Search Results (1,103)

A systematic evaluation of salt tolerance at the seedling stage was conducted using 143 maize inbred lines under a 150 mM mixed-salt solution (NaCl:Na₂SO₄ = 9:1, EC = 16.78 dS/m) that mirrors the ionic composition of saline groundwater in the Yellow River Delta. The comprehensive salt tolerance index (D value) ranged from 0.15 to 0.85 across the population, with the elite line B114 exhibiting the highest D value (0.835) and the sensitive line PHT55 ranking near the bottom. Under salt stress, B114 displayed remarkable growth stability, with plant height decreasing by only 25.9%, fresh weight by 13.3%, and dry weight remaining unchanged, whereas PHT55 suffered severe growth inhibition (plant height: 61.5% decrease; fresh weight: 63.2% decrease; dry weight: 33.3% decrease). Time-series RNA-seq of root tissues across four time points (5, 8, 11, and 14 days) revealed markedly distinct transcriptional dynamics: B114 exhibited relatively stable temporal regulation (2261–9124 DEGs), whereas PHT55 showed a pronounced early transcriptional burst that progressively intensified (3728–10,108 DEGs). Using random forest-based machine learning, 50 core salt tolerance-related genes were unbiasedly identified from 16,194 significantly differentially expressed genes. Functional enrichment analysis revealed that these genes were primarily involved in redox regulation, ion homeostasis maintenance, and stress signal transduction pathways. qRT-PCR validation confirmed biphasic expression patterns, with Zm00001d024160 showing the strongest early induction (48-fold at 5 h). This study established a maize salt tolerance evaluation system closely aligned with field conditions and demonstrated that coordinated temporal transcriptional regulation represents a core molecular mechanism underlying high salt tolerance in maize. The elite salt-tolerant germplasm and key candidate genes identified here provide valuable genetic resources and a theoretical foundation for molecular breeding of salt-tolerant maize adapted to saline-alkaline soils. 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

Learning from high-dimensional, low-sample-size (HDLSS) data remains a persistent challenge in machine learning, as models must infer reliable patterns from limited observations while handling an excessive number of variables—a scenario particularly prevalent in biomedical applications. Such data structures render predictive modeling highly vulnerable to erratic optimization and overfitting. To address this challenge, we propose the Global Interaction Network with Dynamic Generalization Control (GINet-DGC), an artificial intelligence (AI) framework that integrates feature-wise structural priors with dynamic generalization monitoring. Rather than directly learning an unconstrained first-layer weight matrix, GINet-DGC generates task-specific weights from multi-view feature descriptors, encompassing latent semantic, global distributional, local topological, and hierarchical representations. This structure-constrained weight generation strategy effectively narrows the feature-interaction search space and acts as an inductive regularizer against noise and redundant molecular features. Furthermore, we introduce an Overfitting-aware Index (OFI) to monitor the training trajectory and effectively identify the generalization saturation point for adaptive termination. Empirical evaluations on eight public real-world biomedical HDLSS gene-expression datasets, using a repeated stratified 5 × 5 cross-validation protocol, demonstrate that GINet-DGC achieves competitive and stable performance against 17 baselines. These findings support the effectiveness of the proposed framework within the evaluated public biomedical HDLSS benchmark setting. Full article

In this study, a double-sheet detection system for automated metal stamping is developed and modeled. This study aims to address the issue of die damage caused by overlapping workpieces (double sheeting), a significant contributor to production line stoppages. The proposed solution involves a control system utilizing a programmable logic controller (PLC), combined with inductive proximity sensors installed on the die, for real-time processing of workpiece status. The system is designed to immediately halt machine operation upon detecting any abnormalities. The experimental results demonstrate that the developed system successfully reduced double-sheet incidents from one occurrence to zero and eliminated production downtime (reducing it from 4 days to zero), resulting in a total of 32,972.26 USD saved in potential damage costs per incident. Furthermore, the system reduced the production cycle time from 11 s to 9.5 s per piece, increasing the production capacity by 1240 pieces per day. The economic assessment indicates that an initial equipment investment of only 483.2 USD yielded a return on investment (ROI) of 6723.73%, a payback period of 5.35 days, a net present value (NPV) of 89,333 USD, and an internal rate of return (IRR) of approximately 6830%. These figures demonstrate a very high level of economic feasibility. Therefore, this system is a cost-effective, reliable, and practical approach to enhance productivity and sustainably support the Smart Factory concept in the metal stamping industry, aligning with the automotive parts manufacturing industry. Full article

