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Automatically constructing a health indicator for lithium-ion battery state-of-health estimation via adversarial and compound staked autoencoder

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JOURNAL OF ENERGY STORAGE 2024年 84卷

作者： Cai, Lei Li, Junxin Xu, Xianfeng Jin, Haiyan Meng, Jinhao Wang, Bin Wu, Chunling Yang, Shengxiang Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Changan Univ Sch Energy & Elect Engn Xian 710064 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England Xian Key Lab Intelligent Highway Informat Fus & Co Xian 710064 Peoples R China

Precisely assessing the state of health (SOH) has emerged as a critical approach to ensuring the safety and dependability of lithium -ion batteries. One of the primary issues faced by SOH estimate methods is their susceptibility to the influence of noise in the observed variables. Moreover, we prefer to automatically extract explicit features for data -driven methods in certain circumstances. In light of these considerations, this paper proposes an adversarial and compound stacked autoencoder for automatically constructing the SOH estimation health indicator. The compound stacked autoencoder consists of two parts. The first one is a denoising autoencoder that learns three different denoising behaviors. The second is a feature -extracting autoencoder that employs adversarial learning to improve generalization ability. The experimental results show that the proposed compound stacked autoencoder can not only get explainable explicit features but also can achieve accurate SOH estimation results compared with its rivals. In addition, the results with transfer learning demonstrate that the proposed method not only can provide high generalization ability but also be easily transferred to a new SOH estimation task.

关键词： Lithium-ion battery State of health Explicit feature autoencoder Denoising Feature construction

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A novel unsupervised deep learning approach for vibration-based damage diagnosis using a multi-head self-attention LSTM autoencoder

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MEASUREMENT 2024年 229卷

作者： Ghazimoghadam, Shayan Hosseinzadeh, S. A. A. Islamic Azad Univ Dept Civil Engn Shahrood Branch Shahrood Iran Golestan Univ Fac Engn Dept Civil Engn Gorgan Golestan Iran

While vibration-based structural health monitoring (SHM) has seen advances with unsupervised deep learning, limitations remain in localizing and quantifying structural damage from raw ambient vibration signals. Moreover, attention mechanisms can help identify salient patterns in acceleration response data by capturing temporal relationships. However, integrating attention mechanisms into unsupervised deep learning for vibrationbased damage diagnosis is not widely explored, with no comparative evaluations against standard unsupervised methods. To address these gaps, this study develops a multi-head self-attention long short-term memory autoencoder (MA-LSTM-AE). The proposed algorithm identifies damage by comparing reconstruction error disparities from the trained undamaged state against unknown structural conditions. Through comparative evaluations on two laboratory structures and a full-scale bridge against an LSTM autoencoder (LSTM-AE) and a basic autoencoder (AE), the MA-LSTM-AE demonstrates superior damage diagnosis performance in detecting minor damage from loosened joint bolts and identifying multiple damage locations on the bridge structure. It accurately identifies, localizes, and quantifies various damage scenarios using ambient vibration data. Results provide evidence of multi-head self-attention's potential to enhance unsupervised structural damage diagnosis.

关键词： autoencoder Damage detection Long short-term memory Multi -head attention Structural health monitoring Unsupervised deep learning

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An autoencoder-based self-supervised learning for multimodal sentiment analysis

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INFORMATION SCIENCES 2024年 675卷

作者： Feng, Wenjun Wang, Xin Cao, Donglin Lin, Dazhen Xiamen Univ Dept Artificial Intelligence Xiamen Fujian Peoples R China

Representation learning is a crucial and challenging task within multimodal sentiment analysis. Effective multimodal sentiment representations contain two key aspects: consistency and difference. However, the state-of-the-art multimodal sentiment analysis approaches failed to capture the difference and consistency of sentiment information across diverse modalities. To address the multimodal sentiment representation problem, we propose an autoencoder-based self -supervised learning framework. In the pre -training stage, an autoencoder is designed for each modality, leveraging unlabeled data to learn richer sentiment representations for each modality through sample reconstruction and modality consistency detection tasks. In the fine-tuning stage, the pre -trained autoencoder is injected into MulT (AE -MT) and enhance the model's ability to extract deep sentiment information by incorporating a contrastive learning auxiliary task. Our experiments on the popular Chinese sentiment analysis benchmark (CH-SIMS v2.0) and English sentiment analysis benchmark (MOSEI) demonstrate significant gains over baseline models.

