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Deep convolutional self-paced clustering

APPLIED INTELLIGENCE 2022年第5期52卷 4858-4872页

作者： Chen, Rui Tang, Yongqiang Tian, Lei Zhang, Caixia Zhang, Wensheng Foshan Univ Dept Automat Foshan Peoples R China Chinese Acad Sci Inst Automat Res Ctr Precis Sensing & Control Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

Clustering is a crucial but challenging task in data mining and machine learning. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, has achieved state-of-the-art performance in various applications and attracted considerable attention. Nevertheless, most of these approaches fail to effectively learn informative cluster-oriented features for data with spatial correlation structure, e.g., images. To tackle this problem, in this paper, we develop a deep convolutional self-paced clustering (DCSPC) method. Specifically, in the pretraining stage, we propose to utilize a convolutional autoencoder to extract a high-quality data representation that contains the spatial correlation information. Then, in the finetuning stage, a clustering loss is directly imposed on the learned features to jointly perform feature refinement and cluster assignment. We retain the decoder to avoid the feature space being distorted by the clustering loss. To stabilize the training process of the whole network, we further introduce a self-paced learning mechanism and select the most confident samples in each iteration. Through comprehensive experiments on seven popular image datasets, we demonstrate that the proposed algorithm can consistently outperform state-of-the-art rivals.

关键词： Deep clustering convolutional autoencoder Local structure preservation Self-paced learning

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Deep-Learning-Based Proxy Modeling for Microscopic Process of Plasma Etching

Deep-Learning-Based Proxy Modeling for Microscopic Process o...

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Conference of Science and Technology for Integrated Circuits (CSTIC)

作者： Chen, Shuhang Liu, Yujun Ni, Dong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310012 Peoples R China Zhejiang Univ Sch Mirconano Elect Hangzhou 311200 Peoples R China

ISBN: (纸本)9798350362206;9798350362190

Dynamic modeling of the microscopic process of plasma etching is crucial for reducing production costs and improving production efficiency. To alleviate issues such as poor real-time performance and limited transferability of typical numerical simulation algorithms, researchers have employed neural networks. Nonetheless, the considerable expenses often pose challenges in acquiring sufficient real datasets. Hence, this paper proposes the Deep-Learning-Based proxy model, combined with the convolutional autoencoder and Recurrent Neural Network. The model not only accelerates the simulation speed of plasma etching, but also achieves accurate predictions.

关键词： Plasma Etching Proxy Model convolutional autoencoder Recurrent Neural Network

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Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio

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ICT EXPRESS 2023年第3期9卷 398-402页

作者： Pablos, Cesar Andrade, Angel G. Galaviz, Guillermo Univ Autonoma Baja California Fac Ingn Blvd Benito Juarez S-N Mexicali 21280 Baja California Mexico

This work proposes a Spectrum Sensing (SS) scheme based on a convolutional autoencoder (CAE) for application in Cognitive Radio Networks. The channel occupancy is modeled as an anomaly detection problem. The CAE is trained only with noise-signal samples so that any random modulated signal observed in the wireless channel will be regarded as an outlier. The relationship between the detection threshold and the system performance is evaluated. Experiments demonstrate that the SS proposal can identify primary signal presence with better accuracy (high detection and low false alarm rate) than conventional Energy Detection.& COPY;2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Anomaly detection Cognitive radio convolutional autoencoder Spectrum sensing

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Unsupervised Learning for Anomaly Detection of Electric Motors

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2022年第4期23卷 421-427页

作者： Son, Jonghwan Kim, Chayoung Jeong, Minjoong Yonsei Univ Seoul 03722 South Korea Kyonggi Univ 154-42 Gwanggyosan Ro Suwon 16227 Gyeonggi Do South Korea Univ Sci & Technol Korea Inst Sci & Technol Informat Daejeon 34141 South Korea

This paper presents a novel approach for discriminating abnormal electric motors from normal motors using real sound data. The proportion of abnormal data among all training data is extremely low. Feature extraction techniques are applied to the sound signals. After the extraction, those features are used in unsupervised learning algorithms, including the convolutional autoencoder and one-class support vector machine. Results show that the proposed methods successfully distinguished normal data from abnormal data. The techniques can be applied to determine whether an electric motor is faulty during quality inspection.

