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Atypical Sample Regularizer autoencoder for Cross-Domain Human Activity Recognition

INFORMATION SYSTEMS FRONTIERS 2021年第1期23卷 71-80页

作者： Prabono, Aria Ghora Yahya, Bernardo Nugroho Lee, Seok-Lyong Hankuk Univ Foreign Studies 81 Oedae Ro Yongin 17035 Gyeonggi Do South Korea

The sensor-based human activity recognition (HAR) using machine learning requires a sufficiently large amount of annotated data to realize an accurate classification model. This requirement stimulates the advancement of the transfer learning research area that minimizes the use of labeled data by transferring knowledge from the existing activity recognition domain. Existing approaches transform the data into a common subspace between domains which theoretically loses information, to begin with. Besides, they are based on the linear projection which is bound to linearity assumption and its limitations. Some recent works have already incorporated nonlinearity to find a latent representation that minimizes domain discrepancy based on an autoencoder that includes statistical distance minimization. However, such approach discovers latent representation for both domains at once, which causes sub-optimal representation because both domains compensate each other's reconstruction error during the training. We propose an autoencoder-based approach on domain adaptation for sensor-based HAR. The proposed approach learns a latent representation which minimizes the discrepancy between domains by reducing statistical distance. Instead of learning representation of both domains simultaneously, our method is a two-phase approach which first learns the representation for the domain of interest independently to ensure its optimality. Subsequently, the useful information from the existing domain is transferred. We test our approach on the publicly available sensor-based HAR datasets, using cross-domain setup. The experimental result shows that our approach significantly outperforms the existing ones.

关键词： Transfer learning autoencoder Human activity recognition Machine learning

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Deep autoencoder for false positive reduction in handgun detection

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NEURAL COMPUTING & APPLICATIONS 2021年第11期33卷 5885-5895页

作者： Vallez, Noelia Velasco-Mata, Alberto Deniz, Oscar Univ Castilla La Mancha ETSI Ind VISILAB Ave Camilo Jose Cela SN Ciudad Real 13071 Spain

In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is run over the test set. However, this typically translates into an unacceptable rate of false alarms when the system is deployed in a real surveillance scenario. To deal with this situation, which often leads to system shutdown, we propose to add a filter step to discard part of the new false positive detections that are typical of the new scenario. This step consists of a deep autoencoder trained with the false alarm detections generated after running the detector over a period of time in the new scenario. Therefore, this step will be in charge of determining whether the detection is a typical false alarm of that scenario or whether it is something anomalous for the autoencoder and, therefore, a true detection. In order to decide whether a detection must be filtered, three different approaches have been tested. The first one uses the autoencoder reconstruction error measured with the mean squared error to make the decision. The other two use thek-NN (k-nearest neighbors) and one-class SVMs (support vector machines) classifiers trained with the autoencoder vector representation. In addition, a synthetic scenario has been generated with Unreal Engine 4 to test the proposed methods in addition to a dataset with real images. The results obtained show a reduction in the number of false positives between 22.5% and 87.2% and an increase in the system's precision of 1.2%-47% when the autoencoder is applied.

关键词： Handgun detection False positive reduction autoencoder One-class classification

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Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: autoencoder and Bayesian Neural Network Approaches

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2024年第3期71卷 354-365页

作者： Tehrani, Ali K. Z. Cloutier, Guy Tang, An Rosado-Mendez, Ivan M. Rivaz, Hassan Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Univ Montreal Dept Radiol Radiooncol & Nucl Med Montreal PQ H2L 2W5 Canada Univ Montreal Inst Biomed Engn Montreal PQ H2L 2W5 Canada Univ Wisconsin Dept Med Phys Madison WI 53792 USA

Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QU Sparameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution(HK-distribution) is one of the most comprehensive distributions that can model US backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter (alpha) and the ratio of the coherent to diffuse scattering power (k) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic US. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input tosuch algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this article, we follow two approaches to reduce the effect of sample features'error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. The network weight and a demo code are available online at ***://***.

关键词： autoencoder deep learning (DL) homodyned K-distribution quantitative ultrasound (QUS)

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Classification of data on stacked autoencoder using modified sigmoid activation function

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1-18页

作者： Kumar, Arvind Sodhi, Sartaj Singh Comp Sci & Engn USICT GGSIPU Delhi India

A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.

关键词： autoencoder sigmoid activation function logsigmoid neural network classification stacked autoencoder

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Deep denoising autoencoder for seismic random noise attenuation

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GEOPHYSICS 2020年第4期85卷 V367-V376页

作者： Saad, Omar M. Chen, Yangkang Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang Hangzhou 310027 Zhejiang Peoples R China NRIAG ENSN Lab Seismol Dept Helwan 11731 Egypt

Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE). In this approach, the time-series seismic data are used as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, and then it decodes those levels to reconstruct the seismic signal without noise. The DDAE is pretrained in a supervised way using synthetic data;following this, the pretrained model is used to denoise the field data set in an unsupervised scheme using a new customized loss function. We have assessed the proposed algorithm based on four synthetic data sets and two field examples, and we compare the results with several benchmark algorithms, such as f-x deconvolution (f-x deconv) and the f-x singular spectrum analysis (f-x SSA). As a result, our algorithm succeeds in attenuating the random noise in an effective manner.

