Innovations in Applied Engineering and Technology

Sensorless Control of PMSM across the Full Speed Range Based on Fuzzy Logic

Jin Dai — 2026-07-29

To realize full-speed-range operation of the permanent magnet synchronous motor (PMSM) under sensorless control, this paper presents a full-speed-domain sensorless control scheme that relies on fuzzy-logic-based switching between high-frequency injection (HFI) and sliding mode observer (SMO). The core concept is to apply HFI within the zero-low speed region and transition to SMO once the rotor enters the medium-high speed region. To mitigate the chattering inherent in the conventional SMO, an improved SMO incorporating a hyperbolic tangent function together with an adaptive sliding mode gain is developed; meanwhile, a normalized quadrature phase-locked loop is adopted to recover the rotor position and speed, which raises the observation accuracy of the motor angle and speed. Furthermore, to refine the switching process, a fuzzy logic controller is embedded to online tune the weighting factors of HFI and SMO throughout the transition, thereby guaranteeing a smooth handover between the two control schemes. Simulation results indicate that the proposed approach can accomplish stable strategy switching. Relative to conventional switching techniques, the proposed strategy narrows the motor speed error by roughly 50% under no load and by roughly 30% under load. Hardware experiments further confirm the validity of the proposed method, cutting speed fluctuation during switching by approximately 35%.

Depositional Architecture of the Middle and Late Ordovician Reef–shoal Complexes and the Distribution Characteristics of the Reservoirs in the Tazhong Area, Tarim Basin

Liang Zhang — 2026-07-28

Reef-shoal complexes were extensively developed during the Middle and Late Ordovician in the Tarim Basin. Based on core observations from 18 drilling wells, petrographic analysis of more than 500 large thin sections, and correlation of outcrop sections, this study investigates the reefal deposits of the Middle–Upper Ordovician. The results indicate that the formation of these reefs is primarily associated with reef-building organisms, including receptaculites-sponges, corals, stromatoporoids, and calcimicrobes. Reef-shoal complexes consist mainly of framework reefs, lime-mud mounds and grainstone shoal or banks, with “small” reef and “big” shoal, as well as shallowing upward sequences. Laterally, these complexes are discontinuously distributed in sheets along the carbonate platform margin of the No. I fault zone in the northern margin of the Tazhong area; vertically, they exhibit multi-storey superimposed stacking, demonstrating a distinctive depositional architecture and forming the most important reservoir facies belt in the region. Furthermore, this study reveals that meter-scale sea-level cycles resulting in episodic meteoric freshwater dissolution, epikarstification during the late depositional stage of the Lianglitage Formation in the Tazhong area, as well as faulting and fracturing, collectively played a critical role in controlling the formation of favorable porous-vuggy and fractured layered reservoirs within the reef-shoal bodies. These insights have provided crucial guidance for the discovery of the largest Ordovician reef-shoal condensate gas field in China within the Tarim Basin.

MobileMamba-HC: Medical Image Disease Detection in Healthcare Integrating Frequency Adaptive Dilated Convolution and Spatial-Channel Synergistic Attention

Yuhan Dai — 2026-05-13

To address the challenges in disease detection tasks within the medical and healthcare domain, particularly those associated with biopharmaceutical applications, such as insufficient feature representation, limited capability for multi-scale lesion recognition, and severe interference from complex backgrounds, this paper proposes a detection model named MobileMamba-HC, which integrates frequency-domain adaptive convolution with a spatial-channel collaborative attention mechanism. The proposed method aims to improve the perception of fine-grained lesion regions while maintaining model efficiency, thereby enabling more accurate and robust disease detection in medical images. Built upon the MobileMamba architecture, the proposed model fully exploits its strengths in long-sequence modeling and global dependency capture to effectively model long-range spatial relationships in medical images. On this basis, a Frequency-domain Adaptive Dilated Convolution (FADC) module is introduced. By dynamically modulating feature responses in the frequency domain, this module achieves adaptive perception of components at different scales and frequencies, thereby enhancing the model’s ability to represent multi-scale lesion structures. Meanwhile, a Spatial and Channel Synergistic Attention (SCSA) mechanism is designed to jointly model critical regions and discriminative features from both the spatial and channel dimensions, suppressing redundant information and background noise interference and further improving the discriminability of feature representations. Through the synergistic effect of these three components, the proposed model significantly strengthens its capacity for modeling complex medical images while preserving its lightweight characteristics. In the experimental section, extensive evaluations are conducted on multiple public medical imaging datasets, and comparative experiments are performed against various mainstream deep learning detection models. The experimental results demonstrate that the proposed MobileMamba-HC achieves superior performance over the compared methods across multiple evaluation metrics. In particular, it exhibits stronger robustness in small lesion detection and low-contrast scenarios. Furthermore, ablation studies verify the effectiveness and complementarity of the FADC module and theSCSA mechanism in improving model performance, confirming the contribution of each proposed component to the overall enhancement. These findings validate that the proposed MobileMamba-HC model achieves a favorable balance between performance and efficiency in medical image disease detection tasks, providing an effective solution for intelligent computer-aided diagnosis in complex medical scenarios.

Minimal Intervention, Maximum Throughput: Micro-Scale Optimization of Urban Rail Hubs

Wenjun Dai — 2026-05-13

The global stock of operational urban rail hubs faces a performance challenge: designed under earlier demand assumptions, many now exhibit severe transfer bottlenecks, fragmented pedestrian connectivity, and poorly resolved station-city interfaces. Conventional responses—structural reconfiguration and large-scale reconstruction—are increasingly constrained by fiscal austerity and spatial fixity in dense built-up areas. This paper addresses the absence of a systematic framework for micro-scale optimization of existing hubs under such constraints. A Diagnosis–Optimization–Evaluation framework is proposed, operationalized through the Station-City Minimal Intervention Maturity Model (SC-MIMM), integrating three-dimensional spatial conflict detection, quantifiable intervention thresholds, demand-based facility allocation, and closed-loop implementation verification. The framework is tested through a case study of a constrained interchange hub in a large Asian metropolitan rail network. Results indicate that a 30% spatial compression of vertical circulation elements—reconfiguring a 4.3-m staircase into a combined stair-escalator system—yields an approximately 5.6-fold improvement in transfer capacity under simulation-based evaluation conditions at approximately one-fifth of reconstruction cost. Four generalizable threshold conditions are identified under which micro-intervention constitutes the optimal upgrading strategy. These findings challenge the assumption that significant hub performance improvements require major reconfiguration, and provide a transferable methodological foundation for incremental hub upgrading in dense urban contexts.