Multi-scale Convolutional Neural Network Research Articles

Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer

The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger’s (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger “Hua An Long” was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model.

An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction

An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation

BackgroundVirtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue. ObjectiveIn this paper, a convolutional neural network (CNN) EEG detection model based on multi-scale feature correlation is proposed for detecting VRMS. MethodsThe model uses multi-scale 1D convolutional layers to extract multi-scale temporal features from the multi-lead EEG data, and then calculates the feature correlations of the extracted multi-scale features among all the leads to form the feature adjacent matrixes, which converts the time-domain features to correlation-based brain network features, thus strengthen the feature representation. Finally, the correlation features of each layer are fused. The fused features are then fed into the channel attention module to filter the channels and classify them using a fully connected network. Finally, we recruit subjects to experience 6 different modes of virtual roller coaster scenes, and collect resting EEG data before and after the task to verify the model. Results: The results show that the accuracy, precision, recall and F1-score of this model for the recognition of VRMS are 98.66 %, 98.65 %, 98.68 %, and 98.66 %, respectively. The proposed model outperforms the current classic and advanced EEG recognition models. SignificanceIt shows that this model can be used for the recognition of VRMS based on the resting state EEG.

A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

Scene text image super-resolution using multi-scale convolutional neural network with skip connections

Scene text image super-resolution using multi-scale convolutional neural network with skip connections

A novel multi-sensor hybrid fusion framework

Multi-sensor data fusion has emerged as a powerful approach to enhance the accuracy and robustness of diagnostic systems. However, effectively integrating multiple sensor data remains a challenge. To address this issue, this paper proposes a novel multi-sensor fusion framework. Firstly, a vibration signal weighted fusion rule based on Kullback–Leibler divergence-permutation entropy is introduced, which adaptively determines the weighting coefficients by considering the positional differences of different sensors. Secondly, a lightweight multi-scale convolutional neural network is designed for feature extraction and fusion of multi-sensor data. An ensemble classifier is employed for fault classification, and an improved hard voting strategy is proposed to achieve more reliable decision fusion. Finally, the superiority of the proposed method is validated using modular state detection data from the Kaggle database.

Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network.

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.

Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection

This paper introduces an innovative approach, Deep Multiscale Soft-Threshold Support Vector Data Description (DMS-SVDD), designed for the detection of anomalies and prediction of faults in heavy-duty gas turbine generator sets (GENSETs). The model combines a support vector data description (SVDD) with a deep autoencoder backbone network framework, integrating a multiscale convolutional neural network (M) and soft-threshold activation network (S) into the Deep-SVDD framework. In comparison with conventional methods, such as One-Class Support Vector Machine (OCSVM) and autoencoder (AE), DMS-SVDD demonstrates improvements in accuracy (by 22.94%), recall (by 32%), F1 score (by 12.02%), and smoothness (by 39.15%). The model excels particularly in feature extraction, denoising, and early fault detection, offering a proactive strategy for maintenance. Furthermore, the DMS-SVDD demonstrated enhanced training efficiency with a reduction in the convergence rounds by 66% and overall training times by 34.13%. The study concludes that DMS-SVDD presents a robust and efficient solution for gas turbine anomaly detection, with practical advantages for decision support in turbine maintenance. Future research could explore additional refinements and applications of the DMS-SVDD model across diverse industrial contexts.

Research on fault diagnosis method based on the Markov transition field with enhanced properties and AM-MSCNN under different external environmental interference

The mechanical equipment often faces complex working environments in practical operating conditions, and the external environmental interference generated by operating conditions, environmental factors and other components causes the vibration signals to exhibit characteristics with frequency distortion and multi-modality. The existing fault diagnosis methods rarely consider the issue of external environmental interference. Aiming at the background of fault diagnosis under external environment interference, a fault diagnosis method based on Markov transfer field (MTF) with enhanced properties and multi-scale convolutional neural network with attention mechanism (AM-MSCNN) is proposed. The fault features embedded in vibration signals under external environmental interference can be extracted, and an important contribution to the fault diagnosis method under external environmental interference can be made. Firstly, an interference mode selection model based on symplectic geometry modal decomposition is constructed to address the issues of distortion and multi-modality caused by external environmental interference. Next, a two-dimensional feature extraction method based on the MTF with enhanced properties is established. The challenge of extracting temporal correlation features from one-dimensional vibration signals affected by external environmental interference is addressed by Markov transition probability. The impact of external environmental interference can be mitigated, and that has strong anti-interference capability and robustness. Finally, an attention mechanism that can adaptively assign weights is designed, and the AM-MSCNN model is designed to effectively extract global features by incorporating attention mechanisms in the parallel layers of MSCNN and the attention mechanism helps to suppress external environmental interference and improve the diagnostic results. An experimental platform for simulating the typical faults under external environmental interference is constructed, and the experimental results demonstrate that the proposed method exhibits superior generalization performance under varying degrees of different interference environments. The overall average accuracy reaches 92.2%, and the highest accuracy reaches 94.0% for external interference working conditions.

