A thorough analysis and cross-comparison of recent relevant works was provided, outlining a closed-loop process for EEG data analysis based on deep learning. EEG data were introduced, and the application of deep learning in three key stages: preprocessing, feature extraction, and model generalization was unfolded. The research ideas and solutions provided by deep learning algorithms in the respective stages were delineated, including the challenges and issues encountered at each stage. The main contributions and limitations of different algorithms were comprehensively summarized. The challenges faced and future directions of deep learning technology in handling EEG data at each stage were discussed.

A spatial-temporal multi-graph convolution traffic flow prediction model by integrating static and dynamic knowledge graphs was proposed, as current traffic flow prediction methods focus on the spatial-temporal correlation of traffic information and fail to fully take into account the influence of external factors on traffic. An urban traffic knowledge graph and four road network topological graphs with distinct semantics were systematically constructed, drawing upon the road traffic information and the external factors. The urban traffic knowledge graph was inputted into the relational evolution graph convolutional neural network to realize the knowledge embedding. The traffic flow matrix and the knowledge embedding were integrated using the knowledge fusion module. The four road network topology graphs and the traffic flow matrix with fused knowledge were fed into the spatial-temporal multi-graph convolution module to extract spatiotemporal features, and the traffic flow prediction value was outputted through the fully connected layer. The model performance was evaluated on a Hangzhou traffic data set. Compared with the advanced baseline, the performance of the proposed model improved by 5.76%-10.71%. Robustness experiment results show that the proposed model has a strong ability to resist interference.

The effects of leg structure design, foot-end design and sensor design on touchdown detection were comprehensively discussed by analyzing the existing legged robot touchdown detection methods. The touchdown method for direct detection of external sensors, the touchdown detection method based on kinematics and dynamics, and the touchdown detection method based on learning were summarized. Touchdown detection methods were summarized in three special scenarios: slippery ground, soft ground, and non-foot-end contact. The application scenarios of touchdown detection technology were analyzed, including the three application scenarios of motion control requirements, navigation applications, and terrain and geological sensing. The development trends were pointed out, which related to the four major touchdown detection methods of hardware improvement and integration, multi-mode touchdown detection, multi-sensor fusion touchdown detection, and intelligent touchdown detection. The specific relationships between various touchdown detection algorithms were summarized, which provided guidance for the development of follow-up technology for touchdown detection and specific applications of touchdown detection.

A new composite bent cap consisting of a shell made of steel plate and ultra-high-performance concrete (UHPC) and cast-in-place core normal concrete (NC) was proposed in order to realize the assembly and rapid construction of ultra-large-scale bent cap for urban viaducts or highway reconstruction and expansion projects. Parametric analysis of different UHPC and steel plate thickness was conducted in order to analyze the influence of the thickness of UHPC and steel mold plate on its stress performance. Results showed that the stiffness of the shell was affected by the thickness of UHPC and steel plate and their ratio together under the action of self-weight. The thicker the UHPC and steel plate are, the better the stress performance of the shell is, but the economy will be reduced when tensioning prestress and casting concrete. It is recommended to use UHPC thickness of 70 mm and steel plate thickness of 6 mm. A piece of 1∶2.5 scaled-down model was designed and static loading test was conducted in order to verify the feasibility and safety of this scheme. Results show that the new UHPC-NC composite bent cap has good force performance and high safety reserve, which can provide reference for the assembly construction of bent cap.

A dynamic 3D reconstruction technology for construction sites was proposed to ensure safety and efficiency in the construction process. A Binocular camera was deployed to scan the reconstruction site in 3D to obtain the model base and target activity trajectory. The YOLOv8 model was enhanced with an attentional scale sequence fusion (ASF) module to form the YOLOv8-ASF framework, which improved the accuracy and performance of the model, to solve the pain points such as target occlusion and target loss. The improved semi-global block matching (SGBM) algorithm was fused, and the YOLOv8-ASF-SGBM algorithm was integrated with the YOLOv8-ASF to achieve near-real-time target recognition and localization based on 2D images. The obtained depth information was used to 3D project the behavior trajectories of dynamic elements into the substrate, to realize the near-real-time and full-view monitoring of the real construction site. Experimental results show that the proposed technology reproduces the movement trajectory of construction dynamic elements in high-precision three-dimensional, and the relative error with the real motion trajectory of dynamic elements is less than 5%, which can realize high-precision full-view three-dimensional monitoring based on two-dimensional image and video information, and has good application scenarios and engineering value.

