A Deep Transfer Convolutional Neural Network Framework for Microgrid Fault Detection

Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wave from one of normalcy using multi-electrode abdominal waveforms of the electrogastrogram (EGG). Human stomach and abdominal models are extracted from computed tomography scans. Normal and abnormal slow waves are simulated along stomach surfaces. Current dipoles at the stomachs surface are propagated to virtual electrodes on the abdomen with a forward model. We establish a deep convolutional neural network (CNN) framework to classify normal and abnormal slow waves from the multi-electrode waveforms. We investigate the effects of non-idealized measurements on performance, including shifted electrode array positioning, smaller array sizes, high body mass index (BMI), and low signal-to-noise ratio (SNR). We compare the performance of our deep CNN to a linear discriminant classifier using wave propagation spatial features. A deep CNN framework demonstrated robust classification, with accuracy above 90% for all SNR above 0dB, horizontal shifts within 3cm, vertical shifts within 6cm, and abdominal tissue depth within 6cm. The linear discriminant classifier was much more vulnerable to SNR, electrode placement, and BMI. This is the first study to attempt and, moreover, succeed in using a deep CNN to disambiguate normal and abnormal gastric slow wave patterns from high-resolution EGG data. These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.

4D digital tomosynthesis (DTS) techniques for image-guided radiation therapy (IGRT) are able to reduce radiation dose, scan and reconstruction time compared to 4D cone-beam computed tomography (CBCT). In spite of these benefits, the 4D DTS techniques cause the degradation of image quality due to an intrinsic imaging strategy and consequently reduce treatment accuracy. In this study, a deep learning-based convolutional neural network (CNN) framework was proposed for 4D DTS imaging. The proposed CNN framework consisted of the data restoration network based on a U-Net and the denoising network combined with a 2D wavelet transform, and the network training was implemented with clinical images. The quality of the 4D DTS images obtained from the proposed model was evaluated in terms of quantitative accuracy, spatial resolution and noise property. The results showed that the proposed CNN framework improved the quantitative accuracy of 4D DTS images by 3–19%, and the spatial resolution and noise for the proposed CNN framework were reduced by 2.24–7.33% and 8.92–40.07%, respectively, in comparison to other imaging models. These results represented that the degradation of the 4D DTS image quality can be recovered using the proposed CNN framework, and the proposed model is suitable for maintaining spatial resolution as well as suppressing noise and artifacts. In conclusion, the proposed CNN framework can be potentially used to improve the quality of 4D DTS images for the IGRT.

Condition monitoring and diagnosis of a bearing are very important for any rotating machine as it governs the safety while the machine is in operating condition. To construct a feature vector selection of suitable signal processing techniques is a challenge for vibration-based condition monitoring techniques. In the methodology proposed, Short Time Fourier Transform (STFT), Walsh Hadamard Transform (WHT) and Variable Mode Decomposition (VMD) are used to generate 2-D time-frequency spectrograms from the various fault conditions of bearing. When Deep learning techniques apply for fault diagnosis, a large amount of dataset is required for training of machine learning model. To overcome this issue single image Generative Adversarial Network (SinGAN) as a data augmentation technique, utilized for generating additional 2-D time-frequency spectrograms from various fault conditions of ball bearing. To detect fault severity, four deep learning algorithms, ResNet 34, ResNet50, VGG16, and MobileNetV2 are used as a classifier. Experiments are conducted on a rolling bearing dataset provided by the bearing data center of Case Western Reserve University (CWRU) for validating the utility of methodology proposed. Results show that the proposed methodology enables to detect fault severity level with high classification accuracy.

