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Analysis and classification of arrhythmia types using improved firefly optimization algorithm and autoencoder model

MULTIAGENT AND GRID SYSTEMS 2023年第1期19卷 43-60页

作者： Sinnoor, Mala Janardhan, Shanthi Kaliyil Dr Ambedkar Inst Technol Dept Elect & Commun Bengaluru India Dr Ambedkar Inst Technol Dept Med Elect Bengaluru India

In the present scenario, Electrocardiogram (ECG) is an effective non-invasive clinical tool, which reveals the functionality and rhythm of the heart. The non-stationary nature of ECG signal, noise existence, and heartbeat abnormality makes it difficult for clinicians to diagnose arrhythmia. The most of the existing models concentrate only on classification accuracy. In this manuscript, an automated model is introduced that concentrates on arrhythmia type classification using ECG signals, and also focuses on computational complexity and time. After collecting the signals from the MIT-BIH database, the signal transformation and decomposition are performed by Multiscale Local Polynomial Transform (MLPT) and Ensemble Empirical Mode Decomposition (EEMD). The decomposed ECG signals are given to the feature extraction phase for extracting features. The feature extraction phase includes six techniques: standard deviation, zero crossing rate, mean curve length, Hjorth parameters, mean Teager energy, and log energy entropy. Next, the feature dimensionality reduction and arrhythmia classification are performed utilizing the improved Firefly Optimization Algorithm and autoencoder. The selection of optimal feature vectors by the improved Firefly Optimization Algorithm reduces the computational complexity to linear and consumes computational time of 18.23 seconds. The improved Firefly Optimization Algorithm and autoencoder model achieved 98.96% of accuracy in the arrhythmia type classification, which is higher than the comparative models.

关键词： Arrhythmia classification autoencoder electrocardiogram (ECG) ensemble empirical mode decomposition firefly optimization algorithm multiscale local polynomial transform

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Batik Image Retrieval using Convolutional autoencoder 14

Batik Image Retrieval using Convolutional Autoencoder

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14th Symposium on Computer Applications & Industrial Electronics (ISCAIE)

作者： Minarno, Agus Eko Soesanti, Indah Nugroho, Hanung Adi Univ Gadjah Mada Dept Elect & Informat Engn Fac Engn Yogyakarta Indonesia Univ Muhammadiyah Fac Engn Yogyakarta Indonesia

ISBN: (纸本)9798350348798;9798350348804

Image retrieval systems have long been a cornerstone of multimedia databases, aiming to extract relevant images from vast datasets. Batik, an intricate traditional art form originating from Indonesia, presents unique challenges in this domain. Its diverse patterns and rich history have led to a broad array of designs, making the retrieval of specific Batik images a formidable task. This research paper introduces a novel approach that employs a convolutional autoencoder to address the challenges of Batik image retrieval. The underlying problem we address is the difficulty in obtaining high precision results from conventional image retrieval systems when dealing with the detailed patterns of Batik. Our methodology hinges on a convolutional autoencoder, a deep learning model adept at extracting pivotal features from images and using them for various tasks, including image retrieval. The effectiveness of our method was evaluated using the Batik Nitik dataset, a comprehensive collection of 960 images representing a wide range of Batik designs. The outcomes were promising. Our proposed method achieved a remarkable precision of 0.98, a testament to its capability in accurately retrieving relevant Batik images. Furthermore, this research not only demonstrates the potential of convolutional autoencoders in the realm of image retrieval but also offers a solution tailored to the unique intricacies of Batik. In conclusion, the convolutional autoencoder presents a groundbreaking approach to Batik image retrieval, merging traditional art with modern deep learning techniques to ensure accuracy and relevance in results.

关键词： Batik pattern autoencoder deep learning pre-trained image retrieval

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LATENT ENHANCING autoencoder FOR OCCLUDED IMAGE CLASSIFICATION 31

LATENT ENHANCING AUTOENCODER FOR OCCLUDED IMAGE CLASSIFICATI...

