The spoofing attack brings more serious threats and challenges to the Global Navigation Satellite System (GNSS) receiver. The rapid and accurate spoofing detection mechanism is of great significance to the credibility...
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The spoofing attack brings more serious threats and challenges to the Global Navigation Satellite System (GNSS) receiver. The rapid and accurate spoofing detection mechanism is of great significance to the credibility and security of GNSS-enabled transport applications. In this paper, an unsupervised classification solution is proposed to detect GNSS spoofing by analyzing the features of Coarse Acquisition (C/A) code Autocorrelation Function (ACF) using a Hybrid Convolutional autoencoder (HCAE) method integrated with an attention-driven memory network. A dynamic threshold-based protection mechanism is introduced to reduce the system's sensitivity to unexpected anomalies, thereby enhancing detection accuracy. The effectiveness of the proposed solution is verified by comparison with referencing detection methods using the Texas Spoofing Test Battery (TEXBAT) and spoofing injection test datasets. Specifically, the performance indices of the proposed method are improved over the involved referencing methods, which demonstrate that this solution can realize accurate and efficient detection of GNSS spoofing under the data-driven scheme.
As a powerful soft computing tool, fuzzy cognitive maps (FCMs) have been successfully employed for time-series modeling and forecasting problems. However, both the rapid time variation and the trends are still open pr...
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As a powerful soft computing tool, fuzzy cognitive maps (FCMs) have been successfully employed for time-series modeling and forecasting problems. However, both the rapid time variation and the trends are still open problems when processing univariate non-stationary time-series forecasting problems via FCM-based models. In this paper, we propose a time-series forecasting model by composing FCMs, gated recurrent unit network (GRU), and autoencoder network (AE). The model is termed GAE-FCM. Firstly, a scheme based on gated recurrent unit networks and autoencoder networks is designed to learn the potential representations and capture the long-term trend of non-stationary time series while decomposing these univariate time series into a group of multivariate feature vectors. Then, the obtained multivariate feature vectors are modeled as a fuzzy cognitive map in which quantifying its connection matrix is regarded as a convex optimization problem. Finally, the time-series trend is predicted by the optimized fuzzy cognitive map and corresponding modeling mechanism. The performance of the proposed model has been validated by comparison with several representative methods on five non-stationary time-series datasets.
Recently, Vision Transformer (ViT) has become a relevant alternative to convolutional neural networks (CNN) for image classification tasks. However, we believe that ViT needs pre-training on large-size datasets, makin...
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Recently, Vision Transformer (ViT) has become a relevant alternative to convolutional neural networks (CNN) for image classification tasks. However, we believe that ViT needs pre-training on large-size datasets, making it unsuitable for certain scientific fields such as infrared imaging where the amount of training data is limited. In this direction, we proposed a Compact image Transformer based on convolutional variational autoencoder with Augmented attention backbone (referred to AA-CiT) for target recognition in infrared images, which can learn efficiently from scratch even with small-size datasets. This is performed by three main adaptations of the original ViT architecture, in which we introduced convolutions in its different parts to fully benefit from the properties of both paradigms: attention and convolution. First, we proposed an improvement in the tokenization step by introducing a new module based on a local convolutional variational autoencoder. Second, convolutional features are incorporated in ViT's encoder, which allows us to introduce some inductive bias of CNN in the proposed transformer. We finally took profit of a new sequence pooling technique on the top of ViT's encoder to make our model compact and more accurate. These modifications allow us to overcome the difficulties of ViT training and also eliminate the need for Class token and the heavy reliance on positional embeddings. We validated our approach by carrying out extensive experiments on FLIR-SEEK dataset. Globally, we achieved a 3% improvement in overall classification accuracy compared to conventional ViT while relying on fewer parameters (14% of ViT's parameters).
Short-term load forecasting (STLF) of heterogeneous multi-agents plays a significant role in smart grid. Faced with special difficulties of multi-agent STLF due to the high heterogeneity, uncertainty and volatility, t...
