Convolutional Neural Network Scheme Research Articles

Digital healthcare framework for patients with disabilities based on deep federated learning schemes

Utilizing digital healthcare services for patients who use wheelchairs is a vital and effective means to enhance their healthcare. Digital healthcare integrates various healthcare facilities, including local laboratories and centralized hospitals, to provide healthcare services for individuals in wheelchairs. In digital healthcare, the Internet of Medical Things (IoMT) allows local wheelchairs to connect with remote digital healthcare services and generate sensors from wheelchairs to monitor and process healthcare. Recently, it has been observed that wheelchair patients, when older than thirty, suffer from high blood pressure, heart disease, body glucose, and others due to less activity because of their disabilities. However, existing wheelchair IoMT applications are straightforward and do not consider the healthcare of wheelchair patients with their diseases during their disabilities. This paper presents a novel digital healthcare framework for patients with disabilities based on deep-federated learning schemes. In the proposed framework, we offer the federated learning deep convolutional neural network schemes (FL-DCNNS) that consist of different sub-schemes. The offloading scheme collects the sensors from integrated wheelchair bio-sensors as smartwatches such as blood pressure, heartbeat, body glucose, and oxygen. The smartwatches worked with wearable devices for disabled patients in our framework. We present the federated learning-enabled laboratories for data training and share the updated weights with the data security to the centralized node for decision and prediction. We present the decision forest for centralized healthcare nodes to decide on aggregation with the different constraints: cost, energy, time, and accuracy. We implemented a deep CNN scheme in each laboratory to train and validate the model locally on the node with the consideration of resources. Simulation results show that FL-DCNNS obtained the optimal results on the sensor data and minimized the energy by 25%, time 19%, cost 28%, and improved the accuracy of disease prediction by 99% as compared to existing digital healthcare schemes for wheelchair patients.

Novel Piecewise Normalized Bistable Stochastic Resonance Strengthened Cooperative Spectrum Sensing

A novel piecewise normalized bistable stochastic resonance (PNBSR) strengthened cooperative spectrum sensing is established by the PNBSR, residual covariance matrices, credibility weighted matrix fusion and a convolutional neural network (CNN) classification at low signal-to-noise ratios (SNRs). First, the PNBSR based on the traditional bistable stochastic resonance (TBSR) is proposed to improve the SNRs of received signals. Second, the output of the PNBSR is demodulated to obtain in-phase (I) and quadrature-phase (Q) covariance matrices. Third, the I/Q covariance matrices from different secondary users (SUs) are Cholesky decomposed to construct residual covariance matrices in a fusion center (FC). Subsequently, a new credibility weighted coefficient is proposed to fuse residual covariance matrices of the SUs. Finally, both training and test samples of the fusion detection statistics are fed into the CNN to train a high-performance classification model of cooperative spectrum sensing. The main innovations include the PNBSR, optimization index, Cholesky decomposition-based matrix cancellation to construct residual covariance matrices and credibility weighted matrix fusion. Simulation results show that the SNR of received signals strengthened by the PNBSR is improved by 3.36 dB other than received signals on average, which is 0.22dB larger than that of the TBSR. The detection probability of the proposed scheme also outperforms those of the support vector machine (SVM) and CNN schemes by 77% and 75% at -15 dB, respectively.

Toward a sustainable transition in automated production: enabling the vision-based approach and synchronous control for textile surface inspection systems

The textile and apparel industry is one of the largest industries in the world, with significant environmental and social impacts, such as manual labor in poor working conditions and resource depletion and environmental degradation due to ineffective production. As a result, there is an increasing need for sustainable practices and a transition toward a more circular and responsible industry. In this paper, we introduce the proposed approach in the context of a sustainable transition for the industrial textiles. Instead of human labor, the automated solution to detect defects is proposed for the 4.0 revolution in general and the textile industry in particular. Specifically, the mechanical design of a three-dimensional model for small- and medium-sized fabric rolls is portrayed. Some computational mechanics are created to ensure a stable mechanism and proper actuators. For acquiring accurate data and measuring the machine state, several installations, that is, a digital camera, load cell, and positioning sensor, and the layout of the light source are improved to intensify the image quality. The control theme, such as the cross-coupling controller, offers superior performance in synchronization and vibration-less motion. Lastly, the excellent detection method is provided by the convolutional neural network scheme for offline training to distinguish defects on products. Instead of a manual check or low rate of defect detection on products, the contributions of this research are as follows: (a) analyzing the mechanical design of the textile machining system in terms of dynamic characteristics; (b) implementing a smooth scheme based on motion control synchronization; and (c) integrating various strategies, including vision-based computation and control topology, to demonstrate superior performance. These results highlight that our approach can be widely adapted to address sustainability challenges not only in the textile field but also in global manufacturing practices.