Existing research on the retrieval task of similar cases ignores the legal logic that the model should imply, and cannot adapt to the requirements of case similarity criteria in practical applications. Few datasets in Chinese for case retrieval tasks are difficult to meet the research needs. A similar case retrieval model was proposed based on legal logic and strong interpretability, and a case event logic graph was constructed based on predicate verbs. The statutory knowledge corresponding to various crimes was integrated into the proposed model, and the extracted elements were input to a neural network-based scorer to realize the task of case retrieval accurately and efficiently. A Confusing-LeCaRD dataset was built for the case retrieval task with a confusing group of charges as the main retrieval causes. Experiments show that the normalized discounted cumulative gain of the proposed model on the LeCaRD dataset and Confusing-LeCaRD dataset was 90.95% and 94.64%, and the model was superior to TF-IDF, BM25 and BERT-PLI in all indicators.

The development and research status of YOLO algorithm in traffic object detection were systematically summarized from the perspective of the three core elements of 'people-vehicle-road' in order to comprehensively analyze the important role of YOLO (You Only Look Once) algorithm in improving traffic safety and efficiency. The commonly used evaluation indexes of YOLO algorithm were outlined, and the practical significance of these indexes in traffic scenarios was elaborately expounded. An overview of the core architecture of YOLO algorithm was provided, its development process was traced, and the optimization and improvement measures in each version iteration were analyzed. The research status and application scenarios of YOLO algorithm for traffic object detection were sorted out and discussed from the perspective of the three traffic objects 'people-vehicle-road'. The limitations and challenges of YOLO algorithm in traffic object detection were analyzed, and corresponding improvement methods were proposed. Future research focuses were anticipated, providing a research reference for the intelligent development of road traffic.

Few existing studies cover the state-of-the-art multi-objective particle swarm optimization (MOPSO) algorithms. To fill the gap in this area, the research background of multi-objective optimization problems (MOPs) was introduced, and the fundamental theories of MOPSO were described. The MOPSO algorithms were divided into three categories according to their features: Pareto-dominated-based MOPSO, decomposition-based MOPSO, and indicator-based MOPSO, and a detailed description of their existing classical algorithms was also developed. Next, relevant evaluation indicators were described, and seven representative algorithms were selected for performance analysis. The experimental results demonstrated the strengths and weaknesses of each of the traditional MOPSO and three categories of improved MOPSO algorithms. Among them, the indicator-based MOPSO performed better in terms of convergence and diversity. Then, the applications of MOPSO algorithms in production scheduling, image processing, and power systems were briefly introduced. Finally, the limitations and future research directions of the MOPSO algorithm for solving complex optimization problems were discussed.

A new arc additive manufacturing process—droplet+arc additive manufacturing (DAAM) technology was applied to manufacture aluminum alloy samples in order to improve the quality and the efficiency of aluminum alloy. A new droplet generation system (DGS) was applied instead of the conventional wire feeding system, which makes the material addition and arc energy independent of each other. The formed material is 2219 aluminum alloy, and a trace amount of Mg element was added through the DGS. A thin-walled structure was deposited using the DAAM system at a significantly higher deposition rate (160 $ {\mathrm{m}\mathrm{m}}^{3}/\mathrm{s} $) than conventional wire and arc additive manufacturing techniques. The microstructure of the cross section of the thin-walled structure was observed and analyzed. Results showed that the grain morphology of the thin-walled structure was dominated by columnar crystals and exhibited a periodic distribution of inner-layer columnar crystals and inter-layer equiaxed crystals. The average tensile strengths in the horizontal and vertical directions were 455.4 MPa and 417.0 MPa after T6 heat treatment, while the yield strengths were 342.2 MPa and 316.4 MPa, respectively. The comparison results with the previous studies show that the addition of Mg element increases the yield strength of 2219 aluminum alloy, but leads to a corresponding decrease in elongation.