关键词： Multimodal sentiment analysis Self-supervised learning autoencoder Contrastive learning

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A Novel One-Class Convolutional autoencoder Combined With Excitation-Emission Matrix Fluorescence Spectroscopy for Authenticity Identification of Food

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JOURNAL OF CHEMOMETRICS 2024年第11期38卷

作者： Yan, Xiaoqin Jia, Baoshuo Long, Wanjun Huang, Kun Wang, Tong Wu, Hailong Yu, Ruqin Hunan Univ Coll Chem & Chem Engn State Key Lab Chemo Biosensing & Chemometr Changsha Peoples R China South Cent Minzu Univ Sch Pharmaceut Sci Modernizat Engn Technol Res Ctr Ethn Minor Med Hu Wuhan Peoples R China

In this work, a novel one-class classification algorithm one-class convolutional autoencoder (OC-CAE) was proposed for the detection of abnormal samples in the excitation-emission matrix (EEM) fluorescence spectra dataset. The OC-CAE used Boxplot to analyze the reconstruction errors and used the LOF algorithm to handle features extracted by the hidden layer in the convolutional autoencoder (CAE). The fused information provides the basis for more accurate pattern recognition, ensures flexibility in model training, and can obtain higher model specificity, which is important in the field of food quality control. To demonstrate the reliability and advantages of OC-CAE, two EEM cases related to the authentication of food including the Zhenjiang aromatic vinegar (ZAV) case and the camellia oil (CAO) case were studied. The results showed that OC-CAE identified all abnormal samples in the two cases, reflecting excellent performance in the detection of abnormal samples, and that it, coupled with EEM, would be an effective tool for the authenticity identification of food.

关键词： abnormal sample detection autoencoder convolutional excitation-emission matrix fluorescence food fraud one-class classification

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Hyperspectral unmixing of autoencoder based on attention and total variation 2

Hyperspectral unmixing of autoencoder based on attention and...

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2nd International Conference on Optics and Image Processing (ICOIP)

作者： Wang, Ying Zhang, Mingbo Zuo, Fang Henan Univ Inst Intelligence Networks Syst Kaifeng Henan Peoples R China Henan Univ Kaifeng Henan Expt Teaching Demonstrat Ctr Modern Networ Kaifeng Henan Peoples R China Henan Univ Kaifeng Intelligent Data Proc Engn Res Ctr Henan Prov Kaifeng Henan Peoples R China Henan Univ Henan Int Joint Lab Theories Kaifeng Henan Peoples R China Henan Univ Key Technol Intelligence Networks Kaifeng Henan Peoples R China Henan Univ Kaifeng Henan Univ Software Engn Henan Higher Educ Inst Intelligent Informat Proc Subject Innovat & Intelligence Intro Base Kaifeng Henan Peoples R China

ISBN: (纸本)9781510657274;9781510657267

In recent years, methods based on autoencoders (AE) in deep learning have received extensive attention for hyperspectral unmixing. The purpose of hyperspectral unmixing is to estimate terminal members and their respective abundances. This is similar to the learning process of an autoencoder, which is trained to find a set of low-dimensional hidden layers and combine them with their corresponding weights to reduce the reconstruction error. Therefore, AE is well-suited to solving the problem of unsupervised hyperspectral unmixing. Aiming at the problems of being unrobust to noise and the unmixing accuracy to be further improved, this paper proposes a convolutional autoencoder unmixing network (CAA-Net) based on attention mechanism. First, an attention mechanism is introduced to improve the unmixing performance. Then, a total variation regularization term is introduced to exploit spatial information and facilitate piecewise smoothness of abundance maps. The paper conducts experiments on the Samson dataset and Jasper dataset, and compares with other classical methods to obtain higher accuracy.

关键词： Hyperspectral unmixing autoencoder deep learning attention

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On LSTM autoencoder-Based Hybrid Precoding for Reconfigurable Intelligent Surface-Aided Multiuser Millimeter-Wave Massive MIMO 6G Systems 95

On LSTM Autoencoder-Based Hybrid Precoding for Reconfigurabl...