EDCNNS: Federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection

Alzheimer’s is a dangerous disease prevalent in human societies, and unfortunately, its incidence is increasing daily. The number of patients is on the rise, while the availability of physical doctors has become limited and their schedules are packed. Consequently, the adoption of digital healthcare systems for Alzheimer’s disease (AD) has become more common, aiming to alleviate the burden on both AD patients and doctors. AD digital healthcare is a highly complex domain that incorporates various technologies, including fog computing, cloud computing, and deep learning algorithms. However, the implementation of these fog, cloud, and deep learning technologies has encountered challenges related to high computational time during AD detection processes. To address these challenges, this paper focuses on the convex optimization problem, which aims to optimize computation time and accuracy constraints in digital healthcare applications for AD. Convex optimization necessitates the use of an evolutionary algorithm that can enhance different AD constraints in distinct phases. The paper introduces a novel scheme called Evolutionary Deep Convolutional Neural Network Scheme (EDCNNS), designed to minimize computation time and achieve the highest prediction accuracy criteria for AD. EDCNNS comprises several phases, including feature extraction, selection, execution, and scheduling on the fog cloud nodes. The simulation results demonstrate that EDCNNS optimized security by 38%, reduced the deadline failure ratio by 29%, and improved selection accuracy by 50% across different Alzheimer’s classes compared to existing studies.

ITS Based on Deep Graph Convolutional Fraud Detection Network Blockchain-Enabled Fog-Cloud

The advancement in transport applications increases at the everyday progress in technologies. Therefore, intelligent transport systems (ITS) gain a lot of progress at the different vehicle levels and in the vehicular area network. However, privacy and security at the network level are critical issues for ITS applications in the existing mechanism. In this paper, the study devises the cost-efficient and secure Serverless Blockchain Enable Task Scheduling (SBETS) ITS system and algorithm framework. The main goal is to reduce processing and security blockchian costs for ITS applications in the system. The processing cost minimizes based on the new proposed function-based price model and secures the data by a suggested deep graph convolutional neural network scheme in the network. The simulation results show that SBETS outperformed all existing ITS systems and minimized processing costs by 10% and fraud detection issues by 50% for transport applications.

Comparison of Three Convolution Neural Network Schemes to Retrieve Temperature and Humidity Profiles from the FY4A GIIRS Observations

FY4A/GIIRS (Geostationary Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical sounder onboard a geostationary satellite. It can achieve observations of atmospheric temperature and humidity profiles with high vertical and temporal resolutions. Presently, convolutional neural network algorithms are relatively less used in the field of atmospheric profile retrieval, and different convolutional neural network approaches have different characteristics. The one-dimensional convolutional neural network scheme 1D-Net and two three-dimensional retrieval schemes U-Net 1 and U-Net 2 are used to achieve atmospheric temperature and humidity profiles under all skies based on GIIRS-observed brightness temperatures in this paper. After validation with test training data, the retrievals of different schemes derived from actual GIIRS observations and level 2 operational products were verified with ERA5 reanalysis data and radiosonde measurements in summer and winter respectively. The retrieved three-dimensional temperature and humidity fields from U-Net 1 and U-Net 2 are closer to the ERA5 reanalysis field in both distribution and value than the retrievals from the 1D-Net scheme and level 2 operational products. In particular, the inversion field of the U-Net 2 scheme is more continuous in space. Compared with radiosonde observations, the accuracy of the level 2 temperature product is the highest when the field of view is completely clear both in winter and summer month. The root mean square error (RMSE) of temperature retrieval of the two U-Net schemes is the second highest, and the RMSE and bias of the 1D-Net scheme are both large. Two U-Net schemes overestimate the temperature and humidity slightly in winter and underestimate it in summer in both clear and all sky cases. Under all sky conditions, the temperature retrieval RMSE and bias of the two U-Net schemes above 800 hPa are lower than those of the level 2 products, especially the U-Net 2 scheme with an RMSE of approximately 2.5 K. The U-Net 2 scheme bias is the smallest, with a value of approximately 0.5 K in winter. Since the level 2 product only provides the atmospheric temperature above the cloud top, it indicates that its temperature product accuracy is very low when the field of view is influenced by clouds. The humidity retrieval RMSEs of the two U-Net schemes is within 2 g/kg, better than that of the 1D-Net scheme. The retrieval accuracy of the U-Net 2 scheme is approximately 0.3 g/kg better than that of the U-Net 1 scheme below 600 hPa in winter. Level 2 does not provide humidity products. The summer humidity retrieval is worse than in winter. In general, among the three deep machine learning algorithms, 1D-Net has a large retrieval error, and the temperature and humidity from U-Net 2 have the highest accuracy. The retrieval speeds of the two U-Net schemes are nearly the same, and both are faster than that of scheme 1D-Net.

