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Minimum distance constrained sparse autoencoder network for hyperspectral unmixing

JOURNAL OF APPLIED REMOTE SENSING 2020年第4期14卷

作者： Zhao, Zhengang Hu, Dan Wang, Hao Yu, Xianchuan Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Univ North Carolina Chapel Hill Dept Radiol Chapel Hill NC USA BRIC Chapel Hill NC USA

Hyperspectral unmixing is an important task in the analyses and applications of hyperspectral images. Recently, the autoencoder network has been intensively studied to unmix hyperspectral image, recovering the material signatures and their corresponding abundance maps from the hyperspectral pixels. However, the autoencoder network cannot get a unique solution since the loss function is nonconvex. In addition, the data often contain a lot of noise. To address these problems, we propose an autoencoder network, referred to as MDC-SAE, that introduces two different constraints to optimize the spectral unmixing problem. Specifically, we adopt the L-1/2 norm regularizer to constrict the abundance vectors, making them sparse. At the same time, we apply the minimum distance constraint on the endmember matrix to push each endmember toward its centroid. We evaluate our method on both synthetic and real data sets, and experimental results demonstrate that the proposed method can achieve the desired solutions and outperforms several state of the art methods. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral unmixing autoencoder network deep learning L-1/2 regularizer minimum distance constraint

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Heart sound classification with signal instant energy and stacked autoencoder network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年 64卷

作者： Deperlioglu, Omer Afyon Kocatepe Univ Vocat Sch Afyon ANS CampusEskisehir St 8 Km TR-03200 Afyon Turkey

Recently, different signal processing and classification methods have been tried to increase the success of classification for a heart sound analysis. For this purpose, in many studies, S1 and S2 segments of heart sounds were obtained by using methods such as Shannon energy, discrete time wavelet transform, Hilbert transform, and then classified. In this study, the use of signal energy, which is generally used to segment S1-S2 sounds in heart sounds, in direct classification was investigated. The instant energies of the heart sounds obtained by the resampled energy method were used directly for classification. The classification was done with a stacked autoencoder network. Experiments were carried out with the PASCAL B-training data set to test the performance of the proposed method. The results were compared with the data from previous studies for the same data set. As a result of the research, it is seen that the classification performance criterias obtained with the proposed method are as similar as the segmented classification. Thus, it was concluded that the instant energy of the heart sounds, and a stacked autoencoder networks can be very easily used for the diagnosis of heart diseases from heart sounds and a more efficient, and effective classification performance can be obtained.

关键词： Heart sound Signal instant energy Re-sampled energy Deep learning autoencoder network Classification of heart sounds

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Gated autoencoder network for Spectral-Spatial Hyperspectral Unmixing

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REMOTE SENSING 2021年第16期13卷

作者： Hua, Ziqiang Li, Xiaorun Jiang, Jianfeng Zhao, Liaoying Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Key Lab Special Equipment Safety Testing Technol Hangzhou 310020 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China

Convolution-based autoencoder networks have yielded promising performances in exploiting spatial-contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult to balance their effects on unmixing results. In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. Gating mechanism is adopted in the networks to filter and regularize spatial features to construct an unmixing algorithm based on spectral information and supplemented by spatial information. In addition, abundance sparsity regularization and gating regularization are introduced to ensure the appropriate implementation. Experimental results validate the superiority of the proposed method to the state-of-the-art techniques in both synthetic and real-world scenes. This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing.

