Search Results (556)

Monitoring large-gradient surface displacement caused by underground mining remains a significant challenge for conventional Synthetic Aperture Radar (SAR)-based techniques. This study introduces optical flow methods to monitor large-gradient displacement in mining areas and conducts a comprehensive comparison with Small Baseline Subset Interferometric SAR (SBAS-InSAR) and Pixel Offset Tracking (POT) methods. Using 12 high-resolution TerraSAR-X (TSX) SAR images over the Daliuta mining area in Yulin, China, we evaluate the performance of each method in terms of sensitivity to displacement gradients, computational efficiency, and monitoring accuracy. Results indicate that SBAS-InSAR is only capable of detecting displacement at the decimeter level in the Dalinta mining area and is unable to monitor rapid, large-gradient displacement exceeding the meter scale. While POT can detect meter-scale displacements, it suffers from low efficiency and low precision. In contrast, the proposed optical flow method (OFM) achieves sub-pixel accuracy with root mean square errors of 0.17 m (compared to 0.26 m for POT) when validated against Global Navigation Satellite System (GNSS) data while improving computational efficiency by nearly 30 times compared to POT. Furthermore, based on the optical flow results, mining parameters and three-dimensional (3D) displacement fields were successfully inverted, revealing maximum vertical subsidence exceeding 4.4 m and horizontal displacement over 1.5 m. These findings demonstrate that the OFM is a reliable and efficient tool for large-gradient displacement monitoring in mining areas, offering valuable support for hazard assessment and mining management. Full article

Reliable multilevel inverter IGBT modules require precise loss and heat management, particularly in severe traction applications. This paper presents a comprehensive modeling framework for three-level T-type neutral-point clamped (TNPC) inverters using a high-power Insulated Gate Bipolar Transistor (IGBT) module that combines model predictive control (MPC) with space vector pulse width modulation (SVPWM). The particle swarm optimization (PSO) algorithm is used to methodically tune the MPC cost function weights for minimization, while achieving a balance between output current tracking, stabilization of the neutral-point voltage, and, consequently, a uniform distribution of thermal stress. The proposed SVPWM-MPC algorithm selects optimal switching states, which are then utilized in a chip-level loss model coupled with a Cauer RC thermal network to predict transient chip-level junction temperatures dynamically. The proposed framework is executed in MATLAB R2024b and validated with experiments, and the SemiSel industrial thermal simulation tool, demonstrating both control effectiveness and accuracy of the electro-thermal model. The results demonstrate that the proposed control method can sustain neutral-point voltage imbalance of less than 0.45% when operating at 25% load and approximately 1% under full load working conditions, while accomplishing a uniform junction temperature profile in all inverter legs across different working conditions. Moreover, the results indicate that the proposed control and modeling structure is an effective and common-sense way to perform coordinated electrical and thermal management, effectively allowing for predesign and reliability testing of high-power TNPC inverters. Full article

With the continuous increase in the penetration of single-phase microgrids in low-voltage distribution networks (LVDNs), the phase asymmetry of source–load distribution has made the problem of three-phase imbalance increasingly prominent. To address this issue, this paper proposes an active–reactive power coordinated optimization model for distribution network–microgrid clusters considering three-phase imbalance mitigation. The model is formulated within a master–slave game framework: in the upper level, the distribution network acts as the leader, formulating time-of-use prices for active and reactive power based on day-ahead forecast data with the objective of minimizing operating costs. These price signals guide the flexible loads and photovoltaic (PV) inverters of the lower-level microgrids to participate in mitigating three-phase imbalance. In the lower level, each microgrid responds as the follower, minimizing its own operating cost by determining internal scheduling strategies and power exchange schemes with the distribution network. Finally, the resulting leader–follower game problem is transformed into a unified constrained model through strong duality theory and formulated as a mixed-integer second-order cone programming (MISOCP) problem, which is efficiently solved using the commercial solver Gurobi. Simulation results demonstrate that the proposed model fully exploits the reactive power compensation potential of PV inverters, significantly reducing the degree of three-phase imbalance. The maximum three-phase voltage unbalance factor decreases from 3.98% to 1.43%, corresponding to an overall reduction of 25.87%. The proposed coordinated optimization model achieves three-phase imbalance mitigation by leveraging existing resources without the need for additional control equipment, thereby enhancing power quality in the distribution network while ensuring economic efficiency of system operation. Full article

