Search Results (574)

To address the challenge of heat dissipation in electric vehicle in-wheel motors within limited space, this study conducted temperature field analysis and cooling structure optimization based on magnetic–thermal coupling. The three-dimensional finite element model and the magnetic–thermal-coupled mathematical models of synchronous permanent magnet in-wheel motors were established. Using a coupled electromagnetic–thermal finite element analysis method, numerical simulations were performed to investigate the transient temperature fields of the in-wheel motor under different driving conditions. The effects of three different cooling channel structures on the temperature rise and the pressure drop of the in-wheel motor were compared, and a parallel channel structure suitable for the in-wheel motor was selected. The influences of channel quantity, channel width, and coolant flow rate on the temperature field were analyzed. Furthermore, a multi-objective optimization of the cooling structure was carried out using the NSGA-II genetic algorithm. The simulation results demonstrated a significant improvement in the overall thermal performance of the optimized cooling structure. The maximum temperature of the hub motor decreased by 2.25% and 3.32% under the rated and peak speeds, respectively, while the pressure drop in the water channel was reduced by 58.52%. This study provides a theoretical reference for temperature field calculation and cooling structure design of hub motors. Full article

This paper proposes a method to analyze the effect of the rotor’s harmonic winding design and the output of a brushless wound rotor synchronous machine (WRSM) for optimal excitation power transfer. In particular, the machine analyzed by the finite-element method was a 48-slot eight-pole 2D model. The subharmonic magnetomotive force was additionally created in the air gap flux, which induces voltage in the harmonic winding of the rotor. This voltage is rectified and fed to the field winding through a full bridge rectifier. Eventually, a direct current (DC) flows to the field winding, removing the need for external excitation through brushes and sliprings. The effect of the number of harmonic winding turns is analyzed and the field winding turns were varied with respect to the available rotor slot space. Optimization of the harmonic excitation part of the machine will maximize the rotor excitation for regulation purposes and optimize the torque production at the same time. Two-dimensional finite-element analysis has been performed in ANSYS Maxwell 19 to obtain the basic results for the design of the machine. Full article

The rapid growth of Internet of Medical Things (IoMT) devices offers promising avenues for real-time, personalized healthcare while also introducing critical challenges related to data privacy, device heterogeneity, and deployment scalability. This paper presents E-DMFL (Enhanced Distributed Multimodal Federated Learning), an Enhanced Distributed Multimodal Federated Learning framework designed to address these issues. Our approach combines systems analysis principles with intelligent model design, integrating PyTorch-based modular orchestration and TensorFlow-style data pipelines to enable multimodal edge-based training. E-DMFL incorporates gated attention fusion, differential privacy, Shapley-value-based modality selection, and peer-to-peer communication to facilitate secure and adaptive learning in non-IID environments. We evaluate the framework using the EarSAVAS dataset, which includes synchronized audio and motion signals from ear-worn sensors. E-DMFL achieves a test accuracy of 92.0% in just six communication rounds. The framework also supports energy-efficient and real-time deployment through quantization-aware training and battery-aware scheduling. These results demonstrate the potential of combining systems-level design with federated learning (FL) innovations to support practical, privacy-aware IoMT applications. Full article

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value ($\mu$) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations. Full article

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

The Voith Schneider Propeller (VSP) operates with blades undergoing an approximately sinusoidal periodic motion along a circular path. Hydrodynamically, the continuous significant variation in the angle of attack between the blades and incoming flow, together with additional inertial effects caused by accelerated rotation, complicates the computation and measurement of hydrodynamic performance. To investigate the unsteady hydrodynamic behavior resulting from this coupled motion, a numerical model incorporating adaptive mesh refinement was developed to simulate VSP performance. Based on insights into the interaction between blade motion and hydrodynamics, an experimental platform was designed using servo motors to achieve precise synchronized blade control, enabling mutual validation between numerical simulations and transient hydrodynamic measurements. Results demonstrate that the coupled blade motion induces nonlinear variations in hydrodynamic forces. Rotational power loss limits VSP efficiency, and a negative thrust regime occurs at high advance coefficients. Rapid blade flipping leads to flow separation, identified as the primary cause of nonlinear lateral forces. The consistency between numerical and experimental results provides reliable data supporting theoretical studies. These findings offer valuable insights for optimizing motion control strategies in cycloidal propeller applications. Full article

