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Stock market network based on bi-dimensional histogram and autoencoder

INTELLIGENT DATA ANALYSIS 2022年第3期26卷 723-750页

作者： Choi, Sungyoon Gwak, Dongkyu Song, Jae Wook Chang, Woojin Seoul Natl Univ Dept Ind Engn Seoul 08826 South Korea Hanyang Univ Dept Ind Engn Seoul South Korea Seoul Natl Univ Inst Ind Syst Innovat Seoul South Korea Seoul Natl Univ SNU Inst Res Finance & Econ Seoul South Korea

In this study, we propose a deep learning related framework to analyze S&P500 stocks using bi-dimensional histogram and autoencoder. The bi-dimensional histogram consisting of daily returns of stock price and stock trading volume is plotted for each stock. autoencoder is applied to the bi-dimensional histogram to reduce data dimension and extract meaningful features of a stock. The histogram distance matrix for stocks are made of the extracted features of stocks, and stock market network is built by applying Planar Maximally Filtered Graph(PMFG) algorithm to the histogram distance matrix. The constructed stock market network represents the latent space of bi-dimensional histogram, and network analysis is performed to investigate the structural properties of the stock market. we discover that the structural properties of stock market network are related to the dispersion of bi-dimensional histogram. Also, we confirm that the autoencoder is effective in extracting the latent feature of the bi-dimensional histogram. Portfolios using the features of bi-dimensional histogram network are constructed and their investment performance is evaluated in comparison with other benchmark portfolios. We observe that the portfolio consisting of stocks corresponding to the peripheral nodes of bi-dimensional histogram network shows better investment performance than other benchmark stock portfolios.

关键词： autoencoder complex network dimensionality reduction latent space visualization histogram stock portfolio

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autoencoder-based OFDM for Agricultural Image Transmission 10

Autoencoder-based OFDM for Agricultural Image Transmission

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10th International Conference on Advanced Cloud and Big Data (CBD)

作者： Li, Dongbo Liu, Xiangyu Shao, Yuxuan Sun, Yuchen Cheng, Siyao Liu, Jie Harbin Inst Technol Fac Comp IoT & Ambient Intelligence Res Ctr Harbin Peoples R China Harbin Inst Technol Fac Comp Harbin Peoples R China Harbin Inst Technol Shenzhen Int Res Inst AI Shenzhen Peoples R China

ISBN: (纸本)9798350309713

With the rapid development of the Internet of Things (IoT), smart agricultural puts forward higher demands on the transmission of agricultural big data. This paper proposes an end-to-end learning communication with autoencoderbased orthogonal frequency division multiplexing (OFDM-AE) for agricultural image transmission, which solves the problems of delay, congestion and high complexity caused by the processing method to information of independent modularization for the conventional OFDM. It is proposed to construct AE based on convolutional neural network (CNN) to realize global joint optimization of end-to-end communication system. In this paper, the network architecture of OFDM-AE is designed and trained on massive agricultural image data. We analyze the performance of the proposed OFDM-AE in different signal-to-noise ratio (SNR) cases. The experimental results show that the OFDM-AE can retain the image feature information and has a very advantageous complexity performance compared to the conventional OFDM with various modulation methods.

关键词： Internet of Things big data autoencoder OFDM agricultural image transmission

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Multi-local Collaborative autoencoder

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷 107844-107844页

作者： Chu, Jielei Wang, Hongjun Liu, Jing Gong, Zhiguo Li, Tianrui Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Inst Artificial Intelligence Chengdu 611756 Peoples R China Sichuan Univ Sch Business Chengdu 610065 Sichuan Peoples R China Univ Macau Dept Comp & Informat Sci State Key Lab Internet Things Smart City Macau Peoples R China

The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper presents a Multi-local Collaborative autoencoder (MC-AE), which consists of novel multi local collaborative representation RBM (mcrRBM) and multi-local collaborative representation GRBM (mcrGRBM) models. Here, the Locality Sensitive Hashing (LSH) method is used to divide the input data into multi-local cross blocks which contains multi-local collaborative relationships of the unlabeled data and features since the similar multi-local instances and features of the input data are divided into the same block. In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure. Then, the local hidden features converges on the center of each local collaborative block. Under the collaborative joint influence of each local block, the proposed MC-AE has powerful capability of representation learning for unsupervised clustering. However, our MC-AE model perhaps perform training process for a long time on the large-scale and high-dimensional datasets because more local collaborative blocks are integrate into it. Five most related deep models are compared with our MC-AE. The experimental results show that the proposed MC-AE has more excellent capabilities of collaborative representation and generalization than the contrastive deep models. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Restricted Boltzmann machine autoencoder Deep collaborative representation Feature learning Unsupervised clustering