关键词： hyperspectral unmixing spectral-spatial model autoencoder network gating mechanism

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Waste classification using autoencoder network with integrated feature selection method in convolutional neural network models

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MEASUREMENT 2020年 153卷

作者： Togacar, Mesut Ergen, Burhan Comert, Zafer Firat Univ Dept Comp Technol Elazig Turkey Firat Univ Fac Engn Dept Comp Engn Elazig Turkey Samsun Univ Fac Engn Dept Software Engn Samsun Turkey

Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the autoencoder network. The feature sets are then extracted using two datasets by Convolutional Neural network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： autoencoder network Deep learning Recycling waste Feature selection

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An Optimization Method of Logistics Distribution Route based on autoencoder network

An Optimization Method of Logistics Distribution Route based...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Miao, Jiaqi Xu, Panfeng Sun, Han Wang, Ruoxi Wang, Youming Xian Univ Posts & Telecommun Sch Automat Xian Peoples R China

ISBN: (纸本)9781728124858

As the problem of path optimization becomes more and more complex, it is found that the expected results of path optimization cannot he obtained by using the traditional mathematical modeling method, which has a long operation time. The path optimization problem is not only a combinatorial optimization problem, the uncertainty of influencing factors increases the difficulty of the path optimization problem, and the road conditions will be different with the change of time. An autoencoder network prediction model based on deep learning is proposed in this study. First, the autoencoder network is used to construct a prediction model of road conditions. Factors such as weather and road condition information were input into the network as raw data for training and learning. Adam optimizer is used to optimize and predict road conditions. Dijkstra path optimization algorithm is used to solve the optimal path. The accuracy of the proposed algorithm for predicting the optimal path is 97.22% and that of the traditional Dijkstra algorithm is 90.82%.

关键词： deep learning logistics distribution path optimization autoencoder network

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Quality assessment of building footprint data using a deep autoencoder network

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2017年第10期31卷 1929-1951页

作者： Xu, Yongyang Chen, Zhanlong Xie, Zhong Wu, Liang China Univ Geosci Dept Informat Engn Wuhan Hubei Peoples R China Natl Engn Res Ctr Geog Informat Syst Wuhan Hubei Peoples R China

Volunteered geographic information (VGI), OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated. However, the quality of VGI remains a valid concern. In this paper, we use the matched results between OSM building footprints and official data as the samples for training an autoencoder network, which encodes and reconstructs the sample populations according to unknown complex multivariate probability distributions. Then, the OSM data are assessed based on the theory that small probability samples contribute little to the autoencoder network and that they can be recognized by the higher reconstructed errors during training. In the method described here, the selected measures, including data completeness, positional accuracy, shape accuracy, semantic accuracy and orientation consistency between OSM and official data, are used as the inputs for a deep autoencoder network. Finally, building footprint data from Toronto, Canada, are evaluated, and experiments show that the proposed method can assess the OSM data comprehensively, objectively and accurately.

关键词： autoencoder network OpenStreetMap quality assessment building footprint

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Recognition of geochemical anomalies using a deep autoencoder network

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COMPUTERS & GEOSCIENCES 2016年 86卷 75-82页

作者： Xiong, Yihui Zuo, Renguang China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China

In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions. During the training, small probability samples contribute little to the autoencoder network. These samples can be recognized by the trained model as anomalous samples due to their comparatively higher reconstructed errors. The southwestern Fujian district (China) is chosen as a case study area. A variety of learning rates, iterations, and the size of each hidden layer are constructing and training the deep autoencoder networks on all the geochemical samples. The reconstruction error (or, anomaly score) of each training sample is used to recognize multivariate geochemical anomalies associated with Fe polymetallic mineralization. By comparing the results obtained with a continuous restricted Boltzmann machine, we conclude that the autoencoder network can be trained to recognize multivariate geochemical anomalies. Most of the known skarn-type Fe deposits are located in areas with high reconstruction errors or anomaly scores in the anomaly map, indicating that these anomalies may be related to Fe mineralization. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： autoencoder network Multivariate geochemical data Geochemical exploration Skarn-type iron deposits

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network based Intrusion Detection using Time aware LSTM autoencoder 22

Network based Intrusion Detection using Time aware LSTM Auto...

