Search Results (7,171)

Induction motors (IMs) are widely used in variable-speed electric drive systems, where the motor is supplied by a voltage source inverter (VSI). Thus, PWM inverter–IM combination presents several issues that can degrade system performance, particularly overvoltage phenomena when long cables are used. In inverter-fed drive systems, the physical separation between the converter and the motor often requires long motor cables, which can significantly affect voltage stress. As the inverter’s output pulses propagate through the cable, voltage reflections and high-frequency oscillations occur at the motor terminals. We theoretically and experimentally investigate the effect of three PWM methods, namely Space Vector (SVPWM), Selective Harmonic Elimination PWM (SHEPWM), and Random PWM (RPWM) strategies, on overvoltage at the terminals of an induction motor fed by a PWM inverter through a long cable. The simulation results exhibit the validity and efficiency of SVPWM control to reduce overvoltage for different cable lengths. In addition, in order to reduce and eliminate all overvoltage peaks, three filters are proposed and evaluated: an RC filter, an RLC filter, and a compensator. The proposed PWM strategies are assessed using equivalent experimental results obtained on an induction motor fed by a two-level VSI. The experimental tests demonstrate also the efficiency of the SVPWM compared to other strategies. Full article

To further enhance economic efficiency and optimize energy conservation and emission reduction performance, an optimized energy management strategy (EMS) tailored for the hybrid power system of rubber-tyred gantry cranes is proposed. Wavelet packet decomposition (WPD) was employed as the signal processing approach, and this method was further integrated with EMS for hybrid power systems. Through a three-layer progressive architecture comprising WPD frequency–domain decoupling, fuzzy logic real-time adjustment, and PSO offline global optimization, a cooperative optimization mechanism has been established in this study between the frequency-domain characteristics of signals, the physical properties of energy storage components, and the real-time and long-term states of the system. Firstly, the modeling and simulation of the power system were conducted. Subsequently, an EMS based on WPD and limit protection was developed: the load power curve was decomposed into different frequency bands, and power allocation was implemented via the WPD algorithm. Meanwhile, the operating states of lithium batteries and supercapacitors were adjusted in combination with state of charge limits. Simulation results show that this strategy can achieved reasonable allocation of load power, effectively suppressed power fluctuations of the auxiliary power unit system, and enhanced the stability and economy of the hybrid power system. Afterward, a fuzzy controller was designed to re-allocate the power of the hybrid energy storage system (HESS), with energy efficiency and battery durability set as optimization indicators. Furthermore, particle swarm optimization algorithms were adopted to optimize the EMS. The simulation results indicate that the optimized EMS enabled more reasonable power allocation of the HESS, accompanied by better economic performance and control effects. The proposed EMS demonstrated unique system-level advantages in enhancing energy efficiency, extending battery lifespan, and reducing the whole-life cycle cost. Full article

Lower limb exoskeleton systems require accurate recognition of the wearer’s movement intentions prior to action execution in order to achieve natural and smooth human–machine interaction. Surface electromyography (sEMG) signals can reflect neural activation of muscles before movement onset, making them a key physiological source for movement intention recognition. To improve sEMG-based recognition performance, this study proposes an innovative deep learning framework, IFIANet. First, a CNN–TCN-based spatiotemporal feature learning network is constructed, which efficiently models and represents multi-scale temporal–frequency features while effectively reducing model parameter complexity. Second, an IFIA (Frequency-Informed Integration Attention) module is designed to incorporate global frequency information, compensating for frequency components potentially lost during time–frequency transformations, thereby enhancing the discriminability and robustness of temporal–frequency features. Extensive ablation and comparative experiments on the publicly available MyPredict1 dataset demonstrate that the proposed framework maintains stable performance across different prediction times and achieves over 82% average recognition accuracy in within-experiments involving nine participants. The results indicate that IFIANet effectively fuses local temporal–frequency features with global frequency priors, providing an efficient and reliable approach for sEMG-based movement intention recognition and intelligent control of exoskeleton systems. Full article