Automatic detection of ischemic-stroke-lesion with CNN segmentation: a study

The vital organ in human physiology is the brain, and abnormality in the brain will reason for various behavioural problems. Ischemic-Stroke is a medical emergency, and early detection and action will help the patient recover quickly. This scheme aims to implement Convolutional-Neural-Network (CNN) segmentation method to extract and evaluate the infected portion from the MRI slice of the brain. In our study the pre-trained UNet scheme is adopted to extract the stroke region from the Flair modality MRI slice with axial-, coronal- and sagittal plane. In this work, the ISLES2015 database is used for the experimental investigation. The segmented portion is further evaluated to the ground-truth and the metrics such as Jaccard, Dice and Accuracy are computed. The experimental investigation is implemented using Python software. The experimental outcome of this research proves that the proposed CNN scheme aids to improve segmentation accuracy on axial-plane images compared with other images. The performance of the CNN segmentation scheme is then validated with other related results existing in the literature. The outcome of this study confirms that UNet supported technique helps extract the stroke lesion from the MRI slice with more accurate accuracy.

Modified layer deep convolution neural network for text-independent speaker recognition

peaker recognition is the task of identifying the spokesman automatically using speaker-specific features. It has been a popular and most involved topic in the field of speech technology. This field opens a wide opportunity for research and finds its application in the areas such as forensics, authentication, security, etc. In this work, a modified deep-convolutional neural network structure has been proposed for speaker identification that has improved convolution, activation, and pooling layers along with Adam’s optimiser. The proposed architecture yielded the increase of prediction accuracy and reduction of Loss function when compared to the generic Convolutional Neural Network scheme. The execution of the proposed architecture is validated by various datasets and the outcomes show that the modified CNN performs better than the other state-of-the-art models regarding both accuracy (avg 99%) and loss function (avg 1%). From the analysis, it is found that the Modified-CNN suits the best for real-time speaker identification applications as the efficacy of the model does not degrade due to the effects of noise and interferences that are caused in the recording environment. Relevance of the work: Speaker Recognition is an area of interest in which ML and DL schemes, when combined, have the potential to make history in the areas of Automation and Authentication. Using a modified CNN can enhance the process by ignoring many issues such as false positives, background noise, and so on. This process can be expanded to create a Raga Identification and Therapy mechanism that can be used to treat diseases.

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images.

This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity. This work employed the pretrained CNN schemes, such as VGG-UNet and VGG-SegNet, to mine the ER network from the chosen FMI test images. The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. The considered FMI dataset consists of 223 test images, and image augmentation is then implemented to increase these images. The result of this scheme is then confirmed against other CNN methods, such as U-Net, SegNet, and Res-UNet. The experimental outcome confirms a segmentation accuracy of >98% with VGG-UNet and VGG-SegNet. The results of this research authenticate that the proposed pipeline can be considered to examine the clinical-grade FMI.

Serial and parallel convolutional neural network schemes for NFDM signals

Two conceptual convolutional neural network (CNN) schemes are proposed, developed and analysed for directly decoding nonlinear frequency division multiplexing (NFDM) signals with hardware implementation taken into consideration. A serial network scheme with a small network size is designed for small user applications, and a parallel network scheme with high speed is designed for places such as data centres. The work aimed at showing the potential of using CNN for practical NFDM-based fibre optic communication. In the numerical demonstrations, the serial network only occupies 0.5 MB of memory space while the parallel network occupies 128 MB of memory but allows parallel computing. Both network schemes were trained with simulated data and reached more than 99.9% accuracy.

