Multi-scale Convolutional Neural Network Research Articles

SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction

As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine learning methods have been developed for functional peptide prediction. However, the main research heavily relies on statistical features and few consider multifunctional peptide identification. So, we propose SME-MFP, a novel predictor in the imbalanced multi-label functional peptide datasets. First, we employ physicochemical and evolutionary information to represent the peptide sequence’s initialization features from multiple perspectives. Second, the features are fused and then put into spatial feature extractors, where the residual connection and multiscale convolutional neural network extract more discriminative features of different lengths’ peptide sequences. Besides, we also design AFT-based temporal feature extractors to fully capture the global interactions of the sequences. Finally, devising a new loss to replace the traditional cross entropy loss to settle the class imbalance problems. The results show that our framework not only enhances the model's ability to capture sequence features effectively, but also accuracy improves by 3.89% over existing methods on public peptide datasets.

EEG-FRM: a neural network based familiar and unfamiliar face EEG recognition method.

Recognizing familiar faces holds great value in various fields such as medicine, criminal investigation, and lie detection. In this paper, we designed a Complex Trial Protocol-based familiar and unfamiliar face recognition experiment that using self-face information, and collected EEG data from 147 subjects. A novel neural network-based method, the EEG-based Face Recognition Model (EEG-FRM), is proposed in this paper for cross-subject familiar/unfamiliar face recognition, which combines a multi-scale convolutional classification network with the maximum probability mechanism to realize individual face recognition. The multi-scale convolutional neural network extracts temporal information and spatial features from the EEG data, the attention module and supervised contrastive learning module are employed to promote the classification performance. Experimental results on the dataset reveal that familiar face stimuli could evoke significant P300 responses, mainly concentrated in the parietal lobe and nearby regions. Our proposed model achieved impressive results, with a balanced accuracy of 85.64%, a true positive rate of 73.23%, and a false positive rate of 1.96% on the collected dataset, outperforming other compared methods. The experimental results demonstrate the effectiveness and superiority of our proposed model.

A Sequential End-to-End Neonatal Sleep Staging Model with Squeeze and Excitation Blocks and Sequential Multi-Scale Convolution Neural Networks.

Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This limits the application of these methods to multiple scenarios. In this paper, a sequential end-to-end sleep staging model, SeqEESleepNet, which is competent for parallelly processing sequential epochs and has a fast training rate to adapt to different scenarios, is proposed. SeqEESleepNet consists of a sequence epoch generation (SEG) module, a sequential multi-scale convolution neural network (SMSCNN) and squeeze and excitation (SE) blocks. The SEG module expands independent epochs into sequential signals, enabling the model to learn the temporal information between sleep stages. SMSCNN is a multi-scale convolution neural network that can extract both multi-scale features and temporal information from the signal. Subsequently, the followed SE block can reassign the weights of features through mapping and pooling. Experimental results exhibit that in a clinical dataset, the proposed method outperforms the state-of-the-art approaches, achieving an overall accuracy, F1-score, and Kappa coefficient of 71.8%, 71.8%, and 0.684 on a three-class classification task with a single channel EEG signal. Based on our overall results, we believe the proposed method could pave the way for convenient multi-scenario neonatal sleep staging methods.

Deep learning applications in MRI for brain tumor detection and image segmentation

Deep learning holds great potential in the field of MRI applications. By leveraging its advanced algorithms and neural networks, it can effectively analyze and interpret intricate patterns in medical images, aiding in precise disease detection, segmentation, and classification. Integrating deep learning techniques with MRI technology is expected to revolutionize radiology practice, facilitating enhanced diagnostic accuracy and customized treatment strategies, ultimately leading to improved patient outcomes. This article provides an overview of of the latest advancements in deep learning techniques applied to magnetic resonance imaging, specifically focusing on brain tumor detection and segmentation. The study examines eight different deep learning methods, including a multi-scale convolutional neural network, U-Net-based fully convolutional networks, cascaded anisotropic convolutional neural networks, missing modality-based tumor segmentation, Hough-CNN for deep brain region segmentation, k-Space deep learning for accelerated MRI, Multi-level Kronecker Convolutional Neural Network, and a heuristic approach for clinical brain tumor segmentation. Each method is analyzed, highlighting its specific techniques, advantages, and limitations. The comparative performance of these methods in terms of accuracy and efficiency, addressing key factors such as computational requirements, training time, and robustness, was discussed in this article. By assessing the merits and limitations of different approaches, this review seeks to offer valuable perspectives on effective utilization of deep learning techniques in clinical MRI settings for detecting and delineating brain tumors.