Net-cage aquaculture faces challenges from biofouling, which reduces water exchange and threatens structural integrity. Automated cleaning robots provide an alternative to human divers but require effective, low-cost localization. Conventional acoustic–inertial systems are expensive and complex, while vision-only or IMU-based methods suffer from drift in turbid, low-texture waters. This paper presents a lightweight Visual–Inertial–Depth (VID) fusion framework for underwater net-cage cleaning robots. Built on the VINS-Fusion system the method estimates scene scale using optical flow and stereo matching, and incorporates IMU pre-integration for high-frequency motion prediction. A pressure-based depth factor constrains Z-axis drift, and reflective-anchor initialization ensures global alignment. The system runs in real time on a Jetson Orin NX with ROS. Experiments in air, tank, pool, and ocean settings demonstrate its robustness. In controlled environments, the mean anchor coordinate error (ACE) was 0.05–0.16 m, and loop-closure drift (LCD) was ≤0.5 m per 5 m. In ocean trials, turbulence and biofouling led to drift (LCD 1.32 m over 16.05 m, 8.3%), but IMU and depth cues helped maintain vertical stability. The system delivers real-time, cost-effective localization in structured underwater cages and offers insights for improvements in dynamic marine conditions. Full article

The Changjiang River Estuary and the adjacent East China Sea is one of the world’s largest coastal carbon sinks, with a steadily increasing sink capacity over recent decades. However, the potential changes in its carbon sink and control mechanisms at decadal scales under climate change remain unclear. This study, based on 27 years (1998–2024) of continuous satellite remote sensing data, investigates the spatiotemporal distribution and long-term evolution of this coastal carbon sink. The results reveal a typical carbon sink with a capacity of −5.23 ± 3.73 mmol m⁻² d⁻¹ and significant seasonal variation. High-frequency remote sensing data reduces uncertainty compared to traditional shipborne observations. Over the past 27 years, the air–sea CO₂ flux increased at a rate of 0.24 mmol m⁻² d⁻¹ yr⁻¹, with a five-fold enhancement in carbon sink capacity. However, after atmospheric pCO₂ exceeded 400 μatm in 2014, the rate of increase slowed, indicating stabilization. Control mechanism analysis shows that biogeochemical processes have been persistently active, while over the past decade the influence of Changjiang discharge on seawater pCO₂ increased by 50%, shifting the system from primarily physical dilution to enhanced biogeochemical regulation. The findings provide insights into the evolution and management of coastal carbon cycles under climate change. Full article

Background and Objectives: Japan’s aging population faces growing challenges related to oral frailty, a condition characterized by the decline of oral function associated with physical and nutritional deterioration. Impaired oral function contributes to reduced chewing, swallowing, and saliva secretion, leading to poor appetite and frailty progression. Ninjin’yoeito (NYT), a traditional Kampo formula, has been clinically used to improve systemic weakness and oral symptoms. This study aimed to evaluate the efficacy and safety of NYT in improving oral health among elderly individuals with impaired oral function. Materials and Methods: In this open-label prospective study, patients received NYT daily for 12 weeks. Assessments included oral symptom scores, mucosal moisture, repetitive saliva swallowing tests (RSST), gustatory function by visual analogue scale (VAS), an 11-item oral questionnaire, and immune profiling by flow cytometry. Safety was assessed through hematological and biochemical tests. Results: Symptom scores decreased from 8.27 at baseline to 3.64 at 12 weeks (p = 0.006), while oral condition scores improved from 5.09 to 1.36 (p = 0.006). Mucosal moisture increased (25.1 to 28.1, p = 0.03), and RSST frequency improved (2.18 to 4.55, p = 0.046). Questionnaire scores declined from 5.1 to 2.0 (p < 0.001). VAS-taste was unchanged overall (p = 0.21) but improved in low baseline patients. Laboratory findings showed no adverse changes, with favorable lipid trends. Immune analysis revealed a decline in NKG2D expression (p = 0.02), whereas other activating and inhibitory markers remained stable. Conclusions: NYT was well tolerated and associated with gradual improvements in oral and physical symptoms among elderly individuals with impaired oral function. These findings provide preliminary evidence supporting the feasibility of Kampo-based approaches for maintaining oral health in aging populations and warrant further validation in larger controlled trials. Full article