This paper addresses the modeling challenge of significant asymmetry between acceleration and deceleration processes in car-following behavior by proposing an Asymmetric Acceleration and Deceleration Car Following (AAD-CF) model. The model characterizes driving decisions using both desired speed and desired spacing, and incorporates an asymmetric correction factor to capture differences in acceleration and deceleration behavior. Based on real vehicle trajectory data from the I-80 dataset, the model was compared at the microscopic level against classical models such as Gipps in terms of trajectory fitting error. The results show that the AAD-CF model consistently achieves lower trajectory fitting errors across different simulation time-steps, with error reduction exceeding 10%. At the macroscopic traffic flow level, the model successfully reproduced three-phase traffic flow states—free flow, synchronized flow, and wide moving jams. By implementing both startup and emergency braking scenarios, it was further revealed that braking waves propagate approximately 40% faster than startup waves, demonstrating asymmetric wave propagation. This study provides quantitative evidence for understanding the intrinsic relationship between microscopic driving behavior and macroscopic traffic phenomena, and the proposed model can support traffic simulation systems and theoretical analysis. Full article

Formation damage sensitivity is a primary constraint on productivity in coalbed methane (CBM) reservoirs. Conventional experimental methods, which often employ crushed or plug coal samples, disrupt the natural fracture network, thereby overestimating matrix damage and underestimating fracture-related damage. In this study, synchronous comparative experiments were conducted using raw coal and briquette coal cores, integrated with scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR) analyses to characterize coal composition and pore structure. This approach elucidates the underlying mechanisms controlling reservoir sensitivity. The main findings are as follows: The dual-sample comparative system reveals substantial deviations in traditional experimental assessments. Due to post-dissolution compaction, briquette coal samples overestimate acid sensitivity while underestimating water sensitivity. Stress sensitivity is primarily attributed to the irreversible compression of natural fractures. Differences in acid sensitivity are governed by structural integrity: mineral dissolution leads to collapse in briquette coal, whereas fractures help maintain stability in raw coal. Raw coal exhibits a lower critical flow rate for velocity sensitivity and undergoes significant water sensitivity damage below 1 MPa. Both sample types show weak alkaline sensitivity, with damage acceleration observed within the pH range of 7 to 10. Full article

Background/Objectives: With a 17.9 million annual mortality rate, cardiovascular disease is the leading global cause of death. As such, early detection and disease diagnosis are critical for effective treatment and symptom management. Cardiac auscultation, the process of listening to the heartbeat, often provides the first indication of underlying cardiac conditions. This practice allows for the identification of heart murmurs caused by turbulent blood flow. In this exploratory research paper, we propose an AI model to streamline this process to improve diagnostic accuracy and efficiency. Methods: We utilized data from the 2022 George Moody PhysioNet Heart Sound Classification Challenge, comprising phonocardiogram recordings of individuals under 21 years of age in Northeast Brazil. Only patients who had recordings from all four heart valves were included in our dataset. Audio files were synchronized across all recordings and converted to Mel spectrograms before being passed into a pre-trained Vision Transformer, and finally a MiniROCKET model. Additionally, data augmentation was conducted on audio files and spectrograms to generate new data, extending our total sample size from 928 spectrograms to 14,848. Results: Compared to the existing methods in the literature, our model yielded significantly enhanced quality assessment metrics, including Weighted Accuracy, Sensitivity, and F-Score, and resulted in a fast evaluation speed of 0.02 s per patient. Conclusions: The implementation of our method for the detection of heart murmurs can supplement physician diagnosis and contribute to earlier detection of underlying cardiovascular conditions, fast diagnosis times, increased scalability, and enhanced adaptability. Full article

In regions with concentrated load centers in China, the AC transmission network is dense, leading to challenges such as difficulties in power flow control and excessive short-circuit currents. The scale effect of AC grids is approaching saturation, making it imperative to develop new AC/DC hybrid grid structures. To enhance the controllability, security, and stability of AC/DC hybrid power systems, a single-AC multi-DC hybrid grid structure is proposed in this paper. The operational characteristics of this grid are analyzed in terms of power flow control capability, N-1 overload, short-circuit current, frequency stability, voltage stability, and synchronous stability, and a method for constructing the single-AC multi-DC hybrid grid is presented. Finally, simulation analysis is conducted on a typical single-AC multi-DC case, and the results indicate that this hybrid grid structure can simultaneously satisfy the controllability, security, and stability requirements of AC/DC power systems, making it a highly promising grid configuration. Full article

