The researches on data security management and privacy protection technologies at home and abroad were analyzed and summarized aiming at current problems in blockchain security, such as unreasonable data management mode, unreliable data sharing scheme, smart contract vulnerabilities not easily fixed and incomplete privacy protection of multiple types of data. Various security problems and reasonable solutions in current blockchain systems were outlined from four aspects: data storage security, data privacy security, data access security and data sharing security. The challenges and future research directions of data security in blockchain were discussed. Some reference for the future work of researchers was provided in the field of blockchain security.

The research progress of direct air capture CO₂ adsorbents was reviewed. The advantages and disadvantages of alkali/alkaline metal based adsorbents, metal organic framework adsorbents, amine loaded adsorbents and moisture swing adsorbents were compared. Meanwhile, the properties of adsorbents from the aspects of adsorption capacity and amine efficiency, kinetics and supporters, regeneration mode and energy consumption, thermal stability and resistance to degradation were evaluated. Additionally, the related engineering demonstration projects and economic evaluation were briefly discussed. Finally, the problems existing in the current research were summarized, and the future research direction was prospected.

Most of the compound fault diagnosis methods regard the compound fault as a new single fault type, ignoring the interaction of internal single faults, and the fault analysis is vague in granularity and poor in interpretation. An improved Transformer-based compound fault decoupling diagnosis method was proposed for industrial environments with very little compound fault data. The diagnosis process included pre-processing, feature extraction and fault decoupling. With introducing the decoder of the Transformer, the cross-attention mechanism enables each single fault label to adaptively in the extracted feature layer focus on the discriminative feature region corresponding to the fault feature and predicts the output probability to achieve compound fault decoupling. Compound fault tests were designed to verify the effectiveness of the method compared with the advanced algorithms. The results showed that the proposed method had high diagnostic accuracy with a small number of single fault training samples and a very small number of compound fault training samples. The compound fault diagnosis accuracy reached 88.29% when the training set contained only 5 compound fault samples. Thus the new method has a significant advantage over other methods.

A highway tunnel lining disease detection method based on convolutional neural network was proposed aiming at the ever-increasing demand for tunnel maintenance in order to save time and labor costs. The self-developed intelligent rapid detection vehicle for tunnels was used to collect 24 tunnel lining images. A high-quality data set of more than 20000 disease images was constructed. Then single-stage SSD (single shot multiBox detector) models and two-stage R-FCN (region-based fully convolutional networks) models were constructed on a self-made data set combining the causes and characteristics of tunnel lining diseases. The detection results were compared and analyzed, and an offline tunnel lining disease detection scheme was proposed. The experimental results showed that the identification accuracy rate of SSD model was 98%, the total mean average precision (mAP) was 72%, and the detection speed was fast. The SSD model is suitable for rapid diagnosis of tunnels. The identification accuracy rate of R-FCN model was 85%, the total mAP value reached 91%, and the detection accuracy was high. The R-FCN model is suitable for the post-treatment of tunnel diseases. Using these two detection models can improve detection efficiency and accuracy.

A new 3D reconstruction network was proposed in order to resolve the difficulty of 2D detection method to detect defects with depth information. CasMVSNet with multiscale feature enhancement (MFE-CasMVSNet) was combined with the technology of point cloud processing for steel plate surface defect detection. In order to improve the accuracy of 3D reconstruction, a position-oriented feature enhancement module (PFEM) and a multiscale feature adaptive fusion module (MFAFM) were proposed to effectively extract features and reduce information loss. A density clustering method, curvature-sparse-guided density-based spatial clustering of applications with noise (CS-DBSCAN), was proposed for accurately extracting defects in different parts, and the 3D detection box was introduced to locate and visualize defects. Experimental results show that compared with the reconstruction method based on images, MFE-CasMVSNet can realize the 3D reconstruction of steel plate surface more accurately and quickly. Compared with 2D detection, 3D visual defect detection can accurately obtain the 3D shape information of defects and realize the multi-dimensional detection of steel plate surface defects.

