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Traffic classification using distributions of latent space in software-defined networks: An experimental evaluation

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 119卷

作者： Jang, Yehoon Kim, Namgi Lee, Byoung-Dai Kyonggi Univ Div AI & Comp Engn Suwon South Korea

With the emergence of new Internet services and the drastic increase in Internet traffic, traffic classification has become increasingly important to effectively satisfy the quality of service to users. The traffic classification system should be resilient and operate smoothly regardless of network conditions or performance and should be capable of handling various classes of Internet services. This paper proposes a traffic classification method in a software-defined network environment that employs a variational autoencoder (VAE) to accomplish this. The proposed method trains the VAE using six statistical features and extracts the distributions of latent features for the flows in each service class. Furthermore, it classifies the query traffic by comparing the distributions of latent features for the query traffic with the learned distributions of the service classes. For the experiment, the statistical features of network flows were collected from real-world domestic and overseas Internet services for training and testing. According to the experimental results, the proposed method has an average accuracy of 89%. This accuracy was 52%, 47%, 39%, 59%, and 26% higher than conventional statistics-based classification methods, MLP, AE+MLP, VAE+MLP, and SVM, respectively. This result clearly suggests that probability distributions of latent features, rather than specific values for latent features, can be used as more stable features.

关键词： Flow classification Jensen-Shannon divergence Latent features Software-defined network variational autoencoder

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Multimodal representation models for prediction and control from partial information

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ROBOTICS AND AUTONOMOUS SYSTEMS 2020年 123卷 103312-000页

作者： Zambelli, Martina Cully, Antoine Demiris, Yiannis Imperial Coll London Dept Elect & Elect Engn Personal Robot Lab London England Imperial Coll London Personal Robot Lab London England DeepMind London London England Imperial Coll London Dept Comp London England

Similar to humans, robots benefit from interacting with their environment through a number of different sensor modalities, such as vision, touch, sound. However, learning from different sensor modalities is difficult, because the learning model must be able to handle diverse types of signals, and learn a coherent representation even when parts of the sensor inputs are missing. In this paper, a multimodal variational autoencoder is proposed to enable an iCub humanoid robot to learn representations of its sensorimotor capabilities from different sensor modalities. The proposed model is able to (1) reconstruct missing sensory modalities, (2) predict the sensorimotor state of self and the visual trajectories of other agents actions, and (3) control the agent to imitate an observed visual trajectory. Also, the proposed multimodal variational autoencoder can capture the kinematic redundancy of the robot motion through the learned probability distribution. Training multimodal models is not trivial due to the combinatorial complexity given by the possibility of missing modalities. We propose a strategy to train multimodal models, which successfully achieves improved performance of different reconstruction models. Finally, extensive experiments have been carried out using an iCub humanoid robot, showing high performance in multiple reconstruction, prediction and imitation tasks. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multimodal learning Autonomous learning variational autoencoder

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Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model

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REMOTE SENSING 2023年第15期15卷 3890-3890页

作者： Cheng, Ying Zhao, Liaoying Chen, Shuhan Li, Xiaorun Hangzhou Dianzi Univ Comp & Software Sch Hangzhou 310018 Peoples R China Zhejiang Univ Dept Elect Engn Hangzhou 310027 Peoples R China

Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by variations in environmental conditions. These and other factors interfere with the accurate discrimination of source type. Several spectral mixing models have been proposed for hyperspectral unmixing to address the spectral variability problem. The interpretation for the spectral variability of these models is usually insufficient, and the unmixing algorithms corresponding to these models are usually classic unmixing techniques. Hyperspectral unmixing algorithms based on deep learning have outperformed classic algorithms. In this paper, based on the typical extended linear mixing model and the perturbed linear mixing model, the scaled and perturbed linear mixing model is constructed, and a spectral unmixing network based on this model is constructed using fully connected neural networks and variational autoencoders to update the abundances, scales, and perturbations involved in the variable endmembers. Adding spatial smoothness constraints to the scale and adding regularization constraints to the perturbation improve the robustness of the model, and adding sparseness constraints to the abundance determination prevents overfitting. The proposed approach is evaluated on both synthetic and real data sets. Experimental results show the superior performance of the proposed method against other competitors.

关键词： hyperspectral unmixing endmember variability linear mixing model variational autoencoder

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Enhanced Dual-Channel Model-Based with Improved Unet plus plus Network for Landslide Monitoring and Region Extraction in Remote Sensing Images

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REMOTE SENSING 2024年第16期16卷 2990页

作者： Wang, Junxin Zhang, Qintong Xie, Hao Chen, Yingying Sun, Rui Beijing Normal Univ Fac Arts & Sci Zhuhai 519087 Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China

