successfully produced various magnetic structures that exhibit plausible properties under various external fields not provided in the training data. The latent codes were modified by two algorithms. The first algorithm utilizes inversion and translation operation in the latent space and the second algorithm uses recursive flow with a modification bias. The first produced structures preserving the chiral structure of original data and the second produced statistically plausible states. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Machine learning Magnetism autoencoder Spin state generation Interpolation Extrapolation

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3-D Poststack Seismic Data Compression With a Deep autoencoder

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Schiavon, Ana Paula Ribeiro, Kevyn Navarro, Joao Paulo Vieira, Marcelo Bernardes Cruz e Silva, Pedro Mario Univ Fed Juiz de Fora UFJF Dept Ciencia Comp BR-36036900 Juiz De Fora Brazil NVIDIA BR-04576020 Sao Paulo Brazil

We approach the problem of 3-D poststack seismic data compression by training a model based on a deep autoencoder. Our network architecture is trained to consider the similarity between 3-D seismic sections drawn from one or multiple seismic volumes. A whole seismic volume is compressed with the latent representations of each of its composing volumetric sections. The goal is to compress the seismic data at very low bit rates with high-quality reconstruction. Our model is suitable for training general compressors from multiple seismic surveys or for specialized compression of a single seismic volume. Results show that our method can compress seismic data with extremely low bit rates, below 0.3 bits-per-voxel (bpv) while yielding peak signal-to-noise ratio (PSNR) values over 40 dB.

关键词： Bit rate Data compression Task analysis Image coding Decoding Convolution Training 3-D poststack data autoencoder deep learning seismic data compression

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Perceptual Loss-Constrained Adversarial autoencoder Networks for Hyperspectral Unmixing

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Zhao, Min Wang, Mou Chen, Jie Rahardja, Susanto Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Recently, the use of a deep autoencoder-based method in blind spectral unmixing has attracted great attention as the method can achieve superior performance. However, most autoencoder-based unmixing methods use non-structured reconstruction loss to train networks, leading to the ignorance of band-to-band-dependent characteristics and fine-grained information. To cope with this issue, we propose a general perceptual loss-constrained adversarial autoencoder network for hyperspectral unmixing. Specifically, the adversarial training process is used to update our framework. The discriminate network is found to be efficient in discovering the discrepancy between the reconstructed pixels and their corresponding ground truth. Moreover, the general perceptual loss is combined with the adversarial loss to further improve the consistency of high-level representations. Ablation studies verify the effectiveness of the proposed components of our framework, and experiments with both synthetic and real data illustrate the superiority of our framework when compared with other competing methods.

关键词： Image reconstruction Generators Hyperspectral imaging Convolutional neural networks Training Decoding Loss measurement autoencoder fine structure generative adversarial network (GAN) hyperspectral unmixing perceptual loss

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autoencoder-enabled eye-tracking data analytics for objective assessment of user preference in humanoid robot appearance design

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Li, Fan Chen, Chun-Hsien Liu, Yisi Chang, Danni Cui, Jian Sourina, Olga Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore Fraunhofer Singapore Singapore Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

Humanoid robots have been extensively utilized in service industries to provide information and product delivery through direct interactions with users. As the design of humanoid robot appearance significantly impacts human-robot interactions, it is crucial to assess user preference towards it. Traditional evaluation tools, such as surveys, field observations, and interviews, are often time-consuming and subjective. Therefore, this study aims to develop a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. We analyze the critical factors influencing user preference from two perspectives: the attributes of robot appearance and users' selective attention distribution. Accordingly, we propose an integrated machine learning method, combining an autoencoder neural network with a support vector machine to handle the collected visual data. This method, named ASVM, extracts several novel indicators from the eye-tracking data via an unsupervised autoencoder neural network and manual entropy analysis. The proposed ASVM achieves an accuracy of 91%, outperforming other classical machine learning methods, including decision tree, naive Bayes, and support vector machine. ASVM can objectively assess user preference towards humanoid robot appearance design with high time resolution. Furthermore, it can enhance humanoid robot design by revealing the visual attention distribution in assessing robot appearance.

关键词： autoencoder Eye -tracking Robot appearance design User preference

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autoencoder-based composite drought indices

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ENVIRONMENTAL RESEARCH LETTERS 2024年第7期19卷 074007页

作者： Lee, Taesam Kong, Yejin Singh, Vijay Lee, Joo-Heon Gyeongsang Natl Univ Dept Civil Engn 501 Jinju Daero Jinju 52828 Gyeongnam South Korea Texas A&M Univ Dept Biol & Agr Engn 321 Scoates Hall2117 TAMU College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil & Environm Engn 321 Scoates Hall2117 TAMU College Stn TX 77843 USA UAE Univ Natl Water & Energy Ctr Al Ain U Arab Emirates Joongbu Univ Dept Civil Engn 201 Daehkak Ro Geumsan Geumsan 32713 Chungnamdo South Korea