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. These results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that separately estimated the radius of maximum wind (RMW) and the radius of 34 kt wind (R34) of the TC, a multi-task regression model that estimated the RMW and R34 simultaneously, and a multi-task regression model using best-track TC intensity information. For model training, validation, and testing, 29,730, 2505, and 11,624 geostationary satellite images of the region around the center of the TC, respectively, were used, each containing four IR bands: short-wavelength IR (3.7 µm), water vapor (6.7 µm), IR1 (10.8 µm), and IR2 (12.0 µm). The results showed that the multi-task model performed better than the single-task model due to knowledge sharing and its ability to solve multiple interrelated tasks simultaneously. The inclusion of TC intensity information in the multi-task model further improved the performance of the RMW and R34 estimations, with correlations (mean absolute errors) of 0.95 (2.05 nmi) and 0.93 (9.77 nmi), respectively, which represent significant improvements over the performance of existing linear regression statistical methods. The results suggested that this CNN model using geostationary satellite images may be a powerful tool for estimating TC sizes in operational TC forecasts.

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

With the aim to solve the problem of missing or tampering of ship type information in AIS information, in this paper, a novel ship type recognition scheme based on ship navigating trajectory and convolutional neural network (CNN) is proposed. Firstly, according to speed and acceleration of the ship, three ship navigating situations, i.e., static, normal navigation and maneuvering, are integrated into the process of trajectory images generation in the form of pixels. Then, three kinds of modular network structures with different depths are trained and optimized to determine the appropriate convolutional neural network structure. In the validation phase of the model, a large amount of verified data with a time span of one month was used, covering a variety of water conditions including open water, ports, rivers and lakes. Following this approach, a kind of CNN scheme which can be directly used to identify ship types in a wide range of waters is proposed. This scheme can be used to judge the ship type when the static information is completely missing and to test the data when the ship type information is partially missing.

PU-DetNet: Deep Unfolding Aided Smart Sensing Framework for Cognitive Radio

Spectrum sensing in cognitive radio (CR) paradigm can be broadly categorized as analytical-based and data-driven approaches. The former is sensitive to model inaccuracies in evolving network environment, while the latter (machine learning (ML)/deep learning (DL) based approach) suffers from high computational cost. For devices with low computational abilities, such approaches could be rendered less useful. In this context, we propose a deep unfolding architecture namely the Primary User-Detection Network (PU-DetNet) that harvests the strength of both: analytical and data-driven approaches. In particular, a technique is described that reduces computation in terms of inference time and the number of floating point operations (FLOPs). It involves binding the loss function such that each layer of the proposed architecture possesses its own loss function whose aggregate is optimized during training. Compared to the state-of-the-art, experimental results demonstrate that at SNR= -10 dB, the probability of detection is significantly improved as compared to the long short term memory (LSTM) scheme (between 39% and 56%), convolutional neural network (CNN) scheme (between 45% and 84%), and artificial neural network (ANN) scheme (between 53% and 128%) over empirical, 5G new radio, DeepSig, satellite communications, and radar datasets. The accuracy of proposed scheme also outperforms other existing schemes in terms of the F1-score. Additionally, inference time reduces by 91.69%, 90.90%, and 93.15%, while FLOPs reduces by 62.50%, 56.25%, 64.70% w.r.t. LSTM, CNN and ANN schemes, respectively. Moreover, the proposed scheme also shows improvement in throughput by 56.39%, 51.23%, and 69.52% as compared to LSTM, CNN and ANN schemes respectively, at SNR = -6 dB.

Inversion of the Optical Properties of Apples Based on the Convolutional Neural Network and Transfer Learning Methods

Highlights Convolutional neural network and MMD transfer learning methods are applied in inversion of optical properties. The classification accuracy of apples’ peel and pulp absorption coefficients are 84.61% and 92.47%, the accuracy of peel and pulp scattering coefficients are 83.56% and 86.53%, respectively. The depth optical characteristics can better reflect brix and moisture of apple then optical properties and hyperspectral data, the correlations are in the form of 0.98 and 0.98. Abstract. An inversion of optical properties is an important test for determining the quality of fruit. The conventional inversion model of the optical properties uses measured hyperspectral images as the training data. Studies show that the conventional machine learning method for inverting the optical properties results in low inversion accuracy, especially with curved models. Hence, the present study uses a convolutional neural network scheme to train the simulated hyperspectral images. Moreover, the maximum mean discrepancy (MMD) transfer method is used to transfer the simulated hyperspectral images to the measured hyperspectral images of apples. To evaluate the performance of the proposed method, the present study uses it to classify a variety of an apple’s optical properties, including the peel absorption, pulp absorption, peel scattering, and pulp scattering coefficients. The classification accuracies of the peel and pulp absorption coefficients are 84.61% and 92.47%, respectively. The classification accuracies of the peel and pulp scattering coefficients are 83.56% and 86.53%, respectively. These inversion results are compared with convolutional neural networks, neural networks, and support vector machines with measured hyperspectral images. It was found that the proposed inversion model is an effective scheme for optical property inversion. To prove the necessity of optical property inversion, the least squares, decision tree and random forest regression methods are performed to analyze the correlation between the depth of optical characteristics and the brix and moisture. The present study shows that these correlations are in the form of 0.98 and 0.98. The correlation coefficients increase by 0.36 and 0.25 compared to the measured hyperspectral images. The conclusions show that the proposed inversion model is an effective scheme for apple optical property inversion. Keywords: Apple tissue, Hyperspectral, Optical property inversion, Quality inspection.

