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.

A front multi-vehicle target tracking algorithm optimized by DeepSort was proposed in order to improve the awareness of autonomous vehicles to the surrounding environment. Gaussian YOLO v3 model was adopted as the front-end target detector, and training was based on DarkNet-53 backbone network. Gaussian YOLO v3-Vehicle, a detector specially designed for vehicles was obtained, which improved the vehicle detection accuracy by 3%. The augmented VeRi data set was proposed to conduct the re-recognition pre-training in order to overcome the shortcomings that the traditional pre-training model doesn't target vehicles. A new loss function combining the central loss function and the cross entropy loss function was proposed, which can make the target features extracted by the network become better in-class aggregation and inter-class resolution. Actual road videos in different environments were collected in the test part, and CLEAR MOT evaluation index was used for performance evaluation. Results showed a 1% increase in tracking accuracy and a 4% reduction in identity switching times compared with the benchmark DeepSort YOLO v3.

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 garbage image classification method based on improved MobileNet v2 was proposed aiming at the problems of poor real-time performance and low classification accuracy of existing garbage image classification models. A lightweight feature extraction network based on MobileNet v2 was constructed. The parameter numbers of the model were reduced by adjusting its width factor, channel and spatial attention modules were embedded in the model to enhance the network's ability to refine features, a multi-scale feature fusion structure was designed to enhance the adaptability of the network to scale, and transfer learning was used to optimize the model parameters to further improve the model accuracy. Experimental results show that the average accuracy of the algorithm on the self built dataset was 94.6%, which was 2.0%, 3.4%, 3.2%, 2.3% and 1.2% higher than that of MobileNet v2, VGG16, GoogleNet, ResNet50 and ResNet101 models, respectively. The proposed algorithm achieved good performance in two public image classification datasets, CIFAR-100 and tiny-ImageNet. The parameter numbers of the model was only 0.83 M, which was about 2/5 of the basic model. The single inference on edge device JETSON TX2 took 68 ms, which proved the improvement of inference speed and prediction accuracy.

The characteristics of industrial boiler design and the necessity of introducing digital twin technology were summarized. The development and research status of digital design technology for industrial boilers were comprehensively summarized, and it was proposed that the digital design technology of a new generation of industrial boilers, with the design process optimization as the core and the digital twin as the foundation, was the key to improve the design capability and comprehensive performance of industrial boilers. The application characteristics of digital twin technology in industrial boiler design were analyzed, and three key technical problems of digital twin driven industrial boiler design were summarized: digital twin modeling technology for the expression of multiple information in the design process of industrial boiler; design process optimization technology based on human-computer interaction and virtual reality intelligent verification; industrial boiler digital twin data management technology for the full life cycle. On this basis, a digital twin driven digital design technology framework for industrial boilers was proposed, which was expected to provide ideas and valuable references for the research and application of digital design technology for high-performance industrial boilers.

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.

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.

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.

Chitosan aerogel has good biocompatibility, non-toxicity, easy degradation and other excellent properties, which can be used as an ideal green oil adsorbent to effectively solve the major problems of oil leakage and pollution. The researching progress of new petroleum adsorbents based on chitosan aerogel was reviewed. Firstly, the advantages and disadvantages of traditional oil treatment methods and oil absorbents were compared and the superiorities of chitosan aerogels as oil adsorbents were summarized. Then the synthesis and modification methods of chitosan aerogels and their advantage as petroleum adsorbents were analyzed and summarized. Finally, the problems existing in the current research and the future research direction were summarized and prospected.

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.

In order to solve the problems of difficult equipment deployment, velocity measurement and river monitoring in flooding environment, a series of image velocimetry techniques from particle image velocimetry (PIV) to deep learning methods were outlined based on non-invasive, low-cost and efficient measurement means in conjunction with nearly ten years of research in the field of river monitoring. The mechanism and issues of river surface velocimetry were discussed in the sections of image acquisition, image analysis, and image post-processing. By comparing and summarizing the differences of each method, the requirement of the existing methods were proposed, aiming to improve the river flow velocity measurement efficiency.

A novel method based on health index similarity in multiple time scales with autoencoder (AE MTS-HI) was proposed aiming at the shortage of the traditional similarity-based method in extracting health index and similarity matching. Autoencoder was applied to construct the health index based on monitoring data, which can minimize the loss of nonlinear information. The health index in multiple time scales was developed for similarity matching by considering the fluctuation of the length of test degradation trajectories. The method can remove the accuracy limitation caused by fixed time scales and enhance the prediction robustness. Performance of the proposed method was evaluated on public turbofan engines datasets. Results demonstrate that the method can improve the remaining useful life (RUL) prediction accuracy and provide stable support for predictive maintenance.

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.

The characteristics of steam heat network in actual operation were tested using enthalpy drop method and surface heat flow method. Heat loss characteristics and composition characteristics of the heating network were analyzed. Results showed that operating conditions and steam state greatly influenced on the accuracy of enthalpy drop method in evaluating heat loss of steam heating pipe. The actual heat rate of heat supply network was 135.66 W/m², of which the heat rate of pipeline insulation was 67.67 W/m², accounting for about 49.88%. The steam flow resistance loss accounted for 14.98%, and the local heat loss of supports, elbows and traps accounted for 35.14%. The condensation loss coefficient of the heat supply network was defined based on the relationship between actual pipe loss and condensation loss of the heat supply network. Heat loss model was constructed based on heat loss of the heat supply network combined with actual measurement data. The condensation loss coefficient of the heat supply network was 0.16.

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.

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.

A repetitive high-voltage nanosecond pulsed electric field (RnsPEF) generation system was independently developed based on the spark switch and transmission line transformer (TLT) technology in order to analyze the key impact parameters of the process of malignant tumors ablation by high-voltage nanosecond pulsed electric field (nsPEF). The system can stably generate nanosecond exponential pulse. The experimental results proved the effectivity and controllability of RnsPEF on tumor cells ablation. B16 melanoma cells adherently seeded in six-well plates as the research object to analyze the effects of pulse number, peak voltage, repetition frequency and electrode spacing on tumor cells ablation. Cell counting kit-8 (CCK-8) was applied to measure cell viability of B16 tumor cells suspension in the cuvette after treated by pulses. The experimental results show that the pulsed electric field intensity and injected energy density of the applied RnsPEF play the key roles in determining the ablation effect. The repetition frequency hardly affects the ablation results. The pulsed electric field intensity threshold of RnsPEF ablating B16 melanoma cells is 6.8 kV/cm, and the injected energy density threshold is 11.4 J/cm³, as well as the optimal pulse number is 500 pulses.

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.

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 mixed integer linear programming (MILP) model was proposed to optimize the spatial-temporal resources at intersections under the environment of mixed traffic flow with connected and autonomous vehicles (CAVs) and human-driven vehicles. The objective of the model is to maximize the intersection capacity, and the constraints mainly include those regarding lane channelization, flow distribution and signal timing settings. The lane channelization and signal timing scheme at intersections were optimized with different CAV driving behavior settings and different CAV penetration rates by taking a typical four-lane intersection as an example. Results show that the optimal channelization and signal timing scheme need to be adjusted with the change of CAV penetration rate and CAV car following behavior. The increase of the CAV penetration rate and the decrease of CAV headway are both beneficial to the improvement of the intersection capacity. The increase in the intersection capacity is slightly larger when the headway of CAV is not affected by the type of vehicles ahead.