Search Results (681)

Permanent magnet synchronous motors (PMSMs) operating at low speeds are susceptible to parameter mismatches, periodic harmonics, and various internal and external disturbances, which result in steady-state current errors and low-frequency speed oscillations. To address these issues and improve low-speed PMSM performance, an automatic tuning disturbance rejection incremental deadbeat predictive current control (AT-DR-IDPCC) method is proposed. First, an incremental extended-state observer (IESO) is incorporated into the incremental deadbeat predictive current control (IDPCC) framework to estimate and compensate for lumped disturbances caused by resistance and inductance mismatches, thereby improving parameter robustness. Meanwhile, a quasi-resonant controller (QRC) is connected in parallel with the current loop to selectively suppress sixth-order current harmonics induced by inverter nonlinearities and flux harmonics. Furthermore, a deep deterministic policy gradient (DDPG)-based parameter optimization scheme is introduced to automatically tune the controller parameters, overcoming the limitations of conventional trial-and-error tuning and achieving the coordinated optimization of dynamic response, steady-state accuracy, and disturbance rejection capability. Simulation and experimental results demonstrate that, compared with proportional–integral (PI) control and IDPCC incorporating the IESO (IESO-IDPCC), AT-DR-IDPCC reduces the phase current’s total harmonic distortion (THD) by 56.1% and 23.7% while also decreasing the speed fluctuation amplitude by approximately 50% and 20%, respectively. The proposed method significantly enhances the robustness, harmonic suppression capability, and low-speed control performance of PMSM drives. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

High-bandwidth memory (HBM) faces critical power integrity challenges in high-stack configurations due to elongated power delivery paths and increased parasitic inductance. This paper proposes a dual-interposer HBM architecture with an interposer–HBM stack–interposer configuration, integrating an additional top interposer embedded with chip capacitors. This topology redesigns the HBM’s power distribution network, reducing PDN impedance, and this technology enables bidirectional vertical power supply to DRAM chips during moments when they require current. The PDN impedance is systematically compared with a conventional trench-capacitance-enhanced structure (Structure A) and a deep-trench-capacitance-enhanced structure (Structure B). Results show that at 0.1–11.2 GHz, the proposed structure reduces peak PDN impedance by 66.41% and 65.7% versus Structures A and B, respectively, and decreases the loop inductance of the top-layer DRAM chip by 66.71%. The top interposer’s redistribution layer forms a parallel-plate capacitor complementing the embedded chip capacitors, achieving wideband impedance suppression. Without modifying existing protocols, this architecture provides a system-level PDN optimization strategy for high-stack HBM, offering quantitative insights for capacitor selection and layout design. Full article

Immersive visualization can support interpretation of architectural heritage in historical paintings, yet translating 2D pictorial evidence into navigable 3D scenes remains challenging. Conventional workflows rely on physical survey data, while direct generative AI (GenAI) may produce structural hallucinations and lack historical constraints. This study proposes a human-in-the-loop GenAI-assisted framework for producing immersive 3D visualization prototypes rather than historically verified reconstructions. It integrates multi-view image generation, knowledge-informed review, single-image-to-3D generation, topology inspection, and perceptual calibration. Four fragments from the Northern Song Dynasty painting Along the River During the Qingming Festival were examined as a single-case proof of concept. Across three tested model pairs, raw AI assets were generated in approximately 3–4 min and were suitable for distant-background use; close-up visualization required 1–2 h of refinement, while basic structural editability required 4–5 h of post-processing, reducing the initial time advantage. A mixed-methods study with nine domain experts and 30 non-expert participants used the UES-SF, an adapted VisAWI, and semi-structured interviews analyzed through inductive thematic analysis. All eight subscale scores exceeded their neutral midpoints after Bonferroni correction (all adjusted $p<0.001$), indicating favorable perceptions of the guided experience. Interviews suggested potential for spatial exploration, museum interpretation, and education. However, geometric discontinuities, detail loss, color deviation, and historical-semantic errors remained, requiring expert review and manual correction. Transferability beyond this artwork and architectural tradition remains untested. Full article

