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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hua-Hao Liu Pei-Jie Huang Pi-Yuan Lin Wen-Hu Lin Pei-Heng Qi Chong-Hua Song College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (纸本)9781467372220

Feature representation is one of the key research issues in machine learning. In some applications with high dimensionality of data, e.g. genomie microarray data, obtaining a good feature representation with effective dimensionality reduction still remains a challenge. In this paper, instead of selecting a subset of original features by feature selection, we use sparse autoencoder to find a reconstructed feature representation for genetic data analysis. The performance of our proposed method is empirically evaluated using one of the genomie microarray dataset provided in ASU Feature Selection Repository. The results show that the proposed method yield better classification accuracy than some representative feature selection methods.

关键词： Feature representation Genetic data analysis Machine learning sparse autoencoder Machine Learning Artificial Intelligence Feature extraction genetic information Data analysis dimensionality reduction Microarray

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A Duo autoencoder-SVM based Approach for Secure Performance Monitoring of Industrial Conveyor Belt System

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COMPUTERS & CHEMICAL ENGINEERING 2023年第1期177卷

作者： Santhi, Thulasi M. Srinivasan, K. Natl Inst Technol Instrumentat & Control Engn Dept Tiruchirappalli 620015 India

Process industries are fascinated by cyber-physical systems because of the potential to integrate physical systems and the cyber realm, resulting in efficient remote monitoring and control. The conveyor belt system has many critical parameters that require continuous attention, necessitating cyber-physical remote monitoring. Due to cloud-based monitoring of parameters, the system is vulnerable to cyber threats. The proposed technique combines a sparse autoencoder and support vector machine (SVM) to detect false data injection attacks (FDIAs) in the presence of sensor bias fault. The sparse autoencoder extracts sparse features and learns anomaly-free dynamics from the input sensor readings. Then, the trained SVM distinguishes attacks and fault by analysing reconstruction residuals of each measurement reading. The residuals also give an idea about the magnitude of abnormality. The proposed method's efficacy is evaluated in terms of accuracy, precision and false-alarm rate with the help of fault and FDIAs models.

关键词： Conveyor belt Cyber-physical system False data injection attacks sparse autoencoder Support vector machine

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Detecting method of optimal exercise posture using autoencoder: Utilizing surface electromyography and textile stretch sensor

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JOURNAL OF INDUSTRIAL TEXTILES 2024年 54卷

作者： Lee, GyuBin Kim, Jooyong Kim, Ji-seon Kim, SangUn Soongsil Univ Mat Sci & Engn Seoul South Korea Soongsil Univ Dept Organ Mat & Fiber Engn Seoul 06978 South Korea Soongsil Univ Smart Wearables Engn Seoul South Korea

This study aims to validate the optimal posture for the Dumbbell Biceps Curl (DBC) exercise using Textile stretch sensors and Surface Electromyography (sEMG), and then detect inaccurate posture using a sparse autoencoder. To validate the optimal DBC exercise posture, we measured the effects of wrist supination and the angle of the upper body and elbow on the biceps and forearm muscles. A wrist sleeve-shaped Textile stretch sensor detects wrist supination, and sEMG measures biceps and forearm muscle activation. The experiment results confirmed that an angle between the upper body and the elbow within 90 degrees, coupled with wrist supination, constitutes the most efficient posture, maximizing the activation of the biceps while minimizing the synergistic effects of the forearm muscles. Subsequently, this posture was learned through a sparse autoencoder, and the Root Mean Square Error values of the trained model were lowest in the optimal posture (Biceps: 0.090, Forearm: 0.076). This suggests that sparse autoencoder could be useful in identifying inaccurate exercise postures. In summary, this study aims to develop an exercise posture feedback system through the integration of modern exercise physiology and technology, particularly the fusion of AI and sensor technology. The goal is to propose the potential to detect and correct inaccurate or unsafe exercise postures.