Landslide disasters pose significant threats to human life and property;therefore, accurate and effective detection and area extraction methods are crucial in environmental monitoring and disaster management. In our study, we address the critical tasks of landslide detection and area extraction in remote sensing images using advanced deep learning techniques. For landslide detection, we propose an enhanced dual-channel model that leverages EfficientNetB7 for feature extraction and incorporates spatial attention mechanisms (SAMs) to enhance important features. Additionally, we utilize a deep separable convolutional neural network with a Transformers module for feature extraction from digital elevation data (DEM). The extracted features are then fused using a variational autoencoder (VAE) to mine potential features and produce final classification results. Experimental results demonstrate impressive accuracy rates of 98.92% on the Bijie City landslide dataset and 94.70% on the Landslide4Sense dataset. For landslide area extraction, we enhance the traditional Unet++ architecture by incorporating Dilated Convolution to expand the receptive field and enable multi-scale feature extraction. We further integrate the Transformer and Convolutional Block Attention Module to enhance feature focus and introduce multi-task learning, including segmentation and edge detection tasks, to efficiently extract and refine landslide areas. Additionally, conditional random fields (CRFs) are applied for post-processing to refine segmentation boundaries. Comparative analysis demonstrates the superior performance of our proposed model over traditional segmentation models such as Unet, Fully Convolutional Network (FCN), and Segnet, as evidenced by improved metrics: IoU of 0.8631, Dice coefficient of 0.9265, overall accuracy (OA) of 91.53%, and Cohen's kappa coefficient of 0.9185 on the Bijie City landslide dataset;and IoU of 0.8217, Dice coefficient of 0.9021, overall accuracy (OA) of 96.68%,

关键词： landslide detection dual-channel model feature fusion variational autoencoder improving Unet plus plus model

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Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm

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SENSORS 2022年第24期22卷 9943-9943页

作者： Chen, Ting Chen, Youjing Li, Hao Gao, Tao Tu, Huizhao Li, Siyu Changan Univ Sch Informat Engn Xian 710064 Peoples R China Tongji Univ Coll Transportat Engn Key Lab Rd Traff Engn Minist Educ Shanghai 201804 Peoples R China

With the rapid development of artificial intelligent technology, the deep learning method is widely applied to predict human driving intentions due to its relative accuracy of prediction, which is one of critical links for security guarantee in the distributed, mixed driving scenario. In order to sense the intention of human-driven vehicles and reduce the self-driving collision avoidance rate, an improved intention prediction method for human-driving vehicles based on unsupervised, deep inverse reinforcement learning is proposed. Firstly, a contrast discriminator module was proposed to extract richer features. Then, the residual module was created to overcome the drawbacks of gradient disappearance and network degradation with the increase in network layers. Furthermore, the dropout layer was generated to prevent the over-fitting phenomenon in the whole training process of the GRU network, so as to improve the generalization ability of the network model. Finally, abundant experiments were conducted on datasets to evaluate our proposed method. The pass rate of self-driving vehicles with conservative driver probabilities of p = 0.25, p = 0.4, and p = 0.6 improved by a maximum of 8%, 10%, and 3%, compared with the classical method LSTM and VAE + RNN. It indicates that the prediction results of our proposed method fit more with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method.

关键词： self-driving vehicles latent states variational autoencoder deep reinforcement learning

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Bearing degradation prediction based on deep latent variable state space model with differential transformation

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 220卷

作者： Ran, Bi Peng, Yizhen Wang, Yu Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Xi An Jiao Tong Univ Coll Mech Engn Xian 710000 Peoples R China

Rolling bearings are a critical component of mechanical transmission equipment. Predicting their degradation trend is crucial for ensuring safe and stable equipment operation. Most existing bearing degradation prediction methods based on state space models (SSMs) use either linear functions or limited nonlinear functions (e.g., exponential/power laws) to construct the state and measurement equations. As such, these models fail to adapt to the complex and varied nonlinear degradation processes that occur in real-world environments. To address this limitation, we developed a deep latent variable-driven state space degradation model and employed it for bearing degradation prediction. Owing to the powerful nonlinear modeling ability of deep learning models, the proposed method extends the applicability of state space equations. In addition, it inherits the advantages of SSMs and can model uncertainties in a structured manner. Furthermore, the model was integrated with differential pre-transformation to improve its longterm prediction performance. Finally, to validate the effectiveness of the proposed model in predicting bearing degradation, experiments were conducted using a bearing dataset from the PRONOSTIA platform and real wind turbine bearing data. Results showed that the proposed method yielded higher bearing degradation prediction accuracy than existing methods, thus demonstrating the superior performance of the proposed model in predicting bearing degradation.