Just-in-time adaptive interventions require timely and low-burden state estimation, while olfaction offers a programmable output channel with limited attentional demand. We describe a prototype architecture that links electroencephalography (EEG)-based emotion estimation to a six-channel odorant device and evaluate only the EEG sensing and decoding module. Forty EEG sessions from 39 adults were recorded with a 14-channel Emotiv EPOC X headset (128 Hz) during six standardized emotion-induction conditions. No odor was administered. Band-power, frontal alpha asymmetry (FAA) and global field power (GFP) were analyzed with rank-based repeated-measures tests and explicit multiple-comparison correction. Emotion decoding used subject-aware cross-validation. Frontal beta power, the beta/alpha ratio and GFP differed across conditions after false-discovery-rate correction, although effect sizes were small (Kendall’s W = 0.089–0.155). On-line affective metrics showed larger effects (W = 0.130–0.365). Six-class accuracy was 45.1% ± 13.2% within participants (n = 29; chance 16.7%; p < 10⁻⁸) and 23.1% across participants after per-subject normalization (macro-F1 = 0.23; permutation p = 0.005). FAA did not differ. Consumer-headset EEG contained person-specific information about laboratory-induced states, but performance was not sufficient to establish a clinically usable regulator. The results validate neither a complete closed loop nor olfactory efficacy; end-to-end latency, artifact and temporal robustness, chemical characterization and controlled odor-regulation effects require prospective evaluation. Full article

Closed-loop regulation of the bispectral index (BIS) during propofol–remifentanil co-administration is challenging because of nonlinear drug interactions, patient variability, model uncertainty, external disturbances, and infusion constraints. This paper proposes a residual-based fractional-order model predictive control (RB-FOMPC) method within the Extended Prediction Self-Adaptive Control (EPSAC) framework. FOMPC is obtained by introducing fractional-order weights into the EPSAC cost function, while an inverse Hill mapping handles the nonlinear BIS–drug relationship outside the online quadratic programming problem. RB-FOMPC further augments the FOMPC prediction with a bounded prediction of future residual variation generated by a delayed low-order residual model. During a predefined induction window, the one-sided correction is applied according to a filtered BIS-derived effect-site residual indicating nominal-model underestimation. A normalized infusion-ratio parameter coordinates the propofol and remifentanil inputs. Monte Carlo analysis showed that RB-FOMPC preserved the nominal FOMPC performance under inter-patient variability and substantially reduced excessive BIS undershoot under intra-patient model perturbations. Overall, the simulation results indicate that the residual-based predictive correction can selectively reduce induction-phase BIS undershoot under the evaluated nominal-model-underestimation conditions, while preserving the nominal performance of FOMPC. Full article

This study identifies two stage-specific configurations of CLIL teacher well-being in Andalusia: an ecology of proximity in Primary Education and a disciplinary-departmental ecology in Secondary Education. Drawing on 14 semi-structured interviews with seven Primary and seven Secondary teachers, the study used a deductive–inductive ecological analysis combining within-case, cross-case, cross-stage, and subsystem-coupling procedures in QDA Miner. Participants described professional well-being not merely as a subjective psychological state but in terms of professional viability: the perceived ecological enactability of pedagogically legitimate and sustainable practice. Primary teachers’ accounts foregrounded sustained pupil contact, tutorial responsibility, family expectations, classroom routines, and adaptation for younger learners. Secondary teachers’ accounts foregrounded subject specialisation, departmental organisation, heterogeneous language proficiency, assessment ambiguity, and the Primary–Secondary transition. Across both stages, participants described compensatory overfunctioning—additional care, coordination, material redesign, translation, and assessment work undertaken to keep CLIL operational when implementation conditions were perceived as insufficient. Protected coordination time, stage-specific training, shared assessment frameworks, appropriate materials, and transition structures emerged as potential regulatory leverage points. The study advances a phase-sensitive systems interpretation of teacher well-being by identifying a fast balancing loop of frontline adaptation and a slower reinforcing stabilisation trap through which restored continuity may conceal persistent underprovision. Full article

