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Nonlinear process monitoring based on load weighted denoising autoencoder

MEASUREMENT 2021年 171卷 108782-108782页

作者： Zhu, Jiazhen Shi, Hongbo Song, Bing Tao, Yang Tan, Shuai Zhang, Tianqing East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Shanghai Shixiang Technol Corp Ind Big Data Res Ctr Shanghai 200237 Peoples R China

Traditional monitoring methods are trained with normal data and map the process variables into latent variables directly. However, for these methods, the process variables would become intertwined in the latent variables, which results in that the fluctuations of process variables would be submerged in noise or neutralized in latent variables space. In order to address the submergence and neutralization problems, a novel algorithm load weighted denoising autoencoder (LWDAE) is proposed. According to the direction and magnitude of online data, the loading matrix of LWDAE is weighted to highlight the useful information of both training data and online data in latent variables space. In addition, to reduce the effect of noise on weighting matrix, LWDAE modifies the loss function by adding two new regularizations and revises the calculation logic of weighting matrix to consider the successive samples. Case studies of continuous stirred tank reactor demonstrate the effectiveness of LWDAE.

关键词： denoising autoencoder Nonlinear process Fault detection Online weighting strategy

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Investigation of denoising autoencoder-based deep learning model in noise-riding experimental data for reliable state-of-charge estimation

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JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Hong, Seonri Kang, Moses Kim, Jonghoon Baek, Jongbok Korea Inst Energy Res Energy ICT Convergence Res Dept Energy Efficiency Res Div Daejeon 16105 South Korea Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea

State-of-charge (SOC) estimation plays a crucial role in battery management systems to ensure safe and reliable operation. However, SOC estimation remains challenging due to the dynamic nature of battery systems and varying ambient conditions. Data-driven methods have emerged as effective tools for analyzing nonlinear dynamical systems, but their performance heavily relies on data quality. In actual applications, data susceptible to distortions caused by external factors such as sensor failure, circuitry, and temperature variations, leading to degraded model performance. To address the performance degradation resulting from data quality deterioration, this paper introduces a denoising autoencoder is implemented as a stacked multi-layer perceptron, which learns to reconstruct distorted data. Furthermore, we propose the ensemble method that combines the autoencoder with an estimation model for SOC estimation in lithium-ion batteries. The effectiveness of the proposed model is demonstrated through tests conducted on a dataset comprising drive cycle profile of Panasonic 18650PF cells. The model validated under two ambient temperatures scenarios: identical and different, using a distorted dataset with added randomly added noise and dropout. The experimental results reveal that the proposed model achieved a 3 % error in training the drive profile relative to the actual values at different ambient temperatures. When compared to the plain model, the proposed ensemble model showed an increased RMSE of 4 %. Additionally, the performance of different estimation models was compared, with the LSTM model achieving an RMSE 0.67 at different ambient temperatures, outperforming the Support Vector Regression (SVR) with an RMSE 1.35 and the Extended Kalman Filter (EKF) with an RMSE of 0.87.

关键词： State-of-charge estimation denoising autoencoder Data-driven method Lithium-ion batteries Long short-term memory

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Sequential application of denoising autoencoder and long-short recurrent convolutional network for noise-robust remaining-useful-life prediction framework of lithium-ion batteries

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期179卷

作者： Hong, Seonri Kang, Moses Kim, Jonghoon Baek, Jongbok Korea Inst Energy Res Energy ICT Convergence Res Dept Energy Efficiency Res Div Daejeon South Korea Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea

Accurate prediction of the lithium-ion battery lifetime is important to maintain the performance of the battery system. Because data-driven methods are extensively used in the analysis of nonlinear dynamical systems in research, the existing literature is largely focused on the application of these methods for the prediction of battery state. Data-driven methods that are highly dependent on data are sensitive to the noise of the measurement data. Distorted data diminish the performance of data-driven models for lifetime prediction. In this study, we propose an artificial neural network-based framework, which is robust to noise, to increase the prediction accuracy of the remaining-useful-life (RUL) of lithium-ion batteries. A denoising autoencoder trained using a distorted dataset with Gaussian noise and dropout is presented to improve the robustness of the model to noise. Artificial neural network models predict RUL based on the state-of-health estimated using measurement data in the initial step. The proposed prediction model is compared with the base model and the training-noise model using distorted data to validate the robustness to noise. The results demonstrate that the proposed framework is robust to noise and has lower cycle errors compared to other methods.

