Deep Convolutional Neural Network Method Research Articles

Deep Complex-Valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

Drone-aided ubiquitous applications play important roles in our daily lives. Accurate recognition of drones is required in aviation management due to their potential risks and disasters. Radiofrequency (RF) fingerprinting-based recognition technology based on deep learning (DL) is considered an effective approach to extracting hidden abstract features from the RF data of drones. Existing deep learning-based methods are either high computational burdens or have low accuracy. In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones. Compared with existing recognition methods, the DC-CNN method has a high recognition accuracy, fast running time, and small network complexity. Nine algorithm models and two datasets are used to represent the superior performance of our system. Experimental results show that our proposed DC-CNN can achieve recognition accuracies of 99.5% and 74.1%, respectively, on four and eight classes of RF drone datasets.

Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction

Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a form of fraud that attracts people and businesses to access malicious uniform resource locators (URLs) and submit their sensitive information such as passwords, credit card ids, and personal information. Enormous intelligent attacks are launched dynamically with the aim of tricking users into thinking they are accessing a reliable website or online application to acquire account information. Researchers in cyberspace are motivated to create intelligent models and offer secure services on the web as phishing grows more intelligent and malicious every day. In this paper, a novel URL phishing detection technique based on BERT feature extraction and a deep learning method is introduced. BERT was used to extract the URLs’ text from the Phishing Site Predict dataset. Then, the natural language processing (NLP) algorithm was applied to the unique data column and extracted a huge number of useful data features in terms of meaningful text information. Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. Then, the data were classified into legitimate and phishing URLs. To evaluate the proposed method, a famous public phishing website URLs dataset was used, with a total of 549,346 entries. However, three scenarios were developed to compare the outcomes of the proposed method by using similar datasets. The feature extraction process depends on natural language processing techniques. The experiments showed that the proposed method had achieved 96.66% accuracy in the results, and then the obtained results were compared to other literature review works. The results showed that the proposed method was efficient and valid in detecting phishing websites’ URLs.

New Benchmark for Household Garbage Image Recognition

Household garbage images are usually faced with complex backgrounds, variable illuminations, diverse angles, and changeable shapes, which bring a great difficulty in garbage image classification. Due to the ability to discover problem-specific features, deep learning and especially convolutional neural networks (CNNs) have been successfully and widely used for image representation learning. However, available and stable household garbage datasets are insufficient, which seriously limits the development of research and application. Besides, the state-of-the-art in the field of garbage image classification is not entirely clear. To solve this problem, in this study, we built a new open benchmark dataset for household garbage image classification by simulating different lightings, backgrounds, angles, and shapes. This dataset is named 30 classes of household garbage images (HGI-30), which contains 18 000 images of 30 household garbage classes. The publicly available HGI-30 dataset allows researchers to develop accurate and robust methods for household garbage recognition. We also conducted experiments and performance analyses of the state-of-the-art deep CNN methods on HGI-30, which serves as baseline results on this benchmark.

An ensemble 3D deep-learning model to predict protein metal-binding site

Predicting metal-binding sites in proteins is critical for understanding the protein’s biological function. Here, we develop an ensemble deep convolutional neural network (CNN) method for predicting metal-binding sites based on their three-dimensional (3D) structure. We build multi-channel 3D voxels based on biophysical characteristics obtained from raw atom coordinates of each protein-binding pocket. Then, we use these 3D voxels as the input of an ensemble 3D CNN model. We train and evaluate the model using a curated dataset of 3D protein structures. Our proposed model shows high performance in predicting metal-binding sites for Zn, Fe, Mg, Mn, Ca, and Na. Our approach offers a framework to use 3D spatial features to train 3D-CNN, which may be used to predict complicated metal-binding sites directly from their biophysical characteristics. The source code and webserver of the model are publicly available.