The convergence of industrial communication networks and electric drive systems has increased the range of risks associated with induction motor operation, with abnormal motor operation potentially arising from either physical motor faults or malicious cyber operations. This study examines the impact of coordinated network attacks on a three-phase induction motor drive system in a real-time laboratory environment through a PLC–SCADA controlled environment. The following operating conditions were investigated experimentally: normal operation, stator disturbance, rotor abnormalities, false data injection attack, replay-based communication manipulation, and cyber-physical events. In the attack scenarios, significant differences were observed in motor speed, electromagnetic torque, stator current distortion, and communication latency compared with normal operating conditions. To differentiate between actual machine failures and cyber-induced anomalies, an explainable AI-based diagnostic framework was introduced that employs both electrical and network-layer features. Experimental results revealed that the proposed model achieved an overall classification accuracy of 93.17%, with precision and recall > 97% across most operating classes. When subjected to a coordinated attack, communication latency rose from 4.8 ms under normal operation to 37.6 ms, and the current THD increased from 3.2% to 14.7%. The proposed framework also successfully distinguished cyber-attack-induced abnormal behavior from genuine motor faults at a 96.9% detection rate, which is lower than that of conventional AI classifiers. Explainability analysis also showed that the top features that affect the diagnostic decision process were packet delay, stator current distortion, torque oscillation, and rotor speed deviation. The results demonstrate the necessity of incorporating cybersecurity awareness into intelligent fault diagnosis systems for modern induction motors in industrial cyber-physical environments. Full article

Rockfall hazards are frequently observed within mountainous road networks. Significant uncertainties regarding the optimal selection of evaluation units and spatial modeling scales are still being identified in this field. A comprehensive comparative framework for rockfall susceptibility mapping is presented in this study, using Wufeng County as the empirical study area. Five evaluation scenarios were constructed to systematically isolate the independent predictive contributions of the spatial domain, the mapping unit morphology, and the physics-informed engineering proxy. These scenarios included a whole-county macro-scale raster; three multi-scale road buffers with widths of 1 km, 2 km, and 3 km; and an object-oriented vector road evaluation unit (REU) framework. To parameterize localized engineering-induced risks, a physics-informed feature defined as the theoretical slope-cutting height ($H_{\mathrm{cut}}$) was structurally introduced into the vector-based assessment. Thirteen representative machine learning, deep learning, and statistical algorithms—including Random Forest, LightGBM, and TabNet—were systematically cross-examined under both unconstrained splits and strict Leave-One-Road-Corridor-Out Validation (LORCOV) protocols. The empirical multi-metric sensitivity analysis explicitly decouples the three structural effects. First, isolating the effect of the spatial domain reveals that restricting the validation extent from a broad countywide area to a narrow road corridor purges unperturbed background terrain noise, shifting the focus from easy negatives to geomorphological hard negatives. Second, evaluating the independent effect of the evaluation unit demonstrates that transitioning from continuous raster pixels to homogeneous vector REUs successfully resolves the terrain smoothing effect, precisely characterizing sharp geomechanical gradients adjacent to cut slopes. Third, isolating the effect of adding $H_{\mathrm{cut}}$ proves that this engineering indicator drives the primary descriptive gain, enabling tree-based ensembles to achieve a peak baseline AUC of 0.7763 and maintain a robust spatial validation AUC of 0.6129 under strict geographic block constraints, whereas legacy deep learning architectures exhibit an inductive bias mismatch on small-scale tabular records. Rather than asserting a single optimal paradigm, this coordinated feature–unit matching framework provides transport authorities with a highly calibrated, target-tiered decision matrix to optimize localized public works safety budgets and protect critical linear infrastructure assets. Full article

