Deep Convolutional Autoencoder Research Articles

Enhancing underwater thruster anomaly detection with support vector glow encoding description

As the driving system of autonomous underwater vehicles (AUVs), the healthy operation of underwater thrusters is crucial to ensure the performance of AUVs. Due to the challenge of obtaining faulty samples during normal operation, anomaly detection of underwater thrusters is often carried out using only normal samples. To address this, Support Vector Glow Encoding Description (SVGED) is proposed for anomaly detection of underwater thrusters trained with only normal samples. Specifically, the deep features of the operational vibration signals collected from the underwater thruster are extracted by a deep convolutional autoencoder based on a glow model. The glow-encoded data are then used to identify the boundary of normal operation for the underwater thruster. When a new sample enters the trained SVGED model, it can be effectively identified as representing a normal or anomalous condition of the underwater thruster. The developed SVGED was evaluated using AUV experiments. When using the proposed SVGED method, the accuracy reaches 99.81%, Results show that the proposed method can effectively real time detect anomalies in the underwater thruster compared to other methods. It lays the foundation for ensuring the healthy operation of AUVs.

A comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder and XGBoost algorithm

Abstract Accurately forecasting the remaining useful life (RUL) stands as a pivotal and formidable task within the realm of prognostics and health management. However, there is limited research that considers integrating early fault diagnosis during the bearing’s lifecycle with the prediction of its RUL. In this article, a comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder (AE) and XGBoost algorithm is proposed. To achieve this, an unsupervised piecewise function-based deep stacked convolutional AE was designed to construct the health indicator (HI) of the bearing for reducing the dependency on prior knowledge and furnishing a dynamic foundation for predicting RUL. The 4σ criterion based on HI’s increment was proposed for determining the fault occurrence time (FOT) of the bearing’s operational process. Subsequently, an XGBoost algorithm model was utilized to predict the RUL of faulty bearings. The efficacy of the bearing prognosis framework was validated by two real bearing test datasets. Results indicate the employed criterion of construct HI can flexibly adjust to various operational conditions and accurately pinpoint the bearing’s FOT. Furthermore, the findings demonstrate the proposed bearing prognosis framework achieves superior performance compared with several conventional anomaly detection methods.

Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning.

This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace-Beltrami operator to derive significant geometric features. By examining the eigenvectors of these operators, we captured the intrinsic geometric properties of the molecules, aiding their classification and clustering. The research utilized four deep learning methods: Deep Belief Network, Convolutional Autoencoder, Variational Autoencoder, and Adversarial Autoencoder, each paired with k-means clustering at different cluster sizes. Clustering quality was evaluated using the Calinski-Harabasz and Davies-Bouldin indices, Silhouette Score, and standard deviation. Nonparametric tests were used to assess the impact of topological descriptors on clustering outcomes. Our results show that the DBN + k-means combination is the most effective, particularly at lower cluster counts, demonstrating significant sensitivity to structural variations. This study highlights the potential of integrating spectral geometry with deep learning for precise and efficient molecular clustering.

A novel deep contrastive convolutional autoencoder based binning approach for taxonomic independent metagenomics data

A novel deep contrastive convolutional autoencoder based binning approach for taxonomic independent metagenomics data

Addressing data limitations in seizure prediction through transfer learning

According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.

Unleashing deep features and radiomics to enhance best overall response prediction to immunotherapy in advanced non-small cell lung cancer.