A road pavement distress situation forecasting method employing graph convolutional networks was introduced, addressing the prediction problem of road pavement distress generation and deterioration. Firstly, a topological network was established through clustering algorithms, selecting the main influencing factors of the target pavement distress during its evolution. Subsequently, to enhance the expressive capability of the graph neural network for distress information, a graph topology enhancement method was employed, constructing views related to distress information from both static and dynamic aspects. Finally, an enhanced graph neural network (GNN) architecture was applied, by incorporating attention mechanisms in the view dimension to adjust the influence of different views and utilizing Transformer and GRU modules in the temporal dimension to enhance the predictive performance of the model for pavement distress states over extended time sequences. The internal calibration tests of the model, including ablation studies, multi-sample testing, and hyperparameter control group validation, demonstrated the applicability and stability of the proposed model. For the large and sparse pavement disease dataset, the mean absolute error of this model converged within 4.0, which was better than the results of the traditional prediction algorithms in terms of comprehensive performance.

An improved non-dominated sorting genetic algorithm II (NSGA-II) was proposed in order to address the challenges of time-consuming manual airspace sectorization and the difficulty in comparing the quality of different sectorization schemes. A three-dimensional multi-objective optimization model for sectorization was established by using a grid-region-sector hierarchy in order to balance controllers’ workload within sectors and reduce workload differences between sectors. A fitness evaluation operator, a probability-adaptive combination crossover operator and a dynamic mutation operator were incorporated in the NSGA-II algorithm in order to enhance the number of feasible solutions, solution diversity and computational efficiency. A simulation was conducted for the automatic 3D sectorization of Xi'an high-altitude airspace. Results showed that the optimized scheme improved workload balance within sectors by 37% and reduced inter-sector workload by 24% compared with the current sectorization configuration. The proposed improved NSGA-II provided a broader range of options for decision-makers with varying preferences compared with traditional weighted multi-objective optimization algorithms.

An unmanned aerial vehicle (UAV) small target detection algorithm based on YOLOv5, termed FDB-YOLO, was proposed to address the significant issue of misidentification and omissions in traditional target detection algorithms when applied to UAV aerial photography of small targets. Initially, a small target detection layer was added on the basis of YOLOv5, and the feature fusion network was optimized to fully leverage the fine-grained information of small targets in shallow layers, thereby enhancing the network’s perceptual capabilities. Subsequently, a novel loss function, FPIoU, was introduced, which capitalized on the geometric properties of anchor boxes and utilized a four-point positional bias constraint function to optimize the anchor box positioning and accelerate the convergence speed of the loss function. Furthermore, a dynamic target detection head (DyHead) incorporating attention mechanism was employed to enhance the algorithm’s detection capabilities through increased awareness of scale, space, and task. Finally, a bi-level routing attention mechanism (BRA) was integrated into the feature extraction phase, selectively computing relevant areas to filter out irrelevant regions, thereby improving the model’s detection accuracy. Experimental validation conducted on the VisDrone2019 dataset demonstrated that the proposed algorithm outperformed the YOLOv5s baseline in terms of Precision by an increase of 3.7 percentage points, Recall by an increase of 5.1 percentage points, mAP₅₀ by an increase of 5.8 percentage points, and mAP_50:95 by an increase of 3.4 percentage points, showcasing superior performance compared to current mainstream algorithms.