关键词： Sound anomaly detection Unsupervised learning convolutional autoencoder One-class support vector machine Feature extraction

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Signal processing of Internet of Vehicles based on intelligent interference

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022年第1期2022卷 1-16页

作者： Wu, Xiangyu Hou, Changbo Deng, Zhian Fang, Chenyu Liu, Guowei Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Harbin Engn Univ Key Lab Adv Marine Commun & Informat Technol Minist Ind & Informat Technol Harbin Peoples R China

With the rapid development of in-vehicle electronic technology and artificial intelligence, Internet of Vehicles (IoV) technology, as an effective integration of the two, greatly reduces the probability of road traffic accidents. However, the current IoV system is not perfect for the control process of abnormal vehicles. Therefore, to strengthen the management and control of abnormal vehicles in the IoV, it is extremely necessary to propose a method for interfering with IoV signals. Among the current popular intelligent interference methods, most of them rely on the prior knowledge of the signal. However, prior knowledge is difficult to obtain in practical applications. Therefore, in view of the shortcomings of the current communication interference technology, this study introduces intelligent interference into the signal processing technology of the IoV. When the service provider identifies abnormal signals, the intelligent interference method can be used to achieve precise interference for a single target and then realize the control of the IoV signals. This study proposes an interference waveform generation technology based on convolutional autoencoders. The convolutional autoencoder was used to change the features on the fully connected layer to generate an interference waveform that is very similar to the received signal waveform. The simulation results show that the interference waveform generation technology proposed in this study can make the bit error rate (BER) reach 38.4% within the signal-to-interference ratio (SIR) from - 10 to - 15 dB.

关键词： IoV signal processing Intelligent interference convolutional autoencoder Interference waveform generation

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An autoencoder-Based Approach for Fault Detection in Multi-Stage Manufacturing: A Sputter Deposition and Rapid Thermal Processing Case Study

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第2期35卷 166-173页

作者： Jebril, Hana T. T. Pleschberger, Martin Susto, Gian Antonio Med Univ Vienna Christian Doppler Lab Artificial Intelligence Ret A-1090 Vienna Austria Kompetenzzentrum Automobil & Ind Elekt GmbH DSC Dept A-9524 Villach Austria Univ Padua Dept Informat Engn I-35131 Padua Italy

Data-driven Fault Detection and Classification approaches are becoming increasingly important in semiconductor manufacturing and in other industries aiming at implementing the Zero-defect paradigm. Two of the main challenges in developing such solutions are: (i) the complexity of sensor data, that typically presents themselves in the form of time-series, requiring the employment of time-consuming and possibly sub-optimal feature extraction approaches;(ii) the fact that faults/defects may be caused by more than a single process, but in many cases they are generated by a cascade of processes. In this paper, we tackle the first issue, by considering a two-stage case study consisting of a deposition process and a rapid thermal process. The proposed approach is based on convolutional deep autoencoders employed to perform feature extraction from time-series sensor data in frontend production equipment. We will show on the reported case study, how the proposed approach outperfoms key numbers-based approaches typically used in the industry. To allow reproducibility of the reported results and to foster research in the field, we publicly share the data used in this work.

关键词： Feature extraction Convolution Production Semiconductor device manufacture Task analysis Fault detection Decoding convolutional autoencoder deep learning fault detection and classification machine learning multi-stage fabrication semiconductor manufacturing

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Making Noise - Improving Seismocardiography Based Heart Analysis With Denoising autoencoders 23

Making Noise - Improving Seismocardiography Based Heart Anal...

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8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (IWOAR)

作者： Burian, Jonas Toedtmann, Helmut Haescher, Marian Aehnelt, Mario Kuijper, Arjan Fraunhofer IGD Rostock Germany Fraunhofer IGD Darmstadt Germany

ISBN: (纸本)9798400708169

Seismocardiography is a method commonly used to monitor and prevent cardiovascular diseases. However, noise and artifacts in the signals often interfere with the assessment of cardiac health and the analysis of the signal morphology. Therefore, this work presents a new approach to denoise seismocardiography signals by applying fully convolutional denoising autoencoders. In order to investigate the suitability and robustness of this approach, a series of experiments have been carried out with respect to the optimal configuration for the denoising task and a comparison with wavelet denoising as a traditional approach. Furthermore, the practical applicability of the method is tested with the use case of transforming noisy seismocardiography signals into electrocardiography signals. Our approach using autoencoders outperforms the commonly used wavelet denoising. Additionally, we demonstrate that the widespread usage of Butterworth filters may not only be unnecessary but even detrimental. Finally, the generalizability of the method is verified on unseen data. With those combined improvements in noise reduction, the assessment of cardiac health using seismocardiography in the presence of noise may be facilitated in the future.

关键词： seismocardiography cardiac health assessment signal denoising convolutional autoencoder signal processing

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A Cycle-GAN Based Image Encoding Scheme for Privacy Enhanced Deep Neural Networks 19th

A Cycle-GAN Based Image Encoding Scheme for Privacy Enhanced...