Pyrite occurs in a wide range of ore-forming environments, and its trace element and rare earth element (REE) compositions are important geochemical indicators for determining its genetic origin, thereby providing valuable constraints for ore deposit research and mineral exploration. Machine learning, as a data-driven technique, offers new insights into the genesis types of pyrite by comprehensively analyzing data and uncovering underlying patterns. In this study, representative pyrite samples with diverse morphologies, including both sedimentary and hydrothermal types, were collected. Based on electron probe microanalysis (EPMA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), a comprehensive analysis of pyrite genesis was conducted. Furthermore, an artificial neural network (ANN) machine learning algorithm was employed to establish a classification model, and the predictive performance of this trained model was compared against that of traditional discrimination methods. The results show that when only trace elements were used as input features, the training set yielded a classification accuracy of 64.3% for sedimentary pyrite and 93.9% for hydrothermal pyrite. In the testing set, sedimentary pyrite was classified with an accuracy of 100%; however, this result may reflect the limited sample size rather than the model’s true generalization ability and therefore should be interpreted with caution. Feature importance analysis identified Cu, Zn, Te, Bi, and Pb as the key variables of this model. When both trace and rare earth elements (REEs) were used, the detection accuracy for sedimentary pyrite and hydrothermal pyrite in the training set was 87.5% and 100%. However, the combined trace + rare earth elements model exhibited a clear overfitting tendency: its overall training accuracy reached 95.2%, but its validation accuracy (88.9%) was lower than that of the trace-only model (93.8%). Feature importance analysis indicated that Pb, Ho, Zn, Ag, and Ce contribute substantially to the model. The machine learning model proposed in this study is convenient and efficient, providing a novel basis for determining the genetic types of complex pyrite. Full article

This paper proposes a low-complexity speed- and voltage-margin scheduled (SVS) current-reference generator for wide-speed-range permanent magnet synchronous motor (PMSM) drives. It blends current-efficient and voltage-relieving affine anchors using bounded speed and voltage-margin pressures. Unlike speed-only scheduling, the voltage term responds to torque-dependent voltage utilization and DC-link variation. A common scheduling structure covers the evaluated salient machines with and without conventional maximum torque per voltage (MTPV) endpoints. Endpoint quantities are computed offline or during initialization; the runtime candidate path uses scalar arithmetic and stored coefficients without multidimensional current-reference lookup tables, square roots, quartic solutions, or iterative optimization. Torque-consistent projection and derating enforce the modeled limits. Across three parameter sets and nine model-speed cases, SVS reduced the average RMS current-reference error from 0.8859 to 0.8041 A and the case-averaged mean effective-loss-index penalty from 9.6456% to 7.4698% relative to speed-only blending. Closed-loop Model A simulations showed comparable nominal tracking. Under a nonideal 12% voltage sag at 375 rad/s and 4.5 Nm, SVS reduced RMS speed error from 29.846 to 0.155 rad/s and saturation duration from 119.7 to 15.3 ms. Uncompensated q-axis-inductance underestimation remained the principal high-speed limitation. Full article

Shallow defects in ferromagnetic components may produce weak and unstable magnetic flux leakage responses in compact inspection devices, whereas an inductive response alone does not provide the same depth-related magnetic information. To obtain complementary defect information, this study proposes a magnetic–inductive dual-channel inspection method that integrates an MLX90393 three-axis digital magnetic sensor with a PCB planar spiral coil and an LDC1612 inductance-to-digital converter. Because the two sensing units are physically separated on the detection board, peak-position offset analysis and spatial registration were introduced to associate their responses to the same defect region. Controlled experiments were conducted on a laboratory pipeline inspection platform using a Q235 defect specimen installed at the internal inspection position of the pipe. The defects were machined on the inner surface, which was also the inspection surface. For each defect, five motor-driven axial scans were performed at 10 mm/s, with 400 samples acquired at 100 Hz during each 4 s scan. Response amplitude, signal-to-noise ratio, peak position, and repeatability were evaluated. For the square-hole defects, the magnetic response amplitude decreased from 675.0 to 98.5 a.u. as the depth ratio decreased from 50% to 10%, while the magnetic-channel SNR decreased from 30.5 to 4.7. For the 10% depth defect, the inductive channel retained an average SNR of 434.4 and a coefficient of variation of 0.23%, providing a stable auxiliary response. However, the pre-registration peak-position offset measured for this shallow defect was $21.2\pm 20.9$ sampling points, indicating substantial uncertainty in using a single magnetic extremum for scan-specific registration when the magnetic response was weak. These results demonstrate that the two channels provide different and complementary information under controlled laboratory conditions, while further validation is required for irregular corrosion, other ferromagnetic components, and practical inspection conditions. Full article

