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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zheng, Pan Wu, Zhaoyue Paoletti, Mercedes E. Haut, Juan M. Hu, Jun Su, Hongjun Hohai Univ Sch Earth Sci & Engn Nanjing Peoples R China Univ Extremadura Dept Technol Comp & Commun Badajoz Spain

ISBN: (纸本)9798350360332;9798350360325

Hyperspectral sensors carried by lunar rovers or satellites can effectively invert the mineral abundance of the lunar surface. Due to special environment of the lunar surface and small number of samples, it is a challenge to analyze typical minerals on the lunar surface using hyperspectral images. In this paper, a spectral-spatial diffusion autoencoder unmixing model (SSDiffAU) is proposed for mineral mapping. This is the first time the diffusion model is invoked in the unmixing field. The 3D-CNN is utilized as an encoder to represent deep spectral-spatial information. Additionally, the diffusion model is used to obtain high quality abundance maps, and then the endmember matrix is obtained by the decoder. Finally, the performance of the proposed algorithm was verified using popular unmixing datasets. The hyperspectral data acquired by Chang'e-3 Yutu rover are unmixed to estimate typical mineral spectra and their abundance.

关键词： Hyperspectral unmixing Diffusion model autoencoder network Change'E-3 Yutu Rover

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Protocol Aware Unsupervised network Intrusion Detection System 22

Protocol Aware Unsupervised Network Intrusion Detection Syst...

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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

In recent years the number of attacks on computer networks has increased exponentially due to the easy availability of sophisticated tools and attack techniques. These attacks are possible due to existing vulnerabilities in networking protocols. Most of the machine learning based intrusion detection systems proposed earlier, to mitigate these attacks, consider training a model for the group of attacks, which doesn't consider protocol-specific properties into account and is biased toward attacks where most of the data is available. In this paper, we propose protocol aware unsupervised method based on an 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. Our proposed method is based on building protocol aware model by combining individual protocol-specific encoders and learning the protocol channel importance using attention mechanism. We perform various experiments on different recent datasets like CICDDoS2019, and CICIDS2018, and experimental results show that the proposed protocol aware model performs better than the non-protocol aware method.

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

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Few-Shot Unsupervised Specific Emitter Identification Based on Density Peak Clustering Algorithm and Meta-Learning

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IEEE SENSORS JOURNAL 2022年第18期22卷 18008-18020页

作者： Xie, Cunxiang Zhang, Limin Zhong, Zhaogen Naval Aviat Univ Yantai Peoples R China Tianjin Univ Tianjin Peoples R China

Deep learning methods are becoming increasingly popular for improving the efficiency of specific emitter identification (SEI). However, in noncooperative scenarios, it is difficult to both obtain a large number of signal samples for the training of deep-learning models and assign labels to the acquired samples. To solve these two problems, a few-shot unsupervised SEI method is proposed in this study. First, the original radio-frequency (RF) signal is preprocessed based on the Hilbert-Huang transform (HHT) to obtain the Hilbert time-frequency spectrum, which can highlight RF fingerprints (RFFs) that can be used as signal training samples. Then, the Hilbert time-frequency spectrum is input to an improved autoencoder network for training to obtain the latent vector that can represent hidden features of the received signal. Next, the clustering by fast search and find of a density peaks [i.e., density peak clustering (DPC)] algorithm is used to cluster and label the latent vector, which is later reconstructed by the improved autoencoder network to obtain training samples with labeled information. Finally, the meta-learning algorithm is used to train the few-shot SEI network under the few-shot condition, so that it can distinguish different types of RF signals, corresponding to specific emitters. The experimental results show that the unlabeled signal training samples can be clustered and labeled accurately with the proposed method to perform SEI efficiently in few-shot scenarios. In comparison with state-of-the-art solutions, the proposed method in this study exhibited its superiority with regard to few-shot unsupervised SEI.

关键词： autoencoder network density peak clustering (DPC) Hilbert-Huang transform (HHT) meta-learning specific emitter identification (SEI)

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Parameter selection of Touzi decomposition and a distribution improved autoencoder for PolSAR image classification

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2022年第0期186卷 246-266页

作者： Wang, Jianlong Hou, Biao Ren, Bo Zhang, Yake Yang, Meijuan Wang, Shuang Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China