Knowledge graph embedding in a uniform space

Knowledge graph embedding (KGE) is typically used for link prediction to automatically predict missing links in knowledge graphs. Current KGE models are mainly based on complicated mathematical associations, which are highly expressive but ignore the uniformity behind the classical bilinear translational model TransE, a model that embeds all entities of knowledge graphs in a uniform space, enabling accurate embeddings. This study analyses the uniformity of TransE and proposes a novel KGE model called ConvUs that follows uniformity with expressiveness. Based on the convolution neural network (CNN), ConvUs proposes constraints on convolution filter values and employs a multi-layer, multi-scale CNN architecture with a non-parametric L2 norm-based scoring function for the calculation of triple scores. This addresses potential uniformity-related issues in existing CNN-based KGE models, allowing ConvUs to maintain a uniform embedding space while benefiting from the powerful expressiveness of CNNs. Furthermore, circular convolution is applied to alleviate the potential orderliness contradictions, making ConvUs more suitable for conducting uniform space KGE. Our model outperformed the base model ConvKB and several baselines on the link prediction benchmark WN18RR and FB15k-237, demonstrating strong applicability and generalization and indicating that the uniformity of embedding space with high expressiveness enables more efficient knowledge graph embeddings.

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Under the conditions of a mechanical fault in a motor, mechanical vibration of a specific frequency can be generated. The electrical contact points directly connected to the motor can vibrate at the same frequency. The electrical contact points with poor contact can easily produce a series arc fault under vibration conditions, which affects the reliability of the power supply. In order to detect the series arc fault under different vibration conditions, the arc fault generator is connected between the back end of the frequency converter and the motor. An arc fault experiment under different vibration conditions was carried out and the fault phase current and arc voltage signals were collected. In this paper, the noise-assisted multivariate empirical mode decomposition and the correlation coefficient between each intrinsic mode function are used to select the fault feature signals. Then, the reconstructed signal is input into the series arc fault model combining a multi-scale convolutional neural network and a bidirectional long short-term memory network for training. The research results show that the series arc fault detection method proposed in this paper can effectively detect the series arc fault and can preliminarily identify the type of motor fault causing the mechanical vibration of the motor; the model has good noise immunity and generalization.

A proposed video super-resolution reconstruction strategy using wavelet multi-scale convolutional neural networks

A proposed video super-resolution reconstruction strategy using wavelet multi-scale convolutional neural networks

Accurate authentication based on ECG using deep learning

Biometric-based authentication methods have been widely used, for example on portable devices (e.g., Android and iOS devices). However, there are several known limitations in existing authentication methods based on biometrics (e.g., those using facial, iris, and fingerprint). For example, in a healthcare context, a user may be physically incapable of completing the authentication due to his/her medical conditions. Hence, as a complementary authentication mechanism, there have been attempts to also utilize electrocardiogram (ECG). In this work, we propose an ECG authentication system that leverages deep learning. Specifically, to achieve generalization ability, complementary ensemble empirical decomposition (CEEMD) is introduced in our design. Moreover, a 1-D Multi-scale Convolutional Neural Network (1-D MCNN) is implemented to achieve accurate authentication. To evaluate the usability of our proposed approach, we have performed extensive experiments on eight databases, and the findings show that our proposed approach achieves good performance even on abnormal databases and can be adapted for different application environments. In addition, our adopted data from eight public databases requires theoretical statistical treatment for practical applications in real authentication scenarios.

