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Estimation of the Global Equivalence Ratio of a Swirl Combustor from a Small Number of Absorption Spectra Using Machine Learning

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APPLIED SPECTROSCOPY 2024年第10期78卷 1078-1088页

作者： Bong, Cheolwoo Im, Seong-kyun Do, Hyungrok Bak, Moon Soo Sungkyunkwan Univ Sch Mech Engn Suwon South Korea Korea Univ Dept Mech Engn Seoul South Korea Seoul Natl Univ Dept Mech Engn Seoul South Korea Sungkyunkwan Univ Dept Smart Fabricat Technol Suwon South Korea

A new optical diagnostic method that predicts the global fuel-air equivalence ratio of a swirl combustor using absorption spectra from only three optical paths is proposed here. Under normal operation, the global equivalence ratio and total flow rate determine the temperature and concentration fields of the combustor, which subsequently determine the absorption spectra of any combustion species. Therefore, spectra, as the fingerprint for a produced combustion field, were employed to predict the global equivalence ratio, one of the key operational parameters, in this study. Specifically, absorption spectra of water vapor at wavenumbers around 7444.36, 7185.6, and 6805.6 cm-1 measured at three different downstream locations of the combustor were used to predict the global equivalence ratio. As it is difficult to find analytical relationships between the spectra and produced combustion fields, a predictive model was a data-driven acquisition. The absorption spectra as an input were first feature-extracted through stacked convolutional autoencoders and then a dense neural network was used for regression prediction between the feature scores and the global equivalence ratio. The model could predict the equivalence ratio with an absolute error of +/- 0.025 with a probability of 96%, and a gradient-weighted regression activation mapping analysis revealed that the model leverages not only the peak intensities but also the variations in the shape of absorption lines for its predictions. Graphical abstract This is a visual representation of the abstract.

关键词： Swirl combustor status monitoring tunable diode laser absorption spectroscopy TDLAS convolutional autoencoder CAE gradient-weighted regression activation mapping grad-RAM

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A novel self-attention deep subspace clustering

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第8期12卷 2377-2387页

作者： Chen, Zhengfan Ding, Shifei Hou, Haiwei China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ People S Republ China Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China

Most of the existing deep subspace clustering methods leverage convolutional autoencoders to obtain feature representation for non-linear data points. These methods commonly adopt the structure of a few convolutional layers because stacking many convolutional layers may cause computationally inefficient and optimization difficulties. However, long-range dependencies can hardly be captured when convolutional operations are not repeated enough, thus affect the quality of feature extraction which the performance of deep subspace clustering method highly lies in. To deal with this issue, we propose a novel self-attention deep subspace clustering (SADSC) model, which learns more favorable data representations by introducing self-attention mechanisms into convolutional autoencoders. Specifically, SADSC leverages three convolutional layers and add the self-attention layers after the first and third ones in encoders, then decoders have symmetric structures. The self-attention layers maintain the variable input sizes and can be easily combined with different convolutional layers in autoencoder. Experimental results on the handwritten recognition, face and object clustering datasets demonstrate the advantages of SADSC over the state-of-the-art deep subspace clustering models.

关键词： Deep subspace clustering convolutional autoencoder Self-attention

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Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2021年第5期41卷 678-689页

作者： Bansal, S. Singh, M. Dubey, R. K. Panigrahi, B. K. Indian Inst Technol Delhi Comp Sci & Engn Dept New Delhi 110016 India Robert Bosch Engn & Business Solut Private Ltd He 123 Hosur Rd7th Block Bengaluru 560095 Karnataka India Indian Inst Technol Delhi Elect Engn Dept New Delhi 110016 India

Purpose In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. Methods In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT scans. The model proposed in this study uses a three-step architecture, consisting of a convolutional autoencoder based unsupervised feature extractor, a multi-objective genetic algorithm (MOGA) based feature selector, and a Bagging Ensemble of support vector machines based binary classifier. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID ***). A dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. Results The best performing classifier within 127 ms per image achieved an accuracy of 98.79%, the precision of 98.47%, area under curve of 0.998, and an F1 score of 98.85% on 497 test images. The proposed model outperforms the current state of the art COVID-19 diagnostic techniques in terms of speed and accuracy. Conclusion The experimental results prove the superiority of the proposed methodology in comparison to existing *** study also comprehensively compares various feature selection techniques and highlights the importance of feature selection in medical image data problems.