Touzi decomposition provides more scattering information corresponding to parameters describing different targets. It is possible to explore the selection method of parameters, which plays an important role in polarimetric synthetic aperture radar (PolSAR) image classification. Therefore, this paper presents an innovative parameter ordering scheme based on the histogram of parameters obtained by Touzi decomposition. Then, a parameter selection scheme is proposed by analyzing the influence of the increase of parameters on the overall accuracy obtained by the Softmax classifier. Based on the Gamma distribution, the parameter selection method of eigenvalue decomposition is also given. As the data distribution of the selected parameters does not always satisfy the Gaussian distribution, it may not be sufficient to directly apply the autoencoder network. Thus, the loss function of the autoencoder network is modified by the construction of different data error terms according to a different data distribution form of the selected parameters, and then an improved autoencoder network is proposed. The process of feature extraction and classification can be regarded as a whole network to better accomplish the task of classification. An extensive set of experiments are done on four real PolSAR images. Compared with the classification results of all parameters, the gap in the overall accuracy of the parameters obtained by the proposed selection scheme is only about 1%. In terms of the classification overall accuracy, considering the distribution of parameters in the autoencoder network and the construction of the whole classification network can improve it by about 6-7% for four data sets. Code is available at https://***/ Justin20220123/ISPRS2022.

关键词： Polarization decomposition Data distribution Touzi decomposition Parameter selection Feature extraction autoencoder network

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Improved extreme learning machine with autoencoder and particle swarm optimization for short-term wind power prediction

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NEURAL COMPUTING & APPLICATIONS 2022年第6期34卷 4643-4659页

作者： El Bourakadi, Dounia Yahyaouy, Ali Boumhidi, Jaouad Sidi Mohammed Ben Abdellah Univ Fac Sci Dhar Mehraz Dept Comp Sci LISAC Lab Fes 30003 Morocco

Wind energy is a green source of electricity that is growing faster than other renewable energies. However, dependent mainly on wind speed, this source is characterized by the randomness and fluctuation that makes challenging optimal management. In order to remedy this inconvenience, it is essential to predict meteorological data or power produced by generators. In this paper, we present a wind power forecasting approach based on regularized extreme learning machine algorithm (R-ELM), particle swarm optimization method (PSO), and autoencoder network (AE) so-called autoencoder-optimal regularized extreme learning machine (AE-ORELM). Firstly, we train the AE model by the ELM algorithm. Then, the output weights resulting are used as the input weights of the R-ELM model. Furthermore, the PSO method is used to optimally select hyperparameters of the whole model, namely the regularization parameter and the number of hidden nodes in the hidden layer. The simulation results show that the proposed AE-ORELM can achieve better testing accuracy with a faster training time compared to related models.

关键词： Wind energy Wind power prediction Extreme learning machine autoencoder network Regularized extreme learning machine Particle swarm optimization

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A Probability Metric-Based autoencoder for Hyperspectral Unmixing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Min, Anyou Li, Hong Huazhong Univ Sci & Technol Sch Math & Stat Wuhan 430074 Peoples R China

In recent years, hyperspectral unmixing (HU) has been a crucial preprocess in hyperspectral imagery analysis. Current learning-based methods of HU are built on the autoencoder framework. Most of them hold a pointwise loss function only comparing the individual difference without the overall difference. As a remedy, we propose a probability metric-based autoencoder (PMAE) for HU. Specifically, we combine a distribution distance called maximum mean discrepancy (MMD) with underlying loss spectral angle distance (SAD) as the final loss function for training the unmixing autoencoder. The MMD can be seen as a regularization that is designed to match all orders of statistics between the input spectrums and the reconstructed spectrums. The SAD is to compare the individual difference, while the MMD compares the overall difference. Combing these two points, we can obtain more prior information to guide the autoencoder training toward boosting the unmixing performance. In order to validate this viewpoint, we conduct model analysis and a series of ablation studies, showing that adding the MMD into the autoencoder unmixing model, indeed, boosts the unmixing performance a lot compared to the single SAD objective. Experimental results on five real hyperspectral scenes demonstrate the effectiveness and competitiveness of the proposed PMAE compared with several classical and learning-based approaches.

关键词： Hyperspectral imaging Training Task analysis Mathematical models Image reconstruction Linear programming Probability distribution autoencoder network hyperspectral unmixing (HU) maximum mean discrepancy (MMD) spectral angle distance (SAD)

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A Self-Adaptive Discriminative autoencoder for Medical Applications

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第12期32卷 8875-8886页

作者： Ge, Xiaolong Qu, Yanpeng Shang, Changjing Yang, Longzhi Shen, Qiang Dalian Maritime Univ Coll Artificial Intelligence Dalian 116026 Peoples R China Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Wales Northumbria Univ Dept Comp & Informat Sci London E1 7HT England

Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this paper. The proposed SADAE system is implemented under a deep metric learning framework which consists of $K$ local autoencoders, employed to learn the $K$ subspaces that represent the diverse distribution of the underlying data, and a global autoencoder to restrict the spatial scale of the learned representation of images. Such community of autoencoders is aided by a self-adaptive metric learning method that extracts the discriminative features to recognize the different categories in the given images. The quality of the extracted features by SADAE is compared against that of those extracted by other state-of-the-art deep learning and metric learning methods on five popular medical image data sets. The experimental results demonstrate that the medical image recognition results gained by SADAE are much improved over those by the alternatives.