Novel Multi-flow Multi-scale Convolutional Neural Network Developed for Quality Prediction of Batch Processes to Fuse Data With Different Sampling Frequencies

Novel Multi-flow Multi-scale Convolutional Neural Network Developed for Quality Prediction of Batch Processes to Fuse Data With Different Sampling Frequencies

CDAM-Net: Channel shuffle dual attention based multi-scale CNN for efficient glaucoma detection using fundus images

Glaucoma is a typical eye disorder that induces damage to the optic nerve due to increased intraocular pressure. It ultimately leads to partial or complete blindness without clinical reversal. Hence, it is of utmost importance to screen and detect glaucoma at an early stage. Most of the earlier glaucoma diagnosis methods rely on manual feature engineering, which is time-consuming and requires domain experts. Although recent methods, particularly, convolutional neural networks (CNNs) facilitate learning high-level feature representations from fundus images, they need extensive parameters and pose overfitting issues due to insufficient training samples. Further, conventional CNNs often ignore the minute changes in the lesion region. To overcome these issues, we propose a lightweight multi-scale CNN architecture called as CDAM-Net for effective glaucoma identification from retinal fundus images. Additionally, we introduce an attention module called channel shuffle dual attention (CSDA), comprising of a channel attention block, a spatial attention block and a channel shuffle unit, to focus on important regions in the fundus images, thereby extracting class-specific features. The CDAM-Net mainly consists of multi-scale feature representation (MFR) blocks that enable the extraction of multi-scale features from fundus images. Each MFR block is followed by a CSDA module, which further helps enrich the feature representation. The CDAM-Net is evaluated on a retinal fundus image (RFI) dataset containing 1426 fundus images (837 glaucoma and 589 normal), and the results indicate that CDAM-Net yields promising classification performance compared to existing techniques. Also, ablation studies are carried out to test the effectiveness of each component of the CDAM-Net.

A multiscale electricity theft detection model based on feature engineering

With the widespread adoption of smart meters and the growing availability of data mining and machine learning algorithms, there is a pressing demand for methods that are both accurate and explicable in identifying electricity theft patterns among end-users. To address this need, this study proposes a multi-scale anomaly detection model based on feature engineering.Specifically, tsfresh is utilized in feature engineering to extract electricity consumption features from the raw data, and XGBoost is employed to select features that are highly correlated with anomalous behavior, which have clear physical interpretations. Multi-scale convolutional neural networks are then used to analyze and process the data at different temporal and frequency scales. Attention mechanisms are applied to assign weights to different feature channels, and all of the extracted information is fused for anomaly detection. The combination of feature engineering and multi-scale convolutional neural networks not only enhances the interpretability of the model but also improves its performance, as demonstrated by the experimental results, which show that the proposed method outperforms traditional anomaly detection approaches across multiple evaluation metrics.

An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration

To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.

Study on vibration signals identification method for pipeline leakage detection based on deep learning technology

For natural gas pipeline leakage systems based on distributed feedback fiber laser vibration sensors (DFB-FL), a novel neural network model (mCNN-LFLBs) integrating multi-scale convolutional neural networks (mCNN) with local feature extraction blocks (LFLBs) is proposed in this study. To address the problem of feature loss in the traditional signal image transfer process, the one-dimensional original signal is directly used as the input of the model, effectively maintaining the time-domain correlation of the leaked signal. The time-domain correlation of the leakage signal is preserved. To address the problem that traditional pattern recognition algorithms have a weak ability to extract deep features and local features, mCNN is established by this model, which realizes the use of convolution kernels of different scales to extract features of different frequency bands in one-dimensional signals. Additionally, LFLBs are introduced, which enhances the local expression ability of the model, further improves the feature mining quality of the model, and better presents the deep features of leaked signals. Experimental results demonstrate that the mCNN-LFLBs model has a classification accuracy of up to 98.6%, with a testing response time of approximately 10 ms, and outstanding performance in terms of convergence speed. These results highlight the potential practical application of the mCNN-LFLBs model in natural gas leak detection.

Fake news detection in Dravidian languages using multiscale residual CNN_BiLSTM hybrid model

Fake news detection is the process of identifying news that contain purposeful misinformation disseminated through traditional news sources or social media platforms. Detecting fake news in Dravidian languages poses unique challenges due to the linguistic diversity and limited resources available for these languages. In this study, we propose a novel approach for fake news detection in Dravidian languages, leveraging contextualized word embeddings from MuRIL. MuRIL provides contextual embeddings that capture nuanced linguistic nuances, making it well-suited for the complexities of Dravidian languages. The proposed hybrid model combines multiscale residual CNN and BiLSTM layers to capture local and global dependencies in textual data effectively. The multiscale architecture allows the model to extract features at various levels of granularity while the BiLSTM layer captures long-range dependencies and contextual information. We performed extensive experiments on diverse datasets in Dravidian languages to evaluate the proposed approach. The results demonstrate the effectiveness of our hybrid model in discerning between genuine and fake news articles.