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Graph Laplacian-Improved Convolutional Residual autoencoder for Unsupervised Human Action and Emotion Recognition

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IEEE ACCESS 2022年 10卷 131128-131143页

作者： Paoletti, Giancarlo Beyan, Cigdem Del Bue, Alessio Fdn Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Res Line Genoa Italy Fdn Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Res Line Genoa Italy Fdn Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Res Line Genoa Italy

This paper presents an unsupervised feature learning approach based on 3D-skeleton data for human action and human discrete emotion recognition. Relying on the time series of skeleton data analysis to perform such tasks is effective and important to preserve the individual's privacy better. Besides, such methods can represent a viable alternative to emotion recognition applications, in which most works use frontal or profile facial images disclosing the subject's appearance. On the other hand, current unsupervised methods are able to encode the high variety of contexts and nature of the data, but often at the expense of a higher model complexity or longer computational time. To lessen these shortcomings, this paper proposes a convolutional residual autoencoder that models the skeletal geometry across the temporal dynamics of the data without relying on computationally expensive recurrent architectures. Our approach also implements a Graph Laplacian Regularization leveraging upon the implicit skeleton joints connectivity, further improving the robustness of the feature embeddings learned without using action or emotion labels. It was validated on large-scale datasets, having variability in the domain, the input skeleton data (e.g. the number of joints, adjacency matrices), and sensor technology. The results show its effectiveness by notably surpassing the performance of the state-of-the-art unsupervised methods while also achieving better recognition scores compared to the several fully supervised approaches. Extensive experimental analysis proves the usefulness of the proposed method under various evaluation protocols with observed higher-quality feature representations, even if when it is trained with fewer data. The results highlight the proposed method's remarkable transfer-ability across various domains, and its faster inference time.

关键词： Action recognition autoencoder emotion recognition full-body movement graph Laplacian skeletal data unsupervised feature learning

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A Graph Attention-Based autoencoder for Critical Path Anomaly Detection in Microservices 15

A Graph Attention-Based Autoencoder for Critical Path Anomal...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Naderi, Mahdi Momeni, Hossein Shahini, Shayan Computer Engineering Department Golestan University Gorgan Iran

ISBN: (纸本)9798331522254

In complex microservice architectures, detecting performance anomalies is a critical challenge for ensuring system stability and efficiency. This study introduces CPAnoGAT (Critical Path Anomaly detection with Graph Attention Network and autoencoder), a novel real-time anomaly detection model leveraging causal graphs and critical path analysis. The model utilizes Graph Attention Networks (GAT) and a critical-path-based edge weighting strategy to focus on crucial system relationships. Features such as operation name, duration, and status codes are embedded as graph nodes, with interrelations modeled for optimal analysis. Experiments on the TrainTicket dataset, a benchmark for microservice architectures, demonstrate that CPAnoGAT outperforms state-of-the-art models such as TraceAnomaly and MultimodalTrace, achieving superior metrics including 99.98% precision, 73.38% recall, and an F1 score of 0.8464. By reducing false positives and enhancing accuracy, CPAnoGAT provides a robust tool for monitoring distributed systems. Future directions include evaluating the model on diverse datasets and integrating real-time anomaly detection in streaming environments. © 2024 IEEE.

关键词： Anomaly Detection autoencoder Critical Path Analysis Distributed Tracing Graph Attention Network Microservice

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Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an autoencoder

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SENSORS 2022年第16期22卷 1401-1425页

作者： Alwan, Nuha A. S. Hussain, Zahir M. Univ Baghdad Coll Engn Baghdad 10017 Iraq Edith Cowan Univ Sch Engn Joondalup WA 6027 Australia

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system.

关键词： deep learning adaptive inverse control robust control nonlinear plant autoencoder sensor control

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Multi-subband fusion algorithm based on autoencoder

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IET SIGNAL PROCESSING 2022年第9期16卷 1071-1084页

作者： Jiang, Yilin Zhang, Liting Li, Li Li, Jinxin Yang, Yaozu Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Dalian Naval Acad Dept Informat Syst Dalian Peoples R China

A novel algorithm for multi-subband signal fusion achieves performance superior to traditional all-pole model, matrix pencil algorithm and deep-neural-network (Deep neural network (DNN)). The method uses a deep-learning autoencoder more fully described as a multi-subband fusion autoencoder (MSFAE). This autoencoder comprises two parts: a multi-subband encoder and a full-band decoder. Full-band echo distance envelopes are used as training data for the full-band autoencoder, to obtain the full-band coding and the full-band decoder. Then, the multi-subband echo distance envelopes are used as training data, and the full-band coding is used as labels, to train the multi-subband encoder. Finally, the multi-subband encoder and the full-band decoder are combined to obtain the MSFAE. The multi-subband distance envelopes are input to the MSFAE to obtain the full-band distance envelopes, improving the radar distance resolution and obtaining high-resolution range profiles. In contrast with the traditional all-pole model and matrix pencil algorithm, the authors' MSFAE directly processes the information in the frequency domain, avoiding the error of pole estimation in the echo domain. In contrast with DNN, the authors' MSFAE needs only multi-subband distance envelopes as input, avoiding noise subband redundancy. The experimental results show that the fusion accuracy of MSFAE is higher than the traditional all-pole model, matrix pencil algorithm and DNN. The MSFAE has superior performance using the fusion method, even at low signal-to-noise ratio.

