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Evaluation of Machine Learning Methods for Monitoring the Health of Guyed Towers

SENSORS 2022年第1期22卷 213-213页

作者： Martinez Ricardo, Diana Marcela Castaneda Jimenez, German Efrain Vaqueiro Ferreira, Janito de Oliveira Nobrega, Euripedes Guilherme de Lima, Eduardo Rodrigues de Almeida, Larissa M. Univ Estadual Campinas Fac Mech Engn Dept Computat Mech BR-13083860 Campinas Brazil Inst Pesquisas Eldorado Exploratory Hardware Desing Dept BR-13083898 Campinas Brazil Transmissora Alianca Energia Eletr SA BR-20010010 Rio De Janeiro Brazil

This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil's interconnected electrical system. The proposed method increases system reliability and minimizes the risk of service failure and tower collapse through the introduction of predictive maintenance methods based on artificial intelligence, which will ultimately benefit the end consumer. The proposed signal processing and interpretation methods are based on a machine learning approach, where the tower vibration is acquired from accelerometers that measure the dynamic response caused by the effects of the environment on the towers through wind and weather conditions. Data-based models were developed to obtain a representation of health degradation, which is primarily based on the finite element model of the tower, subjected to wind excitation. This representation is also based on measurements using a mockup tower with different types of provoked degradation that was subjected to ambient changes in the laboratory. The sensor signals are preprocessed and submitted to an autoencoder neural network to minimize the dimensionality of the resources involved, being analyzed by a classifier, based on a Softmax configuration. The results of the proposed configuration indicate the possibility of early failure detection and evolution evaluation, providing an effective failure detection and monitoring system.

关键词： guyed tower finite element wind loading machine learning autoencoder

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Deep subclass reconstruction network for fault diagnosis of rotating machinery under various operating conditions

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APPLIED SOFT COMPUTING 2021年 112卷 107755-107755页

作者： Yu, Hui Wang, Kai Li, Yan He, Mengfan Sichuan Univ Sch Mech Engn Innovat Method & Creat Design Key Lab Sichuan Pro 24 South Sect 1Yihuan Rd Chengdu 610065 Sichuan Peoples R China

In the real-world industrial application, all measured vibration signals are usually suffered from substantial spurious factors and characterized as large intra-class and intra-subclass variations due to changes in the operating conditions (i.e., working loads, shaft speeds), which seriously deteriorates most of the machine learning methods' ability to learn the discriminative feature representations. In this work, we propose a novel subclass reconstruction network (SCRN) to learn discriminative feature representations from raw vibration signals under different working conditions by suppressing the intra-class and intra-subclass variations in the feature space. Specifically, a novel and simple average strategy is developed to represent the cluster centroid of each subclass information as an effective supervised representation which is effectively embedded into the SCRN model. Furthermore, a new cost function of SCRN is formulated by jointly minimizing the basic and subclass-level reconstruction errors. To better exploit the discriminative information and improve classification performance, we further develop a deep subclass reconstruction network (DSCRN) model by stacking multiple SCRN models together through non-linear transformations to learn better deep feature representations. Extensive comparative evaluations on three benchmarks CWRU, 2009 PHM, and MFPT demonstrate that the proposed methods achieve consistently better diagnosis performance than existing state-of-the-art methods. Especially, when only 1% training samples from CWRU are used, the proposed DSCRN has significant improvement with classification performance of 94.55% over compared method with a gain of at least 9%. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Fault diagnosis Representation learning autoencoder Subclass reconstruction

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AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

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IEEE ACCESS 2021年 9卷 146810-146821页

作者： Wei, Yuanyuan Jang-Jaccard, Julian Sabrina, Fariza Singh, Amardeep Xu, Wen Camtepe, Seyit Massey Univ Cybersecur Lab Comp Sci Info Tech Auckland 0632 New Zealand Cent Queensland Univ Sch Engn & Technol Sydney NSW 2000 Australia CSIRO Data61 Marsfield NSW 2015 Australia

Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multi-layer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.

关键词： Denial-of-service attack Feature extraction Computer crime Random forests Deep learning Neurons Electronic mail Distributed denial of service DDoS deep learning multi-class classification autoencoder MLP CICDDoS2019

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Comprehensive Genomic Subtyping of Glioma Using Semi-Supervised Multi-Task Deep Learning on Multimodal MRI

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IEEE ACCESS 2021年 9卷 167900-167910页

作者： Tupe-Waghmare, Priyanka Malpure, Piyush Kotecha, Ketan Beniwal, Manish Santosh, Vani Saini, Jitender Ingalhalikar, Madhura Symbiosis Int Deemed Univ Symbiosis Inst Technol Pune 412115 Maharashtra India Symbiosis Int Deemed Univ Symbiosis Ctr Med Image Anal Pune 412115 Maharashtra India Natl Inst Mental Hlth & Neurosci Bengaluru 560029 India