e20611 Background: Predicting immunotherapy response in advanced non-small cell lung cancer (NSCLC) through non-invasive means could be a groundbreaking advancement. Deep features extracted from medical images by deep Convolutional Neural Networks (CNNs) along with radiomics have demonstrated promising results in different oncologic diseases. However, the method for extracting these deep features influences the predictive capabilities of the model. Methods: A retrospective analysis of a multicenter study was conducted in advanced NSCLC patients with high PD-L1 expression with the aim of developing an imaging features-based model for the prediction of best overall response (BOR) to a checkpoint inhibitor immunotherapy. All visible lung lesions, beyond RECIST 1.1, were manually segmented in baseline CT scans. Radiomics features were extracted at lesion-level, and various preprocessing methods and CNN architectures were tested for deep features extraction. Radiomics and deep features were harmonized using ComBat methodology. A distinctive feature signature was created for each patient by calculating the mean across all lesions. Using this data, machine learning models were trained to predict treatment response, specifically distinguishing between complete response (CR) and partial response (PR) from stable disease (SD) and progressive disease (PD). The study compared the performance of different deep features extraction methods and assessed shapley additive explanations (SHAP) values of radiomics for interpretability. Results: In the cohort of 116 patients (mean age: 67, 93% ever-smokers, 73% male), 1024 to 5475 quantitative imaging features per lesion were extracted. Among these patients, 67 patients were CR+PR, and 49 were SD+PD. The top-performing predictive model, trained with radiomics and deep features from a deep convolutional autoencoder, achieved an average AUC of 85%. SHAP values showed that impactful radiomics features were correlated with intensity changes in adjacent pixels describing the tumor heterogeneity, along with shape features potentially associated with malignancy. Ultimately, combining both features demonstrated statistical significance, leading to a performance improvement of ~2-5% compared to models exclusively using deep features. Conclusions: Diverse techniques for extracting deep features were evaluated, revealing autoencoder training as the most effective strategy for predicting immunotherapy response. Consequently, the findings suggest that employing deep learning to extract imaging features from baseline CT scans, combined with radiomics, represents a promising non-invasive approach for the predicting immunotherapy outcomes in advanced NSCLC patients.

1DCAE-TSSAMC: Two-Stage Multi-Dimensional Spatial Features Based Multi-View Deep Clustering for Time Series Data

At present, as a research hotspot for time series data (TSD), the deep clustering analysis of TSD has huge research value and practical significance. However, there still exist the following three problems: (1) For deep clustering based on joint optimization, the inevitably mutual interference existing between deep feature representation learning progress and clustering progress leads to difficult model training especially in the initial stage, the possible feature space distortion, inaccurate and weak feature representation; (2) Existing deep clustering methods are difficult to intuitively define the similarity of time series and rely heavily on complex feature extraction networks and clustering algorithms. (3) Multidimensional time series have the characteristics of high dimensions, complex relationships between dimensions, and variable data forms, thus generating a huge feature space. It is difficult for existing methods to select discriminative features, resulting in generally low accuracy of methods. Accordingly, to address the above three problems, we proposed a novel general two-stage multi-dimensional spatial features based multi-view deep clustering method 1DCAE-TSSAMC (One-dimensional deep convolutional auto-encoder based two-stage stepwise amplification multi-clustering). We conducted verification and analysis based on real-world important multi-scenario, and compared with many other benchmarks ranging from the most classic approaches such as K-means and Hierarchical to the state-of-the-art approaches based on deep learning such as Deep Temporal Clustering (DTC) and Temporal Clustering Network (TCN). Experimental results show that the new method outperforms the other benchmarks, and provides more accurate, richer, and more reliable analysis results, more importantly, with significant improvement in accuracy and spatial linear separability.

Recurrent flow patterns as a basis for two-dimensional turbulence: Predicting statistics from structures

A dynamical systems approach to turbulence envisions the flow as a trajectory through a high-dimensional state space [Hopf, Commun. Appl. Maths 1, 303 (1948)]. The chaotic dynamics are shaped by the unstable simple invariant solutions populating the inertial manifold. The hope has been to turn this picture into a predictive framework where the statistics of the flow follow from a weighted sum of the statistics of each simple invariant solution. Two outstanding obstacles have prevented this goal from being achieved: 1) paucity of known solutions and 2) the lack of a rational theory for predicting the required weights. Here, we describe a method to substantially solve these problems, and thereby provide compelling evidence that the probability density functions (PDFs) of a fully developed turbulent flow can be reconstructed with a set of unstable periodic orbits. Our method for finding solutions uses automatic differentiation, with high-quality guesses constructed by minimizing a trajectory-dependent loss function. We use this approach to find hundreds of solutions in turbulent, two-dimensional Kolmogorov flow. Robust statistical predictions are then computed by learning weights after converting a turbulent trajectory into a Markov chain for which the states are individual solutions, and the nearest solution to a given snapshot is determined using a deep convolutional autoencoder. In this study, the PDFs of a spatiotemporally chaotic system have been successfully reproduced with a set of simple invariant states, and we provide a fascinating connection between self-sustaining dynamical processes and the more well-known statistical properties of turbulence.

Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites.