A dead band compensation strategy for CTPS control was proposed aiming at the problem that the occurrence of return power as well as the failure of soft switching was caused after the addition of bridge arm dead band to the triple phase-shift cooperative control (CTPS) of dual active bridge (DAB) converter. The coupling relationships between different mode shift ratios and the power transfer model and the switching conditions of CTPS control modes were corrected based on the principle of return power generation by analyzing the changes of transformer primary and secondary side voltages and leakage currents caused by the dead band of the bridge arm in different modes of CTPS control. Then the effective control of the impact of dead band on CTPS control was realized. The proposed compensation scheme suppressed the return power caused by the dead band, restored the soft-switching performance of the CTPS control, and had better current stress than before compensation. Experiments before and after dead band compensation were conducted separately to verify the analysis of the dead band effects and the proposed compensation strategy.

Traditional computational intelligence technology was found to lack real-time capabilities and adaptability, and computational intelligence technology based on parallel computing made computational efficiency improve and addressed the issue of compatible processing of multimodal information. From three branches of computational intelligence: neural networks, evolutionary algorithms, and swarm intelligence algorithms, the current states were reviewed on the integration of computational intelligence and big data-parallel computing. Problems present in parallel computing intelligence were summarized, and some thoughts were given to the development direction of related studies.

A 3D target inversion algorithm based on multi-feature reconstruction was proposed in order to solve the problems of large memory occupation and time-consuming training in deep learning-based three-dimensional inversion methods. Four types of features, horizontal area, center depth, vertical thickness and residual density of the target were extracted by feature decomposition to realize the compression of the three-dimensional model and reduce the memory occupation. The multi-feature reconstruction of inversion network (MRNet) was designed to realize the prediction of the four types of target features by different Decoder, and the four types of features were used to reconstruct the three-dimensional model to realize the inversion of the 3D target. The gradient union was introduced at the input of the network to realize the enhancement of target boundary information. The CA attention mechanism was introduced at the cross-layer connection to realize the differentiation of Decoder’s prediction function and optimize the inversion effect. The simulation results showed that the local relative accuracy of MRNet was improved by more than 30% compared with 3D U-Net, reaching 88.91%, and the training time per round was only 1/13 of 3D U-Net. MRNet was applied to Vinton Salt Mound, and the distribution of caprocks was obtained more accurately, which verified that MRNet had certain generalizability.

A multi-distortion type underwater image enhancement algorithm based on improved CycleGAN was proposed, aiming at the difficulties of underwater image blurring, low contrast and image distortion recognition caused by various factors such as scattering, absorption and color deviation. Firstly, in order to improve the image enhancement effect, Auto-Encoder+Skip-connection network structure was used in the generator of CycleGAN, and global color correction structure was added for global enhancement in terms of pixel as well as color, so as to better capture the color information in underwater images. Secondly, a multidimensional perceptual discriminator was designed to learn the global and local features of the image. This discriminator payed more attention to the local details of the image, effectively targeted scattering and color noise, perceived the image from a multidimensional space, and had a stronger ability to extract the features, thereby enhancing the accuracy of image discrimination. Finally, the experimental results on EUVP, UIEB and U45 datasets showed that the proposed method achieved better results, compared with other algorithms. In processing multi-distortion types of underwater images, the algorithm’s SSIM indicator was higher than that of the second place by an average of 1.57%, the PSNR indicator was higher by 1.836%, the UIQM indicator was higher by 1.324%, and the UCIQE indicator was higher by 1.086%. The proposed method performed well in processing color and noise details.

In order to portray the public emotion orientation caused by the public opinion on traffic incidents disseminated in short videos, a physiological feature graph was constructed by the text sentiment analysis and the multimodal physiological signal feature extraction. This work collected 136 highly-liked videos with 38 805 comments on TikTok. Considering all videos as a document set, with each video treated as a document and comments as words, the latent Dirichlet allocation topic model was adopted to obtain the distribution of comments under different topics and the distribution of topics under different videos. Naive Bayes-based SnowNLP was utilized to calculate the sentiment scores of comments and analyze the sentiment tendencies expressed by different opinion topics. Neuroscience experiments were carried out to collect multimodal physiological signals such as EEG, eye movement, ECG, and respiration as well as emotion ratings. Statistical test results show that videos with different sentiment tendencies induce different emotions, and the multimodal physiological features such as the relative spectral power of EEG, blinking frequency, respiration standard deviation, and the very low-frequency power of ECG are specific under different emotions. The emotional semantics embedded in the comments influence public emotion in various ways beyond that evoked by videos.