We propose a Hybrid Parallel Temporal–Spatial CNN-LSTM (HPTS-CL) architecture for optimized indoor environment modeling in sports halls, addressing the computational and scalability challenges of high-resolution spatiotemporal data processing. The sports hall is partitioned into distinct zones, each processed by dedicated CNN branches to extract localized spatial features, while hierarchical LSTMs capture both short-term zone-specific dynamics and long-term inter-zone dependencies. The system integrates model and data parallelism to distribute workloads across specialized hardware, dynamically balanced to minimize computational bottlenecks. A gated fusion mechanism combines spatial and temporal features adaptively, enabling robust predictions of environmental parameters such as temperature and humidity. The proposed method replaces monolithic CNN-LSTM pipelines with a distributed framework, significantly improving efficiency without sacrificing accuracy. Furthermore, the architecture interfaces seamlessly with existing sensor networks and control systems, prioritizing critical zones through a latency-aware scheduler. Implemented on NVIDIA Jetson AGX Orin edge devices and Google Cloud TPU v4 pods, HPTS-CL demonstrates superior performance in real-time scenarios, leveraging lightweight EfficientNetV2-S for CNNs and IndRNN cells for LSTMs to mitigate gradient vanishing. Experimental results validate the system’s ability to handle large-scale, high-frequency sensor data while maintaining low inference latency, making it a practical solution for intelligent indoor environment optimization. The novelty lies in the hybrid parallelism strategy and hierarchical temporal modeling, which collectively advance the state of the art in distributed spatiotemporal deep learning. Full article

Impedance Spectroscopy (IS) is widely used to analyze the dynamic behavior and degradation of electrochemical systems such as batteries. IS has also been successfully applied to study the performance and degradation mechanisms of photovoltaic (PV) devices. Traditionally, IS is performed with Frequency Response Analyzers (FRA), which apply small-signal perturbations and measure the impedance response of the system. However, those instruments are costly and not suitable for in situ diagnostics. This work proposes a methodology to perform IS measurements on PV systems using a power converter, thereby eliminating the need for external specialized equipment. The proposed approach includes a theoretical analysis of the converter dynamics to derive an expression for the duty cycle amplitude, which is required to maintain a constant perturbation magnitude across a range of frequencies. The methodology is experimentally validated using a synchronous Boost converter connected to a PV panel and controlled by a Texas Instruments F28379D digital signal processor (DSP), which injects the perturbation signal in the converter’s duty cycle. Moreover, the voltage and current measurements are performed with an oscilloscope. The results demonstrate that the proposed converter-based IS method accurately reproduces the impedance spectra obtained with a commercial FRA, confirming its feasibility as a low-cost, flexible, and scalable solution for PV impedance characterization and diagnostics. Full article

The rapid proliferation of Electric Vehicle (EV) infrastructures has led to the massive generation of high-frequency streaming data uploaded to cloud platforms for real-time analysis, while such data supports intelligent energy management and behavioral analytics, it also encapsulates sensitive user information, the disclosure or misuse of which can lead to significant privacy and security threats. This work addresses these challenges by developing a secure and scalable scheme for protecting and verifying streaming data during storage and collaborative analysis. The proposed scheme ensures end-to-end confidentiality, forward security, and integrity verification while supporting efficient encrypted aggregation and fine-grained, time-based authorization. It introduces a lightweight mechanism that hierarchically organizes cryptographic keys and ciphertexts over time, enabling privacy-preserving queries without decrypting individual data points. Building on this foundation, an electric vehicle key management and query system is further designed to integrate the proposed encryption and verification scheme into practical V2X environments. The system supports privacy-preserving data sharing, verifiable statistical analytics, and flexible access control across heterogeneous cloud and edge infrastructures. Analytical and experimental evidence show that the designed system attains rigorous security guarantees alongside excellent efficiency and scalability, rendering it ideal for large-scale electric vehicle data protection and analysis tasks. Full article