In order to alleviate the constraints of global warming and sustainable development, digitalization has made significant contributions to promoting agricultural carbon reduction through resources, technology, and platforms. Under this situation, China insists on developing agricultural scale management. However, what impact will scale management in agricultural digital emission reduction have on mechanisms and pathways? Based on three rounds of follow-up surveys conducted by the Digital Countryside Research Institute of Nanjing Agricultural University in Jiangsu Province from 2022 to 2024, in this study a total of 258 valid questionnaires on the rice and wheat industry were collected. Methods such as member checking and audit trail were employed to ensure data reliability and validity. Using econometric approaches including Tobit, mediation, and moderation models, this study quantified the Scale Management Level (SML), examined the mechanism pathways of digital emission reduction in a scaled environment, further demonstrated the impact of scale management on digital emission reduction, and verified the mediating and moderating effects of internal and external scale management. We found that: (1) In scale and carbon reduction, the SBM-DEA model calculates that the scale of agricultural land in Jiangsu showed an “inverted S” trend with SML and an “inverted W” trend with the overall agricultural green production efficiency (AGPE), and the highest agricultural green production efficiency is 0.814 in the moderate scale range of 20–36.667 hm². (2) In digitalization and carbon reduction, the Tobit regression model results indicate that Network Platform Empowerment (NPE) significantly promotes carbon reduction (p < 1%), but its squared terms exhibit an inverted U-shaped relationship with agricultural green production efficiency (p < 1%), and SML is significant at the 5% level. From a local regression perspective, the strength of SML’s impact on the three core variables is: NPE > DRE > DTE. (3) Adding scale in agricultural digital emission reduction, the intermediary mechanism results showed that the significant intensity (p < 5%) of the mediating role of Agricultural Mechanization Level (AML) is NPE > DTE > DRE, and that of the Employment of Labor (EOL) is DRE > NPE > DTE. (4) Adding scale in agricultural digital emission reduction, the regulatory effect results showed that the Organized Management Level (OML) and Social Service System (SSS) significantly positively regulate the inhibitory effect of DRE and DTE on AGPE. Finally, we suggest controlling the scale of land management reasonably and developing moderate agricultural scale management according to local conditions, enhancing the digital literacy and agricultural machinery training of scale entities while encouraging the improvement of organizational level and social service innovation, and reasonably reducing labor and mechanization inputs in order to standardize the digital emission reduction effect of agriculture under the background of scale. Full article

This article updates and extends a prior longitudinal study on adolescents’ psychological adjustment during short-term study-abroad programs, analyzing a newly collected larger cohort with the same design and measures. Using the same assessment schedule (pre-departure, mid-sojourn, post-return) with a larger cohort, we confirmed the adequate reliability and longitudinal comparability of the Teacher’s Report Form. Mean-level analyses replicated earlier patterns: internalizing symptoms increased during the sojourn and remained elevated at reentry, whereas externalizing problems followed an inverted-U, rising abroad and returning to baseline after return. Person-centered models identified three trajectory classes for both domains: a low-stable group, a transient-elevated group showing a mid-sojourn spike with subsequent recovery, and a small high-persistent group with enduring elevations. Clinical threshold transitions showed a temporary mid-sojourn rise in borderline/clinical cases for both domains, with partial normalization after return. Reliable-change estimates further distinguished transient from sustained change. Together, the findings characterize studying abroad as a moderate, time-bound stressor for most adolescents, with a minority at persistent risk. The implications of these findings include suggestions for front-loaded and reentry supports, pre-departure screening, and targeted mid-sojourn monitoring. The strengths include longitudinal measurement invariance and person-centered modeling; the limitations include teacher-only reports and a short post-return follow-up. Full article

Induction motors piloted by voltage source inverters constitute a major source of acoustic noise in industry. The discrete tonal bands generated by induction motor stator current spectra controlled by the fixed Pulse Width Modulation (PWM) technique have damaging effects on the electronic noise source. Nowadays, the investigation of new advanced control techniques for variable speed drives has developed a potential investigation field. Finite state model predictive control has recently become a very popular research focus for power electronic converter control. The flexibility of this control shows that the switching times are generated using all the information on the drive status. Predictive Torque Control (PTC), space vector PWM and random PWM are investigated in this paper in terms of acoustic noise emitted by an induction machine fed by a three-phase two-level inverter. A comparative study based on electrical and mechanical magnitudes, as well as harmonic analysis of the stator current, is presented and discussed. An experimental test bench is also developed to examine the effect of the proposed PTC and PWM techniques on the acoustic noise of an induction motor fed by a three-phase two-level voltage source converter. Full article