Variable-speed induction motor drives spend most of their service life at partial load, where rated-flux field-oriented control (FOC) is inefficient and where loss-minimizing control (LMC) recovers a large part of the loss. LMC, however, is brittle in two ways that matter in industry: it is tuned isothermally, so as the windings heat, the rotor-resistance drift detunes the field orientation and corrupts torque; and it treats the loss-optimal flux as a quasi-static set-point, so an abrupt load rise from a light-load, low-flux condition forces a slow flux rebuild that throttles torque. This paper proposes a thermally adaptive economic model predictive controller (TA-EMPC) that retains the energy optimum of LMC while removing both weaknesses. A temperature-coupled total-loss model (machine copper and core loss plus inverter conduction and switching loss) is minimized over a finite horizon subject to a torque-delivery constraint; a reduced-order two-node thermal observer updates the loss-defining resistances online without a temperature sensor; and a load-demand-aware flux-reservation term pre-magnetizes the machine ahead of anticipated torque rises. In simulations on a representative 7.5 kW drive, TA-EMPC matched the energy of static LMC to within 0.3% across pump, conveyor, and fast-cycling duty profiles—both saving 1.4–2.3% of cycle energy relative to rated-flux FOC, and up to about 14.7 efficiency points at very light load—while, unlike LMC, holding the steady torque error below 0.5% when the winding temperature rose by about 95 °C, to a hot steady state near 115 °C (a stator-resistance increase of roughly 37%) (against an 8% error for the non-adaptive scheme) and reducing the torque undershoot during a light-to-heavy load step from about 23% to near zero. All quantitative results reported in this work are obtained entirely in simulation. A per-step operation-count analysis—not an on-target timing measurement—indicates that the condensed quadratic-program formulation with move blocking is executable within the 100 µs sampling interval on a production digital signal controller for the chosen control horizon; experimental validation on a loaded dynamometer bench, together with on-target timing measurement, is identified as future work. The contribution is thus energy-efficient operation delivered with the torque robustness that loss minimization alone does not provide. Full article

Active-support motor equipment combining a doubly fed induction machine (DFIM) with a flywheel energy storage unit possesses fast active and reactive power support capability, exhibiting promising prospects for enhancing power system stability. However, the startup process of a DFIM involves multiple dynamic stages, including flux linkage establishment, excitation converter connection, and rotor-speed acceleration, which may result in large starting currents, electromagnetic-torque fluctuations, and converter power transients. And existing soft-start methods still involve trade-offs among startup inrush suppression, additional hardware requirements, control complexity, and startup performance. Moreover, the coordinated control of rotor-side closing synchronization, closed-loop acceleration, and converter power constraints during the startup of active-support motor equipment requires further investigation. To address these issues, this paper proposes a rotor-side pre-synchronization method based on virtual impedance. A virtual impedance model is established between the rotor-induced electromotive force and the output voltage of the excitation converter. According to the voltage difference and the virtual impedance, a virtual current is calculated, and rotor-side pre-synchronization is achieved by regulating this virtual current to zero, thereby effectively suppressing the inrush current at the closing instant. On this foundation, a complete soft-start control strategy is formulated. Finally, simulation and hardware-in-the-loop (HIL) experiments are carried out to verify the method. The results demonstrate that the proposed strategy reduces the absolute peak value of the three-phase rotor current from 157 A to 21 A, the rotor-current RMS value during the first electrical cycle after closing from 91.287 A to 9.614 A, and the DC-link voltage overshoot from 2.81% to 0.078%, corresponding to reductions of 86.62%, 89.47%, and 97.22%, respectively. These results confirm that the proposed strategy can effectively suppress the rotor-side closing inrush current and improve the smoothness and reliability of the startup process of the active-support equipment. Full article

Induction machines play a crucial role in industrial applications, making preventive maintenance combined with fault diagnosis techniques essential for ensuring reliable operation. One of the most widely used diagnostic methods for induction machines is Motor Current Signature Analysis (MCSA). However, this technique has certain limitations, particularly in the detection of incipient or small faults. Another well-established technique is Motor Square Current Signature Analysis (MSCSA), which overcomes some of the limitations of MCSA by extracting additional fault-related information from the motor current signals. This paper proposes a new diagnostic technique, designated MSCSA-APT (Motor Square Current Signature Analysis–Alternative Park Transform), based on the spectral analysis of motor currents. Compared with the conventional MSCSA method, the proposed approach provides additional information from the frequency-domain analysis, thereby improving fault detection capability. The method is based on the square of the motor square current signal and employs an Alternative Park Transform (APT) to enhance the extraction of fault signatures. Simulation and experimental results are presented to validate the proposed approach. Although the method has been evaluated for the identification of different types of faults, it is particularly effective in detecting stator short-circuit faults. Full article