Convolution Method Research Articles

Detection of colorization based image forgeries using convolutional autoencoder method

Detection of colorization based image forgeries using convolutional autoencoder method

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

A new approach to improve the Earth's polar motion prediction: on the deconvolution and convolution methods

A new approach to improve the Earth's polar motion prediction: on the deconvolution and convolution methods

MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction

Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverage the true advantages of graph convolution. There is still room for improvement in simultaneously addressing multi-graph convolution, optimizing graphs, and simulating road conditions. Based on this, this paper proposes MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction. This method overcomes the aforementioned issues by dividing the process into different stages and achieves promising prediction results. In the first stage, we construct a latent similarity adjacency matrix and address the randomness interference features in similarity features through two optimizations using the proposed ConvGRU Attention Layer (CGAL module) and the Causal Similarity Capture Module (CSC module), which includes Granger causality tests. In the second stage, we mine the potential correlation between roads using the Correlation Completion Module (CC module) to create a global correlation adjacency matrix as a complement for potential correlations. In the third stage, we utilize the proposed Auto-LRU autoencoder to pre-train various weather features, encoding them into the model’s prediction process to enhance its ability to simulate the real world and improve interpretability. Finally, in the fourth stage, we fuse these features and use a Bidirectional Gated Recurrent Unit (BiGRU) to model time dependencies, outputting the prediction results through a linear layer. Our model demonstrates a performance improvement of 29.33%, 27.03%, and 23.07% on three real-world datasets (PEMSD8, LOSLOOP, and SZAREA) compared to advanced baseline methods, and various ablation experiments validate the effectiveness of each stage and module.

Convolutional neural networks method for folded naira currency denominations recognition and analysis

Sorting banknotes automatically in payment facilities or freewill donation boxes involves many tasks such as recognizing banknote denomination, fitness classification, and counterfeit detection. Different studies have addressed these problems using foreign currencies. In other words, very few researches have been conducted on naira banknote denomination recognition. A few studies did not consider folded naira banknote denomination recognition. To overcome this, we propose a Machine Learning Based Folded Banknote Denomination Recognition System. The proposed system automatically extracts relevant features and characteristics of the naira currency denomination in different positions, orientations, and folds. This automatic representation of the naira currency denomination enables the proposed system to reduce over-reliance on suggested characteristics, improve performance accuracy, and generalize the proposed system to new problems. Moreover, the proposed system was modeled using object-oriented analysis and design methodology, and implemented in the Keras framework interfaced with Tensorflow. Then, the Softmax classification algorithm was used to classify each Naira banknote denomination. The proposed machine learning-based folded banknote denomination recognition achieved an average accuracy of 95%, which shows the generalization ability of the proposed system to recognize folded banknote denominations in different orientations, positions, and folded forms.

Spatial Resolution Enhancement Framework Using Convolutional Attention-Based Token Mixer

Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token mixer method. This approach leveraged spatial context and semantic information to improve the spatial resolution of images. This method used the multi-head convolutional attention block and sub-pixel convolution to extract spatial and spectral information and fused them using the same technique. The multi-head convolutional attention block can effectively utilize the local information of spatial and spectral dimensions. The method was tested on two kinds of data types, which were the visual-thermal dataset and the visual-hyperspectral dataset. Our method was also compared with the state-of-the-art methods, including traditional methods and deep learning methods. The experiment results showed that the method was effective and outperformed state-of-the-art methods in overall, spatial, and spectral accuracies.

Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions.

A dual encoder LDCT image denoising model based on cross-scale skip connections

LDCT image denoising is crucial in medical imaging as it aims to minimize patient radiation exposure while maintaining diagnostic image quality. However, current convolutional neural network-based denoising methods struggle to incorporate global contexts, often focusing solely on local features. This limitation poses a significant challenge. To address this, a dual encoder denoising model is introduced that utilizes the Transformer model’s proficiency in capturing long-range dependencies and global context. This model integrates the Transformer branch and the convolutional branch in the encoder. By concatenating the features of these two different branches, the model can capture both global and local image features, substantially enhancing denoising efficacy. A cross-scale skip connection mechanism is introduced to integrate the encoder’ s low-level features with the decoder’ s high-level features, enriching contextual information and preserving image details. In addition, to meet the requirements of multi-scale feature fusion, the decoder is equipped with different multi-scale convolution modules to optimize feature processing. The number of layers in these modules gradually decreases as the depth of the decoder increases. In order to enhance the discriminative ability of the model, a multi-scale discriminator is also introduced, which effectively improves the recognition ability of the image by extracting features from four different scales. Consequently, our approach demonstrates remarkable performance in reducing noise and improving LDCT image quality, as evidenced by the substantial improvements in PSNR (17.75%) and SSIM (7.31%) values.

