Deep Convolutional Network Research Articles

Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure Series

Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure Series

Semantic-empowered visible light communication for image transmission based on deep convolutional generative adversarial network

In this paper, a semantic communication-based scheme was proposed to tackle the optimization challenge of transmission efficiency and link stability in indoor visible light communication (VLC) systems utilizing light-emitting diodes for image transmission. The semantic model, established by deep convolutional generative adversarial network (DCGAN) and vector quantization method, can effectively extract the essential characteristics of images. In addition, indoor VLC channel models including line-of-sight (LOS) and non-line-of-sight (NLOS) links are established in a 5*5*3 room, while incorporating noise interference encountered during signal transmission into the training process of the semantic model to enhance its anti-interference capability. Besides, the performance disparity between the semantic VLC system and the conventional VLC system using Better Portable Graphics (BPG) is assessed across various modulation formats, transmission distances, and receiving locations. The simulation results demonstrate that the semantic VLC system effectively enhances link stability and achieves a signal-to-noise ratio (SNR) gain exceeding 6 dB. Additionally, the semantic VLC system achieves superior performance compared to the conventional VLC system employing convolutional code at a rate of 1/2, without incurring additional bits consumption for error correction. Moreover, the practicality of the semantic VLC system is experimentally validated on an indoor physical testbed online enabled by the field programmable gate array (FPGA) with up to 1 Gbps data rate. In comparison with existing schemes, the semantic VLC system effectively reduces communication overhead by 40% while maintaining similar received image quality. Furthermore, it significantly enlarges the communication coverage of the VLC system by a factor of two without necessitating any hardware modifications and ensures stable transmission of images in high-speed scenarios.

Data augmentation aided excavator activity recognition using deep convolutional conditional generative adversarial networks

Excavator activity recognition is a crucial task with significant practical applications such as assessing production efficiency, injury prevention, and enabling intelligent control. However, implementing deep learning-based activity recognition methods presents considerable challenges due to the requirement of large training datasets. And sensor-based activity recognition demands more convenient and reliable sensors. This paper proposes a generalized data augmentation framework for excavator activity recognition. Main pump pressure and flow signals are generated using Deep Convolutional Conditional Generative Adversarial Networks (DC-CGAN). The proposed framework integrates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for activity recognition. Results indicate that the framework outperforms traditional data augmentation methods regarding the quality of generated data and shallow and single deep networks regarding model accuracy and generalization. Additionally, the framework was implemented on three sizes of excavators and a wheel loader, demonstrating excellent versatility.

Prediction of multi-layer metasurface design using conditional deep convolutional generative adversarial networks

Designing metasurfaces is a challenging task. Traditional methodologies, which primarily depend on iterative procedures, are both time-intensive and require specialized expertise. The proposed algorithm uses conditional deep convolutional generative adversarial networks (cDCGAN) to design metasurfaces. This method instantly create a 2D image of a multi-layer metasurface using the scattering parameter S11 as the input vector. The algorithm significantly reduces the size of the training dataset by applying pre-training and post-generating steps. The pre-training step involves aliasing and modifying images using a limited color palette. The post-generating step consists of separating the color channels, converting the pixels to vector based images, and fine-tuning the borders. The algorithm is evaluated for three metasurfaces that have unique features compared to the training dataset samples: a single-band metasurface unitcell, a dual-band metasurface unitcell, and a partially trained sample improved by magnetic field analysis. The results show that the proposed algorithm can accurately predict the images of these metasurface unitcells, demonstrating its potential for fast and efficient metasurface design.

A Dynamic Position Embedding-Based Model for Student Classroom Complete Meta-Action Recognition.

The precise recognition of entire classroom meta-actions is a crucial challenge for the tailored adaptive interpretation of student behavior, given the intricacy of these actions. This paper proposes a Dynamic Position Embedding-based Model for Student Classroom Complete Meta-Action Recognition (DPE-SAR) based on the Video Swin Transformer. The model utilizes a dynamic positional embedding technique to perform conditional positional encoding. Additionally, it incorporates a deep convolutional network to improve the parsing ability of the spatial structure of meta-actions. The full attention mechanism of ViT3D is used to extract the potential spatial features of actions and capture the global spatial-temporal information of meta-actions. The proposed model exhibits exceptional performance compared to baseline models in action recognition as observed in evaluations on public datasets and smart classroom meta-action recognition datasets. The experimental results confirm the superiority of the model in meta-action recognition.