Ultrasonic testing is widely used for defect detection in polymer composites owing to advantages such as fast processing speed, simple operation, high reliability, and real-time monitoring. However, defect information in ultrasound images is not easily detectable because of the influence of ultrasound echoes and noise. In this study, a stable three-dimensional deep convolutional autoencoder (3D-DCA) was developed to identify defects in polymer composites. Through 3D convolutional operations, it can synchronously learn the spatiotemporal properties of the data volume. Subsequently, the depth receptive field (RF) of the hidden layer in the autoencoder maps the defect information to the original depth location, thereby mitigating the effects of the defect surface and bottom echoes. In addition, a dual-layer encoder was designed to improve the hidden layer visualization results. Consequently, the size, shape, and depth of the defects can be accurately determined. The feasibility of the method was demonstrated through its application to defect detection in carbon-fiber-reinforced polymers.

New Health Indicator Construction and Fault Detection Network for Rolling Bearings via Convolutional Auto-Encoder and Contrast Learning

As one of the most important components in rotating machinery, if bearings fail, serious disasters may occur. Therefore, the remaining useful life (RUL) prediction of bearings is of great significance. Health indicator (HI) construction and early fault detection play a crucial role in data-driven RUL prediction. Unfortunately, most existing HI construction methods require prior knowledge and preset trends, making it difficult to reflect the actual degradation trend of bearings. And the existing early fault detection methods rely on massive historical data, yet manual annotation is time-consuming and laborious. To address the above issues, a novel deep convolutional auto-encoder (CAE) based on envelope spectral feature extraction is developed in this work. A sliding value window is defined in the envelope spectrum to obtain initial health indicators, which are used as preliminary labels for model training. Subsequently, CAE is trained by minimizing the composite loss function. The proposed construction method can reflect the actual degradation trend of bearings. Afterwards, the autoencoder is pre-trained through contrast learning (CL) to improve its discriminative ability. The model that has undergone offline pre-training is more sensitive to early faults. Finally, the HI construction method is combined with the early fault detection method to obtain a comprehensive network for online health assessment and fault detection, thus laying a solid foundation for subsequent RUL prediction. The superiority of the proposed method has been verified through experiments.

Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition.

We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.12%, or with fewer measurements or shorter acquisition time if the image quality is given. We further demonstrate that it has better anti-noise performance by introducing noises in the SPI systems, and we show that it has better generalizability by applying the systems to targets outside the training dataset. We expect that the developed method will find potential applications based on single-pixel imaging beyond the visible regime.

Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning

Microspace design styles on pottery fragments offer a captivating window into the artistic heritage and cultural practices of past societies. These intricate details and recurring patterns hold valuable clues about rituals, beliefs, and artistic expressions. However, existing approaches struggle to capture the relationships between design elements within a single piece. This limitation hinders the ability to capture the holistic meaning conveyed by the microspace design style. To overcome this limitation, this research proposes a novel approach called MoANN-DSOA for predicting microspace design styles based on ancient village pottery. MoANN-DSOA utilizes a Mosaic Attention Neural Network (MoANN) to analyze both the visual image and the relationships between design elements. This allows for a more detailed understanding of the artistic message encoded within each fragment. Additionally, a Dove Swarm Optimization Algorithm (DSOA) optimizes the MoANN architecture, potentially enhancing the accuracy of capturing intricate details. The proposed MoANN-DSOA method attains 7.78%, 27.89% and 33.335% higher accuracy and 14%, 20.81% and 32.36% higher F-Score compared to the existing methods like Categorization and Retrieval of Painted Pottery with CNNs (CNN), Utilizing CNN-VGG16-VGG19 Approach for Distinguishing Surface Treatments in Wheel-Thrown Pottery (CNN-VGG16-VGG19) and Unsupervised Feature Extraction of Ceramic Profiles using a Deep Variational Convolutional Autoencoder (DVCA) respectively. By this, the proposed MoANN-DSOA methodology paves the way for efficient information extraction from pottery image, unlocking deeper understanding and facilitating the construction of more informed historical narratives.