Near-infrared quality monitoring modeling with multi-scale CNN and temperature adaptive correction

Considering the impact of temperature on near-infrared spectroscopy (NIRS), a temperature adaptive correction neural network model called TI-CNN has been developed. This model takes into account the temperature influence. First, a multi-scale CNN model is established to extract the global features of NIRS. Then, by establishing the quantitative relationship between the sample and the modeling error of the neural network, the influence of temperature on the spectral features. Based on the degree of influence, the weight is adaptively adjusted to quantify the effect of temperature on near-infrared spectral features. Finally, to verify the model performance, EPO-PLSR, OSC-PLSR, CNN, and TA-CNN were used for comparison on the test set. The results showed that traditional temperature correction models performed poorly, with a coefficient of determination R2< 0.80. However, the neural network models based on multi-scale CNN, R2> 0.80. Of these, the TI-CNN based on multi-scale CNN and temperature adaptive correction, demonstrated the best performance with R2> 0.95. Therefore, the method proposed in this paper demonstrates superior performance compared to existing methods. It utilizes a multi-scale CNN to extract near-infrared spectral features and also incorporates temperature to adaptively correct the spectral features, resulting in superior model performance across various temperature conditions.

TECD_Attention: Texture-enhanced and cross-domain attention modeling for visual place recognition

Visual place recognition (VPR) is a challenging task for visual computing in the field of robot navigation. However, most of the existing methods fail to learn the most salient features of place images by simple CNN feature or popular Transformer feature due to the inconsistency problem commonly existing in VPR datasets, which limits the robustness and interpretability of the model. In addition, existing state-of-the-art methods only capture general features of original places with multi-scale CNN or transformer features and ignore texture characteristics existing in place images, resulting in suboptimal recognition performance. To cope with the above issues, we propose a novel visual place recognition network, named Texture-enhanced Cross-domain Attention Transformer (TECD_Attention). Specially, a cross-attention Transformer is first used for fusing deep attentive local and global features to improve the multi-scale feature representation of the recognition model. Second, a texture-enhanced cross-domain attention block is designed to construct the final feature descriptor by fusing texture features and attentive local–global features. Then, a tripled loss function is used for matching top-ranked reference places from the place database to a query place. Last, effective and efficient place re-ranking is achieved by training an adapted weakly supervised re-ranking network relying on the similarity computing between the query place and the top-ranked places. Our approach is carried out in extensive experiments on four challenging datasets. Our model has achieved 96.2%, 94.6%, 95.9%, and 96.8% average recall based on top 1% Candidate scenario on Tokyo 24/7, Pitts250k, VPRiCE, and SUN397 datasets, respectively. Therefore, Compared with the existing state-of-the-art VPR methods, TECD_Attention performs superior on robot place recognition in challenging environments. Hence, we can conclude that this is a robust model for robot visual place recognition in challenging environments.

An Electrocardiogram Classification Using a Multiscale Convolutional Causal Attention Network

Electrocardiograms (ECGs) play a pivotal role in the diagnosis and prediction of cardiovascular diseases (CVDs). However, traditional methods for ECG classification involve intricate signal processing steps, leading to high design costs. Addressing this concern, this study introduces the Multiscale Convolutional Causal Attention network (MSCANet), which utilizes a multiscale convolutional neural network combined with causal convolutional attention mechanisms for ECG signal classification from the PhysioNet MIT-BIH Arrhythmia database. Simultaneously, the dataset is balanced by downsampling the majority class and oversampling the minority class using the Synthetic Minority Oversampling Technique (SMOTE), effectively categorizing the five heartbeat types in the test dataset. The experimental results showcase the classifier’s performance, evaluated through accuracy, precision, sensitivity, and F1-score and culminating in an overall accuracy of 99.35%, precision of 96.55%, sensitivity of 96.73%, and an F1-recall of 96.63%, surpassing existing methods. Simultaneously, the application of this innovative data balancing technique significantly addresses the issue of data imbalance. Compared to the data before balancing, there was a significant improvement in accuracy for the S-class and the F-class, with increases of approximately 8% and 13%, respectively.