The research works of Chinese font style transfer were classified according to different stages of research development. The traditional methods were briefly reviewed and the deep learning-based methods were combed and analyzed. The commonly used open data sets and evaluation criteria were introduced. The future research trends were expected from four aspects, which were to improve the generation quality, enhance personalized differences, reduce the number of training samples, and learn calligraphy font style.

A multimodal image retrieval model based on semantic-enhanced feature fusion (SEFM) was proposed to establish the correlation between text features and image features in multimodal image retrieval tasks. Semantic enhancement was conducted on the combined features during feature fusion by two proposed modules including the text semantic enhancement module and the image semantic enhancement module. Firstly, to enhance the text semantics, a multimodal dual attention mechanism was established in the text semantic enhancement module, which associated the multimodal correlation between text and image. Secondly, to enhance the image semantics, the retain intensity and update intensity were introduced in the image semantic enhancement module, which controlled the retaining and updating degrees of the query image features in combined features. Based on the above two modules, the combined features can be optimized, and be closer to the target image features. In the experiment part, the SEFM model was evaluated on MIT-States and Fashion IQ datasets, and experimental results show that the proposed model performs better than the existing works on recall and precision metrics.

For the poor real-time detection capability of the current object detection model in the production environment of electronic components, GhostNet was used to replace the backbone network of YOLOv5. And for the existence of small objects and objects with large scale changes on the surface defects of electronic components, a coordinate attention module was added to the YOLOv5 backbone network, which enhanced the sensory field while avoiding the consumption of large computational resources. The coordinate information was embedded into the channel attention to improve the object localization of the model. The feature pyramid networks (FPN) structure in the YOLOv5 feature fusion module was replaced with a weighted bi-directional feature pyramid network structure, to enhance the fusion capability of multi-scale weighted features. Experimental results on the self-made defective electronic component dataset showed that the improved GCB-YOLOv5 model achieved an average accuracy of 93% and an average detection time of 33.2 ms, which improved the average accuracy by 15.0% and the average time by 7 ms compared with the original YOLOv5 model. And the improved model can meet the requirements of both accuracy and speed of electronic component surface defect detection.

The dense small target detection algorithm LSA_YOLO based on YOLOv5s for UAVs with complex backgrounds and multiples of small targets with dense distribution was proposed for UAV images. A multi-scale feature extraction module LM-fem was constructed to enhance the feature extraction capability of the network. A new hybrid domain attention module S-ECA relying on multi-scale contextual information has been put forward and a algorithm focus on target information was established aiming to suppress the interference of complex backgrounds. The adaptive weight dynamic fusion structure AFF was designed to assign reasonable fusion weights to both shallow and deep features. The capability of algorithm in detecting dense small targets in complex backgrounds was improved given the application of S-ECA and AFF in the structure of PANet. The loss function Focal-EIOU was utilized instead of the loss function CIOU to accelerate model detection efficiency. Experimental results on the public dataset VisDrone2021 public dataset show that the average detection accuracy for all target classes improves from 51.5% for YOLOv5s to 57.6% for LSA_YOLO when the set input resolution is set to 1 504 × 1 504.

A novel EEGNet variation based on the fusion of the Inception and attention mechanism modules was proposed, called IncepA-EEGNet, in order to achieve more efficient P300 signal feature extraction. Convolutional layers with different receptive fields were connected in parallel. The network’s ability to extract and express EEG signals were enhanced. Then the attention mechanism was introduced to assign weights to the features of different filters, and important information was extracted from the P300 signal. The validation experiment was conducted on two subjects of BCI Competition III dataset II. Results showed that the IncepA-EEGNet recognition accuracy reached 75.5% after just 5 epochs compared with other deep learning models. The information transmission rate was up to 33.44 bits/min on subject B after 3 epochs. These experimental results demonstrate that the IncepA-EEGNet effectively improves the recognition accuracy of the P300 signal, reduces the time of repeated trials, and enhances the applicability of the P300 speller.