Modelling appearance variations in expressive and neutral face image for automatic facial expression recognition

AbstractIn automatic facial expression recognition (AFER) systems, modelling the spatio‐temporal feature information in a specific manner, coalescing, and its effective utilization is challenging. The state‐of‐the‐art studies have examined integrating multiple features to enhance the recognition rate of AFER systems. However, the feature variations between expressive and neutral face images are not fully explored to identify the expression class. The proposed research presents an innovative approach to AFER by modelling appearance variations in both expressive and neutral face images. The prominent contributions of the work are developing a novel and hybrid feature space by integrating the discriminative feature distribution derived from expressive and neutral face images; preserving the highly discriminative latent feature distribution using autoencoders. Local binary pattern (LBP) and histogram of oriented gradients (HOG) are the feature descriptors employed to derive the discriminative texture and shape information, respectively. The component‐based approach is employed, wherein the features are derived from the salient facial regions instead of the whole face. The three‐stage stacked deep convolutional autoencoder (SDCA) and multi‐class support vector machine (MSVM) are employed to address dimensionality reduction and classification, respectively. The efficacy of the proposed model is substantiated by empirical findings, which establish its superiority in terms of accuracy in AFER tasks on widely recognized benchmark datasets.

Open Set Bearing Fault Diagnosis with Domain Adaptive Adversarial Network under Varying Conditions

Bearing fault diagnosis is a pivotal aspect of monitoring rotating machinery. Recently, numerous deep learning models have been developed for intelligent bearing fault diagnosis. However, these models have typically been established based on two key assumptions: (1) that identical fault categories exist in both the training and testing datasets, and (2) the datasets used for testing and training are assumed to follow the same distribution. Nevertheless, these assumptions prove impractical and fail to accurately depict real-world scenarios, particularly those involving open-world assumption fault diagnosis in multi-condition scenarios. For that purpose, an open set domain adaptive adversarial network framework is proposed. Specifically, in order to improve the learning of distribution characteristics in different fields, comprehensive training is implemented using a deep convolutional autoencoder model. Additionally, to mitigate the negative transfer resulting from unknown fault samples in the target domain, the similarity of each target domain sample and the shared classes in the source domain are estimated using known class classifiers and extended classifiers. Similarity weight values are assigned to each target domain sample, and an unknown boundary is established in a weighted manner. This approach is employed to establish the alignment between the classes shared between the two domains, enabling the classification of known fault classes, while allowing the recognition of unknown fault classes in the target domain. The efficacy of our suggested approach is empirically validated using different datasets.

Hierarchical functional differences between gyri and sulci at different scales.

Gyri and sulci are 2 fundamental cortical folding patterns of the human brain. Recent studies have suggested that gyri and sulci may play different functional roles given their structural and functional heterogeneity. However, our understanding of the functional differences between gyri and sulci remains limited due to several factors. Firstly, previous studies have typically focused on either the spatial or temporal domain, neglecting the inherently spatiotemporal nature of brain functions. Secondly, analyses have often been restricted to either local or global scales, leaving the question of hierarchical functional differences unresolved. Lastly, there has been a lack of appropriate analytical tools for interpreting the hierarchical spatiotemporal features that could provide insights into these differences. To overcome these limitations, in this paper, we proposed a novel hierarchical interpretable autoencoder (HIAE) to explore the hierarchical functional difference between gyri and sulci. Central to our approach is its capability to extract hierarchical features via a deep convolutional autoencoder and then to map these features into an embedding vector using a carefully designed feature interpreter. This process transforms the features into interpretable spatiotemporal patterns, which are pivotal in investigating the functional disparities between gyri and sulci. We evaluate the proposed framework on Human Connectome Project task functional magnetic resonance imaging dataset. The experiments demonstrate that the HIAE model can effectively extract and interpret hierarchical spatiotemporal features that are neuroscientifically meaningful. The analyses based on the interpreted features suggest that gyri are more globally activated, whereas sulci are more locally activated, demonstrating a distinct transition in activation patterns as the scale shifts from local to global. Overall, our study provides novel insights into the brain's anatomy-function relationship.