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2024 International Conference on Image Processing

作者： Kotwal, Ketan Deshmukh, Tanay Gopal, Preeti Idiap Res Inst Martigny Switzerland

ISBN: (纸本)9798350349405;9798350349399

Large occlusions result in a significant decline in image classification accuracy. During inference, diverse types of unseen occlusions introduce out-of-distribution data to the classification model, leading to accuracy dropping as low as 50%. As occlusions encompass spatially connected regions, conventional methods involving feature reconstruction are inadequate for enhancing classification performance. We introduce LEARN: Latent Enhancing feAture Reconstruction Network- An auto-encoder based network that can be incorporated into the classification model before its classifier head without modifying the weights of classification model. In addition to reconstruction and classification losses, training of LEARN effectively combines intra- and inter-class losses calculated over its latent space-which lead to improvement in recovering latent space of occluded data, while preserving its class-specific discriminative information. On the OccludedPASCAL3D+ dataset, the proposed LEARN outperforms standard classification models (VGG16 and ResNet-50) by a large margin and up to 2% over state-of-the-art methods. In cross-dataset testing, our method improves the average classification accuracy by more than 5% over the state-of-the-art methods. In every experiment, our model consistently maintains excellent accuracy on in-distribution data.

关键词： Image Classification Occlusion Handling Out-of-distribution autoencoder Latent Enhancement

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Muscle Fatigue Identification Using a Time Frequency Deep autoencoder 11th

Muscle Fatigue Identification Using a Time Frequency Deep Au...

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11th International Conference on E-Health and Bioengineering (EHB)

作者： Leung, Eunice Johns Hopkins Univ Calgary AB Canada

ISBN: (纸本)9783031625015;9783031625022

This paper proposes a novel unsupervised method for muscle fatigue recognition. It uses a convolutional autoencoder to extract time-frequency EMG features and clusters the features into fatigue and non-fatigue groups. Experimental results show that the proposed method is more effective in discriminating muscle fatigue compared to conventional approaches. In addition, the clusters provide an effective way to determine a threshold for identifying muscle fatigue.

关键词： autoencoder Clustering EMG Signals Muscle Fatigue Time Frequency Feature

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Channel Reciprocity Based Attack Detection for Securing UWB Ranging by autoencoder

Channel Reciprocity Based Attack Detection for Securing UWB ...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Gou, Wenlong Yu, Chuanhang Ma, Juntao Wu, Gang Mordachev, Vladimir Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu Peoples R China Belarusian State Univ Informat & Radioelect Minsk BELARUS

ISBN: (纸本)9798350378412

A variety of ranging threats represented by Ghost Peak attack have raised concerns regarding the security performance of Ultra-Wide Band (UWB) systems with the finalization of the IEEE 802.15.4z standard. Based on channel reciprocity, this paper proposes a low complexity attack detection scheme that compares Channel Impulse Response (CIR) features of both ranging sides utilizing an autoencoder with the capability of data compression and feature extraction. Taking Ghost Peak attack as an example, this paper demonstrates the effectiveness, feasibility and generalizability of the proposed attack detection scheme through simulation and experimental validation. The proposed scheme achieves an attack detection success rate of over 99% and can be implemented in current systems at low cost.

关键词： Ultra-Wideband channel reciprocity Channel Impulse Response autoencoder

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Health Prediction Method for Photovoltaic Inverters Based on autoencoder and Fusion of Multiple Operating Conditions 24

Health Prediction Method for Photovoltaic Inverters Based on...

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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Wang, Can Yuan, Wei Shen, Xiaokun Xu, Yue Gu, Yue Longyuan Beijing Wind Power Engn Technol Co Ltd Beijing 100034 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Shaanxi 710126 Peoples R China

ISBN: (纸本)9798400716638

Photovoltaic inverter health prediction is a crucial aspect of the reliability and performance maintenance of photovoltaic power generation systems. With the rapid development of solar energy, the inverter, as one of the core components of photovoltaic power generation systems, plays a vital role in ensuring the effective conversion of energy. Traditional methods for predicting the health of photovoltaic inverters involve simple weighted summation of device-generated data or basic classification assessments. These approaches often lack precision in predicting device health. This paper proposes a data-driven health prediction method that integrates operational environment data from photovoltaic inverters with performance data during operation. Different autoencoders are trained as environmental benchmark models based on various working conditions. Real-time operational data is input into the health model to generate health scores reflecting the device's condition. Experimental results demonstrate that the constructed health model effectively fits the dataset and accurately assesses the operating status of photovoltaic inverters. By enabling real-time health assessment and prompt maintenance actions, this method provides an effective guarantee for increasing photovoltaic power generation efficiency, potentially significantly reducing maintenance costs, and enhancing system reliability and maintainability. This, in turn, contributes significantly to the sustainable development of renewable energy in the field.