Can Deep Learning Solve Parametric Mathematical Programming? An Application to 0–1 Linear Programming Through Image Representation

Deep learning has been widely applied in many fields. Efficient optimization algorithms contribute a lot to the enhancement of deep learning. However, reverse studies on how deep learning can solve optimization problems, especially mathematical programming, are relatively scarce. In this work, we aim to initiate a discussion on using deep learning to solve parametric mathematical programming, which can be converted to find a mapping from parameter space to solution space. Given that deep convolutional neural networks (DCNNs) have been successfully applied in the field of computer vision, converting a mathematical programming problem into an image representation may provide the solution. This work takes the 0–1 knapsack problem (KP) as an example to build an original image representation method. After modifying the image details, the proposed method is extended to the general 0–1 linear programming (LP) problem, and a deep learning architecture is designed to solve the transferred computer vision problem. The efficiency of the proposed DCNNs method is validated on a large number of problems, and results show that this method can solve the 0–1 LP accurately and efficiently without iteration. The solution speed is 20 times faster than that of traditional optimization solvers.

Uji Kinerja Metode Deep Convolutional Neural Networks Untuk Identifikasi Gangguan Daya Listrik

The identification model development of the power disturbance signals with the deep convolutional neural networks (CNNs) method involves a large amount of data. However, the real signal data is limited. Therefore, researchers employ synthetic signal data. These signals can be generated by the formula IEEE standardized. In these formulas, two categories have a similar formula i.e interruption and sag. The difference is only in the intensity parameter (α). This paper analyzed the model performance of identifying those disturbances where the intensity values are set differently for training and testing datasets based on the upper bound value α of sag and the lower bound value α of interruption. Several noise levels are included in the signals. So, there are several datasets with noises in this simulation. Furthermore, those datasets are trained using the model based on deep CNN. The test results show that the true positive (TP) of the model's performance in identifying the interruption signal is 93.54% and the sag signal is 78.78%. In addition, the performance of the model using a dataset without noise obtained a high percentage in accuracy, precision, and f1-score parameters with 92.4%, 97.4%, and 92,76%, respectively.

Wearable Inertial Sensor-Based Limb Lameness Detection and Pose Estimation for Horses

Accurate objective, automated limb lameness detection and pose estimation play an important role for animal well-being and precision livestock farming. We present a wearable sensor-based limb lameness detection and pose estimation for horse walk and trot locomotion. The gait event and lameness detection are first built on a recurrent neural network (RNN) with long short-term memory (LSTM) cells. Its outcomes are used in the limb pose estimation. A learned low-dimensional motion manifold is parameterized by a phase variable with a Gaussian process dynamic model. We compare the RNN-LSTM-based lameness detection method with a feature-based multi-layer classifier (MLC) and a multi-class classifier (MCC) that are built on support vector machine/K-nearest-neighbors and deep convolutional neural network methods, respectively. Experimental results show that using only accelerometer measurements, the RNN-LSTM-based approach achieves 95% lameness detection accuracy and also outperforms the feature-based MLC or MCC in terms of several assessment criteria. The pose estimation scheme can predict the 24 limb joint angles in the sagittal plane with average errors less than 5 and 10 degs under normal and induced lameness conditions, respectively. The presented work demonstrate the successful use of machine learning techniques for high performance lameness detection and pose estimation in equine science. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Automation technologies are increasingly used for precision agriculture but few have focused on monitoring individual animals in open field for precision livestock farming. Limb lameness detection and pose estimation in open field is labor-intensive, unsafe for farmers, and inefficient. The presented machine learning-enabled, wearable inertial sensor-based design provides an effective and efficient approach for horse limb lameness detection and pose estimation applications. We present an RNN-LSTM for lameness detection and an integrated manifold learning model is used to predict the horse limb joint angles in walk and trot gaits under normal and induced lameness conditions. We also present a systematic analysis and experiments to demonstrate the impacts of the wearable sensor locations and signal information on lameness detection and pose estimation performance. Several other machine learning-based lameness detection methods are also presented and compared. The extensive multi-horse testing results are presented to demonstrate the superior accuracy and higher performance than other types of machine learning methods. One attractive feature of the proposed design lies in its high performance and fast computational capability for potential real-time applications in open field.

FCP-Net: A Feature-Compression-Pyramid Network Guided by Game-Theoretic Interactions for Medical Image Segmentation.