Recently, in a massive multiple-input multipleoutput (MIMO) system, deep learning (DL)-based beamforming method has been proposed for reducing the overhead associated with downlink training and uplink feedback. However, the DL-based approach is sensitive to the variation of the communication environment and requires a huge number of training data to ensure a certain level of performance. To reduce the number of required channel data for training a deep neural network (DNN), we introduce deep transfer learning (DTL), which exploits the information from the pre-trained DNN for training other DNNs to find the beamforming vector in the specific channel. Through DTL, DNN can be trained suitably for the communication environment at each BS with fewer channel data. Moreover, we propose ‘step-by-step’ DTL to flexibly apply DTL considering the uncertainties of the realistic system. Simulation results show that DTL has better performance than the conventional DLapproaches even with a small amount number of channel data. Therefore, the DTL-based approach can be a good framework to train DNN when high overhead occurs or designing the beamformer is complicated such as a massive MIMO system.

In recent decades, gait recognition has garnered a lot of attention from the researchers in the IT era. Gait recognition signifies verifying or identifying the individuals by their walking style. Gait supports in surveillance system by identifying people when they are at a distance from the camera and can be used in numerous computer vision and surveillance applications. This paper proposes a stupendous Color-mapped Contour Gait Image (CCGI) for varying factors of Cross-View Gait Recognition (CVGR). The first contour in each gait image sequence is extracted using a Combination of Receptive Fields (CORF) contour tracing algorithm which extracts the contour image using Difference of Gaussians (DoG) and hysteresis thresholding. Moreover, hysteresis thresholding detects the weak edges from the total pixel information and provides more well-balanced smooth features compared to an absolute one. Second CCGI encodes the spatial and temporal information via color mapping to attain the regularized contour images with fewer outliers. Based on the front view of a human walking pattern, the appearance of cross-view variations would reduce drastically with respect to a change of view angles. This proposed work evaluates the performance analysis of CVGR using Deep Convolutional Neural Network (CNN) framework. CCGI is considered a gait feature for comparing and evaluating the robustness of our proposed model. Experiments conducted on CASIA-B database show the comparisons of previous methods with the proposed method and achieved 94.65% accuracy with a better recognition rate.

Carrier signal detection is a complicated and essential task in many domains because it demands a quick response to the existence of several carriers in the wideband, while also precisely predicting each carrier signal’s frequency centers and bandwidths, including single-carrier and multi-carrier modulation signals. Multi-carrier modulation signals, such as FSK and OFDM, could be incorrectly recognized as several single-carrier signals by using the spectrum center net (SCN) or FCN-based method. This paper designed a deep convolutional neural network (CNN) framework for multi-carrier signal detection by fusing the features of multiple consecutive frames of the broadband power spectra and estimating the information of each single-carrier or multi-carrier modulation signal in the broadband, called frame fusion spectrum center net (FFSCN), including FFSCN-R, FFSCN-MN, and FFSCN-FMN. FFSCN includes three base parts, the deep CNN-based backbone, the feature pyramid network (FPN) neck, and the regression network (RegNet) head. FFSCN-R and FFSCN-MN fusing the FPN out features, which use the Residual and MobileNetV3 backbone, respectively, and FFSCN-MN cost less inference time. To further reduce the complexity of FFSCN-MN, the designed FFSCN-FMN modifies the MobileNet blocks and fuses the features at each block of the backbone. The multiple consecutive frames of broadband power spectra not only preserve the high-resolution ratio of the broadband frequency, but also add the features of the signal changes in the time dimension. Extensive experimental results demonstrate that the proposed FFSCN can effectively detect multi-carrier and single-carrier modulation signals in the broadband power spectrum and outperform SCN in accuracy and efficiency.

Timely identification of tumors is essential for reducing cancer-related deaths and improving therapeutic outcomes, particularly in the case of lung cancer, which continues to be a major global health concern. Conventional image-based diagnostic approaches are often limited by errors in distinguishing benign from malignant nodules, inconsistencies among radiologists, and difficulties in processing large-scale medical datasets. To overcome these challenges, this study introduces a Deep Convolutional Neural Network (DCNN) framework designed for automated tumor detection and classification, with emphasis on lung cancer screening and recognition of non-malignant nodules. The approach incorporates preprocessing of CT images through denoising, intensity normalization, and augmentation, followed by hierarchical DCNN feature extraction and classification to separate benign from malignant growths. The primary goal is to improve diagnostic precision, minimize false alarms, and provide an effective decision-support mechanism for clinicians. Experimental analysis indicates that the proposed DCNN achieves higher performance than traditional machine learning and baseline deep models, yielding significant gains in accuracy, precision, sensitivity, and F1-score. These findings demonstrate the promise of deep learning for delivering robust, efficient, and accurate lung cancer detection in real-world clinical settings.