A novel prediction method for low wind output processes under very few samples based on improved W‐DCGAN

AbstractThe threat of long‐term low wind output processes (LWOP) on the supply ability of the power system is escalating with the increasing integration of wind power. Accurate prediction of LWOP is crucial for maintaining the stable operation of the power system. However, the occurrence probability of LWOP is low and the available samples are lacking, limiting the high‐accuracy predictive modeling of LWOP. Therefore, a novel prediction method for LWOP under very few samples based on improved Wasserstein deep convolutional generative adversarial networks (W‐DCGAN) is proposed here. Firstly, a multi‐dimensional identification method is proposed to accurately identify historical LWOP. Then, an LWOP sample generation model based on improved W‐DCGAN is established. The model integrates a long short‐term memory layer into the deconvolutional layer of the generator to enhance the temporal characteristics of generated samples. Finally, three prediction algorithms are used to construct LWOP prediction models based on both generated and actual samples, respectively. The wind power operation data from a province in China is taken as an example to verify the effectiveness of the proposed method. The results show that the prediction accuracy of LWOP can be improved by 14.36%–55.85%.

A graph residual generation network for node classification based on multi-information aggregation

The key to improving the performance of graph convolutional networks (GCN) is to fully explore the correlation between neighboring and distant information. Aiming at the over-smoothing problem of GCN, in order to make full use of the relationship among features, graphs and labels, a graph residual generation network based on multi-information aggregation (MIA-GRGN) is proposed. Firstly, aiming at the defects of GCN, we design a deep initial residual graph convolution network (DIRGCN), which connects the initial input through residuals, so that each layer node retains part of the information of the initial features, ensuring the localization of the graph structure and effectively alleviating the problem of over-smoothing. Secondly, we propose a random graph generation method (RGGM) by utilizing graph edge sampling and negative edge sampling, and optimize the supervision loss function of DIRGCN in the form of generation framework. Finally, applying RGGM and DIRGCN as inference modules for modeling hypotheses and obtaining approximate posterior distributions of unknown labels, an optimized loss function is obtained, we construct a multi-information aggregation MIA-GRGN that combines graph structure, node characteristics and label joint distribution. Experiments on benchmark graph classification datasets show that MIA-GRGN achieves better classification results compared with the benchmark models and mainstream models, especially for datasets with less dense edge relationships between nodes.

Auto-Correlation and Channel Attention Enhanced Deep Graph Convolution Networks for Gait Phase Prediction Based on Multi-IMU System

Gait phase prediction is important in controlling assistive robotic devices such as exoskeletons, where the control unit must differentiate between gait phases to provide the necessary assistance when the user is wearing the exoskeleton. To achieve the objective of precisely identifying the gait phase of users for the accurate control of the exoskeleton, this study proposes Auto-Correlation and Channel Attention enhanced Deep Graph Convolutional Networks (ACCA-DGCN) for gait phase prediction, and a gait phase prediction model based on multiple inertial measurement units (IMUs) and skeleton graph was established, in order to fully utilize the dependency among joints, and enhance accuracy and reliability of gait phase prediction. First, a human lower limb gait data acquisition equipment was developed, and the gait data of human walking were collected. The skeleton graph of the human lower limb was constructed through the natural connection relationship of joints in the human skeleton. After that, the ACCA-DGCN-based gait phase prediction model was constructed by using the gait data of human walking. Auto-Correlation (AC) and Efficient Channel Attention (ECA) were introduced to effectively capture periodic features of gait data and focus on the channels with high contributions to gait phase prediction. Finally, the effect of the window size on the performance of the ACCA-DGCN model was explored, and the proposed algorithm was compared with the other five deep learning algorithms: CNN, RNN, TCN, LSTM, and DGCN. The experimental results show that the average accuracy of gait phase prediction model based on ACCA-DGCN reaches up to 92.26% and 97.21% in user-independent and user-dependent experiments, respectively, which is superior to the other five algorithms. This study provides a new method for gait phase prediction, which is useful for improving the control of exoskeleton robots.