Medical image segmentation is a crucial step in diagnosis and analysis of diseases for clinical applications. Deep convolutional neural network methods such as DeepLabv3+ have successfully been applied for medical image segmentation, but multi-level features are seldom integrated seamlessly into different attention mechanisms, and few studies have fully explored the interactions between medical image segmentation and classification tasks. Herein, we propose a feature-compression-pyramid network (FCP-Net) guided by game-theoretic interactions with a hybrid loss function (HLF) for the medical image segmentation. The proposed approach consists of segmentation branch, classification branch and interaction branch. In the encoding stage, a new strategy is developed for the segmentation branch by applying three modules, e.g., embedded feature ensemble, dilated spatial mapping and channel attention (DSMCA), and branch layer fusion. These modules allow effective extraction of spatial information, efficient identification of spatial correlation among various features, and fully integration of multi-receptive field features from different branches. In the decoding stage, a DSMCA module and a multi-scale feature fusion module are used to establish multiple skip connections for enhancing fusion features. Classification and interaction branches are introduced to explore the potential benefits of the classification information task to the segmentation task. We further explore the interactions of segmentation and classification branches from a game theoretic view, and design an HLF. Based on this HLF, the segmentation, classification and interaction branches can collaboratively learn and teach each other throughout the training process, thus applying the conjoint information between the segmentation and classification tasks and improving the generalization performance. The proposed model has been evaluated using several datasets, including ISIC2017, ISIC2018, REFUGE, Kvasir-SEG, BUSI, and PH2, and the results prove its competitiveness compared with other state-of-the-art techniques.

Brain programming is immune to adversarial attacks: Towards accurate and robust image classification using symbolic learning

In recent years, the security concerns about the vulnerability of Deep Convolutional Neural Networks (DCNN) to Adversarial Attacks (AA) in the form of small modifications to the input image almost invisible to human vision make their predictions untrustworthy. Therefore, it is necessary to provide robustness to adversarial examples in addition to an accurate score when developing a new classifier. In this work, we perform a comparative study of the effects of AA on the complex problem of art media categorization, which involves a sophisticated analysis of features to classify a fine collection of artworks. We tested a prevailing bag of visual words approach from computer vision, four state-of-the-art DCNN models (AlexNet, VGG, ResNet, ResNet101), and the Brain Programming (BP) algorithm. In this study, we analyze the algorithms' performance using accuracy. Besides, we use the accuracy ratio between adversarial examples and clean images to measure robustness. Moreover, we propose a statistical analysis of each classifier's predictions' confidence to corroborate the results. We confirm that BP predictions' change was below 2\% using adversarial examples computed with the fast gradient sign method. Also, considering the multiple pixel attack, BP obtained four out of seven classes without changes and the rest with a maximum error of 4\% in the predictions. Finally, BP also gets four categories using adversarial patches without changes and for the remaining three classes with a variation of 1\%. Additionally, the statistical analysis showed that the predictions' confidence of BP were not significantly different for each pair of clean and perturbed images in every experiment. These results prove BP's robustness against adversarial examples compared to DCNN and handcrafted features methods, whose performance on the art media classification was compromised with the proposed perturbations.

Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays.

The rapid spread of COVID-19 across the globe since its emergence has pushed many countries’ healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary COVID-19 screening test, RT-PCR, although accurate and reliable, has a long turn-around time. More recently, various researchers have demonstrated the use of deep learning approaches on chest X-ray (CXR) for COVID-19 detection. However, existing Deep Convolutional Neural Network (CNN) methods fail to capture the global context due to their inherent image-specific inductive bias. In this article, we investigated the use of vision transformers (ViT) for detecting COVID-19 in Chest X-ray (CXR) images. Several ViT models were fine-tuned for the multiclass classification problem (COVID-19, Pneumonia and Normal cases). A dataset consisting of 7598 COVID-19 CXR images, 8552 CXR for healthy patients and 5674 for Pneumonia CXR were used. The obtained results achieved high performance with an Area Under Curve (AUC) of 0.99 for multi-class classification (COVID-19 vs. Other Pneumonia vs. normal). The sensitivity of the COVID-19 class achieved 0.99. We demonstrated that the obtained results outperformed comparable state-of-the-art models for detecting COVID-19 on CXR images using CNN architectures. The attention map for the proposed model showed that our model is able to efficiently identify the signs of COVID-19.

Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion.

Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer representation of the observed phenomenon, e.g., system degradation, which can facilitate analysis and decision-making processes. This work addresses the topic of predictive maintenance by exploiting sensor data fusion and artificial intelligence-based analysis. With a dataset such as vibration and sound from sensors, we focus on studying paradigms that orchestrate the most optimal combination of sensors with deep learning sensor fusion algorithms to enable predictive maintenance. In our experimental setup, we used raw data obtained from two sensors, a microphone, and an accelerometer installed on a brushless direct current (BLDC) motor. The data from each sensor were processed individually and, in a second step, merged to create a solid base for analysis. To diagnose BLDC motor faults, this work proposes to use data-level sensor fusion with deep learning methods such as deep convolutional neural networks (DCNNs) for their ability to automatically extract relevant information from the input data, the long short-term memory method (LSTM), and convolutional long short-term memory (CNN-LSTM), a combination of the two previous methods. The results show that in our setup, sound signals outperform vibrations when used individually for training. However, without any feature selection/extraction step, the accuracy of the models improves with data fusion and reaches 98.8%, 93.5%, and 73.6% for the DCNN, CNN-LSTM, and LSTM methods, respectively, 98.8% being a performance that, according to our reading, has never been reached in the analysis of the faults of a BLDC motor without first going through the extraction of the characteristics and their fusion by traditional methods. These results show that it is possible to work with raw data from multiple sensors and achieve good results using deep learning methods without spending time and resources on selecting appropriate features to extract and methods to use for feature extraction and data fusion.

Deep Convolutional Neural Network to improve the performances of screening process in LBVS

Drug design is a research process with a goal of creating a chemical drug to produce the desired biological effect. Because of the long time and the high cost issues associated with traditional drug discovery, there is a need to develop new techniques and strategies to increase the diminishing effectiveness of traditional approaches. Ligand-Based Virtual Screening (LBVS) plays a vital role in the early stage of the drug discovery. It could constitute a possible solution to solve the time and cost problems. Subsequently, the researchers are looking for new methods to find new active compounds and bring them to market in a short time. LBVS can be enhanced by different methods and strategies such as Machine Learning and Deep Learning.In this paper, a Deep Convolutional Neural Network method is proposed to enhance the performances of Ligand-Based Virtual Screening process (DCNNLB). Two main contributions are presented in this paper, The first contribution consists of designing a model based on Deep Convolutional Neural Network (DCNN) for LBVS. We propose several topological network models to find the one that gives the best performance such as accuracy and recall. For this, many network topology configurations have been proposed, and a variety of parameters have been taken into account. Furthermore, our proposed model is trained on all compounds of all activity classes of the Drug Data Report Database (MDDR). Thus, it presented a mean accuracy of 0.98 for all three MDDR Datasets. The second contribution is to generate a new learning representation in order to better represent chemical compound. This representation is based on the extraction of the automatic features learning from the weights of our proposed model. Consequently, it is very efficient in calculating molecular similarity and performances of the LBVS process. The obtained results with the three different datasets drawn from the MDDR and the performance evaluation with ANOVA test, have proved the superiority in performance of our proposed method compared to the different conventional methods.

Time analysis of regional structure of large-scale particle using an interactive visual system

N-body numerical simulation is an important tool in astronomy. Scientists used this method to simulate the formation of structure of the universe, which is key to understanding how the universe formed. As research on this subject further develops, astronomers require a more precise method that enables expansion of the simulation and an increase in the number of simulation particles. However, retaining all temporal information is infeasible due to a lack of computer storage. In the circumstances, astronomers reserve temporal data at intervals, merging rough and baffling animations of universal evolution. In this study, we propose a deep-learning-assisted interpolation application to analyze the structure formation of the universe. First, we evaluate the feasibility of applying interpolation to generate an animation of the universal evolution through an experiment. Then, we demonstrate the superiority of deep convolutional neural network (DCNN) method by comparing its quality and performance with the actual results together with the results generated by other popular interpolation algorithms. In addition, we present PRSVis, an interactive visual analytics system that supports global volume rendering, local area magnification, and temporal animation generation. PRSVis allows users to visualize a global volume rendering, interactively select one cubic region from the rendering and intelligently produce a time-series animation of the high-resolution region using the deep-learning-assisted method. In summary, we propose an interactive visual system, integrated with the DCNN interpolation method that is validated through experiments, to help scientists easily understand the evolution of the particle region structure.