关键词： Coronavirus (COVID-19) convolutional autoencoder Multi-objective genetic algorithm Feature subset selection

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A Framework for Anomaly Classification and Segmentation in Remanufacturing using autoencoders and Simulated Data

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Procedia CIRP 2024年 126卷 1005-1010页

作者： Jan-Philipp Kaiser Carl-Leandro Enslin Erik Tabuchi Barczak Florian Stamer Michael Heizmann Gisela Lanza wbk Institute of Production Science Karlsruhe Institute of Technology (KIT) Kaiserstr. 12 76131 Karlsruhe IIIT – Institute of Industrial Information Technology Karlsruhe Institute of Technology (KIT) Hertzstr. 16 76187 Karlsruhe Global Advanced Manufacturing Institute (GAMI) KIT China Branch 215123 Suzhou China

In remanufacturing, reusable end-of-life products must be separated from those for which remanufacturing is not economical. In this process, the focus is initially on the detection and evaluation of surface defects of the used product. Due to the use phase of the product, these show a great variety with strongly differing degrees of severity. Deep-learning-based autoencoders offer the advantage of defect-independent detection of anomalies by detecting deviations from a defect-free product condition. In the present work, anomaly detection based on synthetically generated image data is investigated. In addition, a framework for classification and segmentation is developed and presented. The results show that the approach presented in the paper is effective in detecting anomalies with potential for improvement through adjustments such as perceptual loss or image processing methods. However, challenges remain in accurately detecting anomalies in the presence of missing parts, large anomalies, and image data with high variance.

关键词： Remanufacturing Anomaly Detection Unsupervised Learning convolutional autoencoder

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An elevator door anomaly detection method based on improved deep multi-sphere support vector data description

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Xie, Pengdong Zhang, Linxuan Li, Minghong Qiu, Chaojie Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518057 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Xinjiang Univ Sch Elect Engn Urumqi 830017 Peoples R China

Various types of elevator door faults and difficulties in fault data acquisition make it difficult to use supervised learning methods for fault diagnosis. This paper proposes a semi-supervised anomaly detection method based on improved deep multi-sphere support vector data description. Multiple distinguishing hyper-spheres, characterized by minimum volume, are established on the foundation of normal data by this method. These hyper-spheres represent the multi- modal distribution exhibited by the normal data. In addition, the method fuses multi-sensor source data such as tri-axial acceleration, dual-axial tilt angle, and introduces the structure of InceptionTime to realize the fusion of multivariate data and feature extraction in multiple resolutions. Experiments verify the feasibility of the method with an overall AUC of 96.50%, and comparative experiments demonstrate the superior detection performance. This contributes a novel, accurate, and more appropriate method to the elevator door anomaly detection.

关键词： Anomaly detection Support vector data description Semi-supervised learning convolutional autoencoder

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Machine learning-based prediction of swirl combustor operation from flame imaging

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Bong, Cheolwoo Ali, Mohammed H. A. Im, Seong kyun Do, Hyungrok Bak, Moon Soo Sungkyunkwan Univ Sch Mech Engn Suwon 16419 South Korea Korea Univ Dept Mech Engn Seoul 02841 South Korea Seoul Natl Univ Dept Mech Engn Seoul 08826 South Korea Sungkyunkwan Univ Dept Smart Fab Technol Suwon 16419 South Korea

This study proposes a novel data-driven model to distinguish normal operation of a swirl combustor and predict key operation conditions using a flame image taken with a low-cost monochrome camera. The model, in the form of a convolutional neural network (CNN), is designed to take a flame image as input and provide either the total air flow rate (Q) or the fuel-air equivalence ratio (phi) as an output. However, since the type of problem in this study is regression, it is necessary to make predictions only on normal operation images, as it is not feasible to collect flame images for all unstable combustion modes. Thus, the stacked convolutional layers were first trained as a convolutional autoencoder (CAE) in an unsupervised manner using only flame images under normal operation modes, so that the CAE can perform well only on normal operation images. Then, a regressor that outputs either Q or phi is connected to the trained encoder and trained in a supervised manner. It was found that the model can predict Q and phi within +5.17 L/min (equivalent to 3.4% of the total flow rate) and +0.026, respectively, with a 96% probability, along with detecting abnormalities based on large reconstruction errors of input images. Predictions and image collection can be performed within 50 ms, demonstrating the potential for real-time monitoring of combustor status.

关键词： Swirl combustor Operation condition monitoring Abnormality detection convolutional autoencoder Gradient-weighted activation mapping

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Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution autoencoder and Artificial Neural Network

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SENSORS 2024年第3期24卷 851页

作者： Zaman, Wasim Ahmad, Zahoor Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea Prognosis & Diagnost Technol Co Ltd Ulsan 44610 South Korea

This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps facilitate fluid transport through the energy generated by the impeller. Throughout the operation, variations in the fluid pressure at the pump's inlet may impact the generalization of traditional machine learning models trained on raw statistical features. To address this concern, first, vibration signals are collected from centrifugal pumps, followed by the application of a lowpass filter to isolate frequencies indicative of faults. These signals are then subjected to a continuous wavelet transform and Stockwell transform, generating two distinct time-frequency scalograms. The Sobel filter is employed to further highlight essential features within these scalograms. For feature extraction, this approach employs two parallel convolutional autoencoders, each tailored for a specific scalogram type. Subsequently, extracted features are merged into a unified feature pool, which forms the basis for training a two-layer artificial neural network, with the aim of achieving accurate fault classification. The proposed method is validated using three distinct datasets obtained from the centrifugal pump under varying inlet fluid pressures. The results demonstrate classification accuracies of 100%, 99.2%, and 98.8% for each dataset, surpassing the accuracies achieved by the reference comparison methods.