Identification of multi-mineral-species geochemical anomalies using Bayesian maximum entropy and the spectrum separable module-constrained convolutional autoencoder

In this paper, we present a dual-drive multi-mineral-species anomaly detection system which involves the combined use of Bayesian maximum entropy, a spectral separable module, high-order factor analysis, a geologically constrained loss function, as well as a mixed Gaussian distribution-based thresholding algorithm, attaching to a deep convolutional autoencoder. We have achieved several firsts rarely considered in the existing literature, for example: previous works focus mainly on separating single-mineral-species rather than multi-elemental anomalies, while we have attempted to recognize multi-mineral-species anomalies; previous works pay more attention to data, while we suggest how to discover the ore-related correlations hidden within the input data; previous works fail to integrate soft data in a quantitative fashion, while we have achieved that by capitalizing on Bayesian maximum entropy and the stratigraphic combination entropy. A series of comparative experiments have demonstrated the advantages over other state-of-the-art approaches. Finally, we obtained a mineral-occurrence identification rate ( δ ) ranging from 36.87 to 61.46% v. the anomaly area ranging from 33.75 to 55.38% for each metalliferous anomaly division.

One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

Fiber-reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive testing (NDT) methods to ensure structural integrity and safety. Active infrared thermography (AIRT) is a fast and cost-efficient NDT technique for inspecting FRP composites. However, this method is easily affected by factors such as inhomogeneous heating, leading to a low level of visualization of defects. To address this issue, this study proposes a novel method called one-dimensional deep convolutional autoencoder active infrared thermography (1D-DCAE-AIRT) to enhance the visualization of internal defects in FRP composites. This method first preprocesses the thermal image sequence acquired by AIRT inspections. Subsequently, high-level thermal features at the pixel level are extracted from the aforementioned preprocessed thermal image sequence using a designed one-dimensional deep convolutional autoencoder (1D-DCAE) model. Finally, the extracted high-level thermal features are employed to generate enhanced visualization results that exhibit improved defect visibility. The results of three kinds of AIRT (eddy current pulsed thermography, flash thermography, and vibrothermography) experiments on FRP composite specimens with artificially introduced defects show that 1D-DCAE-AIRT can effectively enhance the visualization of internal defects. The enhancement effect is better than the conventional techniques of fast Fourier transform (FFT), principal component analysis (PCA), independent component analysis (ICA), and partial least-squares regression (PLSR).

HRTF low-dimensional representation based on deep convolutional autoencoder and attention mechanism

HRTF low-dimensional representation based on deep convolutional autoencoder and attention mechanism

A Deep Convolutional Autoencoder–Enabled Channel Estimation Method in Intelligent Wireless Communication Systems

Through modeling the characteristics of wireless transmission channels, channel estimation can improve signal detection and demodulation techniques, enhance the spectrum utilization, optimize communication performance, and enhance the quality, reliability, and efficiency of intelligent wireless communication systems. In this paper, we propose a deep convolutional autoencoder–based channel estimation method in intelligent wireless communication systems. At first, the channel time‐frequency response matrix between the transmitter and receiver can be represented as 2D images. Then they are fed into the convolutional autoencoder to learn key channel features. To reduce the structural complexity of the deep learning model and improve its inference efficiency, we adopt the method of removing redundant parameters to achieve model compression. Iterative training and pruning based on stochastic gradient descent (SGD) and weight importance evaluation are alternated to obtain a lightweight deep learning model for channel estimation. Finally, extensive simulation results have verified the effectiveness and superiority of the proposed method.

The integration of machine learning and IoT for the early detection of tomato leaf disease in real-time

The impact of climate change, pests, and inadequate agricultural practices on crop health is becoming a growing concern. It is estimated that 20-40% of global crop yield is adversely affected by pests and diseases. This has a direct negative impact on food security and nutritional well-being, as staple cereal crops (such as rice, maize, and wheat) and tuber crops (like potato, onion, and tomato) are affected. Advancements in Artificial Intelligence (AI), Computer Vision (CV), and IoT have a significant influence on reducing crop losses by detecting crop diseases at early stages. The primary focus of this research is to develop a real-time system that can detect tomato crop diseases at an early stage by integrating Machine Learning (ML) algorithms and IoT. The most common tomato leaf diseases, such as Tomato Mosaic Virus (TMV), Tomato Bacterial Leaf Spot (TBLS), Tomato Early Blight (TEB), and Tomato Late Blight (TLB), are considered in this work. The hybrid discriminative feature space is derived from the integration of low- and high-level features via Convolution Neural Network (CNN) Layers. The 3-stage Stacked Deep Convolutional Autoencoder is used to optimize the CNN performance by reducing computation complexity. The proposed model is implemented on the Plantvillage benchmark dataset and achieves the highest recognition accuracy of 95.6% for the 5-class problem using 5-fold cross-validation.