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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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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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Modeling Learner Memory Based on LSTM autoencoder and Collaborative Filtering 25th

Modeling Learner Memory Based on LSTM Autoencoder and Collab...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Li, Tengju Bian, Cunling Wang, Ning Xie, Yangbin Chen, Kaiquan Lu, Weigang Ocean Univ China Dept Educ Technol Qingdao 266100 Peoples R China Ocean Univ China Coll Comp Sci & Technol Qingdao 266100 Peoples R China Zhejiang Lab Res Ctr Data Hub & Secur Hangzhou 311100 Peoples R China

ISBN: (纸本)9783031643118;9783031643125

Memory modeling, aimed at predicting the memory states of learners throughout their learning process, has become an integral component in online learning systems. This is particularly crucial for applications such as spaced repetition scheduling and knowledge tracking. However, existing machine learning-based memory modeling methods encounter challenges with weak supervision signals and sparse interaction data. To tackle these issues, we propose a novel approach named LSTM autoencoder Collaborative Filtering (LACF) for modeling learner memory. Our model utilizes an LSTM autoencoder to extract temporal features from user-item interaction sequences. Additionally, a collaborative filtering module, based on the deepFM architecture, is employed to effectively learn interactions between different features, facilitating comprehensive low-order and high-order feature combinations. Experimental results demonstrate that LACF significantly outperforms existing state-of-the-art memory modeling methods, offering enhanced accuracy and precision in predictions.

关键词： Memory modeling autoencoder Collaborative filtering Time series prediction

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Lossless DNA Sequence Compression: Exploring Spatial and Sequential Redundancies of DNA Sequence with 2-D CNN autoencoder 10

Lossless DNA Sequence Compression: Exploring Spatial and Seq...

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10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Sheena, K. S. Nair, Madhu S. Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 Kerala India

ISBN: (纸本)9798350385939;9798350385922

The volume of genomic data being generated is growing due to the technological breakthroughs in genome sequencing and the economic affordability of sequencing. Consequently, the need for compression techniques that are specifically designed for genomic data is rising. Deep learning provides robust methods for reducing the data size and unlocking the full potential of genomic information. The proposed method minimizes storage requirements by applying a 2-D convolutional neural network autoencoder, which learns spatial and sequential redundancies in the sequence to compress the data losslessly. In contrast to conventional compression methods, which handle data as a one-dimensional sequence, the proposed approach uses the spatial structure of the data to produce more compact representations. We evaluated the proposed method with various compression approaches, including state-of-the-art DNA sequence compression algorithms, on two homo sapiens genomes. Experimental results indicate that the proposed method can compress the genomic data effectively, with an improvement of 31.3% compression over the best existing method.

关键词： DNA sequence Deep Learning autoencoder FASTA Lossless Compression

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MULTI-MODAL INTERMEDIATE FEATURE INTERACTION autoencoder FOR OVERALL SURVIVAL PREDICTION OF ESOPHAGEAL SQUAMOUS CELL CANCER 21

MULTI-MODAL INTERMEDIATE FEATURE INTERACTION AUTOENCODER FOR...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Wu, Chengyu Zhang, Yatao Wang, Yaqi Wang, Qifeng Wang, Shuai Shandong Univ Dept Mech Elect & Informat Engn Shandong Peoples R China Commun Univ Zhejiang Coll Media Engn Hangzhou Peoples R China Univ Elect Sci & Technol China Sch Med Sichuan Canc Hosp & InstDept Radiat Oncol Radiat Oncol Key Lab Sichuan ProvSichuan Canc Ct Chengdu Peoples R China Hangzhou Dianzi Univ Sch Cyberspace Hangzhou Peoples R China Shandong Univ Suzhou Res Inst Suzhou Peoples R China

ISBN: (纸本)9798350313345;9798350313338

Survival prediction for esophageal squamous cell cancer (ESCC) is crucial for doctors to assess a patient's condition and tailor treatment plans. The application and development of multi-modal deep learning in this field have attracted attention in recent years. However, the prognostically relevant features between cross-modalities have not been further explored in previous studies, which could hinder the performance of the model. Furthermore, the inherent semantic gap between different modal feature representations is also ignored. In this work, we propose a novel autoencoder-based deep learning model to predict the overall survival of the ESCC. Two novel modules were designed for multi-modal prognosis-related feature reinforcement and modeling ability enhancement. In addition, a novel joint loss was proposed to make the multi-modal feature representations more aligned. Comparison and ablation experiments demonstrated that our model can achieve satisfactory results in terms of discriminative ability, risk stratification, and the effectiveness of the proposed modules.

关键词： Survival Prediction Deep Learning Multi-Modal autoencoder Transformer

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