Vision foundation model for agricultural applications with efficient layer aggregation network

Agricultural production is transitioning from traditional tools to IoT-connected automation devices. The integration of computer vision and agricultural automation is becoming closer with the rapid development of deep learning technology. Agricultural vision tasks, nevertheless, are relatively simple and less diverse compared to large-scale vision tasks, and there is a scarcity of plant data. This is also the reason why researchers need to make significant adjustments when directly applying advanced algorithms to the agricultural domain. In practice, downstream vision models should be designed to be concise and efficient. In this paper, we meticulously designed a deep convolutional network called TasselELANet for agricultural vision applications. It features a concise and efficient global architecture, including a 16-fold downsampling layer encoder and a decoder utilizing only 2 feature layers, which is greatly different from the general approaches. At its core is the efficient layer aggregation network, which utilizes the cross-stage fusion strategy to effectively optimize the gradient propagation path, thereby enhancing the learning ability and inference speed of the network. We validated the performance of TasselELANet on three challenging publicly available plant detection and counting datasets: maize tassels, wheat ears, and rice panels. The experimental tests achieved an average Accuracy of 0.899 and an average determination coefficient R2 of 0.867, remarkably outperforming other advanced computer vision methods in terms of accuracy and efficiency, and demonstrating sufficient generalizability. We firmly believe that TasselELANet can serve as a powerful and reliable vision tool for agricultural practitioners while also providing a solid foundation for future research. The code, datasets can be accessed at https://github.com/Ye-Sk/TasselELANet.git.

Multi-scale feature extraction and TrasMLP encoder module for ocean HABs segmentation

Due to tiny edge and texture details of harmful algae blooms(HABs), existing segmentation networks are not effective for HABs segmentation. In order to solve the above problems, this paper proposes a Multi-scale Feature extraction and TrasMLP Encoder Fusion-based network (MFTS). To tackle the complex morphological characteristics and the complex backgrounds of HABs, a TrasMlp module which can effectively identify long-range patterns and adapt network parameters is introduced, enabling accurately parsing of complex algae images. Secondly, the deep convolution module is constructed by combining deep separable convolution with a two-channel attention mechanism to separate the target region from the background. In addition, this paper proposes a Weighted Feature Fusion of Deep Convolution and INRS Encoder Module classification network(FDIR) is proposed to quantify the performance of the image segmentation network. The segmentation results on HABs dataset from AICO Lab show that our proposed MFTS model achieves a miou of 90.02%, outperforming the performance of classical segmentation networks such as U-Net and Mask R-CNN. Compared to original HABs dataset, the segmented result shows a 5.1% improvement in the classification accuracy of the FDIR model.

Euclidean-Distance-Preserved Feature Reduction for efficient person re-identification

Person Re-identification (Re-ID) aims to match person images across non-overlapping cameras. The existing approaches formulate this task as fine-grained representation learning with deep neural networks, which involves extracting image features using a deep convolutional network, followed by mapping the features into a discriminative space through another smaller network, in order to make full use of all possible cues. However, recent Re-ID methods that strive to capture every cue and make the space more discriminative have resulted in longer features, ranging from 1024 to 14336, leading to higher time (distance computation) and space (feature storage) complexities. There are two potential solutions: reduction-after-training methods (such as Principal Component Analysis and Linear Discriminant Analysis) and reduction-during-training methods (such as 1 × 1 Convolution). The former utilizes a statistical approach aiming for a global optimum but lacking end-to-end optimization of large data and deep neural networks. The latter lacks theoretical guarantees and may be vulnerable to training noise such as dataset noise or initialization seed.To address these limitations, we propose a method called Euclidean-Distance-Preserving Feature Reduction (EDPFR) that combines the strengths of both reduction-after-training and reduction-during-training methods. EDPFR first formulates the feature reduction process as a matrix decomposition and derives a condition to preserve the Euclidean distance between features, thus ensuring accuracy in theory. Furthermore, the method integrates the matrix decomposition process into a deep neural network to enable end-to-end optimization and batch training, while maintaining the theoretical guarantee. The result of the EDPFR is a reduction of the feature dimensions from fa and fb to fa′ and fb′, while preserving their Euclidean distance, i.e.L2(fa,fb)=L2(fa′,fb′). In addition to its Euclidean-Distance-Preserving capability, EDPFR also features a novel feature-level distillation loss. One of the main challenges in knowledge distillation is dimension mismatch. While previous distillation losses, usually project the mismatched features to matched class-level, spatial-level, or similarity-level spaces, this can result in a loss of information and decrease the flexibility and efficiency of distillation. Our proposed feature-level distillation leverages the benefits of the Euclidean-Distance-Preserving property and performs distillation directly in the feature space, resulting in a more flexible and efficient approach. Extensive on three Re-ID datasets, Market-1501, DukeMTMC-reID and MSMT demonstrate the effectiveness of our proposed Euclidean-Distance-Preserving Feature Reduction.