A comprehensive evaluation and categorization of text-to-image generation tasks were conducted. Text-to-image generation tasks were classified into three major categories based on the principles of image generation: text-to-image generation based on the generative adversarial network architecture, text-to-image generation based on the autoregressive model architecture, and text-to-image generation based on the diffusion model architecture. Improvements in different aspects were categorized into six subcategories for text-to-image generation methods based on the generative adversarial network architecture: adoption of multi-level hierarchical architectures, application of attention mechanisms, utilization of siamese networks, incorporation of cycle-consistency methods, deep fusion of text features, and enhancement of unconditional models. The general evaluation indicators and datasets of existing text-to-image methods were summarized and discussed through the analysis of different methods.

A ship object detection algorithm was proposed based on a multi-head self-attention (MHSA) mechanism and YOLO network (MHSA-YOLO), aiming at the characteristics of complex backgrounds, large differences in scale between classes and many small objects in inland rivers and ports. In the feature extraction process, a parallel self-attention residual module (PARM) based on MHSA was designed to weaken the interference of complex background information and strengthen the feature information of the ship objects. In the feature fusion process, a simplified two-way feature pyramid was developed so as to strengthen the feature fusion and representation ability. Experimental results on the Seaships dataset showed that the MHSA-YOLO method had a better learning ability, achieved 97.59% mean average precision in the aspect of object detection and was more effective compared with the state-of-the-art object detection methods. Experimental results based on a self-made dataset showed that MHSA-YOLO had strong generalization.

A visible-infrared image matching method based on generative adversarial model was proposed based on the style transfer of generative adversarial network and traditional local feature extraction capability in order to analyze the problems of large modal difference, difficult matching and poor robustness of existing multi-sensor images matching methods. The loss function calculation path was added and a new loss function was constructed according to the idea of style transfer in GAN network in order to improve the transfer effect of the model on the multi-sensor images. The feature information of the transformed homologous images was extracted by using SIFT algorithm. Then the position and scale of the points to be matched were determined. The feature matching and similarity measurement between the two images were indirectly completed according to the matching strategy. Experiments were conducted on the realistic aerial dataset. Results show that the proposed method can effectively deal with multi-modal data and reduce the difficulty of multi-sensor image matching. The method can provide a new solution for multi-sensor images matching.

An underwater image enhancement algorithm was proposed based on generative adversarial networks (GAN) and improved convolutional neural networks (CNN) in order to solve the problems of haze blurring and color distortion of underwater image. Generative adversarial network was used to synthesize underwater images to effectively expand the paired underwater data set. The underwater image was decomposed by multi-scale wavelet transform without losing the feature resolution. Then, combined with CNN, the compact learning method was used to extract features from multi-scale images, and skip connection was used to prevent gradient dispersion. Finally, the fog blur effect of the underwater image was resolved. In order to improve the color correction ability of the model and overcome the problem of color distortion of underwater images, the correlation between different channels of color images was learned by using the style cost function. Experimental results show that, in subjective visual and objective indicators, the proposed algorithm is superior to the contrast algorithm in comprehensive performance and robustness.

Traditional and new geotechnical engineering problems such as compressed air energy storage, intercepting water with compressed air, carbon dioxide sequestration and oil and gas underground reserve project are all involving air-water two-phase flow and stress coupling problems. For this engineering reality, based on the weak coupling theory of gas-water two-phase seepage and stress in unsaturated soil, a air-water two-phase percolation-stress coupling calculation program based on coupled TOUGH2 and FLAC3D was developed. The calculation program can simulate real air-water two phase flow, and can investigate the gas-water interaction of seepage process. The calculation program considers the direct interaction between gas-water two-phase seepage and soil skeleton deformation, reflects the process of porosity, permeability, capillary pressure and the change of soil physical and mechanical parameters, and achieve a more perfect gas-water two-phase seepage-stress coupling analysis. Furthermore, by comparing with classical drainage test and model test, it is verified that the program can accurately simulate the gas-water two-phase flow-stress interaction.