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Short-term load forecasting (STLF) of heterogeneous multi-agents plays a significant role in smart grid. Faced with special difficulties of multi-agent STLF due to the high heterogeneity, uncertainty and volatility, traditional local methods usually make predictions based on load aggregation or clustering, the complexity of which will rise greatly with the increase of agent types. It has become more and more difficult for traditional methods to meet the STLF demand of smart grid. Meanwhile, global time series forecasting method emerges gradually in many fields, which can make predictions for many agents only by one model with much lower complexity. However, there is few researches on the multi-agent global STLF. This study proposes a global deep-Long Short-Term Memory (LSTM) STLF model based on a pretraining method. The model fully applies the information such as electricity consumption pattern, weather and calendar in a structural and orderly way. The empirical results show that our model can effectively predict the daily load of heterogeneous households, with prediction accuracies of 87.9-90.2% on the test set across various tasks. Compared with the base model, our model achieves an 8.7% higher accuracy with a much faster convergence speed. Furthermore, the fluctuations of accuracy on different tasks are within 2.3%, showing that our model is robust. The superiority of the global method is proved through the comparison with local method in the empirical results. This study is expected to make contribution to global STLF of heterogeneous households and providing experience for the global prediction of heterogeneous time series based on deep-LSTM and pretraining method.
作者：
Lee, JaehyukKyung Hee Univ
Dept Ind Univ Cooperat Osan Gyeonggi Do South Korea Hanshin Univ
Dept Ind Univ Cooperat 137 Hanshindae gil Osan Gyeonggi Do South Korea
Background and objective: This study investigated the spatial and temporal features of muscle synergy during two types of curved walking (CW), according to whether the analyzed legs were located on the outside (OCW) o...
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Background and objective: This study investigated the spatial and temporal features of muscle synergy during two types of curved walking (CW), according to whether the analyzed legs were located on the outside (OCW) or inside (ICW) on the basis of the curve direction during CW, in patients with stroke. Methods: Thirteen patients with stroke and seven age-matched healthy controls participated in this study. Using the autoencoder technique, four muscle synergies were extracted from eight muscles of the paretic legs in patients with stroke and the dominant legs in healthy controls. Walking speed, variance accounted for (VAF) of the four synergies, and each synergy with the same number were compared. Pearson's correlation and activation peak timing calculation were used to identify spatial and temporal features, ***: Regarding walking speed in patients with stroke, ICW was significantly faster than OCW (P = 0.027). Regarding spatial features, muscle weighting values of patients with stroke in synergy 3 that were mainly involved in the early swing phase had the lowest similarity [r = 0.30] during OCW, and synergy 4 that was mainly involved in the late swing phase had the lowest similarity [r = 0.39] during ICW compared to the healthy group. Meanwhile, in terms of temporal features, activation peak timings of patients with stroke in synergy 1, which was mainly involved in the early stance phase, and synergy 2, which was mainly involved in the mid-late stance phase, were significantly delayed during OCW (P < .001, P = 0.003), while peak timings of synergy 1 and synergy 3 were delayed during ICW (P = .004, P = .002).Significance: Based on distinctive features of spatial synergy during the swing phase of CW and temporal synergy during the swing-stance transition phase of CW in patients with stroke in gait rehabilitation, specific approaches need to be considered depending on the curve direction and each gait phase.
Anomaly detection of multivariate time series (MTS) is crucial in industrial intelligent systems. To address the challenges of absence of anomaly labels, fast inference time, multi-source and multi-modality in anomaly...