Rare earths are a strategically vital mineral resource on a global scale; the security of their supply chain and their recycling face severe challenges amid dual pressures of resource scarcity and environmental protection. This study focuses on the reverse supply chain for rare earth permanent magnet materials—specifically those based on praseodymium and neodymium—constructing a two-level game model comprising a rare earth oligopoly (Stackelberg leader) and two recyclers (Cournot followers). It systematically analyzes the dual decision-making behavior of recyclers between “recycling and selling” and “in-house remanufacturing”, and examines the impact of two policy instruments—government subsidies and environmental taxes—on the supply chain equilibrium. The study employs reverse induction to solve the game equilibrium and combines this with numerical simulation methods to compare the differing effects of the two policies on key indicators such as product output, market share, profit distribution, waste recovery volume, and recycling rates. The results indicate that there is strategic coordination and resource competition in recyclers’ decisions regarding recycling and remanufacturing, causing them to assume dual roles as both “suppliers” and “competitors” within the supply chain. Subsidy policies significantly incentivize recycling and remanufacturing activities, thereby increasing the recycling rate, but exert a slight squeeze on the profits of rare earth conglomerates. Environmental tax policies effectively curb primary mining and promote resource circulation, but may have a negative impact on the remanufacturing industry. Currently, value within the closed-loop rare earth supply chain is highly concentrated among upstream oligopolistic enterprises, whilst the recycling segment suffers from insufficient economic incentives and significant policy dependency. This study provides theoretical support and decision-making references for the government to optimize policy combinations and promote the high-quality development of the rare earth recycling industry. Full article

This paper presents the mathematical modeling, numerical implementation, and experimental validation of a decoupling control algorithm for a five-port multiport modular active bridge (MMAB) converter in DC microgrid applications. Based on an analytically derived generalized state-space framework of the MMAB topology, a matrix-based method for suppressing non-linear mutual cross-couplings among individual ports is proposed. The study addresses parametric uncertainties within the system matrix caused by parasitic bus inductances; by formulating a linear system of equations solved via the numerical least-squares method, the equivalent parameter identification error was reduced from over 18% to a valid threshold. The decoupling performance and dynamic responsiveness of the closed-loop system were experimentally verified on a dual-core TMS320F28379D digital signal processor. The experimental results demonstrate that the proposed algorithm effectively isolates transient step-load perturbations, maintaining voltage stability on adjacent undisturbed ports within a strict deviation of less than $+0.51\%$ and achieving a recovery time below 5 ms. Furthermore, the real-time execution of the online Jacobian matrix inversion via the Newton–Raphson method confirms the computational feasibility and convergence of the iterative approach under tight sampling periods. The obtained results provide a robust, experimentally validated foundation for advanced algebraic and numerical control strategies in high-stability multiport power conversion systems. Full article

The model predictive current control (MPCC) of an interior permanent magnet synchronous machine (IPMSM) requires an accurate motor parameter model to predict future currents and achieve high control performance. However, the inductance parameters of an IPMSM are easily affected by factors such as magnetic field saturation, leading to large current prediction errors, high current ripple, and poor stability. Therefore, an MPCC strategy for an IPMSM based on parameter adaptive feedback correction is proposed. First, based on the mathematical model of the IPMSM in the synchronous rotary coordinate, the cross-coupling relationship between the dq-axis inductance deviations and the current prediction error is derived to form an explicit prediction error model. Then, the influence of the d-axis and q-axis inductance parameter deviations of the IPMSM on the current prediction error is discussed in detail. Next, based on the established mathematical model of the prediction error, the recursive least squares scheme is adopted to identify the d-axis and q-axis deviations of the inductance parameters online. Finally, unlike conventional open-loop RLS correction, a PI-based closed-loop correction loop is designed that feeds the prediction error back to adjust the inductance deviations, thereby forcing the prediction error toward zero while inherently compensating for inverter dead-time effects. Simulations and experiments were conducted, and the results show that the proposed scheme greatly improves the accuracy of current prediction and inductance parameter estimation, and enhances robustness against parameter mismatch and dead-time disturbances. The key novelty lies in the PI-feedback-driven RLS closed-loop structure that simultaneously achieves error elimination and dead-time compensation. Full article

