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.

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 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.

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.

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.

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 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.

A prediction method based on the multi-channel graph aggregated attention mechanism was proposed, to address the challenges of limited spatial scope, insufficient spatiotemporal information capture, and low accuracy in short-term bike-sharing demand prediction. Firstly, the city was divided into multiple bike-sharing virtual stations using a flow-adjusted virtual station partitioning method according to bike flows in different areas. A dynamic adjacency matrix was constructed using the origin-destination (OD) matrix between stations to form a bike-sharing graph network structure. Next, spatial information of stations across different time periods was captured via a multi-channel graph aggregation module, which was combined with a multi-head self-attention module to capture temporal correlations. Finally, a cross-attention mechanism, along with exogenous variables, was introduced to uncover potential relationships among various variables. Experiments conducted in Shenzhen and New York demonstrated that the model significantly outperformed other deep learning methods across various time periods and regions, maintaining stable and low prediction errors. The results confirmed that the dynamic adjacency matrix and the cross-attention mechanism integrating external features could effectively enhance the prediction accuracy of shared bike usage.

Existing blockchain-based crowdsourcing methods for fingerprint data lack effective utilization of fingerprint distribution characteristics in data quality control consensus and incentive mechanism reward distribution, affecting data collection quality. To address this issue, a decentralized indoor positioning crowdsourcing data quality control method considering fingerprint distribution features was proposed. A data quality control consensus algorithm was designed based on fingerprint distribution characteristics. Characteristic parameters were estimated by retrieving historical data within identical label classes. User-submitted fingerprint vectors were chained only when their weighted mean square error with historical mean vectors was below a given threshold. Duplicate data were rejected to resist replay attacks. To protect user identity privacy, data were anonymously uploaded with the block ownership verified using the Schnorr protocol. An appropriate incentive function was determined according to the data quality errors between user-submitted and on-chain fingerprint distributions. Experiments on three fingerprint datasets (UJIIndoorLoc, MALL and WiFi-RSS) demonstrated that compared with the original datasets, fingerprint data filtered by the proposed method improved training accuracy of indoor positioning models by approximately 10, 3 and 10 percentage points respectively, effectively enhancing fingerprint data acquisition quality.

An improved YOLOv5s sleeper crack target detection algorithm was proposed, in response to the safety hazards posed by the increasing number of crack defects in high-speed rail sleepers due to extended service life, as well as the issues of missed and false detections of surface fine cracks in high-speed rail sleepers. In the backbone network of the YOLOv5s algorithm, the full-dimensional dynamic convolution based on the multi-dimensional attention mechanism was used instead of the traditional convolution to enhance the overall feature extraction ability of the network and improve the detection accuracy of fine cracks. An improved lightweight C3 structure was proposed based on the ConvNeXt module and depth-separable convolution to compress the model volume and accelerate the convergence of the network to improve the detection efficiency. The scale-optimized weighted GFPN feature fusion network was used to solve the problem of detail feature loss in the sampling process of small targets at multiple scales. The improved YOLOv5s sleeper crack target detection algorithm could solve the problem of missed detection of fine cracks on the sleeper surface effectively. The experimental results showed that the parameter count of the improved algorithm model was decreased by 19.7%, the accuracy rate, recall rate and mean average precision were increased by 1.8, 2.4 and 4.2 percentage points respectively, and the detection speed was up to 96 frames per second. The results verify that the proposed lightweight YOLOv5s-OCG algorithm model provides an effective solution for the real-time detection of surface cracks on sleepers.

A dual-channel graph neural network method was proposed for user-level fraud detection tasks on E-commerce platforms to address the limitations of existing approaches that overemphasized global modeling of user behavior while insufficiently exploiting comment information. Multi-dimensional user behavior was modeled through the construction of two complementary graphs: an entity interaction graph and a comment semantic graph. The entity interaction graph was designed to capture global interaction patterns based on purchase and rating behaviors, while the comment semantic graph was built to model time-sensitive semantic relations between comments for characterizing fine-grained behavioral features. Parallel modeling of the dual graphs was performed using graph neural networks. Dynamic interaction optimization between dual-channel features was achieved through an attention mechanism, and higher-order node features containing multi-hop neighborhood information were generated. A comprehensive user-level behavior representation was produced by adaptively fusing different neighborhood ranges and feature spaces with a multi-head additive attention mechanism. Experimental evaluations were conducted on public datasets to validate the proposed method, and significant improvements were observed in multiple evaluation metrics compared to traditional approaches. Results show that the proposed method effectively enhances fraud detection performance at the user level.

Problems in industrial robot kinematic calibration were addressed. These included measurement redundancy caused by positioning error similarity at sampling points, and kinematic parameter compensation affected by measurement uncertainty. A parameter calibration method combining de-redundant trajectory measurement and measurement uncertainty was proposed. Firstly, the spatial positioning error variation function was measured to characterize the Cartesian space similarity between the joint and the end effector, and a spatial de-redundant measurement trajectory for the ball bar instrument with multi-joint synchronous driving was constructed. Secondly, an improved moth-flame optimization algorithm (MFO) with enhanced encirclement and search strategy was developed to enhance the accuracy and efficiency of inverse kinematics and error parameter identification. Thirdly, a dynamic correction strategy for identification parameters based on measurement parameter uncertainty was formulated, and a nested optimization method for kinematic compensation parameters was established. Finally, the error compensation test results showed that based on the results of de-redundant measurement and identification, the accuracy of the robot was improved by 49.8% after error compensation without considering uncertainty, and by 53.5% after error compensation considering uncertainty. The processing test showed that after the error compensation considering the uncertainty, the size error of the impeller workpiece was reduced by 32.3% on average and the shape and position error was reduced by 38.9% on average, compared with the impeller workpiece processed before compensation.

A day-ahead market economic dispatch model that incorporated the flexible ramping products (FRPs) provided by energy storage systems was proposed to fully harness the flexible regulatory capabilities of various resources to ensure real-time flexibility, thereby addressing the problem that conventional thermal power units cannot meet the power system’s flexibility demands against the backdrop of the construction of a new type of power system. The demand composition and the opportunity cost of FRPs were introduced. A decision tree model for FRPs participation in flexible ramping deployment in the day-ahead and real-time markets was obtained by conducting the probabilistic analysis of the decision-making schemes for FRPs in both the day-ahead and real-time markets, and the cost and revenue analyses were performed under the scenarios where FRPs are either abundant or scarce. Based on this, a day-ahead market economic dispatch model that considered energy storage providing FRPs was established. After the economic dispatch in the day-ahead market, the cost and FRPs revenue settlement was achieved based on the probability of accepting FRPs in the day-ahead market and the expected deployment probability in the real-time market. A case study analysis using an improved IEEE 30-bus system was conducted to validate the superiority of the model with energy storage participation, and the impact of the acceptance probability in the day-ahead market and the expected deployment probability in the real-time market on the system was discussed.

To address the challenges of high randomness on both the supply and demand sides, difficulties in integrating renewable resources, and low flexibility in industrial parks, a study on the optimal configuration of a steam heating system coupled with green electricity in industrial parks was conducted, considering time-of-use steam pricing. The aim was to enhance system economic efficiency and green electricity utilization level. A system demand response management strategy was developed based on the modeling of the steam heating system in a green electricity-coupled industrial park. By introducing time-of-use steam pricing, users were encouraged to adjust their steam usage behavior, thereby regulating user-side loads and improving system flexibility. Based on the optimized time-of-use pricing results, a bi-level optimal configuration model considering both planning and operation stages was established for the steam heating system in industrial parks. A case study based on a power plant in Zhejiang Province was conducted for validation. The comparative results showed that the proposed optimization method reduced the peak-valley load difference by 58.32%. Additionally, the optimized configuration scheme decreased the total system cost by 98200, 253700, and 182500 yuan under different scenarios with combined heat and power unit output limits of 70%, 60%, and 50%. This time-of-use steam pricing-based optimization approach provided valuable guidance for the low-carbon transition of steam heating systems in industrial parks.

To address the energy consumption optimization problem in cooperative control of mixed vehicle platoon while ensuring passenger comfort, a collaborative control method combining real-time optimization with distributed model predictive control and an intelligent driver model was proposed. For connected autonomous vehicles in the platoon, passenger comfort constraints were established. Utilizing a precise fuel consumption model, a real-time optimized distributed model predictive control method was designed to reduce real-time energy consumption while ensuring the consistency and stability of the platoon. For human-driven vehicles in the platoon, an intelligent driver-following model that ensures passenger comfort and low energy consumption was adopted. The following stability condition was then derived. Simulation experiments were conducted in the scenarios of constant speed and variable speed leader vehicles to verify the tracking performance of the proposed control method under the constraints of passenger comfort. The average engine power from the initial state to the steady state was used as the energy consumption optimization index, and multiple sets of comparative simulation experiments were conducted. Simulation results show that, compared with the comparative algorithm, the proposed control method can effectively reduce the energy consumption of the mixed vehicle platoon.

A reinforcement learning-based cloud-edge collaborative computing resource scheduling algorithm, KNCS, was proposed aiming at the problem of insufficient resource utilization in cloud-edge collaborative computing scenarios due to imbalances in network and computational resources, as well as uncertainties in task types and arrival times. This algorithm achieved shorter transmission time, processing time, and turnaround time by comprehensively considering the state of network resource and computational resource. A unified information transmission platform was designed to aggregate information from computational nodes and various tasks, facilitating the definition of task dependencies, dynamically adjusting subsequent tasks based on the type of running tasks, and providing a more realistic task scheduling scenario. The experimental results show that the performance of the KNCS algorithm surpasses that of the default Kubernetes scheduling algorithm in cloud-edge collaborative computing scenario.

The freight volume data between regions of highway has mutual influence, and the temporal and spatial problems cannot be handled simultaneously by traditional long short-term memory (LSTM) models. An improved LSTM model (TS-LSTM) based on spatiotemporal information fusion was designed, and a method for reconstructing the dataset was proposed according to the importance of spatiotemporal information. To verify the effectiveness of the model, the highway toll system data (25 563 256 in total) for a consecutive 12-month period in a certain region was used as the original dataset, and TS-LSTM was compared and analyzed with a time-based LSTM model (T-LSTM), a space-based LSTM model (S-LSTM), a fully connected neural network, an unidirectional LSTM, a bidirectional LSTM, and the Transformer. Results showed that the performance of TS-LSTM varied across different regions, and compared to other machine learning models, the reduction range of the mean absolute error was between 40% and 85%. The mean absolute error of TS-LSTM was 10% lower than that of Transformer, and the mean absolute percentage error was 21 percentage points lower. The prediction performance of TS-LSTM were superior to those of the comparison model.

A bi-level optimization strategy for active distribution networks with smart soft open point (SOP) considering carbon-guided electric vehicle (EV) clustering was proposed, in order to improve the consumption of wind-solar new energy and fully exploit the potential of EV clusters in optimal operation of active distribution networks for carbon emission reduction. Firstly, under the premise of EV cluster integration, the active/reactive power outputs of distributed generators and SOPs were coordinated and optimized by the upper layer to minimize system operation costs. Secondly, the lower-layer generalized energy storage optimization model for EV clusters based on Minkowski sums was constructed to minimize charging-discharging costs. A dynamic tariff mechanism based on dynamic carbon emission factor was proposed to guide vehicle-grid energy interaction and achieve win-win benefits for both parties. Finally, simulation verification on the improved IEEE 33-node system showed that the strategy could effectively promote friendly interaction between active distribution network and EV cluster, reducing the risk of system voltage overruns.

A desensitization design method for parallel robots considering multi-source uncertain hybrid perturbation was proposed aiming at the problem of optimal design of high-performance parallel robots. A probabilistic error model was established by using the first-order perturbation method for error modeling. The optimal dimension design parameters were obtained by using multi-target subregion meta-heuristic iterations after analyzing the high-value targets corresponding to the working subregion. A performance sensitivity index was constructed to optimally allocate the design tolerances. The sensitivity of maintenance to parameters was calculated by establishing an in-service accuracy performance sensitivity model, and a low-sensitivity preventive maintenance strategy was obtained. An additive manufacturing parallel robot was used as an example for validation. Results show that the static performance and dynamic in-service accuracy maintenance can be effectively improved via desensitization design.

A decision-making and planning algorithm integrating reachable sets with reinforcement learning (RL) was proposed to address the limitations of traditional reachable sets in effectively handling behavioral interactions between intelligent vehicles and adjacent vehicles in dynamic and uncertain environments, as well as excessive computational complexity. An RL model was incorporated into the algorithm framework to guide multi-step decision-making, clearly defining continuous macro driving behaviors over the planning horizon. Firstly, a reinforcement learning decision model was established and formulated as a Markov decision process (MDP), with state space, action space, and reward function designed. Secondly, feasible driving regions were partitioned based on driving semantics. Lateral and longitudinal behavioral predicates were introduced to segment reachable regions at each time step into finite feasible areas via a two-stage (lateral-first, then longitudinal) segmentation. Finally, the ego vehicle’s position was predicted from RL model outputs to determine optimal driving regions and form a driving corridor. The proposed algorithm’s effectiveness was validated through long-duration cyclic tests in dynamic and uncertain scenarios and comparative analysis of typical cases. Experimental results demonstrated that, compared with existing reachable set algorithms, the proposed method achieved better overall performance in enhancing driving efficiency and ensuring safety, comfort, and real-time responsiveness.