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Anomaly detection of multivariate time series (MTS) is crucial in industrial intelligent systems. To address the challenges of absence of anomaly labels, fast inference time, multi-source and multi-modality in anomaly detection, researchers have primarily investigated unsupervised reconstruction-driven methods. However, the existing reconstruction-driven methods mainly focus on minimizing reconstruction errors while neglecting the importance of training methods that increase errors between normal and abnormal classes. Furthermore, accurately constructing the feature space of normal and abnormal classes during the reconstruction process remains a challenge. In this paper, we propose an innovative model, namely the confidence adversarial autoencoder (CAAE). The proposed CAAE combines a confidence network, based on window credibility judgment, with an autoencoder to provide credibility support for anomaly detection. We further introduce fake labels to provide the confidence network with a discriminative knowledge for identifying reconstructed data. Additionally, we implement the confidence adversarial training method to generate fake labels to construct an adversarial loss aiming to expand the decision boundary of anomaly scores. Extensive experimental results on publicly available time series datasets are provided to demonstrate the efficiency of our proposed CAAE. It reveals that excellent generalization ability and superior average performance are achieved on different datasets compared with the state-of-the-art methods.
Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily ...
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Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.
Due to the complicated production mechanism in multivariate industrial processes, different dynamic features of variables raise challenges to traditional data-driven process monitoring methods which assume the process...
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Due to the complicated production mechanism in multivariate industrial processes, different dynamic features of variables raise challenges to traditional data-driven process monitoring methods which assume the process data is static or dynamically consistent. To tackle this issue, this paper proposes a novel process monitoring method based on the long short-term memory (LSTM) and autoencoder neu-ral network (called LSTMED) for multivariate process monitoring with uneven dynamic features. First, the LSTM units are arranged in the encoder-decoder form to construct an end-to-end model. Then, the constructed model is trained in an unsupervised manner to capture long-term time dependency within variables and dominant representation of high dimensional process data. Afterward, the kernel density estimation (KDE) method is performed to determine the control limit only based on the reconstruction error from historical normal data. Finally, effective online monitoring for uneven dynamic process can be achieved. The performance and advantage of the process monitoring method proposed are explained through typical cases, including the numerical simulation and Tennessee Eastman (TE) benchmark process, and comparative experimental analysis with state-of-the-art methods.(c) 2022 Elsevier Ltd. All rights reserved.
It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to ...
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It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.
作者：
Yuan, LinXu, ZhijieMeng, BoyuanYe, LanQilu Univ Technol
Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist EducShandong Comp Sci Ctr 3501 Daxue Rd Jinan 250353 Peoples R China Qilu Univ Technol
Shandong Acad Sci Fac Comp Sci & Technol Shandong Engn Res Ctr Big Data Appl Technol 3501 Daxue Rd Jinan 250353 Peoples R China Shandong Fundamental Res Ctr Comp Sci
Shandong Prov Key Lab Ind Network & Informat Syst 3501 Daxue Rd Jinan 250353 Peoples R China Shandong Univ
Hosp 2 Canc Ctr 247 Beiyuan St Jinan 250033 Peoples R China
BackgroundClustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limi...
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BackgroundClustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First, they do not fully exploit cellular features. Second, they are developed based on gene expression information and lack of flexibility in integrating intercellular relationships. Finally, the performance of these methods is affected by dropout *** propose a novel deep learning (DL) model based on attention autoencoder and zero-inflated (ZI) layer, namely scAMZI, to cluster scRNA-seq data. scAMZI is mainly composed of SimAM (a Simple, parameter-free Attention Module), autoencoder, ZINB (Zero-Inflated Negative Binomial) model and ZI layer. Based on ZINB model, we introduce autoencoder and SimAM to reduce dimensionality of data and learn feature representations of cells and relationships between cells. Meanwhile, ZI layer is used to handle zero values in the data. We compare the performance of scAMZI with nine methods (three shallow learning algorithms and six state-of-the-art DL-based methods) on fourteen benchmark scRNA-seq datasets of various sizes (from hundreds to tens of thousands of cells) with known cell types. Experimental results demonstrate that scAMZI outperforms competing *** outperforms competing methods and can facilitate downstream analyses such as cell annotation, marker gene discovery, and cell trajectory inference. The package of scAMZI is made freely available at https://***/10.5281/zenodo.13131559.
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