CTCNet: A CNN Transformer capsule network for sleep stage classification

In this paper, we propose a novel neural network architecture called CTCNet. First, we adopt a multi-scale convolutional neural network (MSCNN) to extract low and high-frequency features, adaptive channel feature recalibration (ACFR) to enhance the model’s sensitivity to important channel features in the feature maps and reduce dependence on irrelevant or redundant features, a multi-scale dilated convolutional block (MSDCB) to capture characteristics of different types among feature channels. Second, we use Transformer to extract global temporal context features. Third, we employ capsule network to capture spatial location relationships among EEG features and refine these features. Besides, the capsule network module is used as our model’s classifier to classify the final results. It is worth noting that our model better solves the problem that previous researches failed to take into account the simultaneous extraction of local features and global temporal context characteristics of EEG signals, and ignored the spatial location relationships between these features. Eventually, we assess our model on three datasets and it achieves better or comparable performance than most state-of-the-art methods.

A Lightweight Network Based on Multi-Scale Asymmetric Convolutional Neural Networks with Attention Mechanism for Ship-Radiated Noise Classification

Ship-radiated noise classification is critical in ocean acoustics. Recently, the feature extraction method combined with time–frequency spectrograms and convolutional neural networks (CNNs) has effectively described the differences between various underwater targets. However, many existing CNNs are challenging to apply to embedded devices because of their high computational costs. This paper introduces a lightweight network based on multi-scale asymmetric CNNs with an attention mechanism (MA-CNN-A) for ship-radiated noise classification. Specifically, according to the multi-resolution analysis relying on the relationship between multi-scale convolution kernels and feature maps, MA-CNN-A can autonomously extract more fine-grained multi-scale features from the time–frequency domain. Meanwhile, the MA-CNN-A maintains its light weight by employing asymmetric convolutions to balance accuracy and efficiency. The number of parameters introduced by the attention mechanism only accounts for 0.02‰ of the model parameters. Experiments on the DeepShip dataset demonstrate that the MA-CNN-A outperforms some state-of-the-art networks with a recognition accuracy of 98.2% and significantly decreases the parameters. Compared with the CNN based on three-scale square convolutions, our method has a 68.1% reduction in parameters with improved recognition accuracy. The results of ablation explorations prove that the improvements benefit from asymmetric convolution, multi-scale block, and attention mechanism. Additionally, MA-CNN-A shows a robust performance against various interferences.

Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM

Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.

Multiscale 1-DCNN for Damage Localization and Quantification Using Guided Waves With Novel Data Fusion Technique and New Self-Attention Module

This article proposes an innovative damage localization and size quantification method named as MSCNNSAM based on new multiscale convolutional neural network (MSCNN) and novel small-weighted zero-setting self-attentive module (SAM) in carbon fiber reinforced plastic structures. Firstly, an improved piecewise aggregate approximation algorithm (IPAA) is developed to compress the guided wave signal and extract a series of damage indexes (DI). Considering the different effects of the damage location on the different sensing paths, a new method of damage information targeting enhancement and multipath data fusion is proposed. Then, a novel MSCNN architecture is also proposed for the inherent multiscale characteristics of the guided wave signal, which takes the multipath fused data as input and uses regression and classification methods to directly predict the location and size of the damage. Finally, to further improve the performance of the MSCNN, a SAM is proposed to effectively avoid the influence of low-information channel features and improve the damage feature extraction capability of the network. The proposed method is evaluated through experiments on a guided wave testing platform. Experimental results and comprehensive comparison analysis with respect to the state-of-the-art damage localization and quantification methods have demonstrated the superiority of the proposed MSCNNSAM.

Fault Diagnosis Method for Bearing Based on Attention Mechanism and Multi-Scale Convolutional Neural Network

Fault Diagnosis Method for Bearing Based on Attention Mechanism and Multi-Scale Convolutional Neural Network

Research on Multi-scale CNN and Transformer-based Multi-level Multi-classification Method for Images

Research on Multi-scale CNN and Transformer-based Multi-level Multi-classification Method for Images

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

Multiscale 3-D–2-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification

Multiscale 3-D–2-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification

A Residual Multi-Scale Convolutional Neural Network with Transformers for Speech Emotion Recognition

A Residual Multi-Scale Convolutional Neural Network with Transformers for Speech Emotion Recognition