Manufacturing tolerances and winding-layout variations can introduce phase-to-phase mismatches in stator resistance and leakage inductance. Under such unbalanced phase impedances, conventional field-oriented control (FOC), typically designed under balanced-parameter assumptions, may produce unequal phase currents, distorted airgap MMF, reduced efficiency, increased torque ripple, and undesired vibro-acoustic behavior. This paper investigates an open-end winding (OEW) induction machine (IM) drive, in which each phase is independently driven by an H-bridge inverter fed by the same DC source. To mitigate phase–current imbalance without parameter estimation, an RMS-based phase–current-balancing controller is proposed. The controller continuously calculates the RMS value of each phase current and adaptively scales the corresponding reference-phase voltage in a low-bandwidth outer loop, while preserving the classical FOC structure. The balancing law is derived directly from the phase-impedance imbalance model; convergence of the three coupled per-phase loops is proven via a Lyapunov argument, and stability of the cascaded structure is established through an analytical bandwidth-separation analysis shown to be robust to ±30% machine-parameter variation and across the 500–1500 rev/min speed range. Simulation and experimental results across multiple operating points demonstrate effective phase–current equalization. Full article

Accurate short-term travel time prediction is foundational for sustainable urban mobility and intelligent transportation systems on urban arterial corridors, where travel conditions are shaped by interacting traffic, weather, and public transport factors. This study proposes a multi-source machine learning framework for segment-direction-level prediction in the Denizli city center. Floating car data (FCD), Traffic Control Center (TCC) inductive loop detector measurements, historical weather, and public transport indicators were integrated into a 15 min time-segment structure. The final dataset includes 60 segment-direction targets. Performance was evaluated using Linear Regression, Random Forest, LightGBM, and LSTM under a chronological train-validation-test design. Tree-based ensemble models produced the most stable overall performance, with LightGBM and Random Forest yielding similarly low pooled test errors. Segment-level analyses revealed clear spatial and temporal heterogeneity, showing no single model is universally superior across all links. By providing reliable traffic-state information, the framework enables efficient traffic management and may indirectly reduce delay, fuel use, and emissions; these environmental effects were not quantified. SHAP-based interpretation showed that temporal and traffic-state variables dominate predictions, while weather and public transport provide complementary value. Full article

This paper proposes a Generalized Predictive Control (GPC) approach for the inner current-control loop of a multiphase induction motor drive. The method is based on a CARIMA model, which inherently provides integral action, allowing steady-state error rejection without the need for state or disturbance observers. In contrast to state-space Model Predictive Control (MPC), the proposed formulation enables the straightforward inclusion of system delays and yields an analytical solution when constraints are not considered, reducing computational complexity. Additionally, it is shown that the GPC is able to cope with modelling errors due to different dynamics at different operating points. The effectiveness of the approach is evaluated on a five-phase induction machine, showing competitive performance and improved robustness against modelling uncertainties. Full article

The doubly fed induction generator (DFIG) dominates the wind energy market, yet its direct stator-to-grid connection makes it vulnerable to grid faults, creating a tension between hardware self-protection and grid-code fault ride-through (FRT) compliance. This paper presents the modelling and FRT analysis of a 9 MW DFIG wind farm combining a 20 MVA Static Synchronous Compensator (STATCOM) with a ten-tier algorithmic protection scheme. A detailed phasor-domain MATLAB/Simulink R2024b model is complemented by physics-based reduced-order models integrated in Python, separating calibration targets, calibration-dependent derived quantities and quantities independent of the DC-link calibration. The aerodynamic model reproduces the power coefficient maximum of 0.48 at a tip–speed ratio of 8.1. The energy-balance model uses two parameters identified per scenario from the detailed DC-link trajectory; its peak-voltage agreement within 0.4% is therefore a calibrated consistency check, while the derived arming times, slopes, chopper sizing and latency budget remain conditional on that calibration. A first-order Thevenin analysis shows that the STATCOM supports a weak 25 kV point of common coupling of order 53 MVA short-circuit level, not the 2500 MVA source. The approximate 0.50-to-0.78 p.u. recovery requires about 29.7 M_var and 1.90 p.u. of STATCOM rated current for 150 ms, conditional on an assumed short-time envelope and adequate converter-voltage headroom; it is not attributable to continuous rated operation. FRT support for the selected recoverable dip is separated from converter survival during a zero-impedance fault, for which a 1700 V chopper pickup with a 1 ms gate delay, not the 10 ms isolation command, is the clamping mechanism. The assessment is explicitly conditional and requires electromagnetic-transient and hardware-in-the-loop confirmation. Full article