关键词： Photovoltaic inverter autoencoder Health prediction Multiple operating conditions

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Assessment of abdominal rehabilitation for diastasis recti abdominis using ensemble autoencoder

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PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2023年第4期89卷 891-901页

作者： Radhakrishnan, Menaka Premkumar, Vinitha Joshy Guddimettla, Mohan Teja Choudhury, Sanjucta Basu, Siddharth Gupta, Satvik Vellore Inst Technol Ctr Cyber Phys Syst CCPS Sch Elect Engn Chennai 600127 India Vellore Inst Technol Sch Elect Engn Chennai 600127 India

Women experience major bodily changes both during pregnancy and post-pregnancy. Diastasis Recti Abdominis (DRA) is a noticeable issue in the postpartum period among the female population in the world. Though postnatal fitness has gained attention in the recent decade, there is scarce knowledge of the abnormal condition called DRA and its consequences. In the presence of an abnormality, women feel less energetic in their daily activities and may experience fatigue in the abdominal muscles. The physical way of regaining strength in core abdominal muscles includes rehabilitation through exercises prescribed by physiotherapists. The sit-up and curl-up exercises engage the core abdominal muscles and when practiced regularly can bring back the separated recti muscles together in time. In order to bring this practice unsupervised by the physicians and monitor the pace of exercises by the patient individually, wearable Inertial measurement unit (IMU) sensors were employed. The utilization of IMU wearable sensors for DRA has been sparsely explored in literature. In this study, two groups of subjects with DRA perform the rehabilitation exercises and respective inertial measurements were observed. When the situation goes unsupervised, the effective contraction of the abdominal recti muscles and the correctness of exercises were uncertain. It's a well-known fact that deep learning algorithms aid in determining the significant features thereby making the unsupervised classification problem more efficient. Here in this study an ensembled autoencoder neural network is implemented in which the IMU datasets were employed for the classification of correct and incorrect exercises. The latent vector generated in the autoencoder model encapsulates the inherent patterns of the input by undertaking all occurrences into a latent space. Thereby in this work, the reconstruction error generated from the autoencoder network is used to determine the correct and incorrect exercise. The ensemble

关键词： Diastasis recti abdominis Inertial measurement sensors Ensemble autoencoder Latent vector

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Linear local tangent space alignment with autoencoder

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COMPLEX & INTELLIGENT SYSTEMS 2023年第6期9卷 6255-6268页

作者： Ran, Ruisheng Wang, Jinping Fang, Bin Chongqing Normal Univ Coll Comp & Informat Sci Coll Intelligent Sci Chongqing 401331 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Linear local tangent space alignment (LLTSA) is a classical dimensionality reduction method based on manifold. However, LLTSA and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively "represent" the original samples. This paper proposes a novel LLTSA method based on the linear autoencoder called LLTSA-AE (LLTSA with autoencoder). The proposed LLTSA-AE is divided into two stages. The conventional process of LLTSA is viewed as the encoding stage, and the additional and important decoding stage is used to reconstruct the original data. Thus, LLTSA-AE makes the low-dimensional embedding data "represent" the original data more accurately and effectively. LLTSA-AE gets the recognition rates of 85.10, 67.45, 75.40 and 86.67% on handwritten Alphadigits, FERET, Georgia Tech. and Yale datasets, which are 9.4, 14.03, 7.35 and 12.39% higher than that of the original LLTSA respectively. Compared with some improved methods of LLTSA, it also obtains better performance. For example, on Handwritten Alphadigits dataset, compared with ALLTSA, OLLTSA, PLLTSA and WLLTSA, the recognition rates of LLTSA-AE are improved by 4.77, 3.96, 7.8 and 8.6% respectively. It shows that LLTSA-AE is an effective dimensionality reduction method.

关键词： Linear local tangent space alignment autoencoder Dimensionality reduction Manifold learning

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Development of deep autoencoder-based anomaly detection system for HANARO

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第2期55卷 475-483页

作者： Ryu, Seunghyoung Jeon, Byoungil Seo, Hogeon Lee, Minwoo Shin, Jin-Won Yu, Yonggyun Korea Atom Energy Res Inst Artificial Intelligence Applicat & Strategy Team 111 Daedeok daero 989 beon gil Daejeon South Korea Korea Atom Energy Res Inst HANARO Management Div 111 Daedeok daero 989 beon gil Daejeon South Korea Univ Sci & Technol Nucl & Radiat Safety 217 Gajeong ro Daejeon South Korea

The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system;simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model.(c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Deep learning autoencoder Anomaly detection Nuclear reactor Research reactor

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A Bi-population Cooperative Optimization Algorithm Assisted by an autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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