Depending on the type, drought events are described using different indices, such as meteorological, agricultural, and hydrological. The use of different indices often causes confusion for making water-related management decisions. One simple summarized index which can describe the different aspects of drought is desired. Several methods have therefore been proposed, especially with the linear combination method which does not adequately describe drought characteristics. Meanwhile, autoencoders, nonlinear transformation in dimensional reduction, have been applied in the deep learning literature. The objective of this study, therefore, was to derive autoencoder-based composite drought indices (ACDIs). First, a basic autoencoder was directly applied as ACDI, illustrating a negative relation with the observed drought indices which was further multiplied by a negative. Also, the hyperbolic tangent function was adopted instead of the sigmoid transfer function due to its higher sensitivity to drought conditions. For better expression of drought indices, positive and unity constraints were applied for weights, denoted as ACDI-C. Further simplification was made as sACDI by excluding the decoding module since it was not necessary. All applied weights of different sites over a country can be unified into one weight, and the same weights were made for all the sites, called as sACDI1. In the context of model evaluation, a comprehensive analysis was undertaken employing metrics as root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficients. The collective findings underscore the superior performance of both the sACDI and sACDI1 models over their counterparts. Notably, these simplified models manifestly diminished RMSE and MAE values, indicating their enhanced predictive capabilities. Of particular note, sACDI1 exhibited a discernibly lower MAE in comparison to alternative models. Further alarm performance metrics was conducted including the false alar

关键词： autoencoder drought index SPI composition reservoir agricultural drought

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Compressing Multiple Sensor Signals for Distributed Sensing by autoencoder Preprocessing 5

Compressing Multiple Sensor Signals for Distributed Sensing ...

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5th International Conference on Control and Robotics (ICCR)

作者： Wang, Junming Shu, Jing Li, Zheng Yu, Hongliu Tong, Raymond Kai-Yu Chinese Univ Hong Kong Dept Biomed Engn Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Surg Shatin Hong Kong Peoples R China Univ Shanghai Sci & Technol Inst Rehabil Engn & Technol Shanghai Peoples R China

ISBN: (纸本)9798350307627

This paper proposes an autoencoder-based approach to effectively extract sensor features by leveraging an autoencoder as a data preprocessing method. The autoencoder constrains the hidden units in a bottleneck structure, resulting in a compressed knowledge representation of sensor readings. In the latent space representation, the encoded data learns and describes the most prominent latent attributes of sensor readings. The algorithm is experimentally validated in a real-world setting, demonstrating its effectiveness in accurately extracting relevant features from sensor data. Nine flexible bending sensors are utilized for posture sensing of a bellow-shaped fluidic elastomer actuator. Compared to previous studies, the results demonstrate that valuable features can be extracted without employing a large dropout rate for overfitting prevention, while maintaining prediction accuracy and reducing the entire sensor signals to half. Additionally, the training time is reduced by 7.2%. By providing a reduced and featured input to the regression neural network, the proposed approach not only prevents overfitting but also alleviates the computational redundancy and complexity brought by an increasing number of sensors.

关键词： autoencoder flexible sensor distributed sensing soft robotics deep learning

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Capsule-based autoencoder Network for Pointcloud Reconstruction 21

Capsule-based Autoencoder Network for Pointcloud Reconstruct...

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IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Hollosi, Janos Ballagi, Aron Pozna, Claudiu Radu Szechenyi Istvan Univ Dept Informat Technol Gyor Hungary Szechenyi Istvan Univ Dept Automat Gyor Hungary

ISBN: (纸本)9798350319866

The theory of capsule networks and the dynamic routing mechanism for capsules was introduced by Geoffrey Hinton and his research team. In this new approach, they tried to solve typical problems of classical convolutional neural networks. For example, that the efficiency of neural networks degrades when a geometric transformation is applied on the input image, or when the data is far away from the training dataset. It became clear early on that capsule networks are state-of-the-art solutions for visual data classification tasks. For other tasks their use is less common and in many cases difficult to apply. For example image segmentation or object detection and localization. The efficiency of the capsule networks theory in the field of pointcloud processing is also an open question. In this work we investigated the pointcloud reconstruction capability of capsule networks. In this approach, three different complexity autoencoder networks was selected. We created a decoder network based on capsules theory, which was fitted to the existing autoencoder networks. The efficiency of the networks was tested using four different datasets. As a result of our work, we show the effectiveness of capsule networks in the field of pointcloud reconstruction compared with the selected autoencoder networks.

关键词： capsule network pointcloud autoencoder reconstruction

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An autoencoder Based ASCII Art Generator 23

An Autoencoder Based ASCII Art Generator

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8th International Conference on Intelligent Information Technology (ICIIT)

作者： Kimura, Masaomi Iqbal, Mohammad Mukhlash, Imam Shibaura Inst Technol Koto Tokyo Japan Inst Teknol Sepuluh Nopember Surabaya East Java Indonesia

ISBN: (纸本)9781450399616

ASCII art is a way to represent an image with character shapes. It is common to carry ASCII art instead of displaying image files on Internet bulletin boards. Multibyte encodings contain various characters that are useful to shape an image. The essential idea required to get ASCII art is to approximate color distribution at the portion of a target image to a character shape. In this study, we make a machine learning model that learns the shapes of characters in a multibyte encoding to convert a partial image of a target image to a font image.

关键词： ASCII art synthesis autoencoder deep learning K-nearest neighborhood

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