关键词： deep learning autoencoder random noise attenuation deep denoising autoencoder local similarity

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Opinion fraud detection via neural autoencoder decision forest

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PATTERN RECOGNITION LETTERS 2020年 132卷 21-29页

作者： Dong, Manqing Yao, Lina Wang, Xianzhi Benatallah, Boualem Huang, Chaoran Ning, Xiaodong Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods. (c) 2018 Elsevier B.V. All rights reserved.

关键词： autoencoder Neural decision forest Opinion fraud detection

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autoencoder and Extreme Value Theory Based Unknown Intrusion Detection System 31

Autoencoder and Extreme Value Theory Based Unknown Intrusion...

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31st IEEE Conference on Signal Processing and Communications Applications (SIU)

作者： Ates, Pelin Damla Ates, Cagatay Koca, Mutlu Anarim, Emin Bogazici Univ Elekt & Elekt Muhendisligi Bolumu Istanbul Turkiye

ISBN: (纸本)9798350343557

Controlling the events occurring in the network traffic and detecting malicious activities are of great importance for the security and sustainability of the system. For this reason, it is necessary to accurately detect different types of attacks that may occur in network traffic. On the other hand, it is very important from the security aspect to be able to distinguish the types of attacks that have not been seen before. In this paper, a two-step procedure is proposed that can both correctly classify known attack types and distinguish unknown attack types. In the first stage, incoming traffic is classified by supervised learning with an autoencoder. In the second stage, the reliability of this classification is checked with the help of the autoencoder and the Extreme Value Theorem (EVT). According to the reliability value, incoming traffic is classified as unknown class. The simulation results were obtained with the IDS 2017 data set, which is widely used in the literature.

关键词： Intrusion detection autoencoder network security extreme value theory

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An Energy-Efficient Data Collection Scheme Using Denoising autoencoder in Wireless Sensor Networks

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Tsinghua Science and Technology 2019年第1期24卷 86-96页

作者： Guorui Li Sancheng Peng Cong Wang Jianwei Niu Ying Yuan School of Computer Science and Engineering Northeastern University School of Information Science and Technology Guangdong University of Foreign Studies State Key Laboratory of Virtual Reality Technology and Systems Beihang University

As one of the key operations in Wireless Sensor Networks(WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data is usually chosen empirically, and the transformed results are not always the sparsest. In this paper, we propose a Data Collection scheme based on Denoising autoencoder(DCDA) to solve the above problem. In the data training phase, a Denoising autoencoder(DAE) is trained to compute the data measurement matrix and the data reconstruction matrix using the historical sensed data. Then, in the data collection phase, the sensed data of whole network are collected along a data collection tree. The data measurement matrix is utilized to compress the sensed data in each sensor node, and the data reconstruction matrix is utilized to reconstruct the original data in the ***, the data communication performance and data reconstruction performance of the proposed scheme are evaluated and compared with those of existing schemes using real-world sensed data. The experimental results show that compared to its counterparts, the proposed scheme results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed.

关键词： wireless sensor networks data collection neural networks autoencoder data reconstruction

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autoencoder and Deep Neural Network based Energy Consumption Analysis of Marine Diesel Engine 19

Autoencoder and Deep Neural Network based Energy Consumption...

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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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User-Centric Online Gossip Training for autoencoder-Based CSI Feedback

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 559-572页

作者： Guo, Jiajia Zuo, Yiping Wen, Chao-Kai Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

Recently, the autoencoder framework has shown great potential in reducing the feedback overhead of the downlink channel state information (CSI). In this work, we further find that the user equipment in practical systems occasionally moves in a relatively stable area for a long time, and the corresponding communication environment is relatively stable. A user-centric online training strategy is proposed to further improve CSI feedback performance using the above characteristics. The key idea of the proposed method is to train a new encoder for a specific area without changes to the decoder at the base station. Given that the CSI training samples are insufficient, two data augmentation strategies, including random erasing and random phase shift, are introduced to improve the neural network generalization. In addition, the proposed user-centric online training framework is extended to the multi-user scenario for considerable performance improvement via gossip learning, which is a fully decentralized distributed learning framework and can use crowd intelligence. The simulation results show that the proposed user-centric online gossip training offers a more substantial increase in the feedback accuracy and can considerably improve autoencoder generalization.

关键词： Training Artificial neural networks Downlink Decoding Correlation Computer architecture 5G mobile communication autoencoder CSI feedback gossip learning user-centric online training

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