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IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE Conference / CSE Conference / EUC Conference / ISCI Conference

作者： Ratti, Ritesh Singh, Sanasam Ranbir Nandi, Sukumar Indian Inst Technol Dept Comp Sci & Engn Gauhati 781039 Assam India

ISBN: (纸本)9798350381993;9798350382006

With the advancement of Internet technologies Cyber attacks have become a significant risk to overall security, therefore, intelligent security systems are required to strengthen the network security against these threats. Machine learning has played a pivotal role in the detection and mitigation of these attacks over the years. However, to identify the zero-day attacks and incorporate frequently changing attack scenarios, techniques need to be developed that can work with minimally labeled data. In this paper, we propose Time aware LSTM autoencoder-based learning approach to detect the attack in network flows by training the model using only normal traffic and using reconstruction error as the parameter to classify the attack event. We perform the experiments on different recent datasets like CICDDoS2019, & CICIDS2018 and experimental results exhibit that the proposed model overall provides better classification metrics.

关键词： Intrusion Detection System Unsupervised Machine Learning Anomaly Detection autoencoder network

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Fault Diagnosis and Isolation for Diesel Engine Combustion Chambers Based on autoencoder and BP Neural network 1

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15th World Congress on Engineering Asset Management (WCEAM)

作者： Yu, Yonghua Hu, Jia Yang, Jianguo Wuhan Univ Technol Wuhan 430063 Peoples R China

ISBN: (数字)9783030967949

ISBN: (纸本)9783030967949;9783030967932

In order to improve the efficiency and accuracy of diesel engine combustion chamber fault isolation, a method of combining the feature dimension reduction of autoencoder network and the fault isolation of BP neural network was proposed based on acoustic emission signals. Taking a Z6170 diesel engine of China ZICHAI company as an example, fault simulation tests of exhaust valve and piston rings under experimental environments were carried out, and the acoustic emission signals of the cylinder head were collected, then the time-domain, frequency-domain and other characteristic parameters of different signal sections in the whole cycle were extracted. The dimension of characteristic parameters was reduced by using autoencoder network, BP neural network was used to fault diagnosis and fault isolation, so that a fault diagnosis and fault isolation model of combustion chamber components was established. After training and verification of the model, it shows that the proposed diagnosis and isolation method is effective with capability of identifying the faults of exhaust valve and piston ring for the combustion chamber parts of diesel engines, therefore, it is promising to detect and isolate the condition of combustion components automatically.

关键词： Diesel engine Fault diagnosis autoencoder network BP neural network Acoustic emission

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Towards Enhanced Interpretability: A Mechanism-Driven domain adaptation model for bearing fault diagnosis across operating conditions

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 225卷

作者： Jiang, Fei Kuang, Yicong Li, Tao Zhang, Shaohui Wu, Zhaoqian Feng, Ke Li, Weihua Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China

Deep learning has emerged as a formidable tool in bearing fault diagnosis, yet its effectiveness is often hampered by the opaqueness of feature interpretation and the scarcity of labeled data under varied industrial conditions. In response to these challenges, this paper introduces a mechanismdriven domain adaptation model with interpretability tailored for bearing fault diagnosis across various operating conditions. Specifically, a customized autoencoder driven by bearing fault mechanism is developed for unsupervised extraction of interpretable features from vibration signal, aiming to guide the fault diagnosis model capture the mechanism features of bearing faults. Furthermore, the mechanism features of each sample signal are constructed into a physical parameter matrix composed of natural frequency, damping ratio, amplitude, etc, transforming the input into meaningful physical indicators of bearing condition rather than mere original data points. Ultimately, the physical parameter matrix of all samples is input into the domain adversarial neural network to adaptively diagnose bearing faults under different operating conditions. The feature extraction capability of the mechanism-driven model is verified through various simulation analyses, and three sets of experiments and comparison methods are utilized to verify the effectiveness and advantages of the proposed method in terms of bearing fault diagnosis and interpretability.

关键词： Bearing fault diagnosis Domain adaptation autoencoder network Interpretable networks

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