High grade glioma (HGG) are the most common, highly infiltrative brain tumors usually with a grim outcome with low survival. Recent comprehensive genomic profiling has greatly elucidated the molecular markers in gliomas that include the mutations in isocitrate dehydrogenase (IDH), 1p/19q co-deletion and alterations in O6 methyl-guanine methyltransferase (MGMT). Prognosis of these markers can support early clinical decision making that can consequently improve the survival outcomes. Multi-modal MRI based phenotypic tools such as radiomics based multi-variate models and state-of-art convolutional neural networks (CNNs) have shown promise in identifying these genotypes. However, current techniques do not facilitate comprehensive genomic profiling of the HGG as these are focused only on a single mutation. Moreover, the models are trained on small datasets and cannot employ unlabeled data, which is abundant as pathological labelling is invasive, expensive and inaccessible in many places. In this work, we build a semi-supervised hierarchical multi-task model that can incorporate unlabeled glioma data and learn to predict multiple molecular markers simultaneously. Our framework employs the latent space from an encoder to incorporate the unlabeled data while the hierarchical multi-task model accounts for the similarity between tasks and utilizes the shared information, resulting in inductive learning that facilitates precise delineation of IDH, MGMT, 1p/19q and grade. We applied our framework to 120 labeled, 149 semi-labeled and 48 unlabeled data using T1-contrast enhanced, T2 and FLAIR images and illustrate that our model performs with an average test accuracy of 82.35% and verified the results using task wise and modality wise ablation analysis. Moreover, the class activation maps computed from each local task branch provide clinical interpretability.

关键词： Tumors Task analysis Multitasking Magnetic resonance imaging Feature extraction Data models Training autoencoder deep learning glioma machine learning MRI molecular markers radiogenomics

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A compressed large language model embedding dataset of ICD 10 CM descriptions

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BMC BIOINFORMATICS 2023年第1期24卷 482-482页

作者： Kane, Michael J. King, Casey Esserman, Denise Latham, Nancy K. Greene, Erich J. Ganz, David A. Yale Univ Sch Publ Hlth Dept Biostat New Haven CT 06520 USA Yale Univ Jackson Sch Global Affairs New Haven CT USA US Healthcare & Life Sci Microsoft Redmond WA USA Brigham & Womens Hosp Boston Claude D Pepper Older Amer Independence Ctr Res Program Mens Hlth Aging & Metab Boston MA USA Univ Calif Los Angeles VA Greater Los Angeles UCLA Los Angeles CA USA

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements, download and use without any other setup. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

关键词： Large language model autoencoder ICD-10-CM Electronic health records EHR NLP

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Mixed-type data generation method based on generative adversarial networks

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2022年第1期2022卷 22-22页

作者： Wei, Ning Wang, Longzhi Chen, Guanhua Wu, Yirong Sun, Shunfa Chen, Peng China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China Beijing Normal Univ Ctr Governance Studies Zhuhai 519087 Peoples R China

Data-driven based deep learing has become a key research direction in the field of artificial intelligence. Abundant training data is a guarantee for building efficient and accurate models. However, due to the privacy protection policy, research institutions are often limited to obtain a large number of training data, which would lead to a lack of training sets circumstance. In this paper, a mixed-type data generation model based on generative adversarial networks is proposed to synthesize fake data that have the same distribution with the real data, so as to supplement the real data and increase the number of available samples. The model first pre-trains the autoencoder which maps given dataset into a low-dimensional continuous space. Then, the generator constructed in the low-dimension space is obtained by training it adversarially with discriminator constructed in the original space. Since the constructed discriminator not only consider the loss of the continuous attributes but also the labeled attributes, the generator nets formed by the generator and the decoder can effectively learn the intrinsic distribution of the mixed data. We evaluate the proposed method both in the independent distribution of the attribute and in the relationship of the attributes, and the experiment results show that the proposed generate method has a better performance in preserve the intrinsic distribution compared with other generation algorithms based on deep learning.

关键词： Generative adversarial network autoencoder Mixed type data

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Autoencoding low-resolution MRI for semantically smooth interpolation of anisotropic MRI

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MEDICAL IMAGE ANALYSIS 2022年 78卷 102393-102393页

作者： Sander, Jorg de Vos, Bob D. Isgum, Ivana Univ Amsterdam Amsterdam Univ Med Ctr Dept Biomed Engn & Phys Locat AMC Amsterdam Netherlands Univ Amsterdam Amsterdam Univ Med Ctr Amsterdam Cardiovasc Sci Locat AMC Amsterdam Netherlands Univ Amsterdam Amsterdam Univ Med Ctr Dept Radiol & Nucl Med Locat AMC Amsterdam Netherlands Univ Amsterdam Informat Inst Amsterdam Netherlands