关键词： centrifugal pump Stockwell transform fault diagnosis rotating machinery convolutional autoencoder vibrational signals deep learning

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RL Based Unsupervised Video Summarization Framework for Ultrasound Imaging 3rd

RL Based Unsupervised Video Summarization Framework for Ultr...

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3rd International Workshop of Advances in Simplifying Medical Ultrasound (ASMUS)

作者： Mathews, Roshan P. Panicker, Mahesh Raveendranatha Hareendranathan, Abhilash R. Chen, Yale Tung Jaremko, Jacob L. Buchanan, Brian Narayan, Kiran Vishnu Chandrasekharan, Kesavadas Mathews, Greeta Indian Inst Technol Palakkad Palakkad India Univ Alberta Edmonton AB Canada Hosp Univ Puerta de Hierro Spain Madrid Spain Govt Med Coll Thiruvananthapuram Thiruvananthapuram Kerala India Sree Chitra Inst Med Sci & Technol Thiruvananthap Thiruvananthapuram Kerala India Bhagwan Mahaveer Jain Hosp Bangalore Bangalore Karnataka India

ISBN: (纸本)9783031169021;9783031169014

The need for summarizing long medical scan videos for automatic triage in Emergency Departments and transmission of the summarized videos for telemedicine has gained significance during the COVID-19 pandemic. However, supervised learning schemes for summarizing videos are infeasible as manual labeling of scans for large datasets is impractical by frontline clinicians. This work presents a methodology to summarize ultrasound videos using completely unsupervised learning schemes and is validated on Lung Ultrasound videos. A convolutional autoencoder and a Transformer decoder is trained in an unsupervised reinforcement learning setup i.e., without supervised labels in the whole workflow. Novel precision and recall computation for ultrasound videos is also presented employing which high Precision and F 1 scores of 64.36% and 35.87% with an average video compression rate of 78% is obtained when validated against clinically annotated cases. Even though demonstrated using lung ultrasound videos, our approach can be readily extended to other imaging modalities.

关键词： Ultrasound Video summarization Unsupervised reinforcement learning convolutional autoencoder Transformer

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Deep Representation for the Classification of Ultrasound Breast Tumors 16

Deep Representation for the Classification of Ultrasound Bre...

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16th International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Song, Mingue Kim, Yanggon Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

ISBN: (纸本)9781665426787

An automated classification of ultrasound breast tumor is a vital step for the early prevention of abnormal breast cells. In general, radiologists manually handle this procedure, but manual analysis performed by individual poses a problem of consistency depending on the experts. One of the standardized alternatives was to apply automated deep learning method in this field. In fact, majority ideas in literature are dominantly based on the supervised learning framework, but even such methods have still failed to present promising discrimination performance. In this work, we assume that unsupervised learning still can be a potential option and beneficial attribute that enables to accelerate discrimination is inherent in it. Hence, we present a deep representation for the ultrasound breast data utilizing two types of independent supervised and unsupervised network to reconstruct the principal features, while the volume of supervised features is set to be minimum and the volume of unsupervised is the maximum. Specifically, we adopted pretrained Resnet34 as a supervised network, and a convolutional autoencoder (CAE) was designed for the unsupervised network. Each representation vector is combined into a single vector, and the generated vector is given to the support vector machine as an input for the final discrimination. The results are verified that the proposed method shows far better performance compared to several conventional deep learning methods and the single use of each method. The value of accuracy, sensitivity and specificity are obtained by 88.18%, 85.25% and 100.00% respectively.

关键词： fusion framework ultrasound breast cancer classification convolutional autoencoder machine-learning

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Deep Clustering for Improved Inter-Cluster Separability and Intra-Cluster Homogeneity with Cohesive Loss

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第5期E104D卷 776-780页

作者： Kim, Byeonghak Loew, Murray Han, David K. Ko, Hanseok Korea Univ Dept Visual Informat Proc Seoul 02841 South Korea George Washington Univ Dept Biomed Engn Washington DC USA Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Korea Univ Sch Elect Engn Seoul 02841 South Korea

To date, many studies have employed clustering for the classification of unlabeled data. Deep separate clustering applies several deep learning models to conventional clustering algorithms to more clearly separate the distribution of the clusters. In this paper, we employ a convolutional autoencoder to learn the features of input images. Following this, k-means clustering is conducted using the encoded layer features learned by the convolutional autoencoder. A center loss function is then added to aggregate the data points into clusters to increase the intra-cluster homogeneity. Finally, we calculate and increase the inter-cluster separability. We combine all loss functions into a single global objective function. Our new deep clustering method surpasses the performance of existing clustering approaches when compared in experiments under the same conditions.

关键词： separate clustering convolutional autoencoder intra-cluster homogeneity inter-cluster separability

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