Microwave, millimeter-wave, and terahertz devices are fundamental to modern 5G/6G communications, automotive imaging radar, and sensing systems. As essential RF front-end elements, passive microwave array components on static platforms remain constrained by fixed geometry and single-frequency excitation, leading to limited spatial resolution and weak interference suppression. Phase-steered arrays offer angular control but lack range-dependent response, preventing true two-dimensional focusing. Frequency-Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) architectures introduce element-wise frequency offsets to enrich spatial–spectral degrees of freedom, yet conventional linear or predetermined nonlinear offsets cause range–angle coupling, periodic lobes, and restricted beamforming flexibility. Existing optimization strategies also tend to target single objectives and insufficiently address target- or scene-induced perturbations. This work proposes a nonlinear frequency-offset design for passive microwave arrays using a Dingo–Gray Wolf hybrid intelligent optimizer. A multi-metric fitness function simultaneously enforces sidelobe suppression, null shaping, and frequency-offset smoothness. Simulations in static scenarios show that the method achieves high-resolution two-dimensional focusing, enhanced interference suppression, and stable performance under realistic spatial–spectral mismatches. The results demonstrate an effective approach for improving the controllability and robustness of passive microwave array components on static platforms. Full article

A monolayer metasurface-based Linear Polarizer and Quarter-Wave Plate (LP&QWP) is proposed for compact and precise polarization control in chip-scale atomic clocks (CSACs). Finite-difference time-domain simulations reveal that the designed metasurface efficiently converts linearly polarized light into right-handed circularly polarized light. Experimental characterization of devices fabricated on optical glass substrates confirms the polarization manipulation performance, achieving a polarization extinction ratio (PER) of 4.8 dB and a degree of polarization (DOP) of 74.2%, confirming its ability to effectively control the state of polarization. The short-term frequency stability of the developed CSAC prototype reaches 9.29 × 10⁻¹¹ at 1 s and 1.59 × 10⁻¹¹ at 10,000 s, demonstrating its potential for integration into miniature timing systems. The novelty of this work lies in the specific application to CSACs and the co-optimization with attenuation, as the metasurface simultaneously realizes polarization control and optical power balancing within a single functional layer. This study bridges metasurface photonics and atomic frequency standards, providing a functional route toward polarization control and frequency stability in miniaturized chip-scale atomic clocks. Full article

RFID (Radio Frequency Identification) technology has become an essential instrument in numerous industrial sectors, enhancing process efficiency and streamlining operations, allowing for the automated tracking of goods and equipment without the need for manual intervention. Nevertheless, the deployment of industrial IoT systems necessitates the establishment of complex sensor networks to enable detailed multi-parameter monitoring of items. Despite these advancements, challenges remain in item-level sensing, data analysis, and the management of power consumption. To mitigate these shortcomings, this study presents a holistic AI-assisted, semi-passive RFID-integrated multi-sensor system designed for robust food quality monitoring. The primary contributions are threefold: First, a compact (45 mm ∗ 38 mm) semi-passive UHF RFID tag is developed, featuring a rechargeable lithium battery to ensure long-term operation and extend the readable range up to 10 m. Second, a dedicated IoT cloud platform is implemented to handle big data storage and visualization, ensuring reliable data management. Third, the system integrates machine learning algorithms (LSTM) to analyze sensing data for real-time food quality assessment. The system’s efficacy is validated through real-world experiments on food products, demonstrating its capability for low-cost, long-distance, and intelligent quality control. This technology enables low-cost, timely, and sustainable quality assessments over medium and long distances, with battery life extending up to 27 days under specific conditions. By deploying this technology, quantified food quality assessment and control can be achieved. Full article