The backgrounds are cluttered, the spot sizes of apple leaf disease are varying in complex environments, and the existing models have the problems of multiple parameters and a large amount of calculation. Thus, an apple leaf disease recognition network, ConvNext network based on attention and multiscale feature fusion (MA-ConvNext), was proposed. A multiscale spatial reconstruction and channel reconstruction block (MSCB) and a feature extraction block with triplet attention fusion (TAFB) were utilized to effectively extract the features at different scales and enhance the focus on leaf disease spots. Additionally, a stepwise relational knowledge distillation method was employed to fuse the "teacher" network (MA-ConvNext) with an "intermediate" network (DenseNet121) to guide the training of the "student" network (EfficientNet-B0) and achieve the model lightweighting. Experimental results showed that MA-ConvNext achieved a recognition accuracy of 99.38%, improving by 3.98 percentage points, 7.55 percentage points and 4.27 percentage points compared to ResNet50, MobileNet-V3, and EfficientNet-V2 networks, respectively. After the stepwise relational knowledge distillation, the recognition accuracy further improved by 1.76 percentage points, with a smaller network size and parameters of 1.56×10⁷ and 5.29×10⁶. respectively. The proposed method offers new insights and technical support for the precise detection of pests and diseases in agriculture.

A low-jitter high-speed charge-pump phase-locked loop (CPPLL) suitable for high-speed SerDes serial link was designed using 65 nm CMOS technology. Loop bandwidth and circuit structure of voltage-controlled oscillator (VCO), charge pump (CP), phase frequency detector (PFD) were optimized to reduce jitter caused by voltage ripple and internal noise. CPPLL can achieve a stable clock signal with the smaller jitter offset while meeting the wide frequency range and high speed requirements of SerDes link. Chip area including the entire pads is 0.309 mm². The measurement results show that CPPLL can generate a 10.9-12 GHz clock signal and exhibit a phase noise of ?111.47 dBc/Hz and a reference spur of ?25.14 dBc and a figure-of-merit (FoM) of ?223.5 dB at 10 MHz offset. It takes 600 μs to generate a stable 11.3 GHz clock signal, and its RMS jitter is 973.9 fs when the reference frequency is 706.25 MHz, which is approximately 0.065 UI. The power consumption is 47.3 mW at the supply voltage of 1.2 V. The proposed phase-locked loop (PLL) is suitable for high-speed communication link systems at 20 Gb/s and above.

The similar test of physical model was designed aiming at the diversion tunnel project of layered surrounding rock of San Gavan Hydropower Station. LSDYNA was used to analyze the propagation characteristics and distribution characteristics of stress wave in layered rock mass by considering the static load, dynamic load and the dip angle. The sensitivity of different factors to the peak stress and secondary equilibrium stress of surrounding rock was analyzed by orthogonal test. A stress prediction model under the influence of multiple factors was established based on the dimensional analysis in order to determine the safety load control range of surrounding rock. Results showed that there was initial stress concentration in the surrounding rock of tunnel under high ground stress. The dynamic load had a significant impact on the value of stress concentration. The stress wave front was discontinuously distributed due to the influence of bedding. The dynamic and static loads were positively linearly correlated with the peak stress and secondary equilibrium stress. The peak stress and secondary equilibrium stress showed '∧' type distribution with the increase of dip angle. The sensitivity order of different factors to the peak stress and secondary equilibrium stress was dynamic load>static load>dip angle. The static load limit values were 0.731, 0.555, 0.479 and 0.456 MPa respectively, and the dynamic load limit values were 0.624, 0.523, 0.477 and 0.463 MPa respectively when the dip angle was 90°(0°), 75°(15°), 60°(30°) and 45°.