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IEEE 95th Vehicular Technology Conference: (VTC-Spring)

作者： Lu, Yi-Hsien Ou, Kai-Hao Chen, Hong-Yunn Wu, Meng-Hsun Yang, Ta-Wei Tsai, Hsin-Han Chou, Cheng-Fu Natl Taiwan Univ GINM Taipei Taiwan Natl Taiwan Univ CSIE Taipei Taiwan

ISBN: (数字)9781665482431

ISBN: (纸本)9781665482431

The sixth-generation (6G) wireless communication networks are anticipated to combine terrestrial, aerial, and marine communications into a dependable, fast network that could handle many devices with ultra-low latency requirements. Reconfigurable intelligent surfaces (RIS)-aided millimeter-wave massive MIMO communication systems could increase wireless link quality by providing passive beamforming gain via low-cost reflecting components. On the other hand, the power consumption and cost could be reduced much by applying a hybrid precoding architecture, which combines digital and analog precoding modules. However, using RIS to solve the problem of hybrid precoding is difficult because how to figure out reflecting coefficients without beam training overhead or large channel estimation is a challenging issue. In this work, we propose a novel hybrid precoding architecture based on geometric mean decomposition (GMD) and jointly consider the design of the Long Short-Term Memory (LSTM) autoencoder scheme. That is, we adopt GMD for diminishing the computational complexity and enhancing the hybrid precoding performance. Since the encoder-decoder design of autoencoder serves as a dimensionality reduction strategy, the LSTM autoencoder are able to capture the temporal and spatial distribution of the sequential data by using the LSTM models sequential and feature extraction capabilities. Numerical results show that our proposed algorithm could significantly improve the RIS-aided millimeter-wave MIMO communication systems performance compared with previous works.

关键词： sixth-generation (6G) Reconfigurable intelligent surface (RIS) Long Short-Term Memory (LSTM) hybrid precoding autoencoder

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A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked autoencoder

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Computers, Materials & Continua 2021年第9期68卷 3915-3929页

作者： Nojood O.Aljehane College of Computer Science and Information Technology University of TabukTabukSaudi Arabia

Cyber physical systems(CPSs)are a networked system of cyber(computation,communication)and physical(sensors,actuators)elements that interact in a feedback loop with the assistance of human ***,CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart *** utilization of CPSs,however,poses many threats,which may be of major significance for *** security issues in CPSs represent a global issue;therefore,developing a robust,secure,and effective CPS is currently a hot research *** resolve this issue,an intrusion detection system(IDS)can be designed to protect *** the IDS detects an anomaly,it instantly takes the necessary actions to avoid harming the *** this study,we introduce a new parameter-tuned deep-stacked autoencoder based on deep learning(DL),called PT-DSAE,for the IDS in *** proposed model involves preprocessing,feature extraction,parameter tuning,and ***,data preprocessing takes place to eliminate the noise present in the ***,a DL-based DSAE model is applied to detect anomalies in the *** addition,hyperparameter tuning of the DSAE takes place using a search-and-rescue optimization algorithm to tune the parameters of the DSAE,such as the number of hidden layers,batch size,epoch count,and learning *** assess the experimental outcomes of the PT-DSAE model,a series of experiments were performed using data from a sensor-based ***,a detailed comparative analysis was performed to ensure the effective detection outcome of the PT-DSAE *** experimental results obtained verified the superior performance on the applied data over the compared methods.

关键词： Cyberphysical system intrusion detection system autoencoder cybersecurity

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HYPERSPECTRAL UNMIXING USING CONVOLUTIONAL autoencoder FOR METAL DETECTION IN LITHIUM-ION BATTERY RECYCLING APPLICATIONS 12

HYPERSPECTRAL UNMIXING USING CONVOLUTIONAL AUTOENCODER FOR M...