E-scooters have rapidly become a popular option for first- and last-mile mobility, yet their integration into urban transportation systems has raised significant safety concerns. This study investigates the feasibility of permitting E-scooter riding on sidewalks under controlled conditions to minimize pedestrian conflicts. Analysis of E-scooter crashes in Daejeon, South Korea, showed that 98.09% of crashes were caused by rider negligence, with “Failure to Fulfill Safe Driving Duty” as the leading factor. To investigate the applicability of safe sidewalk usage, a VR-based simulator experiment was conducted with 41 participants across four scenarios with varying sidewalk widths and pedestrian densities, under speed limits of 10, 15, and 20 km/h. Riding behaviors—including speed stability, braking, steering, and conflict frequency—and gaze behaviors were measured. Results showed that riding at 10 km/h improved riding stability and minimized conflicts. Regression analysis identified pedestrian density as the strongest predictor of conflicts, followed by sidewalk width and riding speed. These findings suggest specific policy needs: ensuring a minimum sidewalk width of 4 m for safe shared use, restricting operation to environments with low-to-moderate pedestrian density, and implementing a 10 km/h speed limit. This study provides evidence-based recommendations for safer integration of E-scooters into pedestrian environments. Full article

Against the backdrop of the carbon peaking and neutrality (“dual-carbon”) goals and evolving new-type power system dispatch, the share of pumped-storage hydropower (PSH) in power systems continues to increase, imposing stricter requirements on units for higher cycling frequency, greater operational flexibility, and rapid, stable startup and shutdown. Focusing on the entire hot-standby-to-generation transition of a PSH plant, a full-flow-path three-dimensional transient numerical model encompassing kilometer-scale headrace/tailrace systems, meter-scale runner and casing passages, and millimeter-scale inter-component clearances is developed. Three-dimensional unsteady computational fluid dynamics are determined, while the surge tank free surface and gaseous phase are captured using a volume-of-fluid (VOF) two-phase formula. Grid independence is demonstrated, and time-resolved validation is performed against the experimental model–test operating data. Internal instability structures are diagnosed via pressure fluctuation spectral analysis and characteristic mode identification, complemented by entropy production analysis to quantify dissipative losses. The results indicate that hydraulic instabilities concentrate in the acceleration phase at small guide vane openings, where misalignment between inflow incidence and blade setting induces separation and vortical structures. Concurrently, an intensified adverse pressure gradient in the draft tube generates an axial recirculation core and a vortex rope, driving upstream propagation of low-frequency pressure pulsations. These findings deepen our mechanistic understanding of hydraulic transients during the hot-standby-to-generation transition of PSH units and provide a theoretical basis for improving transitional stability and optimizing control strategies. Full article

Flywheel energy storage systems play an important role in frequency regulation and power quality control within modern power grids, yet the fault signals generated by defects in their rolling bearings are typically indistinct, making direct diagnosis difficult. Raw noisy signals often yield unsatisfactory diagnostic performance when directly processed by neural networks. Although MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) can effectively extract impulsive fault components, its performance is highly dependent on the selected fault period and filter length. To address these issues, this paper proposes an improved fault diagnosis method that integrates MOMEDA-based periodic extraction with a neural network classifier. The Artificial Fish Swarm Algorithm (AFSA) is employed to adaptively determine the key parameters of MOMEDA using multi-point kurtosis as the optimization objective, and the optimized parameters are used to enhance impulsive fault features. The filtered signals are then converted into image representations and fed into a ResNet-18 network (a compact 18-layer deep convolutional neural network from the residual network family) to achieve intelligent identification and classification of bearing faults. Experimental results demonstrate that the proposed method can effectively extract and diagnose bearing fault signals. Full article