A three-phase inverter generates non-sinusoidal voltages, contains high order harmonics, and concentrates on switching frequency multiples. Supplying an induction machine (IM) with a voltage source inverter (VSI) increases the acoustic noise content which becomes unbearable, particularly for systems needing a moderate level of electric traction. The discrete tonal bands produced by the IM stator current spectrum controlled by the fixed pulse width modulation (PWM) technique have damaging effects on the electronic noise source. Moreover, it has been factually proven that the noise content is strongly associated with the harmonics of the source feeding electric machine. Thus, the harmonic content is influenced by the control strategy VSI to produce pulse width modulation (PWM). Currently, the investigation of new advanced control techniques for variable speed drives has developed into a potential investigation file. Two fundamental topologies for a three-phase inverter have been suggested in the literature, namely two- and three-level topologies. Therefore, this paper investigated the effect of variable and fixed PWM strategies, such as random PWM (RPWM) and space vector PWM (SVPWM), on the noise generated by an IM, powered with a two- or three-level inverter. Simulation results showed the validity and efficiency of the proposed variable RPWM strategy in reducing sideband harmonics for both the two and three levels at different switching frequencies and modulation indexes. The proposed PWM strategies were further evaluated by the results of equivalent experiments on an IM fed by a two-level VSI. The experimental measurements of harmonic current and noise spectra demonstrate that the acoustic noise is reduced and dispersed totally for the RPWM strategy. Full article

Model predictive control (MPC) has emerged as a favorable control approach for PMSM drives, though its practical deployment is frequently hindered by superior computational complexity and execution burden. This paper presents four finite control set MPC (FCS-MPC) techniques applied to a two-level inverter-fed PMSM drive. Two of the approaches are conventional methods, while the other two are novel developed strategies proposed in this paper. The novel techniques focus on significantly decreasing computational burdens by employing an efficient space-vector selection mechanism that quickly selects the optimum switching vector without exhaustive evaluation. A comprehensive comparative assessment of all four control methods is conducted under various operating conditions, evaluating their dynamic and steady-state performance, computational requirements, and real-time feasibility. Simulation results demonstrate that the proposed techniques achieve a significant reduction in computational effort and faster processing, up to 39.65% faster than conventional full-state evaluation, while maintaining control performances comparable to conventional techniques. These results highlight the potential of the proposed MPC approaches to bridge the gap between advanced control theory and practical implementation in real-time PMSM drive systems, providing effective solutions for installing high-performance PMSM drives on hardware with limited resources. Full article

In three-level inverters, high accuracy and low latency sector and region detection are of great importance for control and monitoring processes. This study aims to overcome the limitations of traditional methods and develop a model that can work in real time in industrial applications. In this study, various deep learning (DL) architectures are systematically evaluated, and a comprehensive performance comparison is performed to automate sector and region detection for inverter systems. The proposed approach aims to detect sectors (6 classes) and regions (3 classes) with high accuracy using a Deep Neural Network (DNN), 1D Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) based DL architectures. The performance of the considered DL approaches was systematically evaluated with cross-validation, confusion matrices, and statistical tests. The proposed GRU-based model offers both computational efficiency and high classification performance with a low number of parameters compared to other models. The proposed model achieved 99.27% and 97.62% accuracy in sector and region detection, respectively, and provided a more optimized solution compared to many heavily structured state-of-the-art DL models. The results show that the GRU model exhibits statistically significant superior performance and support that it has the potential to be easily integrated into hardware-based systems due to its low computational complexity. The comprehensive results show that DL-based approaches can be effectively used in sector and region detection in inverter systems, and especially the GRU architecture is a promising method. Full article

Climate change threatens grain production in East Asia. This study assesses the impacts of climate variables and climate change on rice, wheat, and maize total production using an improved economy–climate model (C-D-C model). The innovation is to model a roughly inverted U-shaped relationship between dry-wet conditions (measured by Standardized Precipitation Evapotranspiration Index, SPEI) and production. Building on this, this study introduces a new metric reflecting extent of future climate change impact, the Impact Ratio of Climate Change (IRCC), to project the impact on production under three climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) for 2021–2050. Key findings include: The dry–wet conditions exhibit a significant roughly inverted U-shaped relationship with grain production in some crop areas, with optimal production levels observed near an SPEI of zero. Effective accumulated temperature positively affects wheat production in most regions, while higher effective accumulative temperatures reduce production in warm southern areas. Future climate change in 2021–2050 will likely increase rice production in northern China but decrease it in the south (IRCC > −30%). Overall impacts on wheat will be modestly negative, accounting for about 10% of future total production. Impacts in Japan and Korea will be minimal, with absolute values of IRCC not exceeding 2.5% across all scenarios. Full article

