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A Deep convolutional autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第3期19卷 1477-1490页

作者： Maggipinto, Marco Beghi, Alessandro Susto, Gian Antonio Univ Padua Dept Informat Engn I-35122 Padua Italy Univ Padua Human Inspired Technol Res Ctr I-35122 Padua Italy

In manufacturing industries, it is of fundamental importance to detect anomalies in production in order to meet the required quality goals and to limit the number of defective products that are accidentally delivered to the customers. Nevertheless, monitoring systems currently employed in production are typically very simple and rely on a set of univariate control charts that fail to capture the multivariate and complex nature of real-world industrial systems. In such context, Machine Learning (ML)-based approaches for Anomaly Detection (AD) have proven to be extremely effective in increasing anomalies detectability and, in general, in enhancing monitoring procedures. However, industrial data are typically very complex and not suitable to be fed directly to classical ML-based AD tools making feature extraction procedures a necessary step that unfortunately may lead to information loss and low scalability. Deep Learning, has proven very effective at learning useful representations of complex data in an automatic way. In this paper, we propose an AD pipeline that makes use of convolutional autoencoders to extract useful features from two-dimensional, non-image, data. We test our approach on real world Optical Emission Spectroscopy data that are typical of semiconductor manufacturing and we achieve improved performance over classical monitoring methods.

关键词： Feature extraction Task analysis Monitoring Anomaly detection Manufacturing Standards Deep learning Anomaly detection convolutional autoencoder deep neural networks Industry 4 0 machine learning semiconductor manufacturing unsupervised feature selection

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A Real-Time Bearing Fault Diagnosis Model Based on Siamese convolutional autoencoder in Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 3820-3831页

作者： Hu, He-Xuan Cao, Chengcheng Hu, Qiang Zhang, Ye Lin, Zhen-Zhou Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing 211100 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanjing Univ Finance & Econ Off Teaching Affairs Nanjing 210023 Peoples R China

The extreme environment refers to the abnormal temperature, pressure, or vibration in the environment within a certain period of time, which will cause the fault of bearing equipment. Bearing fault diagnosis model can accurately identify the health status of bearing equipment, which can deal with the influence of extreme environments on the normal operation of bearings in a timely manner. However, current bearing fault diagnosis models have the following challenge: the sample size of faulty data is too small, which makes the parameters in the bearing fault diagnosis model unable to be effectively learned. Therefore, in order to solve the above issue in the field of bearing fault diagnosis, we draw on the siamese network and convolutional autoencoder, and propose a real-time bearing fault diagnosis model based on siamese convolutional autoencoder (RBFDSCA) in this work. First, we use an Industrial Internet of Things (IIoT) platform to collect, store and analyze bearing data. Second, to cope with the challenge of the small sample size of faulty data, RBFDSCA model constructs a siamese convolutional autoencoder. The siamese convolutional autoencoder contains a positive feature extraction network, a negative feature extraction network, and a prediction network. The four evaluation metrics of RBFDSCA model on the real bearing data set are 0.9638, 0.9640, 0.9641, and 0.9639, respectively, which verifies its excellent performance.

关键词： Bearing fault diagnosis model convolutional autoencoder extreme environment Industrial Internet of Things (IIoT) siamese network

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A Lightweight Visual Font Style Recognition With Quantized convolutional autoencoder

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2024年第1期5卷 120-130页

作者： Tonmoy, Moshiur Rahman Rakib, Abdul Fattah Rahman, Rashik Adnan, Md. Akhtaruzzaman Mridha, M. F. Huang, Jie Shin, Jungpil Adv Machine Intelligence Res Lab Dhaka 1213 Bangladesh Univ Asia Pacific Dept Comp Sci & Engn Dhaka 1205 Bangladesh Amer Int Univ Bangladesh Dept Comp Sci Dhaka 1229 Bangladesh Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu 9658580 Japan

Font style recognition plays a vital role in the field of computer vision, particularly in document and pattern analysis, and image processing. In the industry context, this recognition of font styles holds immense importance for professionals such as graphic designers, front-end developers, and UI-UX developers. In recent times, font style recognition using Computer Vision has made significant progress, especially in English. Very few works have been done for other languages as well. However, the existing models are computationally costly, time-consuming, and not diversified. In this work, we propose a state-of-the-art model to recognize Bangla fonts from images using a quantized convolutional autoencoder (Q-CAE) approach. The compressed model takes around 58 KB of memory only which makes it suitable for not only high-end but also low-end computational edge devices. We have also created a synthetic data set consisting of 10 distinct Bangla font styles and a total of 60,000 images for conducting this study as no dedicated dataset is available publicly. Experimental outcomes demonstrate that the proposed method can perform better than existing methods, gaining an overall accuracy of 99.95% without quantization and 99.85% after quantization.