关键词： sparse autoencoder textile stretch sensor surface electromyography dumbbell biceps curl exercise detection of unsafe posture supination movement exercise feedback system

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Evaluating Shape Representations for Maya Glyph Classification

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ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE 2016年第3期9卷 14-14页

作者： Can, Gulcan Odobez, Jean-Marc Gatica-Perez, Daniel Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Idiap Res Inst Ctr Parc Rue Marconi 19 CH-1920 Martigny Switzerland

Shape representations are critical for visual analysis of cultural heritage materials. This article studies two types of shape representations in a bag-of-words-based pipeline to recognize Maya glyphs. The first is a knowledge-driven Histogram of Orientation Shape Context (HOOSC) representation, and the second is a data-driven representation obtained by applying an unsupervised sparse autoencoder (SA). In addition to the glyph data, the generalization ability of the descriptors is investigated on a larger-scale sketch dataset. The contributions of this article are four-fold: (1) the evaluation of the performance of a data-driven auto-encoder approach for shape representation;(2) a comparative study of hand-designed HOOSC and data-driven SA;(3) an experimental protocol to assess the effect of the different parameters of both representations;and (4) bridging humanities and computer vision/machine learning for Maya studies, specifically for visual analysis of glyphs. From our experiments, the data-driven representation performs overall in par with the hand-designed representation for similar locality sizes on which the descriptor is computed. We also observe that a larger number of hidden units, the use of average pooling, and a larger training data size in the SA representation all improved the descriptor performance. Additionally, the characteristics of the data and stroke size play an important role in the learned representation.

关键词： HOOSC sparse autoencoder sketch Maya glyph

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Automatic diagnosis of pulmonary nodules using a hierarchical extreme learning machine model

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2018年第3期11卷 192-201页

作者： Hao, Rui Qiang, Zilin Qiang, Yan Ge, Lei Zhao, Juanjuan Shanxi Univ Finance & Econ Coll Informat Management Taiyuan Shanxi Peoples R China Taiyuan Univ Technol Coll Software Taiyuan Shanxi Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan Shanxi Peoples R China

To effectively improve the diagnosis of pulmonary nodules, this paper proposes a new automatic diagnosis method for pulmonary nodules based on a new hierarchical extreme learning machine (H-ELM) that can automatically carry out feature extraction, model training and pulmonary nodule detection. In our method, an adaptive histogram equalisation is used first to enhance contrast of the original pulmonary nodule image. The processed images are then input into an extreme learning machine (ELM)-based unsupervised multilayer auto-encoder to obtain more compact and meaningful high-level features of the pulmonary nodule image. Finally, supervised feature classification, which uses these high-level features of the pulmonary nodule as input data, is implemented using the ELM classifier. In the experiments, 2,800 pulmonary nodule images are used to validate the proposed method, and compared with existing pulmonary nodule diagnosis methods, our proposed method is more accurate and less time consuming and effectively avoids the complexity of manual feature extraction.

关键词： automatic diagnosis pulmonary nodule hierarchical extreme learning adaptive histogram equalisation sparse autoencoder feature extraction feature classification computer aided diagnosis

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Damage Identification and Health Assessment of Crane Girder Structure Under Moving Loads

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2024年第19期24卷 2450217-2450217页

作者： Hu, Mantang Wang, Guofeng Sheng, Yanliang Tianjin Univ Sch Mech Engn Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300350 Peoples R China

In the degradation evaluation process of the crane girder structure under moving loads, the complex vibration response and low accuracy of health index standards are the main factors limiting the performance of the evaluation method. A method for identifying and evaluating damage to crane girder structures under moving loads has been proposed. In this method, optimized empirical wavelet transform (EWT) is used for decomposing the vibration signal of the crane girder structure to obtain sensitive components. A health indicator based on power spectrum maximum sequence is constructed by combining structural vibration simulation and response characteristics analysis under moving loads. A degradation index acquisition scheme based on an optimized sparse autoencoder is proposed, which utilizes a simulated annealing algorithm to optimize the model parameters of the sparse autoencoder. The effectiveness of the proposed method is verified through girder structure crack tests under moving loads.

关键词： Crane girder structural damage moving loads sparse autoencoder

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Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

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FORESTS 2022年第7期13卷 1129页

作者： Lai, Can Zeng, Shucai Guo, Wei Liu, Xiaodong Li, Yongquan Liao, Boyong Zhongkai Univ Agr & Engn Coll Hort & Landscape Architecture Guangzhou 510225 Peoples R China South China Agr Univ Coll Forestry & Landscape Architecture Guangzhou 510642 Peoples R China

Forests suffer from heavy losses due to the occurrence of fires. A prediction model based on environmental condition, such as meteorological and vegetation indexes, is considered a promising tool to control forest fires. The construction of prediction models can be challenging due to (i) the requirement of selection of features most relevant to the prediction task, and (ii) heavily imbalanced data distribution where the number of large-scale forest fires is much less than that of small-scale ones. In this paper, we propose a forest fire prediction method that employs a sparse autoencoder-based deep neural network and a novel data balancing procedure. The method was tested on a forest fire dataset collected from the Montesinho Natural Park of Portugal. Compared to the best prediction results of other state-of-the-art methods, the proposed method could predict large-scale forest fires more accurately, and reduces the mean absolute error by 3-19.3 and root mean squared error by 0.95-19.3. The proposed method can better benefit the management of wildland fires in advance and the prevention of serious fire accidents. It is expected that the prediction performance could be further improved if additional information and more data are available.