Automatic MRI segmentation of pectoralis major muscle using deep learning

To develop and validate a deep convolutional neural network (CNN) method capable of selecting the greatest Pectoralis Major Cross-Sectional Area (PMM-CSA) and automatically segmenting PMM on an axial Magnetic Resonance Imaging (MRI). We hypothesized a CNN technique can accurately perform both tasks compared with manual reference standards. Our method is based on two steps: (A) segmentation model, (B) PMM-CSA selection. In step A, we manually segmented the PMM on 134 axial T1-weighted PM MRIs. The segmentation model was trained from scratch (MONAI/Pytorch SegResNet, 4 mini-batch, 1000 epochs, dropout 0.20, Adam, learning rate 0.0005, cosine annealing, softmax). Mean-dice score determined the segmentation score on 8 internal axial T1-weighted PM MRIs. In step B, we used the OpenCV2 (version 4.5.1, https://opencv.org) framework to calculate the PMM-CSA of the model predictions and ground truth. Then, we selected the top-3 slices with the largest cross-sectional area and compared them with the ground truth. If one of the selected was in the top-3 from the ground truth, then we considered it to be a success. A top-3 accuracy evaluated this method on 8 axial T1-weighted PM MRIs internal test cases. The segmentation model (Step A) produced an accurate pectoralis muscle segmentation with a Mean Dice score of 0.94 ± 0.01. The results of Step B showed top-3 accuracy > 98% to select an appropriate axial image with the greatest PMM-CSA. Our results show an overall accurate selection of PMM-CSA and automated PM muscle segmentation using a combination of deep CNN algorithms.

Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed using the constrained molecular dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several ten to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than 3 become crucial. To make the CNN applicable in the task of the impact parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact parameter-sensitive observables, that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds, and shows significantly better in terms of predicting the impact parameters and recognizing the central collision events, compared to that of the conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact parameter determination using the deep CNN method, compared to using the the conventional methods. This result reveals the importance to select a reliable impact parameter determination method in the experimental deduction of the nuclear stopping power as well as other observables.

SELM: Siamese extreme learning machine with application to face biometrics.

Extreme learning machine (ELM) is a powerful classification method and is very competitive among existing classification methods. It is speedy at training. Nevertheless, it cannot perform face verification tasks properly because face verification tasks require the comparison of facial images of two individuals simultaneously and decide whether the two faces identify the same person. The ELM structure was not designed to feed two input data streams simultaneously. Thus, in 2-input scenarios, ELM methods are typically applied using concatenated inputs. However, this setup consumes two times more computational resources, and it is not optimized for recognition tasks where learning a separable distance metric is critical. For these reasons, we propose and develop a Siamese extreme learning machine (SELM). SELM was designed to be fed with two data streams in parallel simultaneously. It utilizes a dual-stream Siamese condition in the extra Siamese layer to transform the data before passing it to the hidden layer. Moreover, we propose a Gender-Ethnicity-dependent triplet feature exclusively trained on various specific demographic groups. This feature enables learning and extracting useful facial features of each group. Experiments were conducted to evaluate and compare the performances of SELM, ELM, and deep convolutional neural network (DCNN). The experimental results showed that the proposed feature could perform correct classification at 97.87% accuracy and 99.45% area under the curve (AUC). They also showed that using SELM in conjunction with the proposed feature provided 98.31% accuracy and 99.72% AUC. SELM outperformed the robust performances over the well-known DCNN and ELM methods.

ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome.

Deep convolutional neural network (CNN) and its derivatives have recently shown great promise in the prediction of brain disorders using brain connectome data. Existing deep CNN methods using single global row and column convolutional filters have limited ability to extract discriminative information from brain connectome for prediction tasks. This paper presents a novel deep Connectome-Inception CNN (ConCeptCNN) model, which is developed based on multiple convolutional filters. The proposed model is used to extract topological features from brain connectome data for neurological disorders classification and analysis. The ConCeptCNN uses multiple vector-shaped filters extract topological information from the brain connectome at different levels for complementary feature embeddings of brain connectome. The proposed model is validated using two datasets: the Neuro Bureau ADHD-200 dataset and the Cincinnati Early Prediction Study (CINEPS) dataset. In a cross-validation experiment, the ConCeptCNN achieved a prediction accuracy of 78.7% for the detection of attention deficit hyperactivity disorder (ADHD) in adolescents and an accuracy of 81.6% for the prediction of cognitive deficits at 2 years corrected age in very preterm infants. In addition to the classification tasks, the ConCeptCNN identified several brain regions that are discriminative to neurodevelopmental disorders. We compared the ConCeptCNN with several peer CNN methods. The results demonstrated that proposed model improves overall classification performance of neurodevelopmental disorders prediction tasks.

