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 new multimodal sentiment analysis model (MTSA) was proposed on the basis of cross-modal Transformer, aiming at the difficult retention of the modal feature heterogeneity for single-modal feature extraction and feature redundancy for cross-modal feature fusion. Long short-term memory (LSTM) and multi-task learning framework were used to extract single-modal contextual semantic information, the noise was removed and the modal feature heterogeneity was preserved by adding up auxiliary modal task losses. Multi-tasking gating mechanism was used to adjust cross-modal feature fusion. Text, audio and visual modal features were fused in a stacked cross-modal Transformer structure to improve fusion depth and avoid feature redundancy. MTSA was evaluated in the MOSEI and SIMS data sets, results show that compared with other advanced models, MTSA has better overall performance, the accuracy of binary classification reached 83.51% and 84.18% respectively.

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.

The Kalina cycle (KC) and the organic Rankine cycle (ORC) were used and modeled as the bottom cycles, to economically and efficiently recover the waste heat of the supercritical carbon dioxide Brayton cycle (SCBC). Parametric analysis was conducted and the NSGA-II multi-objective genetic algorithm was performed for these combined systems to optimize the parameters. The optimization results were compared with the SCBC system performance to display the benefits of combined cycles. Parametric analysis results showed that there was an optimal pressure ratio for the thermodynamic performances of both integrated cycle schemes. The thermodynamic performances of the two schemes were improved by raising the turbo expansion ratio of bottom cycles, and the exergoeconomic performances of the systems were promoted by upping the inlet temperature of the bottom cycle turbine. Comparison results showed that the optimized SCBC/KC system’s thermal efficiency and exergy efficiency increased by 9.27% and 8.69% respectively compared with that of the pre-optimized SCBC system, and its exergoeconomic cost increased by 0.92%. The thermal efficiency and exergy efficiency of SCBC/ORC system increased by 10.73% and 10.08% respectively, and the exergoeconomic cost increased by 1.87%. Comparative analysis showed that the SCBC/KC system was more exergy economical, while SCBC/ORC system was more energy efficient.

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.

A multi-scale parallel MRI reconstruction model based on a variational model and Transformer (VNTM) was proposed, to enhance the quality of reconstructed MR images from undersampled multi-coil MR data. First, undersampled multi-coil k-space data were used to estimate sensitivity maps, with an intermediate-stage enhancement strategy applied to improve the accuracy of these maps. Next, the undersampled multi-coil k-space data and estimated sensitivity maps were input into a variational model for reconstruction. In the variational model, resolution was reduced through a pre-processing module to reduce computational load; multi-scale features were then effectively fused through a multi-scale U-shaped network with the Transformer. Finally, a post-processing module was applied to restore resolution, and data consistency operations were performed on the output to ensure fidelity. Extensive quantitative and qualitative experiments were conducted on publicly available datasets to validate the effectiveness of the proposed method. The experimental results indicate that the proposed reconstruction model achieves superior reconstruction quality and more stable performance in terms of peak signal-to-noise ratio, structural similarity, and visual effects. In addition, a series of ablation studies and robustness evaluations with varying auto-calibration signal (ACS) region sizes were carried out, confirming that VNTM maintained consistently high reconstruction performance under diverse conditions.

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.

A multimodal feature fusion model based on non-smooth non-negative matrix factorization (nsNMF-PCNN-GRU-MSA) was proposed to address the problems of poor generalisation ability, single feature extraction mode and model uninterpretability in the fatigue driving detection methods. This model detected the level of driver fatigue by analyzing electroencephalogram (EEG) signals. A channel weighting module was designed in the shallow layer of the network, and the non-smooth non-negative matrix factorization (nsNMF) algorithm was introduced to compute the contribution of the electrode channels. A multimodal feature fusion module was designed in the middle layer of the network, where the Gramian angular field imaging method was introduced to map the 1D EEG data into a 2D image, and the spatio-temporal features of different modes were fused in parallel with the PCNN-GRU module. The multi-head self-attention (MSA) mechanism was fused in the deep layer of the network to complete the task of fatigue driving state classification. The experimental results showed that the fatigue detection accuracies of the model on the mixed samples of the SEED-VIG and SAD datasets were 93.37% and 90.78%, respectively, and the lowest accuracies for single-subject data were 86.60% and 85.59%, respectively, which were higher than those of the state-of-the-art models. The analysis method of mapping the feature activation values onto the brain topology map not only improves the interpretability of the model, but also provides a new perspective on fatigue driving detection.

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.