关键词： Visual font recognition computer vision convolutional autoencoder quantization TinyML edge devices bangla font style

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Development of de-noised image reconstruction technique using convolutional autoencoder for fast monitoring of fuel assemblies

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NUCLEAR ENGINEERING AND TECHNOLOGY 2021年第3期53卷 888-893页

作者： Choi, Se Hwan Choi, Hyun Joon Min, Chul Hee Chung, Young Hyun Ahn, Jae Joon Yonsei Univ Dept Informat & Stat Seoul South Korea Yonsei Univ Dept Radiat Convergence Engn Seoul South Korea

The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based de noising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. convolutional autoencoder (CAE) was employed for de noising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images;the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image. (c) 2020 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： Tomographic imaging Deep learning-based denoising process convolutional autoencoder Verification of fuel assemblies

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A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

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JOURNAL OF STRUCTURAL BIOLOGY 2018年第2期202卷 150-160页

作者： Zeng, Xiangrui Leung, Miguel Ricardo Zeev-Ben-Mordehai, Tzviya Xu, Min Carnegie Mellon Univ Computat Biol Dept Sch Comp Sci Pittsburgh PA 15213 USA Univ Oxford Wellcome Trust Ctr Human Genet Div Struct Biol Oxford OX3 7BN England Univ Utrecht Bijvoet Ctr Biomol Res Cryoelectron Microscopy Utrecht Netherlands

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.

关键词： Cellular electron cryo-tomography Macromolecular complex Subtomogram classification Visual proteomics Particle picking Structural pattern mining Deep learning convolutional neural network convolutional autoencoder Image semantic segmentation Machine learning Unsupervised learning Weakly supervised learning Pose normalization

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Laser stripe image denoising using convolutional autoencoder

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RESULTS IN PHYSICS 2018年 11卷 96-104页

作者： Fang, Zhuoqun Jia, Tong Chen, Qiusheng Xu, Ming Yuan, Xi Wu, Chengdong Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110004 Liaoning Peoples R China

convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser stripe images of the depth sensor, which directly reduces the external noise of the depth sensor so as to increase its accuracy. To reduce the amount of training data and avoid overfitting, a patch size of the laser stripe image is determined, on the basis of which a small-scale dataset called Laser Stripe Image Patch (LSIP) is created. Also, a 14-layers convolutional autoencoder is constructed to reduce the noise of the image patches, which can learn the most salient features on the LSIP dataset. Moreover, the trained convolutional autoencoder is applied to an omnidirectional structured light system. Experimental results demonstrate that the proposed method obtains useful features and superior performance both visually and quantitatively on denoising tasks, and significantly improves the accuracy of the structured light system.

关键词： convolutional autoencoder Deep learning Depth perception Image denoising Laser stripe Structured light

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convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy

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JOURNAL OF ENERGY STORAGE 2023年 60卷

作者： Obregon, Josue Han, Yu-Ri Ho, Chang Won Mouraliraman, Devanadane Lee, Chang Woo Jung, Jae-Yoon Kyung Hee Univ Dept Ind & Management Syst Engn 1732 Deogyeong daero Yongin 17104 Gyeonggi Do South Korea Kyung Hee Univ Dept Chem Engn Integrated Engn 1732 Deogyeong daero Yongin 17104 Gyeonggi Do South Korea Kyung Hee Univ Dept Big Data Analyt 1732 Deogyeong daero Yongin 17104 Gyeonggi Do South Korea

The advancement of consumer electronics and electric vehicles requires heavy use of energy sources, particularly in the form of rechargeable batteries. Although lithium-ion batteries (LiBs) enable the use of such technologies owing to their high energy and power densities, estimating the state-of-health (SOH) of such batteries remains a challenge because of the various environmental operational conditions that affect the charging and discharging cycles of LiBs. In this study, we explore an approach that uses a convolutional autoencoder (CAE) for overcomplete feature extraction from electrochemical impedance spectroscopy (EIS) data. Subsequently, the extracted latent data representation is fed into a deep neural network (DNN) for battery capacity retention and SOH estimation. The proposed end-to-end deep learning-based architecture is called CAE-DNN. To prove the effectiveness of the proposed architecture, we conducted a series of experiments using a public dataset involving EIS spectra collected from fully charged LiBs cycled at different temperatures. The experimental results were compared with those of existing state-of-the-art methods, and with other classic machine learning methods. The results demonstrate that the proposed architecture extracts useful features in an unsupervised manner and estimates the SOH of LiBs more accurately than other baseline estimation methods.