Driver behavior recognition based on dual-branch and deformable convolutional network method

Aiming at the task of driver behavior recognition in the car cockpit, this paper proposes a recognition method based on a dual-branch neural network. The main branch of the network model uses ResNet50 as the backbone network for feature extraction, and uses deformable convolution to adapt the model to the shape and position changes of the driver in the image. The auxiliary branch assists in updating the parameters of the backbone network during the gradient backpropagation process, so that the backbone network can better extract features that are conducive to driver behavior recognition, thereby improving the recognition performance of the model. The ablation experiment and comparative experiment results of the network model on the State Farm public dataset show that the recognition accuracy of the proposed network model can reach 96.23%, and the recognition effect is better for easily confused behavior categories. The research results are of great significance for understanding driver behavior in the car cockpit and ensuring driving safety.

Unpaired Image-to-Image Translation with Diffusion Adversarial Network

Unpaired image translation with feature-level constraints presents significant challenges, including unstable network training and low diversity in generated tasks. This limitation is typically attributed to the following situations: 1. The generated images are overly simplistic, which fails to stimulate the network’s capacity for generating diverse and imaginative outputs. 2. The images produced are distorted, a direct consequence of unstable training conditions. To address this limitation, the unpaired image-to-image translation with diffusion adversarial network (UNDAN) is proposed. Specifically, our model consists of two modules: (1) Feature fusion module: In this module, one-dimensional SVD features are transformed into two-dimensional SVD features using the convolutional two-dimensionalization method, enhancing the diversity of the images generated by the network. (2) Network convergence module: In this module, the generator transitions from the U-net model to a superior diffusion model. This shift leverages the stability of the diffusion model to mitigate the mode collapse issues commonly associated with adversarial network training. In summary, the CycleGAN framework is utilized to achieve unpaired image translation through the application of cycle-consistent loss. Finally, the proposed network was verified from both qualitative and quantitative aspects. The experiments show that the method proposed can generate more realistic converted images.

A Graph Convolutional Network-Based Method for Congested Link Identification

A Graph Convolutional Network-Based Method for Congested Link Identification

Research on Chebyshev Graph Convolutional Neural Network Modeling Method for Rotating Equipment Fault Diagnosis under Variable Working Conditions

Research on Chebyshev Graph Convolutional Neural Network Modeling Method for Rotating Equipment Fault Diagnosis under Variable Working Conditions

The Identification and Quantification of Hidden Hazards in Small Scale Reservoir Engineering Based on Deep Learning: Intelligent Perception for Safety of Small Reservoir Projects in Jiangxi Province

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding–decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by unmanned aerial vehicles (UAVs). The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) were obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers) proposed in this paper outperforms other methods. The main evaluation indicator, mIoU of HHSN-25, is higher than other methods, reaching 87.00%, and the Unet is 85.50%, and the Unet++ is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Statement of Retraction: Computer simulation as an aid to predict fundamental frequency of a sandwich system via discrete singular convolution method

Statement of Retraction: Computer simulation as an aid to predict fundamental frequency of a sandwich system via discrete singular convolution method

Study on frequency dependent convolution methods for sound event detection

Sound event detection (SED) aims to classify target sound events from a given audio clip with the corresponding onset and offset time localization. SED, a core research field in machine listening, has been rapidly growing by adopting the methods by applying deep learning methods in image recognition fields. One representative example is 2D convolution which is applied to 2D time-frequency audio features. However, 2D audio data exhibits physical discrepancies from 2D image data, one of which is that frequency dimension of 2D audio data is translational-variant while position dimension of 2D image data is translational-invariant. Therefore, to make 2D convolution more consistent to 2D audio data, we need to make 2D convolution dependent to frequency dimension. Previously, to address this problem, Frequency Dynamic Convolution (FDYConv) was proposed to replaces 2D convolution. FDYConv is dependent on frequency dimensions since it applies different kernels on frequency dimension. While it has proven its outstanding performance on SED, there are several more alternative frequency dependent convolution methods which are yet to be explored, In this work, we propose other simpler frequency dependent convolution methods and compare them with FDYConv in order to further investigate the effect of frequency dependence of 2D convolution methods.

An efficient CNN accelerator for pattern-compressed sparse neural networks on FPGA

Currently, the sparsity of weights and activations are mainly utilized to improve the energy efficiency and computational performance of CNN accelerators. However, the irregular sparsity of weights and activations can lead to problems such as mapping access conflict and imbalanced workload, which poses great challenges to the efficient computation of sparse networks. To address those problems, this paper proposes a CNN accelerator for pattern-compressed sparse neural networks on FPGA to achieve efficient inference acceleration. It mainly includes three parts: first, we propose a convolutional kernel grouping fusion method to achieve a balanced workload of weights. Second, we propose an activation hash mapping conflict reduction method to drastically reduce the mapping access conflict rate. Finally, we further propose a hierarchical mapping computing architecture to achieve efficient processing of mapping access conflicts and balanced adaptation of weight loads. To verify the effectiveness of the method proposed in this paper, our accelerator is designed and implemented on the Zynq UltraScale+ MPSoC ZCU102 evaluation board and evaluated by running VGG16 and ResNet50 networks. The experimental results show that the method proposed in this paper can reduce the mapping conflicts by more than 97 % and improve the utilization rate of processing element (PE) by 1.67×. Furthermore, the accelerator implemented in this paper can achieve 393.2 GOPs throughput, 1.40–3.25× computational performance, and 1.11–7.34× energy efficiency improvement.