Automated segmentation of leukocyte from hematological images\u2014a study using various CNN schemes

Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m.

Predictability of U.S. Regional Extreme Precipitation Occurrence Based on Large-Scale Meteorological Patterns (LSMPs)

AbstractIn this study, we use analogue method and Convolutional Neural Networks (CNNs) to assess the potential predictability of extreme precipitation occurrence based on Large-Scale Meteorological Patterns (LSMPs) for the winter (DJF) of Pacific Coast California (PCCA) and the summer (JJA) of Midwestern United States (MWST). We evaluate the LSMPs constructed with a large set of variables at multiple atmospheric levels and quantify the prediction skill with a variety of complementary performance measures. Our results suggest that LSMPs provide useful predictability of daily extreme precipitation occurrence and its interannual variability over both regions. The 14-year (2006-2019) independent forecast shows Gilbert Skill Scores (GSS) in PCCA range from 0.06 to 0.32 across 24 CNN schemes and from 0.16 to 0.26 across 4 analogue schemes, in contrast to those from 0.1 to 0.24 and from 0.1 to 0.14 in MWST. Overall, CNN is shown to be more powerful in extracting the relevant features associated with extreme precipitation from the LSMPs than analogue method, with several single-variate CNN schemes achieving more skillful prediction than the best multi-variate analogue scheme in PCCA and more than half of CNN schemes in MWST. Nevertheless, both methods highlight the Integrated Vapor Transport (IVT, or its zonal and meridional components) enables higher skills than other atmospheric variables over both regions. Warm-season extreme precipitation in MWST presents a forecast challenge with overall lower prediction skill than in PCCA, attributed to the weak synoptic-scale forcing in summer.

Smart Fault Diagnostics using Convolutional Neural Network and Adam Stochastic Optimization

Navigation, aviation and several other fields of engineering extensively make use of rotating machinery. The stability and safety of the equipment as well as the personnel are affected by this machinery. Use of deep learning as the basis of intelligent fault diagnosis schemes has and investigation of other relevant fault diagnosis schemes has a large scope for development. Thorough exploration needs to be performed in deep neural network (DNN) based schemes as shallow layer network structure based fault diagnosis schemes that are currently available has several considerable limitations. The nonlinear problems may be processed during intelligent fault diagnosis using deep convolutional neural network, which is a special structure DNN. The convolutional neural network (CNN) based scheme is emphasized in this paper. The principle and basic structure of the model are introduced. In rotating machinery, the fault diagnosis schemes using CNN are analyzed and summarized. Various CNN schemes, the potential mechanisms and performance diagnosis are analyzed. A novel smart fault diagnosis strategy is proposed while highlighting the potential aspects of existing schemes and reviewing the challenges.

Automatic Space-Time Block Code Recognition Using Convolutional Neural Network With Multi-Delay Features Fusion

Automatic signal recognition (ASR) is becoming increasingly important in spectrum identification and cognitive radio, but most existing space-time block code (STBC) recognition algorithms are traditional ones and do not account for the complementarities between different features. To overcome these deficiencies, a multi-delay features fusion scheme for ASR of STBC using a convolutional neural network (CNN) is proposed in this study. The proposed scheme tries to fuse different time-delay features of received STBC signals to obtain more discriminating features. The dilated convolution of different dilation rates is applied to realize automated multi-delay feature extraction. Then, two fusion methods, i.e., max-correlation (MC) fusion and multi-delay average (MDA) fusion, are proposed to combine the features of different time delays, and a residual block is applied to achieve better representation of signals. Finally, the simulation results demonstrate the superior performance of the proposed method. It is notable that the recognition accuracy can reach 97.4% with a signal-to-noise ratio (SNR) of -5 dB. In addition, the proposed multi-delay features fusion CNN (MDFCNN) scheme does not need a priori information, i.e., modulation type, channel coefficients, and noise power, which is well suited to non-collaborative communication.