A spatio-temporal graph attention network for vehicle multimodal trajectory prediction (STGAMT) was proposed to address the challenges of predicting manually-driven vehicle trajectories and investigating their impact on autonomous driving decisions. The temporal and spatial characteristics were modeled based on the historical information about the vehicle. A two-dimensional convolutional neural network was employed to identify transverse and longitudinal lane change states, which were then combined with the output from the spatio-temporal dynamic interaction module to form transverse and longitudinal motion characteristics. The Softmax function was used to determine the vehicle’s driving intention. The multi-mode trajectory output was achieved by using a GRU network based on Gaussian conditional distribution. Experimental results showed that, in short-term predictions, the STGAMT model reduced the average error by 63.8% and 41.0% compared to the other five classic models on HighD and NGSIM datasets, respectively. In long-term predictions, the STGAMT model reduced the RMSE by 62.5% and 19.1% compared to the average RMSE of the other five classic models on HighD and NGSIM datasets, respectively. Results indicated that the STGAMT model could effectively improve the accuracy of manually-driven vehicle trajectory prediction.

A linguistic failure mode and effects analysis (FMEA) method for risk evaluation based on personalized individual semantics was proposed, as the existing FMEA method has deficiencies in linguistic information expression, linguistic modeling, and factor assignment when assessing risk problems in complex situations. A group of evaluation experts used a distributed linguistic preference relation to evaluate failure patterns for each risk factor, while another group also used the distributed linguistic preference relation to evaluate the relative importance of evaluation experts and risk factors. The obtained linguistic preference relation was converted into a corresponding numerical preference relationship through the numerical scale model. A first-stage personalized individual semantic model was constructed to obtain the weight values of risk factors and evaluation experts, and a second-stage personalized individual semantic model was further constructed. Based on the solution results of the two-stage personalized individual semantic model, the final score of the failure mode was calculated, and the prioritization was completed. The risk assessment problems in the development process of a certain aero engine were selected for verification, and the results showed the feasibility and effectiveness of the proposed linguistic FMEA method. Experimental comparison with the uniformly distributed method shows that the personalized individual semantics obtained by the proposed method yield highly consistent risk-assessment outcomes, supporting reliability and accuracy in complex product development.

Existing anomaly detection methods of time series data focus on extracting the temporal variation features, while the spatial dependency features between multiple variables are ignored. To address this problem, a detection method based on a spatiotemporal graph attention network was proposed. The original multivariate time series data were transformed into a time-series graph with spatiotemporal dependencies, and a spatiotemporal graph attention network was designed to separately extract the temporal variation features and spatial dependency features. The periodic patterns of fused spatiotemporal features were learned by a multilayer perceptron, and an anomaly detection was performed based on the anomaly scores between prediction values and observation values. Experimental results on public datasets showed that the proposed method significantly outperformed state-of-the-art baseline methods in terms of anomaly detection accuracy and robustness.

A control strategy integrating braking force redistribution and path tracking was proposed to address the problem that instability and yawing were prone to occur when vehicles equipped with electromechanical brake (EMB) system experience braking failures during cornering. Gaussian perturbation and staged optimization were introduced to improve the algorithm in order to mitigate the deficiencies of the slime mould algorithm (SMA). The enhanced SMA was employed to optimize the weight matrix of the linear quadratic regulator (LQR). The improved LQR algorithm was utilized to compute the vehicle’s yaw moment upon detection of a single-wheel failure in the EMB system, followed by braking force redistribution to maintain vehicle stability. The pure pursuit algorithm was modified by shifting the tracking control point to enhance the response speed of the algorithm. An adaptive fuzzy control algorithm was incorporated to accommodate dynamic factors such as road conditions and vehicle speed, thus improving its adaptability. Path tracking was implemented to guide the vehicle along a predefined trajectory until a safe stop when a double-wheel failure was detected in the EMB system. The experimental results demonstrated that the maximum lateral deviation was reduced by 59.15% for single-wheel failure and by 41.95% for double-wheel failure compared with conventional methods. The proposed control strategy can more effectively ensure driving safety during cornering braking failure.

A multi-behavior aware service recommendation method based on hypergraph graph convolutional neural network (MBSRHGNN) was proposed to resolve the problem of insufficient high-order service feature extraction in existing service recommendation methods. A multi-hypergraph was constructed according to user-service interaction types and service mashups. A dual-channel hypergraph convolutional network was designed based on the spectral decomposition theory with functional and structural properties of multi-hypergraph. Chebyshev polynomial was used to approximate hypergraph convolution kernel to reduce computational complexity. Self-attention mechanism and multi-behavior recommendation methods were combined to measure the importance difference between multi-behavior interactions during the hypergraph convolution process. A hypergraph pooling method named HG-DiffPool was proposed to reduce the feature dimensionality. The probability distribution for recommending different services was learned by integrating service embedding vector and hypergraph signals. Real service data was obtained by the crawler and used to construct datasets with different sparsity for experiments. Experimental results showed that the MBSRHGNN method could adapt to recommendation scenario with highly sparse data, and was superior to the existing baseline methods in accuracy and relevance.

The mechanical and electrochemical characteristics of LiFePO₄ battery under different temperature and electric field were analyzed in order to introduce the in-situ surface expansion force as an additional input variable for the estimation of state of charge (SOC) and thus improve the estimation accuracy. A multi-physics signal acquisition platform was designed and constructed. Open-circuit voltage (OCV) tests, hybrid pulse power characterization (HPPC) tests, and in-situ surface expansion force measurements were conducted at different temperature. The mechanical and electrochemical characteristics of battery and its multi-physics responses under various operating conditions were analyzed. Results show that the in-situ surface expansion force first increases, then decreases, and then increases again as SOC rises, and it is more sensitive to SOC than OCV. The extrema of the expansion force curves are slightly affected by temperature, showing small delays with increasing temperature. They are strongly affected by current, occurring earlier and gradually disappearing as the current increases. The internal resistance decreases significantly with increasing temperature. The OCV curves exhibit high consistency across different temperature. The experimental results demonstrate that the expansion force signal has potential in SOC estimation and provide theoretical foundation and data support for SOC estimation methods based on expansion force signals.

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.

A new steam Carnot battery based on high-temperature and low-temperature phase change materials was proposed in order to analyze the new route of multi-energy complementation of integrated energy system in industrial parks. A thermodynamic cycle calculation model considering the equipment performance and mass flow rate was established. The effects of design parameters and multi-stage compression structure on the system heat pump coefficient, round-trip efficiency, power storage loss and efficiency of the heating were analyzed. The phase change temperature of low-temperature phase change material and the phase change temperature of high-temperature phase change material are the main factors affecting the performance of steam Carnot battery. The high cycle performance region of steam Carnot battery was obtained. The parameters and structure of the steam Carnot battery were optimized. Results showed that the round-trip efficiency could reach 56.96%, the coefficient of performance of the heat pump could reach 2.55, and the efficiency of the heating could reach 68.74%.

A lightweight classification model MB-CNN for optical coherence tomography (OCT) images was proposed to accurately and conveniently identify multiple types of fundus lesions. By reducing the number of convolution cores and adjusting the proportion of convolution blocks in each stage, a lightweight backbone network L-Resnet was designed, and the extraction of deep-layer semantic information was enhanced by deepening the network depth. The multi-scale convolution block MultiBlock was designed using depthwise seperable convolution, and the features of the lesion area was mined. Different convolution kernels were used to extract the lesions features of different sizes to improve the recognition ability of the network to the OCT image of the lesion. The feature fusion module FFM was constructed, and the shallow layer information and deep layer information were fused, the texture and semantic information of the pathological features were extracted, and the recognition ability of small target lesions was improved. Experimental result showed that the overall classification accuracy of MB-CNN in the three datasets of UCSD, Duke and NEH was 97.2%, 99.92% and 94.37% respectively, the amount of model parameters were significantly reduced. The proposed model can classify various fundus lesions.

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.

A GW-XGBoost model considering spatial heterogeneity was constructed, and the SHAP model was used to explain the extent and spatial differences in the role of built environment factors, in order to explore the influence of spatial heterogeneity on the nonlinear relationship between the built environment and bike-sharing trips. Compared with the geographically weighted regression and extreme gradient boosting tree models, the GW-XGBoost model significantly improved the explanatory and predictive power of the model by introducing geospatial weighting and adaptive bandwidth, with the overall goodness-of-fit increased by 15.59% on average, and it could reveal the intensity, direction and local differences of the built environment on the nonlinear impact of bike-sharing trips. The results showed that the built environment factors had a nonlinear impact on bike-sharing trips. When the population density reached 20000 persons per km², the impact turned from negative to positive. When the distance from CBD factor was between 15 and 20 km, its effect shifted from positive to negative, and then became stabilized when moving outward from the city center. When the floor area ratio reached 1.8, the impact effect turned from negative to positive. The research results provide a scientific basis and methodological support for the resource optimization of the urban bike-sharing system.