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12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

作者： Chouhan, Seema Rasti, Behnood Ghamisi, Pedram Lorenz, Sandra Fuchs, Margret Gloaguen, Richard Helmholtz Inst Freiberg Resource Technol HIF Helmholtz Zentrum Dresden Rossendorf HZDR Freiberg Germany

ISBN: (数字)9781665470698

ISBN: (纸本)9781665470698

Recent advancements in hyperspectral imaging systems have opened up possibilities for identifying and distinguishing materials based on their spectral characteristics, as every material has its unique spectral signature. In our work, we present a novel approach for detecting and distinguishing copper and aluminum foils present in shredded lithium-ion batteries (LIBs) using convolutional autoencoder for hyperspectral unmixing. In hyperspectral applications, unmixing is a key procedure for estimating spectral signatures of pure materials (endmembers) as well as the corresponding fractional spatial extent (abundances) of endmembers in mixed pixels of hyperspectral images (HSIs). We perform hyperspectral unmixing on a real hyperspectral dataset using a convolutional autoencoder with sparse regularization. We evaluate the performance of the autoencoder framework using VNIR (visible and near-infrared) HSI data acquired with the Specim FX10 hyperspectral sensor. Our experimental unmixing results demonstrate that convolutional autoencoder showed a significant improvement in unmixing performance compared with competing unmixing methods. To the best of our knowledge, this work is the first to implement hyperspectral unmixing using autoencoder in LIB recycling, which is highly significant for automated sorting of valuable metals in LIB recycling industrial applications.

关键词： Hyperspectral unmixing autoencoder Hyperspectral images (HSIs) Lithium-ion batteries (LIBs) Recycling

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A new framework for damage detection of steel frames using burg autoregressive and stacked autoencoder-based deep neural network

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2022年第5期7卷 1-16页

作者： Viet-Linh Tran Vinh Univ Dept Civil Engn Vinh 461010 Vietnam

In civil engineering, monitoring the structural damage becomes critically important to ensure safety and avoid sudden failures of structures. Therefore, improving the accuracy of methods for Structural Health Monitoring problems remains a priority. This paper proposes a new framework that combines the Burg Autoregressive (BAR) and Stacked autoencoder-based Deep Neural Network (SAE-DNN) for the damage detection of steel frames using time-series data. Firstly, features of the time-series data are extracted using the BAR method. Then, the autoencoder (AE) network is employed to reduce the dimension and learn sensitive features. Finally, the AE and Softmax layers are stacked and trained in a supervised manner of DNN for structural damage detection. The experimental data of two steel frame benchmarks are adopted to verify the performance of the proposed framework. The results show that the proposed framework could achieve high accuracy (97.8 and 99%) in the damage identification of steel frames.

关键词： autoencoder Burg autoregressive Damage detection Deep neural network Steel frame

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Noise Cleaning of ECG on Edge Device Using Convolutional Sparse Contractive autoencoder

Noise Cleaning of ECG on Edge Device Using Convolutional Spa...

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20th IEEE International Conference on Pervasive Computing and Communications (IEEE PerCom)

作者： Banerjee, Rohan Mukherjee, Ayan Ghose, Avik Tata Consultancy Serv TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665416474

Single-lead Electrocardiogram (ECG) can be easily measured by a commercial smartwatch or a dedicated wearable device. The waveforms are often susceptible to background noise and motion artifacts introducing errors in disease interpretation. An effective yet light-weight de-noising of ECG is an open area of research. In this paper, we propose a novel convolutional autoencoder structure considering a number of regularization terms like sparsity constraint, contractive regularization and L2 norm for ECG de-noising. The deep learning model is duly optimized to efficiently run on low-power edge devices. The proposed approach is evaluated on a simulated and a real-world single-lead ECG database recorded from normal subjects as well as patients having Atrial Fibrillation (AF) and other kinds of abnormal heart rhythms. A thorough comparison is performed with a number of related signal processing and deep learning based prior approaches. Experimental results show that the proposed autoencoder yields the least Root Mean Square Error (RMSE) in reconstruction of clean signals from input ECG corrupted due to addition of noise. Our approach is also able to preserve the relevant morphological properties in the reconstructed ECG data for successful detection of AF and other abnormal rhythms. The optimized model is deployed on a low-power single-board computer for real-time noise cleaning.

关键词： Electrocardiogram autoencoder Convolutional Neural Network Noise cleaning TinyML

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