SGG-DGCN: Wind Turbine Anomaly Identification by Using Deep Graph Convolutional Networks with Similarity Graph Generation Strategy

In order to minimize wind turbine failures, fault diagnosis of wind turbines is becoming increasingly important, deep learning methods excel at multivariate monitoring and data modeling, but they are often limited to Euclidean space and struggle to capture the complex coupling between wind turbine sensors. To address this problem, we convert SCADA data into graph data, where sensors act as nodes and their topological connections act as edges, to represent these complex relationships more efficiently. Specifically, a wind turbine anomaly identification method based on deep graph convolutional neural network using similarity graph generation strategy (SGG-DGCN) is proposed. Firstly, a plurality of similarity graphs containing similarity information between nodes are generated by different distance metrics. Then, the generated similarity graphs are fused using the proposed similarity graph generation strategy. Finally, the fused similarity graphs are fed into the DGCN model for anomaly identification. To verify the effectiveness of the proposed SGG-DGCN model, we conducted a large number of experiments. The experimental results show that the proposed SGG-DGCN model has the highest accuracy compared with other models. In addition, the results of ablation experiment also demonstrate that the proposed SGG strategy can effectively improve the accuracy of WT anomaly identification. Conflict of Interest Statement The authors declare no conflicts of interest.

Application of Deep Convolutional Generative Adversarial Network to Identification of Bridge Structural Damage

In recent years, the increase in the number of vehicles on the road has impacted the service life of bridges, and thus, their performance. In China, bridge inspections must be performed and documented at least once per year using labor-intensive and subjective approaches. In this study, a computationally efficient damage identification methodology for bridges was developed based on a deep convolutional generative adversarial network (DCGAN). To predict the damage to bridges, the vibration data of the structural damage are extracted from sensor measurements on the bridges and applied to the DCGAN model. The proposed damage identification method was validated on a simple supported beam bridge and a continuous girder bridge. The effects of sample size and noise on the identification accuracy were also evaluated. The performance of the DCGAN in the damage identification process is robust and reliable. This method for structural health monitoring offers a more objective evaluation of the overall performance and condition of a bridge, thus enabling bridge owners to efficiently allocate finite maintenance resources.

YOSMR: A Ship Detection Method for Marine Radar Based on Customized Lightweight Convolutional Networks

In scenarios such as nearshore and inland waterways, the ship spots in a marine radar are easily confused with reefs and shorelines, leading to difficulties in ship identification. In such settings, the conventional ARPA method based on fractal detection and filter tracking performs relatively poorly. To accurately identify radar targets in such scenarios, a novel algorithm, namely YOSMR, based on the deep convolutional network, is proposed. The YOSMR uses the MobileNetV3(Large) network to extract ship imaging data of diverse depths and acquire feature data of various ships. Meanwhile, taking into account the issue of feature suppression for small-scale targets in algorithms composed of deep convolutional networks, the feature fusion module known as PANet has been subject to a lightweight reconstruction leveraging depthwise separable convolutions to enhance the extraction of salient features for small-scale ships while reducing model parameters and computational complexity to mitigate overfitting problems. To enhance the scale invariance of convolutional features, the feature extraction backbone is followed by an SPP module, which employs a design of four max-pooling constructs to preserve the prominent ship features within the feature representations. In the prediction head, the Cluster-NMS method and α-DIoU function are used to optimize non-maximum suppression (NMS) and positioning loss of prediction boxes, improving the accuracy and convergence speed of the algorithm. The experiments showed that the recall, accuracy, and precision of YOSMR reached 0.9308, 0.9204, and 0.9215, respectively. The identification efficacy of this algorithm exceeds that of various YOLO algorithms and other lightweight algorithms. In addition, the parameter size and calculational consumption were controlled to only 12.4 M and 8.63 G, respectively, exhibiting an 80.18% and 86.9% decrease compared to the standard YOLO model. As a result, the YOSMR displays a substantial advantage in terms of convolutional computation. Hence, the algorithm achieves an accurate identification of ships with different trail features and various scenes in marine radar images, especially in different interference and extreme scenarios, showing good robustness and applicability.