Remaining Useful Life Prediction for Aircraft Engines under High-Pressure Compressor Degradation Faults Based on FC-AMSLSTM

The healthy operation of aircraft engines is crucial for flight safety, and accurate Remaining Useful Life prediction is one of the core technologies involved in aircraft engine prognosis and health management. In recent years, deep learning-based predictive methods within data-driven approaches have shown promising performance. However, for engines experiencing a single fault, such as a High-Pressure Compressor fault, existing deep learning-based predictive methods often face accuracy challenges due to the coupling relationship between different fault modes in the training dataset that includes a mixture of multiple fault modes. In this paper, we propose the FC-AMSLSTM method, a novel approach for Remaining Useful Life prediction specifically targeting High-Pressure Compressor degradation faults. The proposed method effectively addresses the limitations of previous approaches by fault classification and decoupling fault modes from multiple operating conditions using a decline index. Then, attention mechanisms and multi-scale convolutional neural networks are employed to extract spatiotemporal features. The long short-term memory network is then utilized to model RUL estimation. The experiments are conducted using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. The results demonstrate that compared to other prediction models, the FC-AMSLSTM method effectively reduces RUL prediction error for HPC degradation faults under multiple operating conditions.

Deep-learning enhanced high-quality imaging in metalens-integrated camera.

Because of their ultra-light, ultra-thin, and flexible design, metalenses exhibit significant potential in the development of highly integrated cameras. However, the performances of metalens-integrated camera are constrained by their fixed architectures. Here we proposed a high-quality imaging method based on deep learning to overcome this constraint. We employed a multi-scale convolutional neural network (MSCNN) to train an extensive pair of high-quality and low-quality images obtained from a convolutional imaging model. Through our method, the imaging resolution, contrast, and distortion have all been improved, resulting in a noticeable overall image quality with SSIM over 0.9 and an improvement in PSNR over 3 dB. Our approach enables cameras to combine the advantages of high integration with enhanced imaging performances, revealing tremendous potential for a future groundbreaking imaging technology.

Image Classification Method Based on Multi-Scale Convolutional Neural Network

Traditional convolutional neural networks (CNNs) typically use fixed scale convolutional kernels for feature extraction when processing image classification tasks, while ignoring the multi-scale information present in the image. To overcome this limitation, we propose an algorithm based on multi-scale CNNs, which capture features at different levels by introducing convolutional kernels of different scales into the convolutional layer. In this study, we first designed a multi-scale convolutional layer consisting of multiple convolutional kernels of different scales to extract multi-scale features of the image. To further enhance classification performance, we introduced a multi-scale feature fusion module that can effectively fuse features of different scales and classify them through a fully connected layer. Then we conducted extensive experiments on several commonly used image classification datasets. The experimental results show that this network can not only effectively identify and locate hyperspectral image targets in different scenarios, but also reduce missed detections and false positives during the detection process. The average accuracy of the improved model has been improved, and the recognition accuracy of some small markers affected by external factors such as occlusion and lighting has also been improved. In addition, by comparing the detection effect of a single image, the progressiveness and anti-leakage ability of the improved model are proved. The image classification method based on multi-scale CNNs has broad application prospects in image recognition and feature extraction, and can provide valuable reference and reference for research in related fields.

Prediction of CRISPR/Cas9 off-target activity using multi-scale convolutional neural network

Clustered regularly interspaced short palindromic repeat/CRISPR-associated protein 9 (CRISPR/Cas9) is a new generation of gene editing technology, which relies on single guide RNA to identify specific gene sites and guide Cas9 nuclease to edit specific location in the genome. However, the off-target effect of this technology hampers its development. In recent years, several deep learning models have been developed for predicting the CRISPR/Cas9 off-target activity, which contributes to more efficient and safe gene editing and gene therapy. However, the prediction accuracy remains to be improved. In this paper, we proposed a multi-scale convolutional neural network-based method, designated as CnnCRISPR, for CRISPR/Cas9 off-target prediction. First, we used one-hot encoding method to encode the sgRNA-DNA sequence pair, followed by a bitwise or operation on the two binary matrices. Second, the encoded sequence was fed into the Inception-based network for training and evaluating. Third, the well-trained model was applied to evaluate the off-target situation of the sgRNA-DNA sequence pair. Experiments on public datasets showed CnnCRISPR outperforms existing deep learning-based methods, which provides an effective and feasible method for addressing the off-target problems.

Developing a Multi-Scale Convolutional Neural Network for Spatiotemporal Fusion to Generate MODIS-like Data Using AVHRR and Landsat Images

Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications.