Search Results (3,413)

Multi-motor electric heavy-duty vehicles face significant energy efficiency challenges due to the complex coordination of discrete gear selection and continuous energy flow allocation in the powertrains. This study aims to address this coordination dilemma by proposing a multi-agent deep reinforcement learning energy management strategy (EMS). The framework employs collaborative control across three agents to simultaneously optimize middle axle/rear axle gear shifts (DQN) and power distribution (DDPG), effectively handling the hybrid action space. A specialized rule is integrated to accelerate convergence and enhance real-cycle adaptability. Simulation results on CHTC-TT and CHTC-HT cycles show the proposed strategy achieves only 3.14% and 4.65% higher energy consumption, respectively, compared to a rule-optimized benchmark. This validates its practicality and robustness for real-world electric heavy-duty transportation applications. Full article

Urban sustainable mobility requires understanding how people travel, which modes they use, and what impacts these choices generate. This study proposes a smart mobility analytics framework that integrates GPS traces, dynamic traffic variables, and machine learning to infer transport modes and sustainability metrics in Cuenca, Ecuador. Geospatial and kinematic data were collected at 1 Hz from 50 participants over four working weeks, yielding 8.99 million samples across five modes: walking, cycling, tram, bus, and private vehicles. A compact subset of physical and spatial predictors, derived from speed, acceleration, jerk, longitudinal forces, and distance to public transport routes, was selected using the Football Optimization Algorithm. A classification tree trained with a 70/15/15 train–validation–test split achieved an overall accuracy of 84.2%, with class precisions of about 99% for pedestrian and bicycle, 93% for tram, 76% for private vehicles, and 64% for bus. The classified trajectories show that walking and cycling account for approximately 65% of total travel time but only 2% of total distance and 1.7% of CO₂ emissions, whereas motorized modes generate more than 98% of emissions. Buses contribute nearly four times more CO₂ than private vehicles, despite carrying a larger passenger volume. The proposed framework delivers detailed, policy-relevant indicators to support low-carbon urban transport strategies. Full article

The high-temperature engine nozzle is a critical component of a rocket motor, and its stability and performance are significantly influenced by internal high-temperature gas radiative heat transfer. Due to the non-gray nature of the nozzle medium and the complexity of the Radiative Transfer Equation (RTE), rapid and accurate simulation of radiative heat transfer is crucial for engineering applications. This paper presents a high-efficiency solution coupling the Full-Spectrum Correlated k-Distribution (FSCK) model with the Null-Collision Monte Carlo Method (NCMCM). To address the inherent computational bottleneck of linear traversal in unstructured grids, a hybrid ray-localization model integrating KD-tree and Bounding Volume Hierarchy (BVH) is proposed. This model shifts the search mechanism from element-wise iteration to spatial topological indexing, achieving logarithmic search complexity and significantly mitigating the sensitivity of computational cost to grid scale. Furthermore, a collaborative MPI–OpenMP parallel framework is established to maximize hardware utilization, where an optimized guided scheduling strategy effectively counteracts the stochastic load imbalances encountered in traditional static schemes. Results indicate that the proposed method reduces the total execution time to approximately 1/4 compared to traditional models. Simulations identify the convergent section as the primary radiation zone, where CO₂ contributes less to the radiative source term than H₂O under high-temperature conditions. Full article

Brake-by-wire systems eliminate the mechanical linkage between the brake pedal and wheel actuators, resulting in the loss of the natural and familiar braking feel perceived by the driver. To address this issue, this study proposes an active brake pedal simulator based on a linear motor and springs, aiming to simulate the adaptive pedal feel and ensure safety performance. Firstly, this paper established a structural model of the pedal simulator and designed a force compensation strategy to reproduce the target pedal characteristic curve of the traditional hydraulic braking system. Subsequently, the system was verified through Adams simulation and real vehicle experiments under slow, normal, and emergency braking conditions. The experimental results show that the initial design exhibited a relatively “soft” pedal feel, with a brake feel index score of 62.31. By optimizing the spring stiffness and feedback force composition, the brake feel index score was significantly improved to 92.21. The optimized pedal simulator is capable of achieving precise pedal force tracking and adaptive adjustment of pedal feel, and still providing basic and reliable pedal force feedback, even in the event of motor failure. Therefore, the designed pedal simulator provides a practical and effective solution for improving the pedal feel of the brake-by-wire system, demonstrating strong application potential. Full article

High-performance speed control of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicle (EV) applications faces significant challenges due to inherent nonlinearities, parameter variations, and signal non-idealities such as sensor noise and measurement latency. To address these issues, this paper proposes a robust PI-based Fractional-Order PSO-Fuzzy Weight Controller (PI-FOPSOFWC). The proposed strategy integrates a fractional-order PI (FOPI) core to ensure iso-damping robustness, a fuzzy inference mechanism for online gain scheduling against nonlinear load dynamics, and a novel Fractional-Order Particle Swarm Optimization (FOPSO) algorithm for optimal parameter tuning. A key contribution of this study is the validation of the control strategy within a high-fidelity co-simulation framework coupling MATLAB/Simulink with CarSim 2023, which incorporates realistic vehicle dynamics and time-varying road loads unavailable in conventional simplified simulations. Co-simulation results demonstrate that the proposed controller effectively eliminates overshoot in step responses and maintains stability under significant parameter mismatches ($2.0\times$ inertia). Furthermore, under the EPA urban driving cycle, the proposed method reduces the speed tracking Root Mean Square Error (RMSE) by 75.0% compared to the standard PI controller. Computational complexity analysis further confirms the feasibility of the proposed algorithm for real-time implementation in commercial EV traction drives. Full article

Under practical operating conditions, intelligent fault diagnosis of permanent magnet synchronous motors (PMSMs) is often hindered by the shortage of effective fault samples. To address this issue, this paper proposes a twin-data-driven transfer learning-based diagnostic method for PMSM inter-turn short-circuit faults. First, a finite element model of the motor is established in Ansys to generate inter-turn short-circuit twin data, thereby enriching the source-domain samples. Second, continuous wavelet transform (CWT) is employed to convert stator current signals into multi-scale time–frequency feature maps, which are then fed into a feature extraction network constructed by integrating a residual network (ResNet) into an efficient channel attention mechanism (ECA) to achieve effective fusion of local and global time–frequency features. Finally, a joint loss function combining multi-kernel maximum mean discrepancy (MK-MMD) and a domain-adversarial neural network (DANN) is introduced to align feature distributions and perform adversarial optimization, enhancing cross-domain invariance and improving fault recognition capability. Experimental results demonstrate that the proposed REDM method achieves higher diagnostic accuracy and robustness than several existing intelligent fault diagnosis approaches. Full article

This study investigated the acute effects of anodal transcranial direct current stimulation (a-tDCS) applied over the dorsolateral prefrontal cortex (DLPFC) and primary motor cortex (M1) on neuromuscular performance during a velocity-based back squat exercise. Fifteen recreationally trained men participated in a randomized, double-blind, crossover design, completing three experimental conditions (SHAM, DLPFC, and M1 stimulation) consisting of 20 min of 2 mA a-tDCS followed by a squat protocol performed to a 15% velocity loss threshold. Total repetitions, repetitions per set, mean concentric velocity, and rating of perceived exertion (RPE) were recorded. No significant differences between stimulation conditions were observed for any outcome variable. However, two individuals showed reversed responses, consistent with previously reported inter-individual variability in response to tDCS. Given the high inter-individual variability in response to a-tDCS, we additionally performed a post hoc sensitivity analysis based on response direction relative to SHAM. This analysis indicated that a-tDCS over M1 and DLPFC resulted in a significantly greater total number of repetitions compared with SHAM, whereas repetitions per set, mean velocity, and RPE were not different between conditions. Accordingly, a systematic and individualized approach may be needed to address inter-individual variability in response to tDCS to optimize its effect on fatigue tolerance. Full article

Diagnosing cervical spine instability with flexion-extension radiographs is challenging, as current guidelines are based on limited cadaver studies and do not adequately account for level, vertebral size, or patient effort. There is a need for automated cervical instability metrics anchored to normative reference data, accompanied by evidence on how often abnormal findings occur in real clinical populations and which soft-tissue injury patterns they can detect. We developed and evaluated fully automated, radiographic-based cervical intervertebral motion (IVM) metrics—adapted from prior lumbar methods—using an FDA-cleared analysis pipeline that segments C2–C7 and derives rotation, translation, disc heights, and regression-based instability indices. Normative reference data were first established from flexion-extension radiographs of 341 asymptomatic volunteers after excluding radiographically degenerated levels. Abnormality prevalence was then estimated in two symptomatic cohorts: pooled preoperative clinical-trial radiographs and 881 patients with symptoms attributed to motor-vehicle accidents, excluding levels with <5° rotation to reduce unreliable data due to insufficiently stressed spines. Finally, potential diagnostic performance was assessed in a controlled cadaveric ligament-sectioning model (12 cadavers) using ROC analysis and Youden’s J thresholds. Across clinical cohorts, objective IVM abnormalities were uncommon. Prevalence increased when studies demonstrated adequate total C2–C7 motion, emphasizing the importance of patient effort. In cadavers, vertical instability metrics were most discriminative (AUC 0.96–0.97) with high sensitivity (0.89) and perfect specificity at optimal thresholds, whereas translation changed minimally with sectioning. These results support regression-based instability indices as promising candidates for standardized, physiology-guided cervical instability assessment. Full article

Walking is one of many basic human motor functions, yet replicating it in robotic systems remains a complex problem. Historically, the design of walking mechanisms has relied on human intuition and iterative refining. Some well-known mechanisms, like Theo Jansen, have been invented by artists rather than engineers. In this paper, we present a novel, automated pipeline that includes dataset generation, filtering, and an optimization procedure for synthesizing 1-DOF geometrically feasible walking mechanisms. Four million mechanisms were simulated and evaluated for 25 mechanism types, for a total of 100 million mechanisms. Quantitative design criteria for walking motion were identified and applied to retain a total of 23,250 valid, stable walking mechanisms. We then apply a custom optimization process to adjust near-walking mechanisms whose joints run into the ground. A custom function is used to minimize the error related to ground intersection and step uniformity. The computational generation and optimization of walking linkages demonstrated in this work aims to systematically generate a large number of design concepts for walking mechanisms. While the focus of this work is on the synthesis of mechanisms for walking robots, the same methodology could be generalized to identify mechanisms for a wide range of applications beyond walking robots. Full article

Rolling bearings are critical components in rotating machinery, and their failures may lead to significant economic losses and safety hazards. However, early fault signals are often weak and masked by strong background noise, making accurate fault diagnosis extremely challenging. To address this issue, this paper proposes a fault diagnosis method for rolling bearings based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), an autoregressive (AR) model, and observer-based eigenvalue extraction, combined with a particle swarm optimization-based kernel extreme learning machine (PSO-KELM). Targeting rotating machinery with rolling bearings, the approach begins by applying ICEEMDAN as a preprocessing step to decompose non-stationary vibration signals into multiple intrinsic mode functions (IMFs), from which all essential fault-related information is extracted. The preprocessed vibration signal is then reconstructed. Subsequently, an AR model is used to establish a state-space representation for the observer, which processes the reconstructed signal and generates a residual output by comparing it with the actual mechanical signal. Features are then extracted from the residual signal, including its mean, variance, maximum and minimum values, kurtosis, waveform factor, pulse factor, and clearance factor. These features serve as inputs to the PSO-KELM classifier for fault diagnosis. To validate the method, real vibration data from electric motor bearings were employed in a case study, covering normal conditions and three typical fault types: outer race fault, inner race fault, and rolling element fault. The results demonstrate that the proposed method effectively enables fault feature extraction and accurate identification of bearing conditions. Full article

Brake pedals and wheel braking units are mechanically decoupled in brake-by-wire systems. This causes the driver to lose the familiar pedal feel. To address this issue, this paper designed an active braking pedal simulator based on the long-travel Halbach-array linear motor. Firstly, this paper conducted both qualitative and quantitative analyses on the pedal characteristics of a traditional hydraulic braking system and used them as a reference. A dual-coil independent control strategy was designed in order to overcome the thrust instability at the junction of the Halbach-array magnetic field. This enables the linear motor to achieve smooth and continuous thrust output throughout the entire travel range. Secondly, this paper also designed a “linear motor + spring” solution to reduce energy consumption and peak motor thrust. By conducting a quantitative analysis of the relationship between the spring stiffness, motor work and peak thrust, the spring stiffness was optimized. The results show that when the spring stiffness is 3.73 N/mm, the motor work can be reduced to 5.92 Joules while significantly reducing the peak thrust. Finally, this paper also established a testing platform. It was used to verify the performance of the proposed pedal simulator under low-intensity, medium-intensity, and high-intensity braking conditions as well as an anti-lock braking system intervention. The testing results show that the pedal simulator can actively adjust the pedal characteristics according to the braking intensity, and it can provide clear vibration feedback during the anti-lock braking system intervention. Therefore, the proposed pedal simulator effectively simulates the pedal feel of hydraulic braking systems while improving energy efficiency and operational stability. It provides a feasible solution for enhancing the driver–vehicle interaction and the driving comfort of brake-by-wire systems. Full article

Bearing rings are thin-walled components prone to distortion during quenching. Achieving high-precision distortion control for bearing rings remains a critical challenge in high-precision bearing manufacturing. This paper proposes an AI-driven method for distortion control during bearing ring quenching in mesh-belt furnaces. The primary objective is to identify the optimal reverse motor frequency within the furnace. An experimental database is established using distortion results obtained at different reverse motor frequencies. This database serves as training data for deep learning. Subsequently, a large language model (LLM) employs few-shot learning to optimize and predict the reverse motor frequency influencing bearing quenching distortion. Results demonstrate that the LLM method achieves significantly higher prediction accuracy than traditional machine learning approaches. The optimization outcomes validate the effectiveness of generative AI in optimizing the reverse motor frequency of mesh-belt furnaces and controlling distortion during bearing ring quenching. Full article

Parkinson disease (PD) and mood disorders represent two substantial global health burdens that increasingly co-occur as both conditions rise in prevalence worldwide. Diagnosing Parkinson disease in patients with pre-existing mood disorders is clinically challenging due to overlapping symptoms, medication effects, and shared neurobiological mechanisms. Apathy, psychomotor slowing, and fatigue may mimic depressive symptoms, leading to delayed recognition of early parkinsonism. Development of an underlying neurodegenerative disorder could account for some treatment-resistant symptoms or treatment failures if not recognized. Therefore, the identification of PD will change the treatment and management plan significantly. Accurate diagnosis of PD requires a detailed neurologic examination focusing on bradykinesia, rigidity, and resting tremor, supported when appropriate by dopamine transporter imaging (DaT scan) or other emerging biomarkers. Understanding the temporal relationship between psychiatric and motor features helps differentiate prodromal PD from primary mood disorders. Management of patients with both mood disorders and PD integrates dopaminergic replacement therapy for motor symptoms with individualized treatment of psychiatric comorbidities. Levodopa remains the cornerstone for motor control, while dopamine agonists, MAO-B inhibitors, and COMT inhibitors can be added as needed. For depression and anxiety, SSRIs and SNRIs are first-line choices; quetiapine or clozapine are preferred when treatment for psychosis is necessary. Intentional, thoughtful polypharmacy is frequently required. Non-pharmacologic interventions—including cognitive behavioral therapy, structured exercise, and patient–caregiver education—enhance mood, function, and quality of life. Multidisciplinary collaboration between neurology, psychiatry, and allied health professionals is essential for optimal outcomes. This review offers guidance to healthcare providers as well as other interested parties involved in patients with mood disorders who may also be developing or have PD, especially to those who may have limited access to neurologic resources. Full article

Parameter self-tuning of servo control systems is crucial for optimizing automation processes, especially in complex systems such as permanent magnet synchronous motors. In this paper, a nonlinear adaptive whale optimization algorithm (NAWOA) is proposed and applied to parameter self-tuning, which improves the traditional whale optimization algorithm (WOA) by nonlinearly adaptively adjusting two parameters during optimization to enhance fast convergence and global search capabilities. A servo control system with three parameters to be tuned is constructed using both simulation and physical methods. Simulation and experimental results show that the NAWOA outperforms the genetic algorithm, particle swarm optimization, and WOA in parameter self-tuning of the servo control system with lower error indicators and fast convergence speed. Although it still faces the challenge of initial condition dependency, the proposed NAWOA provides a powerful solution for real-time industrial applications. Full article

With the core advantages of high energy efficiency, high power density, and reliable operation, high-voltage permanent magnet motors have become the mainstream development direction of modern motor technology. However, the risk of demagnetization caused by excessive temperature increases in permanent magnets has become a key bottleneck restricting motor performance and operational reliability, which makes research on the flow and heat transfer characteristics of motor cooling systems of great engineering value. Taking the 710 kW high-voltage permanent magnet motors as the research object, this study established a global flow field mathematical model covering the internal and external air duct cooling systems of the motor based on the theories of computational fluid dynamics and numerical heat transfer, and systematically analyzed the flow characteristics and distribution laws of cooling air. The thermal–fluid coupling numerical method was employed to simulate the temperature field of the motor, and the overall temperature distribution of the motor, temperature gradient of key components, and maximum temperature value were accurately obtained. To verify the validity of the established model, a test platform for the cooling system performance was designed and built. Measuring points for wind speed, air temperature, and component temperature were arranged at key positions, such as the stator radial ventilation ducts, and experimental tests were conducted under the rated operating conditions. The results show that the flow field distribution of the internal and external air ducts of the motor is reasonable and that the cooling air flows uniformly, with the external and internal circulating air volumes reaching 1.2 m³/s and 0.6 m³/s, respectively, which meets the heat dissipation requirements. The maximum temperature of 95 °C occurs in the stator winding area, and the maximum temperature of the permanent magnets is controlled within the safe range of 65 °C. The simulation results were in good agreement with the experimental data, with an average relative error of only 4%, which fell within the engineering allowable range, thus verifying the accuracy and reliability of the established global model and thermal–fluid coupling calculation method. This study reveals the thermal–fluid coupling transfer mechanism of high-voltage permanent magnet motors and provides a theoretical basis and engineering reference for the optimal design, precise temperature rise control, and reliability improvement of motor cooling systems. Full article