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19th International Conference on Information Systems Security (ICISS)

作者： Rodriguez, David Krishnan, Ram Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA

ISBN: (纸本)9783031490989;9783031490996

Deep learning model training on cloud platforms typically require users to upload raw input data. However, uploading raw image data to cloud service providers raises serious privacy concerns. To address this problem, we propose a Cycle-Gan based-image transformation scheme that leverages convolutional autoencoder image encoding for domain translation. Our Cycle-GAN based image transformation scheme enhances privacy of deep neural networks while preserving model utility. In this paper, we demonstrate that our Cycle-GAN based image transformation scheme protects visual feature information of sensitive image data. We evaluate the effectiveness of our proposed method to preserve model utility using classification accuracy and robustness against reconstruction attacks using structural similarity index measure (SSIM). The classification accuracy of encoded images using our proposed method is 92.48, 91.05, 90.37 for Chest X-ray, Dermoscopy and OCT datasets, respectively. The SSIM scores for reconstruction attacks where the attacker only has access to the encoded data and corresponding labels are 0.1002, 0.0995 and 0.0329 for Chest X-ray, Dermoscopy and OCT datasets, respectively. Our results demonstrate that the Cycle GAN based encoding scheme effectively enhance privacy while preserving model utility.

关键词： Cycle-GAN Deep Neural Networks convolutional autoencoder Privacy Utility

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MS²A-Net: Multiscale Spectral-Spatial Association Network for Hyperspectral Image Clustering

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 6518-6530页

作者： Shahi, Kasra Rafiezadeh Ghamisi, Pedram Rasti, Behnood Gloaguen, Richard Scheunders, Paul Univ Antwerp Dept Phys Imec Visionlab B-2000 Antwerp Belgium Helmholtz Inst Freiberg Resource Technol Helmholtz Zentrum Dresden Rossendorf D-09599 Freiberg Germany Inst Adv Res Artificial Intelligence A-1030 Vienna Austria

Remote sensing hyperspectral cameras acquire high spectral-resolution data that reveal valuable composition information on the targets (e.g., for Earth observation and environmental applications). The intrinsic high dimensionality and the lack of sufficient numbers of labeled/training samples prevent efficient processing of hyperspectral images (HSIs). HSI clustering can alleviate these limitations. In this study, we propose a multiscale spectral-spatial association network (MS(2)A-Net) to cluster HSIs. The backbone of MS(2)A-Net is an autoencoder architecture that allows the network to capture the nonlinear relation between data points in an unsupervised manner. The network applies a multistream approach. One stream extracts spectral information by deploying a spectral association unit. The other stream derives multiscale contextual and spatial information by employing dilated (atrous) convolutional kernels. The obtained feature representation generated by MS(2)A-Net is fed into a standard k-means clustering algorithm to produce the final clustering result. Extensive experiments on four HSIs for different types of applications (i.e., geological-, rural-, and urban-mapping) demonstrate the superior performance of MS(2)A-Net over the state-of-the-art shallow/deep learning-based clustering approaches in terms of clustering accuracy.

关键词： convolutional autoencoder deep learning dilated (atrous) convolutions hyperspectral imaging multiscale information remote sensing spectral association

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Quantifying Asymmetry Between Medial and Lateral Compartment Knee Loading Forces Using Acoustic Emissions

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第4期69卷 1541-1551页

作者： Jeong, Hyeon Ki An, Sungtae Herrin, Kinsey Scherpereel, Keaton Young, Aaron Inan, Omer T. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA

Objective: Osteoarthritis is the most common type of knee arthritis that can be affected by excessive and compressive loads and can affect one or more compartments of the knee: medial, lateral, and patellofemoral. The medial compartment tends to be the most vulnerable to injuries and research suggests that a better understanding of the medial to lateral load distribution conditions could provide insights to the quantitative usage of knee compartments in activities of daily life. Methods: Prior to study in an osteoarthritic clinical population which may present with various complicating anatomical and physiological changes, we investigate knee acoustical emissions of able-bodied individuals during a varying width squat exercise which simulates loading asymmetries that would typically be seen in this clinical population. To that end, we present a novel method to quantify the directional bias of asymmetry between the medial and lateral compartment knee joint load in healthy individuals by recording knee acoustical emissions and analyzing them using a deep neural network in a subject independent model. We placed four miniature contact microphones on the medial and lateral sides of the patella on both the left and right leg. We compared the handcrafted audio features with the automated features extracted from the convolutional autoencoder which is an unsupervised model that learns the comprehensive representation of the input to determine whether these automated features can better represent the signal's characteristic in regard to the structural asymmetry of the knee joint. The input to the convolutional auto encoder (CAE) is a time-frequency representation and different types of these images such as spectrogram and scalogram are compared. We alsocompared the multi-sensor fusion approach with the performance of a single sensor to determine the robustness of using multiple sensors. Results: Using a representation learning based approach, we developed a subject independen

关键词： Knee Feature extraction Force Joints Electromyography Loading Legged locomotion convolutional autoencoder joint acoustic emissions joint contact force neural network scalogram sensor fusion

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