关键词： Lithium -ion batteries Deep learning State of health Electrochemical impedance spectroscopy Charge capacity estimation convolutional autoencoder

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Low-Dose CT Image Reconstruction using Vector Quantized convolutional autoencoder with Perceptual Loss

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2023年第2期48卷 1-5页

作者： Ramanathan, Shalini Ramasundaram, Mohan Natl Inst Technol Dept CSE Tiruchirappalli Tamil Nadu India

Computed Tomography (CT) has become a useful screening procedure to identify disease or injury within various regions of the human body. The human beings' health issues caused by CT radiation have attracted the interest of the researchers and academic community. Reducing the radiation dose is the solution, but the CT image generated with low-dose radiation results in excessive noise due to lower intensity and fewer angle measurements. Low-dose CT scan images reduce image quality and thus affect a doctor's diagnosis. Deep learning methods have become increasingly popular in recent years, many models have been proposed for Low-Dose CT image reconstruction. Low-Dose CT Image Reconstruction is an active area of modern medical imaging research. Deep learning-based medical image reconstruction methods will be helpful to reduce noise without compromising image quality. Therefore, this paper introduces a novel CT image reconstruction method based on the vector quantization technique utilized in the convolutional autoencoder network. The quality of the results is evaluated based on the perceptual loss function. Experimental evaluations are conducted on the LoDoPaB-CT benchmark dataset. Its result showed that the proposed network obtained better performance metric values and better noise elimination results, in terms of quantitative and visual evaluation, respectively.

关键词： Low-dose CT (LDCT) vector quantization deep learning convolutional autoencoder medical image reconstruction

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Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

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Artificial Intelligence in Agriculture 2021年第1期5卷 90-101页

作者： Punam Bedi Pushkar Gole Department of Computer Science University of DelhiDelhiIndia

Plants are susceptive to various diseases in their growing *** detection of diseases in plants is one of the most challenging problems in *** the diseases are not identified in the early stages,then theymay adversely affect the total yield,resulting in a decrease in the farmers'*** overcome this problem,many researchers have presented different state-of-the-art systems based on Deep Learning and Machine Learning ***,most of these systems either use millions of training parameters or have lowclassification *** paper proposes a novel hybrid model based on convolutional autoencoder(CAE)network and convolutional Neural Network(CNN)for automatic plant disease *** the best of our knowledge,a hybrid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-ofthe-art systems present in the *** this work,the proposed hybrid model is applied to detect Bacterial Spot disease present in peach plants using their leaf images,however,it can be used for any plant disease *** experiments performed in this paper use a publicly available dataset named PlantVillage to get the leaf images of peach *** proposed system achieves 99.35%training accuracy and 98.38%testing accuracy using only 9,914 training *** proposed hybrid model requires lesser number of training parameters as compared to other approaches existing in the ***,in turn,significantly decreases the time required to train the model for automatic plant disease detection and the time required to identify the disease in plants using the trained model.

关键词： Plant disease detection convolutional autoencoder convolutional neural network Deep learning in agriculture

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convolutional autoencoder neural network for fault diagnosis with multi-sensor data

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年

作者： Wang, Xin Cui, Lingli Yang, Na Beijing Univ Technol Key Lab Adv Mfg Technol Beijing Peoples R China Beijing Univ Technol Key Lab Adv Mfg Technol Beijing Chaoyang Dist Pingleyuan 100 Beijing 100124 Peoples R China

With machines in manufacturing industry being automated, complex and intelligent, its monitoring systems are equipped with more and more smart sensors. How to extract useful features from great volume of multi-sensor data become a great challenge to the field of fault diagnosis. To overcome such challenge, an improved convolutional autoencoder neural network (CANN) is proposed to fuse and extract effective features of the color images formed by multi-sensor data in this paper. Firstly, the vibration signals of different channels are jointly transformed into color images. Secondly, an improved CANN is constructed by introducing special convolution kernels and residual connection for multi-sensor data fusion and feature extraction. Finally, the encoder part of CANN is connected with the softmax classifier for fault diagnosis. Two datasets collected from Wind Power Test-Bed and Industrial Blower Fan System are used to fully validate the effectiveness of proposed CANN. The results show that it can effectively fuse multi-sensor data and mine the discriminative features. Furthermore, compared with some related state-of-art methods, the CANN obtains higher diagnostic accuracy, especially for less labeled data.

关键词： Multi-sensor data fusion color image convolutional autoencoder residual connection

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