关键词： autoencoder deep learning HRRP neural network subband fusion

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SILP-autoencoder for face de-occlusion

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NEUROCOMPUTING 2022年第0期485卷 47-56页

作者： Sun, Dengdi Xie, Wandong Ding, Zhuanlian Tang, Jin Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc ICSP Minist Educ Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Comp Hefei 230601 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China

Recognizing faces with partial occlusion is a challenging problem in many real-world applications. Although various methods have been proposed to deal with the facial image de-occlusion tasks, most of them only concern the local features of occluded images, obviously ignoring the global facial expressions and structural prior information. In this paper, we propose a novel end-to-end SILP-autoencoder to effectively restore partial occluded faces. To improve the recovery quality and occlusion removal robustness, our framework mainly consists of two components, Laplacian prior subnetwork, and left-and-right symmetric match module (LR-match module), which preserve the global facial expression features and fully make use of the symmetrical characteristics of facial regions and structures respectively. Based on the above characteristics, a composite loss function is designed to achieve end-to-end training of the entire network. Extensive experiments on the face expression datasets with various shaded areas suggest that our approach achieves superior performance against the state-of-the-art methods. In particular, our method is more useful for facial detail recovery and distortion expression suppression. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Face de-occlusion autoencoder Facial expression

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Sparse autoencoder-based feature extraction from TOF-SIMS image data of human skin structures

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2022年第2期414卷 1177-1186页

作者： Matsuda, Kazuhiro Aoyagi, Satoka Toray Res Ctr Ltd Surface Sci Labs 3-3-7 Sonoyama Otsu Shiga 5208567 Japan Seikei Univ Fac Sci & Technol 3-3-1 Kichijoji Kitamachi Musashino Tokyo 1808633 Japan

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a useful and versatile tool for surface analysis, enabling detailed compositional information to be obtained for the surfaces of diverse samples. Furthermore, in the case of two- or three-dimensional imaging, the measurement sensitivity in the higher molecular weight range can be improved by using a cluster ion source, thus further enriching the TOF-SIMS information. Therefore, appropriate analytical methods are required to interpret this TOF-SIMS data. This study explored the capabilities of a sparse autoencoder, a feature extraction method based on artificial neural networks, to process TOF-SIMS image data. The sparse autoencoder was applied to TOF-SIMS images of human skin keratinocytes to extract the distribution of endogenous intercellular lipids and externally penetrated drugs. The results were compared with those obtained using principal component analysis (PCA) and multivariate curve resolution (MCR), which are conventionally used for extracting features from TOF-SIMS data. This confirmed that the sparse autoencoder matches, and often betters, the feature extraction performance of conventional methods, while also offering greater flexibility.

关键词： TOF-SIMS autoencoder Feature extraction Artificial neural network Bioimaging

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Residual Dense autoencoder Network for Nonlinear Hyperspectral Unmixing

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 5580-5595页

作者： Yang, Xu Chen, Jianguo Wang, Chengbin Chen, Zihao China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China China Univ Geosci Sch Earth Resources Wuhan 430074 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430074 Peoples R China

Hyperspectral unmixing is a popular research topic in hyperspectral processing, aiming at obtaining the ground features contained in the mixed pixels and their proportion. Recently, nonlinear mixing models have received particular attention in hyperspectral decomposition since the linear mixing model cannot suitably apply in the situation that exists in multiple scattering. In this article, we constructed a residual dense autoencoder network (RDAE) for nonlinear hyperspectral unmixing in multiple scattering scenarios. First, an encoder was built based on the residual dense network (RDN) and attention layer. The RDN is employed to characterize multiscale representations, which are further transformed with the attention layer to estimate the abundance maps. Second, we designed a decoder based on the unfolding of a generalized bilinear model to extract endmembers and estimate their second-order scattering interactions. Comparative experiments between the RDAE and six other state-of-the-art methods under synthetic and real hyperspectral datasets demonstrate that the proposed method achieved a better performance in terms of endmember extraction and abundance estimation.

关键词： Hyperspectral imaging Feature extraction Decoding Spatial resolution Scattering Licenses Kernel autoencoder generalized bilinear model (GBM) nonlinear hyperspectral unmixing residual dense network (RDN)

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