Portrait style transfer using deep convolutional neural networks and facial segmentation

Atrial fibrillation (AF) is one of the most common chronic cardiac arrhythmias often associated with stroke, heart failure, coronary artery disease, etc. Based on the stage of progression and chances of restorability, AF can be categorized into paroxysmal, persistent and permanent. Among all, automatic detection of paroxysmal (early stage) AF (PAF), is challenging as it is clinically silent and occurs in short duration (episodic pattern) an has a high chances of being undiagnosed. In this paper, we present a multiscale deep convolution neural network (MS-DCNN) framework for automatic detection of paroxysmal AF episodes from single lead short electrocardiogram (ECG) signals. The MS-DCNN employs the architecture of multiple-stream CNNs for the multiscale decomposed single lead ECG signal. The proposed method is evaluated on the Physionet/CinC Challenge 2017 ECG dataset consists of 5048 normal sinus rhythm (NSR) and 756 AF rhythm subjects acquired using AliveCor hand-held device. The proposed method achieves an average accuracy, sensitivity, specificity and F1-score of 84.31%, 84.80%, 83.82% and 84.31% respectively on the validation dataset.

AimsTo investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical...

Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans

Traffic sign detection and classification is a critical component of intelligent transportation systems, which is applied to inform automatic unmanned driving systems and driving assistance systems about conditions and limits of roads. Although computer vision is widely utilised in traffic sign detection, detection and recognising traffic signs globally remains a great challenge due to the variety of sign types, scale‐variance and geometric variations. To address these problems, this study proposes a region‐based deep convolutional neural network (CNN) framework for traffic sign detection and classification. Specifically, a multi‐branch sample pyramid module is proposed, which is based on multi‐branch CNNs for multi‐scaled feature exaction. A limited deformable convolutional module is then embedded into the CNN layers to learn the distorted information representation for deformation handing. Moreover, a scale‐aware multi‐task region proposal network module is applied to detect traffic signs with various scales. The whole network is trained in an end‐to‐end manner. Finally, experiments are conducted on two public detection data sets to demonstrate the effectiveness of the proposed method.

BackgroundFetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions.MethodsIn this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML.ResultsBased on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectivelyConclusionsOnce the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

The existing identification methods of transmission tower safety state in goafs have some defects, such as large sample data scale, and the identification accuracy is significantly affected by some time-varying parameters. To solve this problem, this paper proposes an identification method based on improved fuzzy theory and transfer learning. Firstly, the image feature data of transmission tower in goafs is extracted according to the YOLOv3 algorithm which belongs to one-stage target detection algorithm, and the image feature data is preprocessed by fuzzy algorithm. Secondly, a deep convolution neural network framework is proposed for security state recognition, and a multi-stage migration learning algorithm is used to complete network training. Finally, the results show that compared with the traditional machine learning algorithm without migration learning training, the proposed algorithm improves the accuracy and precision by more than 4.5% and 3.8%, respectively, which verifies the effectiveness and superiority of the proposed algorithm.

In this paper, we propose a deep convolutional neural network framework to classify dermoscopy images into seven classes. With taking the advantage that these classes can be merged into two (healthy/diseased) ones we can train a part of the network regarding this binary task only. Then, the confidences regarding the binary classification are used to tune the multi-class confidence values provided by the other part of the network, since the binary task can be solved more accurately. For both the classification tasks we used GoogLeNet Inception-v3, however, any CNN architectures could be applied for these purposes. The whole network is trained in the usual way, and as our experimental results on the skin lesion image classification show, the accuracy of the multi-class problem has been remarkably raised (by 7% considering the balanced multi-class accuracy) via embedding the more reliable binary classification outcomes.