关键词： fire prediction imbalanced data deep learning sparse autoencoder deep neural network

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Gait phase recognition of lower limb exoskeleton system based on the integrated network model

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期76卷 000-000页

作者： Zhang, Zaifang Wang, Zhaoyang Lei, Han Gu, Wenquan Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Shanghai Punan Hosp Shanghai 200125 Peoples R China

Exoskeleton robots have become an emerging technology in medical, industrial and military applications. Human gait phase recognition is the crucial technology for recognizing movement intention of the exoskeleton wearer and controlling the exoskeleton robot. As a new biometric recognition method, gait phase recognition also plays an important role in clinical disease diagnosis, rehabilitation training and other fields. This paper proposes an integrated network model SBLSTM that combines sparse autoencoder (SAE), bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) aiming at gait phase recognition during human movement. The model can accurately identify four phases in the gait cycle, including heel strike (HS), foot flat (FF), heel off (HO) and swing phase (SW). Normalization and feature extraction of collected sensor signals are performed to enhance the accuracy of recognition during the gait identification process. The processed data are input into the SBLSTM model. The introduction of SAE into the model can extract key information from gait characteristics. BiLSTM is used to learn temporal patterns and periodic changes in gait data. DNN is adopted to identify gait phases and output classification results. Different algorithms such as DNN, LSTM and SBLSTM are applied to the gait phase detection of subjects. The experimental results show that the SBLSTM algorithm is effective in gait recognition. The accuracy and F-score are outperformed by other algorithms, which verifies the effectiveness of the SBLSTM in practice.

关键词： Exoskeleton robot Gait phase recognition sparse autoencoder Bidirectional long short -term memory Deep neural network

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Classification of multiple power quality events via compressed deep learning

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第6期29卷 e12010.1-e12010.14页

作者： Liu, Hui Hussain, Fida Yue, Shen Yildirim, Ozal Yawar, Sheikh Junaid Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Jiangsu Peoples R China Hamdard Univ Fac Engn Sci & Technol Karachi Pakistan Munzur Univ Dept Comp Engn Tunceli Turkey Sir Syed Univ Engn & Technol Dept Elect Engn Karachi Pakistan

This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine-tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS- and SAE-based DL algorithms can be efficiently used for single and multiple PQDs classifications.

关键词： compressed sensing deep machine learning power quality disturbances classification sparse autoencoder

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A key-factor denoising strategy for quasi periodic non-stationary incipient faults diagnosis

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MEASUREMENT 2022年 197卷

作者： Yang, Jing Xie, Guo Yang, Yanxi Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China

Currently, the analysis method based on monitoring data has become an effective means of machinery fault diagnosis, and the fault diagnosis with obvious data features has achieved fruitful results. However, the incipient fault signals of equipment not only show the characteristics of weak intensity, quasi periodic and non-stationary, but also are submerged in strong background noise, which often makes it difficult to extract effective information directly from the original signals. Therefore, in order to effectively solve the problem of incipient fault diagnosis, and considering the capability of sparse autoencoder (SAE) to extract features automatically, this paper proposes a key-factor denoising strategy and an improved SAE network, and then an improved SAE network with key factor denoising strategy (KF-ISAE) based intelligent diagnosis method for quasi periodic non-stationary incipient faults is proposed. The main contributions of the proposed method are as follows. On the one hand, signal denoising that cannot be ignored in fault diagnosis is achieved by the developed incipient faults sensitivity based key-factor denoising strategy, and on the other hand, for SAE, the blindness of feature learning is handled by the formed weights constraints. In addition, the health condition identification and the fault severity level determination of machinery are completed by the improved SAE network designed in this paper. Finally, verification and comparative experiments show the effectiveness and practicability of the proposed method.

关键词： EEMD Key-factor denoising sparse autoencoder Incipient fault Intelligent fault diagnosis

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