High-resolution medical images are beneficial for analysis but their acquisition may not always be feasible. Alternatively, high-resolution images can be created from low-resolution acquisitions using conventional upsampling methods, but such methods cannot exploit high-level contextual information contained in the images. Recently, better performing deep-learning based super-resolution methods have been introduced. However, these methods are limited by their supervised character, i.e. they require high-resolution examples for training. Instead, we propose an unsupervised deep learning semantic interpolation approach that synthesizes new intermediate slices from encoded low-resolution examples. To achieve semantically smooth interpolation in through-plane direction, the method exploits the latent space generated by autoencoders. To generate new intermediate slices, latent space encodings of two spatially adjacent slices are combined using their convex combination. Subsequently, the combined encoding is decoded to an intermediate slice. To constrain the model, a notion of semantic similarity is defined for a given dataset. For this, a new loss is introduced that exploits the spatial relationship between slices of the same volume. During training, an existing in-between slice is generated using a convex combination of its neighboring slice encodings. The method was trained and evaluated using publicly available cardiac cine, neonatal brain and adult brain MRI scans. In all evaluations, the new method produces significantly better results in terms of Structural Similarity Index Measure and Peak Signal-to-Noise Ratio ( p < 0 . 001 using one-sided Wilcoxon signed-rank test) than a cubic B-spline interpolation approach. Given the unsupervised nature of the method, high-resolution training data is not required and hence, the method can be readily applied in clinical settings. (c) 2022 The Author(s). Published by Elsevier B.V.

关键词： Image synthesis Semantic interpolation autoencoder Latent space interpolation Unsupervised Cardiac MRI Adult brain MRI Neonatal brain MRI

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Robust malware clustering of windows portable executables using ensemble latent representation and distribution modeling

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第8期35卷

作者： Rizvi, Syed Khurram Jah Fraz, Muhammad Moazam Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad Pakistan Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Sect H-12 Islamabad 44000 Pakistan

Malware is a malicious program used for unauthorized access to organizational infrastructure and systems. To overcome challenges of exponential growth of malware, notable research has been made for unsupervised clustering of Windows-based portable executable (PE). Nevertheless, to the best of our knowledge there has been no research for robust cluster prediction of Windows based PEs using static features. To this end, we proposed an ensemble neural network architecture for unsupervised feature learning and its distribution modeling for robust clustering of PE(s). The novel architecture is a cascaded formation of a deep autoencoder (AE) network and latent distribution modeling (LDM) network. The AE performs feature learning using latent representation and LDM performs the distribution modeling of latent representation using Gaussian approximation. An objective function is also devised for model optimization. The network adjusts the Gaussian components to optimize the distribution modeling. It also performs adjustments for data representations toward related Gaussian centers to make the model behave in adaptive manner. A novel malware dataset has also been collected by employing endpoint security management solution over enterprise network to assess proposed architecture. The dataset contains 21,486 samples including 14,497 malicious and 6989 benign ones. We also performed the evaluation of proposed architecture over publicly available benchmark malware dataset including 138,047 samples comprising 96,742 malicious and 41,323 benign PEs. The experimental results demonstrated that the proposed architecture yielded more than 95% accuracy for cluster prediction. The novel architecture has achieved superior performance and outperformed progressive techniques. The dataset along with implementation are accessible at .

关键词： autoencoder clustering of portable executable distribution modeling ensemble neural network static analysis

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A Novel Lidar Signal Denoising Method Based on Convolutional Autoencoding Deep Learning Neural Network

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ATMOSPHERE 2021年第11期12卷 1403页

作者： Hu, Minghuan Mao, Jiandong Li, Juan Wang, Qiang Zhang, Yi North Minzu Univ Sch Elect & Informat Engn North Wenchang Rd Yinchuan 750021 Ningxia Peoples R China Key Lab Atmospher Environm Remote Sensing Ningxia North Wenchang Rd Yinchuan 750021 Ningxia Peoples R China

The lidar is susceptible to the dark current of the detector and the background light during the measuring process, which results in a significant amount of noise in the lidar return signal. To reduce noise, a novel denoising method based on the convolutional autoencoding deep-learning neural network is proposed. After the convolutional neural network was constructed to learn the deep features of lidar signal, the signal details were reconstructed by decoding part to obtain the denoised signal. To verify the feasibility of the proposed method, both the simulated signals and the actually measured signals by Mie-scattering lidar were denoised. Some comparisons with the wavelet threshold denoising method and the variational modal decomposition denoising method were performed. The results show the denoising effect of the proposed method was significantly better than the other two methods. The proposed method can eliminate complex noise in the lidar signal while retaining the complete details of the signal.

关键词： lidar deep learning autoencoder convolutional neural network denoising

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Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

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PEERJ COMPUTER SCIENCE 2018年第5期4卷 e154页

作者： Fernandes, Kelwin Chicco, Davide Cardoso, Jaime S. Fernandes, Jessica INESC TEC Inst Engn Sistemas & Comp Tecnol & Ciencia Porto Portugal Univ Porto Porto Portugal Princess Margaret Canc Ctr Toronto ON Canada Univ Cent Venezuela Caracas Venezuela

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

关键词： Dimensionality reduction Health-care informatics Denoising autoencoder autoencoder Biomedical informatics Binary classification Deep learning Cervical cancer Artificial neural networks Health informatics

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