In this paper, a nonlinear power transfer matrix model is presented for power control of Permanent Magnet Synchronous Generator (PMSG) wind turbines, incorporating the DC link dynamics to account for transient stability, thereby clarifying the technical aspect and purpose. The rising penetration of wind turbines (WTs) into the power grid necessitates that they remain connected during and after faults to ensure system reliability. During voltage dips, the stator and grid-side converter (GSC) of a permanent magnet synchronous generator (PMSG) system are directly impacted by the sudden voltage changes. These disturbances can induce large transient voltages and currents in the stator, which in turn may lead to uncontrolled current flow in the rotor circuit and stress the converter components. Moreover, Low Voltage Ride-Through (LVRT) is a critical requirement for grid connection to Wind Energy Conversion Systems (WECS). It ensures that WTs remain connected and operational during short periods of grid voltage dips (faults), instead of disconnecting immediately. This capability is essential for maintaining grid stability. However, in this paper, the authors propose an LVRT scheme for a grid-connected PMSG-based WECS. A sequence of attempts was performed to validate the effectiveness of the proposed control scheme under fault conditions and to improve its overall performance. Full article

Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (${BER}_{sys}$). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and ${BER}_{sys}$ at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work. Full article

This paper proposes a synchronous measurement method for broadband acoustic modal identification based on a combined microphone array, which is capable of overcoming the acoustic modal aliasing issue arising from a limited number of microphones. In the proposed method, the cross-correlation combination of axial and circumferential arrays is performed by utilizing the relevant characteristics of turbulent noise modes, thereby realizing modal identification of turbulent noise in a wide range with a small number of acoustic measurement points. For fast iteration, the modal cross terms are optimized by leveraging the relevant characteristics of turbulent noise modes. This method can effectively distinguish the distribution information of forward- and backward-propagating acoustic modes. The accuracy of the identified acoustic modes is verified through numerical simulations, and the method is experimentally validated using experimental results from an axial flow compressor. The results show that this method can effectively suppress the aliasing problem. Compared with the traditional rotating axial array method, it has higher testing efficiency in circumferential and radial modal identification, requires fewer sound-pressure measurement points, and is more suitable for rapid evaluation of noise reduction designs. Full article

Soft open point (SOP) offers a viable alternative to traditional tie switches for optimizing power flow distribution between connected feeders, thereby improving power quality and enhancing the reliability of distribution networks (DNs). Among existing medium-voltage (MV) SOP demonstration projects, the modular multilevel converter (MMC) back-to-back voltage source converter (BTB-VSC) is the most commonly adopted configuration. However, MMC BTB-VSC suffers from high cost and significant volume, with device requirements increasing substantially as the number of feeders grows. To address these challenges, this paper proposes a novel star-connected cascaded H-bridge (CHB) STATCOM SOP (SCS-SOP). The SCS-SOP integrates the static synchronous compensator (STATCOM) and low-voltage (LV) BTB-VSC into a single device, enabling reactive power support within feeders and active power exchange between feeders, while achieving reduced component cost and volume, simplified power decoupling control, and increasing power quality management capabilities. The topology derivation, configuration, operational principles, and control strategies of the SCS-SOP are elaborated. Finally, simulation and experimental models of a two-port 3 Mvar/300 kW SCS-SOP are developed, with results validating the theoretical analysis. Full article

The paper proposes a parallel transformerless (TL) static synchronous series compensator (SSSC) for the control of power flow along the power distribution lines under balanced or unbalanced voltages. This new SSSC configuration offers the advantages of a fast dynamic response, light weight, and high efficiency. By connecting multiple SSSCs in parallel, the current rating is increased, which improves the grid power transfer capabilities and flexibility. However, there may be circulating current flowing between the parallel-connected inverters, hence causing losses. A modified model predictive control scheme is thus developed, which ensures that the proposed SSSC accurately tracks the reference currents while effectively mitigating the circulating current. The model and cost function of the controller are derived and analyzed in the paper. A real-time simulation of a power line with the parallel TL SSSC controlled by a hardware-in-loop (HIL) DSP is developed to validate the performance of this device under both balanced and unbalanced line voltages. Full article