A deep learning-based convolutional spatiotemporal network proxy model for reservoir production prediction

Accurate production prediction is crucial in the field of reservoir management and production optimization. Traditional models often struggle with the complexities of nonlinear relationships and high-dimensional data, which hinders their ability to capture the variability of the production process efficiently and results in time-consuming calculations. To overcome these limitations, this paper introduces an innovative proxy modeling technique employing a convolutional spatiotemporal neural network. This method utilizes convolutional neural networks to extract spatial features from high-dimensional data, while the Transformer is used to model and predict complex temporal dynamics in production. To validate the effectiveness of the proposed proxy model, two case studies involving four injection and nine production wells within two-dimensional (2D) and three-dimensional (3D) non-homogeneous reservoirs were conducted, with the R2 coefficient serving as the primary evaluation metric. As the number of training iterations and data volume increase, the proxy model demonstrates rapid convergence. In tests conducted on the 2D and 3D datasets, the average R2 value exceeded 0.96 and 0.94. These results confirm the accuracy and stability of the proxy model. It also shows that the proxy model can accurately describe the geological and fluid seepage characteristics of the reservoir, which in turn can achieve a highly accurate match with the real data. In addition, the computational time is reduced by two orders of magnitude compared to traditional models. Compared with the long short-term memory method, the accuracy of the prediction results is increased by 30%, which greatly enhances efficiency and accuracy. To some extent, the presented proxy model can provide some guidance for the efficient history match of production data.

Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.

Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically-annotated medical images. Many two-dimensional pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain Positron Emission Transmission (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using two-dimensional randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6,000-20,000 trainable parameters) achieved a test mean-absolute-error of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare "classical" and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The mean-absolute-error fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g. DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest mean-absolute-error at test time (~0.45 - 0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.

3D Face Reconstruction Based on ResNet Feature Extraction and CBAM

In view of the scarcity, high cost and lack of diversity of three-dimensional (3D) face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction network, which uses single 2D face image as input. The model bypasses the 3D face datasets and only uses the 2D face datasets for training to achieve high-precision 3D face reconstruction without any 3D face prior. First, the improved ResNet50 feature extraction module is introduced to extract and characterize the input image by deep convolutional network. Then, a lightweight convolutional block attention module is added to the face prediction subnetwork. On the one hand, channel attention extracts different information included in the image, and on the other hand spatial attention finds the location of the information. So, the serialized attention operation could accurately find the features required for different parameter predictions, further improving face reconstruction parameters’ prediction accuracy. Finally, training, ablation and comparison experiments were conducted on CelebFaces Attributes, basel face model and Photoface datasets, and the combined loss function of pixel loss and perception loss was selected. The pixel loss function was calculated at the pixel microscopic level, and the perception loss function was calculated at the image macroscopic convolution level. The combination of the two could complement each other. Compared with the historical optimal results of the same network structure, the scale-invariant depth error and mean angle deviation of the proposed algorithm are improved by 5.2% and 8.2%, respectively. Experimental results strongly prove the effectiveness of the algorithm.

Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples

Abstract Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the improved deep convolutional generative adversarial network-multiscale convolutional neural network with mixed attention (IDCG-MAMCNN) model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds.

HQ-DCGAN: Hybrid quantum deep convolutional generative adversarial network approach for ECG generation

The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability.

High energy absorption design of porous metals using deep learning

Due to its remarkable energy absorption properties, porous metals have widespread applications in engineering. However, the high randomness of pore morphology greatly hinders the effective design and analysis of high energy absorption structures. To address this challenge, this paper first introduces a deep learning-based framework for high energy absorption-oriented design of random porous metals structures. The framework comprises two steps: (i) a generator powered by Wasserstein deep convolutional generative adversarial network is developed to swiftly generate a vast design space (∼one million samples) of porous metals with real random pore morphology. (ii) an inverse search strategy based on convolutional neural network is applied to quickly pick out the optimal structure with the best energy absorption from the design space. Results show that the optimal energy absorption is about 17.71 % higher than the maximum value of initial structures from CT scan. Additionally, a 575-fold increase in computational efficiency is achieved compared to the traversal search using finite element method. Subsequently, the deformation process of the optimal structure is analyzed focusing on the pore morphology and compression performance, showing that random porous metals with uniformly sized pores are capable of withstanding higher stress under the same strain and exhibit no yield band during compression. Inspired by this, a structural homogenization method is introduced and validated to create porous metal structure with stable microstructure evolution, extended plateau stress and high energy absorption.