Slab track suffers material performance decline and structural damage accumulation in the long-term service process under the coupling effect of train load and complex environment, resulting in a gradual deterioration of its service performance. The forms and causes of common interlayer damages on prefabricated slab track and double-block slab track in China were comprehensively discussed. The application of ground penetrating radar method, impact echo method and other local damage identification methods used in slab tracks were summarized. And it was proposed that combining multiple local damage identification techniques was the key to achieve accurate local damage identification of track structures. In addition, the overall damage identification technologies based on modal parameters, slab bed vibration signals and vehicle vibration signals were outlined. The need to expand the detection sample of field damages to improve the generalization of the overall identification method was pointed out. The advantages and limitations of various identification methods were analyzed in detail to provide guidance for improving the identification technology system of slab track structures in China and making scientific and reasonable maintenance strategies.

In order to promote the transformation of industrial cyber security defense mode from static passive defense to active defense, and alleviate the contradiction between the serious shortage of security experts and the sharp increase of cyber security demands, a cyber security active defense system framework of digital twin system was built from the perspective of bionics, and then five kinds of key technologies focusing on active defense were proposed based on the digital twin security brain (DTSB), including security data interaction and systems collaborative defense based on cloud-edge collaboration, cyber security active defense model of parallel digital twin system, situation awareness of parallel digital twin systems based on digital twin security brain, active defense and control technical framework for digital twin system based on immune system, and anti-attack intelligent recognition of digital twin system based on artificial intelligence. A case study of a digital twin workshop was given to demonstrate the successful application of digital twin cyber security in smart manufacturing.

A bearing RUL prediction method based on multi-scale features and attention mechanism was proposed aiming at the problem that the previous remaining useful life (RUL) prediction methods were insufficient in mining bearing degradation information and ignored the difference in the contribution of different features, which affected the prediction accuracy. Several time-domain and frequency-domain features of the original bearing vibration signal at multiple scales were calculated as the input feature set. The multi-scale feature set was input into the network, and the attention module was used to adaptively assign the best weights to different features. Then the convolutional neural network (CNN) module was used for deep feature extraction and multi-scale feature fusion. The RUL prediction value was obtained through the feedforward neural network (FNN) module mapping. The proposed method was applied to the public bearing datasets for comparative studies. Results showed the superior prediction performance of the proposed method.

Two typical cases including the stiff system of ordinary differential equations ROBER problem and the steady-state mixture fraction equation in jet flame were selected in order to efficiently embed the complex physicochemical information of turbulent combustion into physics-informed neural networks (PINNs). The potential of PINNs in solving combustion chemical differential equations was explored. Results show that the PINNs model can correctly capture the evolution of the zero-dimensional stiff reaction system. PINNs solution accorded well with the conventional numerical solution for steady jet flame. The selection of residual points was particularly important for solving complex differential equations in the field of combustion and chemistry, which should be considered based on the specific configuration in detail.

A traffic signal control method based on an improved deep reinforcement learning was proposed for an isolated intersection, aiming at a problem that the traffic signal control methods based on deep reinforcement learning were difficult to update the traffic signal control strategy in time. A new reward function of the proposed method was built by utilizing the real-time change of vehicle numbers at an intersection between two adjacent sampling time steps, whereby the dynamic change process of traffic status at the intersection was tracked and utilized in time. In addition, double network structure and experience playback were respectively used to improve the learning efficiency and convergence rate of the proposed method. SUMO simulation test results show that the proposed method can significantly shorten the average waiting time and average queue length of vehicles at the intersection, and improve the traffic efficiency at the intersection.