关键词： Remaining -useful -life prediction denoising autoencoder Long-term recurrent convolutional network Data -driven method Lithium -ion batteries

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Adaptive Scheme of denoising autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment

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SENSORS 2023年第12期23卷 5544页

作者： Kim, Kyuri Lee, Jaeho Duksung Womens Univ Dept IT Media Engn Seoul 01369 South Korea Duksung Womens Univ Dept Software Seoul 01369 South Korea

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model's performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

关键词： RSSI indoor localization neural networks denoising autoencoder

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All-round improvement in DCT-based blind image watermarking with visual enhancement via denoising autoencoder

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 1页

作者： Hu, Hwai-Tsu Hsu, Ling-Yuan Lee, Tung-Tsun Natl Ilan Univ Dept Elect Engn Ilan Taiwan

Discrete cosine transform (DCT)-based watermarking has received great success in the copyright protection of digital images. This study exploited the orthonormal expansion properties of DCT to achieve superior performance in computational efficiency and watermarking efficacy. In particular, we reformulated the relative modulation to allow the modification of paired DCT coefficients as if operable in the spatial domain. To account for human visual characteristics, we also adjusted the embedding strength adaptively according to image entropy. The use of a range regulation mechanism enabled perfect watermark retrieval. The pursuit of an ideal balance between robustness and imperceptibility could be benefited from the grey wolf optimizer. Using the above-discussed techniques, the proposed watermarking scheme demonstrates superior robustness in terms of bit error rate and normalized correlation coefficient with the peak signal-to-noise ratio tuned at around 38 dB. Compared to the regular DCT approach, the computational acceleration is on the scale of N-2/(log2N)(2), where N signifies the side length of the image block under processing. Furthermore, for the type of character-style watermark, we explored the feasibility of using a denoising autoencoder (DAE) to enhance the retrieved watermark comprehensibility. The proposed DAE was found to be capable of contributing a 60% reduction of the bit error rate, thus making the recovered watermark visually more recognizable.

关键词： Blind image watermarking relative modulation DCT-based orthonormal expansion grey wolf optimizer denoising autoencoder

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Stacked Graph Fusion denoising autoencoder for Hyperspectral Anomaly Detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Zhang, Yongshan Li, Yijiang Wang, Xinxin Jiang, Xinwei Zhou, Yicong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most existing methods cannot simultaneously explore both structural and spatial information from global and local perspectives. In this letter, we propose a stacked graph fusion denoising autoencoder (SGFDAE) for hyperspectral anomaly detection. Specifically, the global and local graphs are constructed from an HSI to explore potential structural and spatial information. With the designed graph fusion strategy, an advanced graph denoising autoencoder with deep architecture is developed in a hierarchical manner. To achieve better reconstruction and detection, a greedy layerwise unsupervised pretraining strategy is presented for network training. Experiments show that SGFDAE achieves 97.17%, 98.43%, and 98.90% detection accuracies by averaging the results of the datasets from three different scenes and outperforms the state-of-the-art methods.

关键词： Detectors Noise reduction Anomaly detection Training Image edge detection Hyperspectral imaging Geoscience and remote sensing denoising autoencoder graph neural network hyperspectral imagery

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Accurate classification of Listeria species by MALDI-TOF mass spectrometry incorporating denoising autoencoder and machine learning

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JOURNAL OF MICROBIOLOGICAL METHODS 2022年 192卷 106378-106378页

作者： Li, Yunhong Gan, Zeyu Zhou, Xi Chen, Zhiwei Xiamen Univ Sch Elect Sci & Engn Dept Elect Sci Fujian Prov Key Lab Plasma & Magnet Resonance Res Xiamen Peoples R China Xiamen Univ Coll Mat Res Ctr Biomed Engn Xiamen Dept BiomatHigher Educ Key Lab Biomed Engn Fujian Xiamen Peoples R China Xiamen Univ Sch Elect Sci & Engn Dept Elect Sci Xiamen 361005 Peoples R China

Listeria monocytogenes belongs to the category of facultative anaerobic bacteria, and is the pathogen of listeriosis, potentially lethal disease for humans. There are many similarities between L. monocytogenes and other nonp-athogenic Listeria species, which causes great difficulties for their correct identification. The level of L. monocytogenes contamination in food remains high according to statistics from the Food and Drug Administration. This situation leads to food recall and destruction, which has caused huge economic losses to the food industry. Therefore, the identification of Listeria species is very important for clinical treatment and food safety. This work aims to explore an efficient classification algorithm which could easily and reliably distinguish Listeria species. We attempted to classify Listeria species by incorporating denoising autoencoder (DAE) and machine learning algorithms in matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). In addition, convolutional neural networks were used to map the high dimensional original mass spectrometry data to low dimensional core features. By analyzing MALDI-TOF MS data via incorporating DAE and support vector machine (SVM), the identification accuracy of Listeria species was 100%. The proposed classification algorithm is fast (range of seconds), easy to handle, and, more importantly, this method also allows for extending the identification scope of bacteria. The DAE model used in our research is an effective tool for the extraction of MALDI-TOF mass spectrometry features. Despite the fact that the MALDI-TOF MS dataset examined in our research had high dimensionality, the DAE + SVM algorithm was still able to exploit the hidden information embedded in the original MALDI-TOF mass spectra. The experimental results in our work demonstrated that MALDI-TOF mass spectrum combined with DAE + SVM could easily and reliably distinguish Listeria species.