A transformer-based convolutional method to model inverse cascade in forced two-dimensional turbulence

The present work proposes a novel transformer-based convolutional neural network (TransCNN) method to effectively model the inverse energy cascade in two dimensional (2D) turbulence. The TransCNN structure combines large-scale features extracted by transformer with small-scale features from convolutional layers, thus is considered suitable for multi-scale modeling. The novel TransCNN method has been applied to model sub-grid scale (SGS) stress for large-eddy simulation (LES) of 2D turbulence, under the extremely challenging situation that the LES grid is too coarse to resolve the external forcing scale. The data-driven model trained by the novel TransCNN structure is compared to two deep CNN models with varying complexities. All models exhibit proficiency during a priori tests. Notably, TransCNN surpasses its counterparts in predictive accuracy and generalizability in a posteriori tests. An investigation into the receptive fields reveals that the TransCNN model can efficiently leverage global information with the transformer structure, which is key to its superior performance in representing the inverse energy cascade in the 2D turbulent simulations.

Mixing efficacy and residence time distribution measurement in heavy water upgradation plant using radiotracer

To evaluate the efficiency of catalyst packing and to characterize the liquid phase flow dynamics in a heavy water distillation column, a series of residence time distribution (RTD) measurements were carried out using radiotracer. For a RTD measurement, about 1 mCi (37 MBq) of 82Br as ammonium bromide was injected as an impulse and its movement was monitored by thirty NaI(Tl) detectors at the inlet and outlet of the five catalyst cages present in the distillation column. The tracer measurements were carried out at varying liquid phase flow rate from 80 to 180 LPH. From the radiotracer data, it was concluded that radial distribution of the liquid phase at the top and bottom of the five cages was uniform. However, a slight non uniformity was observed in the middle cages. The residence time of the heavy water in the five cages varying the liquid flowrate was found to be varying from 4.2 min to 36.7 Min. To characterize the liquid mixing in each section of the distillation column, axial dispersion model was used using convolution method. The model parameter peclet number (Pe) was used to characterize the mixing. The Pe values for the five sections were found to be varying from 7 to 35. The Pe value suggests that flow of the liquid phase in the column was close to the plug flow in bottom three sections as desired.

Measurement method of flexible component pose based on discrete motion actuators

Abstract In order to address the limitations of traditional large discrete motion actuator mechanisms in realizing high-precision inter-axis relationship calibration in manufacturing environments, this paper proposes a new convolutional neural network-based attitude mapping estimation method, component pose using convolutional neural network (CPCNN). The method implicitly encodes the inter-axis relation matrix into the weight parameters of the training neural network, which results in a high degree of integration with existing large discrete motion actuator mechanisms. The CPCNN-based method directly obtains the attitude change of the adjustment cabin by reading the change of each axis of the current motion actuator mechanism in its own coordinate system. This method can overcome the limitations of the experimental process in the traditional calibration methods and improve the accuracy of attitude mapping under the influence of self-weight by selecting better motion parameters through redundant degrees of freedom. The application of this new method will provide an effective solution for the high-precision inter-axis relationship calibration of large discrete motion actuator mechanisms in manufacturing environments, offering new possibilities for improving production efficiency and product quality.

A novel graph convolutional network-based interpretable method for chiller energy consumption prediction considering the spatiotemporal coupling between variables

The prediction of chiller energy consumption is critical for lowering the building’s energy consumption. Deep neural networks have been widely applied in this field. However, traditional deep learning methods only consider operational data and do not incorporate empirical knowledge and working condition information that are beneficial for chiller energy consumption prediction. The graph convolutional network (GCN) can handle this problem well, as it is able to incorporate empirical knowledge and working condition information into the network. However, it is important to avoid the excessive influence of empirical knowledge and working condition information on the model’s accuracy. To adjust the weight of the influence of the association graph and training set on model accuracy in the GCN network, a coefficient θ was introduced. Herein, a novel GCN (θ-GCN)-based method for the energy consumption prediction of chillers is proposed. This proposed method first combines empirical knowledge and working condition information to obtain the adjacency matrix among operational data and then constructs an association graph based on this adjacency matrix. Secondly, due to the temporal correlation of the collected dataset, a gated recurrent unit (GRU) is used for feature extraction. Lastly, the features extracted through GRU are input into the constructed association graph, and the θ-GCN network is used for training and energy consumption prediction. The proposed method was experimentally verified in an actual chiller system and compared with existing methods; the outcomes indicated that the established method could obtain better prediction accuracy.