关键词： Measurement Feature extraction Training Medical diagnostic imaging Data mining Face recognition Deep learning autoencoder network deep learning metric learning computer aided diagnosis

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A Review of Abnormal Personnel Behavior Detection Based on Deep Learning 29

A Review of Abnormal Personnel Behavior Detection Based on D...

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29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Shi Jinfei Zhang Tianqi He Guanghong Hao Fei Nanjing Inst Technol Kangni Ind Technol Res Inst Nanjing Peoples R China Nanjing Inst Technol Sch Mech Engn Nanjing Peoples R China

ISBN: (纸本)9798350325621

With the increasing demand for intelligent security on various occasions, the detection of personnel behavior in a variety of environments has become a current research hotspot through new detection methods. Based on the recent popular deep learning techniques, this paper gives a detailed review of abnormal behavior detection. First, abnormal behavior detection based on machine vision is the focus of this review and divide the research progress of deep learning on this basis into two categories, convolutional neural network and autoencoder network. According to the characteristics of detection methods and applicable environment, the research methods of different categories are described and analyzed. Next, in the light of the situation of person gathering in different environments, some commonly used public datasets are listed, and the experiment results of various detection methods on each dataset are compared and analyzed. Finally, abnormal behavior detection trends are analyzed and prospected.

关键词： behavior detection convolutional neural network autoencoder network datasets

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Optimization Strategies for the k-Nearest Neighbor Classifier

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SN Computer Science 2023年第1期4卷 47页

作者： Yepdjio Nkouanga, Hermann Vajda, Szilárd Department of Computer Science Portland State University 1825 SW Broadway Portland 97201 OR United States Department of Computer Science Central Washington University 400 University Way Ellensburg 98926 WA United States

In this paper, we propose six (6) fast and efficient classification schemes for different type of images (digits, objects, characters) using the classical k-nearest neighbor (kNN) classifier. It is common knowledge that kNN is one of the most popular supervised classification algorithm. However, for large data collections, the classification is time consuming because of the distance calculations which increase with the number of training samples and the data dimensionality. To reduce the number of training samples, we propose two techniques that employ the notions of convex envelope and stratified sampling. For the convex envelope, only the data points building the convex hull will serve as designated prototypes (training samples), thus reducing considerably the computational burden. To reduce the dimensionality of the data, we considered auto-encoder networks and vector embedding. The former is learning a way to compact data representation using statistical machine learning and the latter is changing the data itself emphasizing how each data sample is organized in space compared to others. The experiments on multiple benchmark data collections such as MNIST, Fashion-MNIST and Lampung characters show a considerable classification speed up (up to 32×) with no significant drop in accuracy when compared to results obtained using the complete data. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： autoencoder network Character recognition Classification Convex envelope Digit recognition k-Nearest neighbor Stratified sampling Vector embedding

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A simple yet efficient unsupervised anomaly detection network for industrial machine 6

A simple yet efficient unsupervised anomaly detection networ...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Wu, Danxuan Chen, Qijun Tongji University Shanghai China

ISBN: (纸本)9798350305623

Anomaly detection, aiming to detect items, events, or observations that do not match expected patterns or other items in a data set, is a crucial problem in many fields such as network intrusion detection, fraud detection and so on. Due to the rarity of labeled anomalous datasets, the research aims at detecting anomalies without anomalous samples and only using normal ones which called unsupervised learning. While traditional machine learning devoted to find a hard marginal between abnormal and normal ones, some unavoidable disturbance along with the signal can cause deviation. Moreover, the deep learning research of this subject focus on construct an autoencoder network only using normal data to memory the patterns of the normality. However, considering the generalization ability of neural network, it may occur that abnormal ones can reconstruct well thus leading the mistake of predicting. In this passage, to overcome the above two problem, we design a noise generator to simulate some reasonable noises, utilize and combine the neural network and memory bank to strengthen the ability of the network to memorize the patterns of the normal instances. We experiment and compare our algorithm on the MIMII sound anomaly dataset and the corresponding results shows the effectiveness and efficiency of our approach. It reaches 91.37 % in Accuracy and 93.76 % in F1 score. To our knowledge, it is the state of the art work up to now. © 2023 IEEE.

关键词： autoencoder network noise simulator unsupervised anomaly detection

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