关键词： Listeria species MALDI-TOF denoising autoencoder Machine learning Support vector machines Convolutional neural networks

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A face attribute based recommendation system via integrating denoising autoencoder and hash coding

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 90卷 107020-107020页

作者： Liu, Fan Chen, Zhiyu Ding, Yuhua Yang, Sai Zhang, Tao Hohai Univ Nanjing 210098 Peoples R China Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China Nantong Univ Nantong 226019 Peoples R China Yangzhou Univ Yangzhou 225127 Jiangsu Peoples R China

Nowadays, with the rapid development of commerce, how to effectively improve the performance of an recommendation system has aroused great concern. However, traditional recommendation system requires users to log in their accounts, which brings poor user experience. This paper presents a novel recommendation system by using face recognition technologies to extract face attribute information as the input automatically. The system first obtains the user information of identity, gender, age, and then gets feedback by expression analysis. Based on the acquired face attributes, we propose to extract compact binary user features by integrating denoising autoencoder and hash coding, which can effectively improve the computing *** hash features from DAE-H-Face and DAE are further combined to enhance the representation ability. Finally, Hamming similarity-based collaborative filtering is used for recommendation. Experimental results on the MovieLens database show that the proposed recommendation method has better effectiveness and robustness. Moreover, the results also demonstrate its advantages to the cold start problem.

关键词： Recommendation denoising autoencoder Face attribute Hash coding

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A Multi-Label Classification with an Adversarial-Based denoising autoencoder for Medical Image Annotation

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ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2023年第2期14卷 19-19页

作者： Chai, Yidong Liu, Hongyan Xu, Jie Samtani, Sagar Jiang, Yuanchun Liu, Haoxin Hefei Univ Technol Key Lab Proc Optimizat & Intelligence Decis Makin Minister Educ Liu Sch Management 193 Tunxi Rd Hefei 230009 Anhui Peoples R China Tsinghua Univ Res Ctr Contemporary Management Sch Econ & Management 30 Shuangqing Rd Beijing 100084 Peoples R China Capital Med Univ Beijing Tongren Hosp Beijing Inst Ophthalmol 1 Dongjiaominxiang Beijing 100005 Beijing Peoples R China Indiana Univ Kelley Sch Business 107 S Indiana Ave Bloomington IN 47405 USA

Medical image annotation aims to automatically describe the content of medical images. It helps doctors to understand the content of medical images and make better informed decisions like diagnoses. Existing methods mainly follow the approach for natural images and fail to emphasize the object abnormalities, which is the essence of medical images annotation. In light of this, we propose to transform the medical image annotation to a multi-label classification problem, where object abnormalities are focused directly. However, extant multi-label classification studies rely on arduous feature engineering, or do not solve label correlation issues well in medical images. To solve these problems, we propose a novel deep learning model where a frequent pattern mining component and an adversarial-based denoising autoencoder component are introduced. Extensive experiments are conducted on a real retinal image dataset to evaluate the performance of the proposed model. Results indicate that the proposed model significantly outperforms image captioning baselines and multi-label classification baselines.

关键词： Deep learning adversarial learning medical image annotation multi-label classification denoising autoencoder

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Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

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JOURNAL OF ENGINEERING-JOE 2019年第19期2019卷 6188-6191页

作者： Wang, Jun Wang, Wenfeng Luo, Feixiang Wei, Shaoming Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China

In this article, a machine learning method to classify signal with Gaussian noise based on denoising auto encoder (DAE) and convolutional neural network (CNN) is proposed. We combine denoising autoencoder's denoising ability with CNN's feature extraction capability. First, a six-layer neural network is built, including three CNN layers. Then a dataset containing noiseless signal of 11 modulation is generated. In the simulation, we apply this dataset to train neural network and achieve an accuracy of 94%, which is much higher than performance with noisy signal, meaning that noise can greatly influence the accuracy of neural network. Next, we build a denoising autoencoder and train it with signal of 5dB signal-to-noise ratio (SNR). Compared with neural network without denoising autoencoder, adding a denoising autoencoder can achieve an accuracy of 84% at signal of 18dB SNR, improved by 58%.

关键词： signal classification modulation feature extraction Gaussian noise signal denoising convolutional neural nets software radio machine learning method Gaussian noise convolutional neural network six-layer neural network CNN layers denoising autoencoder modulation classification CNN feature extraction signal-to-noise ratio GNU radio signal classification noise figure 5 0 dB noise figure 18 0 dB

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