关键词： Bearings Degradation prediction variational autoencoder State space model

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Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 125卷 102251-102251页

作者： Munoz-Ramirez, Veronica Kmetzsch, Virgilio Forbes, Florence Meoni, Sara Moro, Elena Dojat, Michel Univ Grenoble Alpes INRIA Grenoble INP CNRSLJK F-38000 Grenoble France Univ Grenoble Alpes Inserm U1216 CHU Grenoble Alpes Grenoble Inst Neurosci F-38000 Grenoble France CHU Grenoble Alpes Div Neurol F-38000 Grenoble France

With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies, such as neurodegenerative diseases. In this work, we leverage deep learning models to detect anomalies in brain diffusion tensor imaging (DTI) parameter maps of recently diagnosed and untreated (de novo) patients with Parkinson's disease (PD). For this purpose, we trained auto-encoders on parameter maps of healthy controls (n = 56) and tested them on those of de novo PD patients (n = 129). We considered large reconstruction errors between the original and reconstructed images to be anomalies that, when quantified, allow discerning between de novo PD patients and healthy controls. The most discriminating brain macro-region was found to be the white matter with a ROC-AUC 68.3 (IQR 5.4) and the best subcortical structure, the GPi (ROC-AUC 62.6 IQR 5.4). Our results indicate that our deep learning-based model can detect potentially pathological regions in de novo PD patients, without requiring any expert delineation. This may enable extracting neuroimaging biomarkers of PD in the future, but further testing on larger cohorts is needed. Such models can be seamlessly extended with additional parameter maps and applied to study the physio-pathology of other neurological diseases.

关键词： Machine learning Unsupervised learning variational autoencoder Neurodegenerative disease Parkinson's disease

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Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第3期5卷 035080页

作者： Su, Yaokun Li, Chen Univ Calif Riverside Mat Sci & Engn Riverside CA 92507 USA Univ Calif Riverside Mech Engn Riverside CA 92507 USA

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model's versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework's two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

关键词： phonon dynamics inelastic neutron scattering variational autoencoder domain adaptation

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Normative ascent with local gaussians for unsupervised lesion detection

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MEDICAL IMAGE ANALYSIS 2021年 74卷 102208-102208页

作者： Chen, Xiaoran Pawlowski, Nick Glocker, Ben Konukoglu, Ender Swiss Fed Inst Technol Zurich Switzerland Imperial Coll London London England

Unsupervised abnormality detection is an appealing approach to identify patterns that are not present in training data without specific annotations for such patterns. In the medical imaging field, methods taking this approach have been proposed to detect lesions. The appeal of this approach stems from the fact that it does not require lesion-specific supervision and can potentially generalize to any sort of abnormal patterns. The principle is to train a generative model on images from healthy individuals to estimate the distribution of images of the normal anatomy, i.e., a normative distribution , and detect lesions as out-of distribution regions. Restoration-based techniques that modify a given image by taking gradient ascent steps with respect to a posterior distribution composed of a normative distribution and a likelihood term recently yielded state-of-the-art results. However, these methods do not explicitly model ascent directions with respect to the normative distribution, i.e. normative ascent direction, which is essential for successful restoration. In this work, we introduce a novel approach for unsupervised lesion detection by modeling normative ascent directions. We present different modelling options based on the defined ascent directions with local Gaussians. We further extend the proposed method to efficiently utilize 3D information, which has not been explored in most existing works. We experimentally show that the proposed method provides higher accuracy in detection and produces more realistic restored images. The performance of the proposed method is evaluated against baselines on publicly available BRATS and ATLAS stroke lesion datasets;the detection accuracy of the proposed method surpasses the current stateof-the-art results. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Unsupervised learning variational autoencoder Lesion detection

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Improving disentanglement in variational auto-encoders via feature imbalance-informed dimension weighting

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KNOWLEDGE-BASED SYSTEMS 2024年 296卷

作者： Liu, Yue Yu, Zhenyao Liu, Zitu Yu, Ziyi Yang, Xinyan Li, Xingyue Guo, Yike Liu, Qun Wang, Guoyin Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Engn Res Ctr Intelligent Comp Syst Shanghai 200444 Peoples R China Imperial Coll London Dept Comp London SW7 2AZ England Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Shanghai Univ Coll Sci Shanghai 200444 Peoples R China

Using variational Auto-Encoder (VAE) to learn disentangled representation holds great promise. But there is a feature imbalance in the learning process of VAEs, and the model usually concentrates on the learning of some dimensions at the expense of others. Instead of attempting to rectify it, we exploit feature imbalance to propose a Dimension Weighting method that can boost the disentanglement effect of VAE-based models. In order to conduct disentanglement learning under a fixed size of the latent space, the intrinsic dimension of raw data is estimated by a Dimension Number Estimator and set as the latent space size. Then, leveraging the feature imbalance, a Dimension Importance Evaluator is constructed to separate each dimension in the latent variable into important, unimportant, and general dimensions. By exerting different learning pressures on specific dimensions, we further optimize the variational lower bound of model and retrain it, thus promoting the disentanglement of important dimensions. The experiments on four benchmark datasets show that the Dimension Weighting can further improve the disentangling effect without compromising model performance, and in approximately 80% of cases, the results in disentangling metric evaluation experiments achieve better disentangling scores than the original models. This reveals that not all dimensions of the latent variables are equally influential. By focusing on the crucial dimensions within the latent representation, the model can achieve better performance.

关键词： variational autoencoder Latent space Disentangled representation Dimension weighting

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