Ripples in the electromagnetic torque of electric vehicle (EV) motors due to poor stator voltage and control cause jerky movements, equipment failure, discomfort for passengers and drivers, and damage to the associated civil works. This paper presents the implementation of Finite Control Set Model Predictive Control (FCSMPC) for a high-level modified W-type inverter (MWI) driving a three-phase induction motor (IM), along with validation of its performance. The proposed control strategy aims to minimize motor torque ripples and has been tested under various driving torque patterns. The results demonstrate a significant reduction in torque ripples—down to less than 1%—and acceptable levels of total harmonic distortion (THD), as verified through quality analysis of the stator currents. Moreover, a comparative assessment of voltage profiles for the electromagnetic torque and rotor speed curves has been presented for nine cases of simultaneous variations in multiple motor parameters; the results indicate that the MWI-fed motor has the best performance and the lowest sensitivity to the variations. Full article

This study aims to develop an event-triggered control strategy of grid-connected inverters, based on the linear parameter-varying (LPV) modeling approach. Regarding the changes in grid voltage, filter capacitance and inductance, and random electromagnetic interference, a stochastic LPV model for three-phase two-level inverters is established. To reduce computation burden, an event trigger with a continuous-time form is adopted to derive the state feedback controller for the LPV plant. Unlike the existing common approach to dealing with event-triggered mechanisms, a predesignated event-triggering threshold is used to determine the triggering instant of the event condition. Using parameter-dependent Lyapunov functions, sufficient conditions reliant on parameters are introduced. Based on the derived conditions, the corresponding event-triggered controllers are engineered to ensure uniform ultimate bounded stability for the resulting event-triggered LPV inverter system subject to exogenous disturbance. The simulation results are presented to confirm the efficacy of the proposed methods. Full article

Empirical research on the impact of the digital economy on sustainable development is hampered by severe methodological challenges. Discrepancies in the theoretical foundations and construction logic of measurement frameworks have led to diverse and often conflicting conclusions, hindering the systematic accumulation of knowledge. This study aims to address this critical gap by proposing a new, logically consistent measurement framework. To overcome the existing limitations, we construct a functional deconstruction framework grounded in General-Purpose Technology (GPT) theory and a “stock–flow” perspective. This framework deconstructs the digital economy into a neutral “digital infrastructure” (stock platform) and two forces reflecting its inherent duality: a “consumption force” (digital industrialization) and an “empowerment force” (industrial digitalization). Based on this, we develop a measurement system adhering to the principle of “logical purity” and apply a “two-step entropy weighting method with annual standardization” to assess 30 provinces in China from 2012 to 2023. Our analysis reveals a multi-scalar evolution. At the micro level, we identified four distinct provincial development models and three evolutionary paths. At the macro level, we found that the overall inter-provincial disparity followed an inverted U-shaped trajectory, with the core contradiction shifting from an “access gap” to a more profound “application gap.” Furthermore, the primary driver of this disparity has transitioned from being “empowerment-led” to a new phase of a “dual-force rebalancing.” The main contribution of this study is the provision of a new analytical tool that enables a paradigm shift from “aggregate assessment” to “structural diagnosis.” By deconstructing the digital economy, our framework allows for the identification of internal structural imbalances and provides a more robust and nuanced foundation for future causal inference studies and evidence-based policymaking in the field of digital sustainability Full article

With the widespread application of multilevel inverters, device losses have become a critical area of research. A key limitation of conventional three-level discontinuous pulse width modulation (DPWM) strategies is their inability to maintain switching device clamping during the peak intervals of the load current, especially under varying load power factor conditions, thereby reducing switching losses. This paper proposes an improved three-level power factor adaptive DPWM (PFA-DPWM) strategy that minimizes switching losses by clamping the power devices during the one-third fundamental period of maximum load current. First, a unified mathematical model of DPWM strategies is established. Theoretical analysis demonstrates that phase disposition (PD) carrier modulation for three-level inverter exhibits superior line voltage harmonic characteristics. Based on this, a theoretical comparison of switching losses and harmonic distortion for various DPWM schemes is conducted. The proposed PFA-DPWM control strategy has the minimum switching loss without compromising harmonic performance. The efficacy and validity of the proposed strategy are confirmed by comprehensive simulation and experimental results. Full article

Recent theories of citizenship call into question the dominance of ancestry (jus sanguinis) and territory (jus soli) as the primary criteria for membership in a polity. Debates around postnationalism, cosmopolitanism, and transnationalism increasingly locate the legitimacy of citizenship in world-level models of rights that extend beyond the state. Yet national citizenship remains remarkably persistent, posing three interrelated puzzles for the sociology of citizenship: (1) How can rights-based and birth-based legitimations of citizenship be reconciled? (2) How can citizenship be conceptualized in non-national terms without eroding the state’s central role? (3) How can we account for the rise of multinational citizenship rights? Drawing on recent global shifts in nationality laws, this article offers a unified analytical framework to address these puzzles through the concept of modular citizenship, which inverts the conventional understanding: it is not the juridical category of citizenship that determines the scope of rights, but the enforceable rights themselves that determine the quantum of citizenship. Full article