Millidegree-Level Direction-of-Arrival Estimation and Tracking for Terahertz Ultra-Massive MIMO Systems

Terahertz (0.1-10 THz) wireless communications are expected to meet 100+ Gbps data rates for 6G communications. Being able to combat the distance limitation with reduced hardware complexity, ultra-massive multiple-input multiple-output (UM-MIMO) systems with hybrid dynamic array-of-subarrays (DAoSA) beamforming are a promising technology for THz wireless communications. However, fundamental challenges in THz DAoSA systems include millidegree-level three-dimensional direction-of-arrival (DoA) estimation and millisecond-level beam tracking with reduced pilot overhead. To address these challenges, an off-grid subspace-based DAoSA-MUSIC and a deep convolutional neural network (DCNN) methods are proposed for DoA estimation. Furthermore, by exploiting the temporal correlations of the channel variation, an augmented DAoSA-MUSIC-T and a convolutional long short-term memory (ConvLSTM) solutions are further developed to realize DoA tracking. Extensive simulations and comparisons on the proposed subspace- and deep-learning-based algorithms are conducted. Results show that both DAoSA-MUSIC and DCNN achieve super-resolution DoA estimation and outperform existing solutions, while DCNN performs better than DAoSA-MUSIC at a high signal-to-noise ratio. Moreover, DAoSA-MUSIC-T and ConvLSTM can capture fleeting DoA variation with an accuracy of 0.1° within milliseconds, and reduce 50% pilot overhead. Compared to DAoSA-MUSIC-T, ConvLSTM can tolerate large angle variation and remain robust over a long duration.

Acoustic Emission Intelligent Identification for Initial Damage of the Engine based on Single Sensor

Detecting the initial damage of the engine precisely is conducive to finding out the failure symptom timely and ensuring the reliable operation of the engine for avoiding malignant accidents. This article proposes a single-sensor acoustic emission (AE) diagnosis method based on deep learning (DL) for structural health monitoring (SHM) and initial damage intelligent identification of the engine. Firstly, the time-frequency domain features of AE signals collected from the engine test bench under different initial damages are extracted by continuous wavelet transform (CWT), and two-dimensional time-frequency images are constructed using the obtained results. After that, the proposed deep convolutional neural networks (DCNN) is employed to directly extract damage features from the converted two-dimensional time-frequency images via a multi-layer fusion and perform initial damage intelligent identification. In addition, the white Gaussian noise with different signal-to-noise ratio (SNR) is added to the raw AE signal, and the robustness of the proposed DCNN method is verified. Finally, to evaluate the performance of the proposed DCNN, the constructed test accuracy Euclidean distance and other indexes including Kappa, microF1 and macroF1 are introduced to compare with other commonly machine learning methods. The results demonstrated the effectiveness of the proposed DCNN model, which lays the foundation for the application of the single-sensor AE diagnosis method based on DCNN in SHM and initial damage intelligent identification of the engine.

Deep Ensemble CNN Method Based on Sample Expansion for Hyperspectral Image Classification

With the continuous progress of computer deep learning technology, convolutional neural network (CNN), as a representative approach, provides a unique solution for hyperspectral image (HSI) classification. However, the parameters of CNN can not be well-tuned when the number of training samples is insufficient, resulting in unsatisfactory classification performance. To tackle the thorny problem, a deep ensemble CNN method based on sample expansion for HSI classification is studied in this paper. Specially, spatial information is first extracted and fused with original spectral bands to help classifiers obtain discriminant spectral-spatial features. Then we use the pixel-pair feature (PPF) to expand the number of training samples so that the parameters of CNN structure can be fully trained. In addition, deep ensemble CNN is employed in this paper, enabling the trained model to obtain better generalization ability and more robust classification results. Ultimately, the proposed method is applied to classify four widely used hyperspectral data sets. Experimental results show that the studied approach yields higher classification accuracy than some CNN-based methods even under the condition of small-size training set.