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Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness. To address this, the mechanism behind these non-ideal characteristics is analyzed based on the principles of space vector pulse width modulation (SVPWM) and its circuit structure. Tests are then conducted to examine the actual driver characteristics and verify the analysis. Building on this, a real-time compensation method is proposed, physically matched to the driver. Using the volt–second equivalence principle, an input–output voltage model of the driver is derived, with model parameters estimated from test data. The driving error is then compensated with a voltage method based on the model. The results of simulations and experiments show that the proposed method effectively mitigates the influence of the driver’s non-ideal characteristics, improving the driving and speed control accuracies by 88.07% (reducing the voltage error from 0.7345 V to 0.0879 V for a drastic command voltage with a sinusoidal amplitude of 10 V and a frequency of 50 Hz) and 53.08% (reducing the speed error from 0.0130°/s to 0.0061°/s for a lower command speed with a sinusoidal amplitude of 20° and a frequency of 0.1 Hz), respectively, in terms of the root mean square errors. This method is cost-effective, practical, and significantly enhances the control performance of PMSMs.

Due to the presence of dozens of error sources in the platform inertial measurement system, these error sources affect the system’s measurement results to varying degrees. This paper introduces a new error model for a four-axis frame system, which can be utilised for multi-axis frame systems’ self-calibration and measurement error compensation. The model takes into account a comprehensive set of 29 error parameters, including accelerometer component errors, measurement errors in the four-axis frame system, and associated non-orthogonal errors. Finally, computer simulations are conducted to evaluate the potential impacts of these error sources on the specific force observations.

In recent years, mobile robots have found extensive application across diverse sectors including industry, agriculture, healthcare, and defense. However, relying solely on a single sensor for mobile robot localization presents several challenges, such as limited accuracy, divergence of localization errors over time, and susceptibility to obstruction from obstacles. This paper proposes an indoor mobile robot localization algorithm assisted by Ultra-Wideband (UWB). The algorithm begins by calculating the credibility of the Line-of-Sight (LOS) environment using UWB ranging measurements and predicted distances, enabling the identification of the Non-Line-of-Sight (NLOS) environment. Subsequently, ranging measurements affected by NLOS errors are compensated by using a complementary filter. Finally, these measurements are utilized for Extended Kalman Filter updates to achieve the best estimation of the mobile robot’s position. Field tests are conducted on a wheeled robot to validate the effectiveness and performance of the developed approach. Results show that the localization approach reduces the maximum localization error from 41 cm to 19 cm compared to UWB trilateration, achieving a 53.7% improvement. Even under a prolonged NLOS environment, the algorithm ensures that the localization error remains below 25 cm. The proposed method is of significant merit in solving the challenge of indoor mobile robot localization.

Wearable Inertial Measurement Units (WIMU) based on Micro-Electro-Mechanical Systems (MEMS) inertial sensors and magnetometers offer advantages such as low cost and user-friendly integration, making it a promising tool for lower-limb human motion analysis. Achieving accurate attitude estimation through effective filtering to fuse the aforementioned heterogeneous sensor data is crucial. However, magnetometer measurements affect the determination of yaw and influence the estimation of pitch and roll angles; magnetic interference can degrade accuracy in situations requiring high-precision pitch and roll estimation. This paper proposes a Double-Complementary Filter (DCF) attitude estimator. It adopts a two-layer filter architecture. In the first layer, the vector cross-product processes the accelerometer output to generate a correction vector, which, in conjunction with gyroscope measurements, serves as the input to the filter. In the second-layer filter, the magnetometer output is decoupled from the roll and pitch estimates, ensuring that only the yaw correction vector is retained. The final attitude is obtained by fusing the outputs of both layers of filters. To verify the feasibility and effectiveness of the proposed algorithm, we conducted comparative experiments using the self-developed WIMU and representative algorithms. The results show that the proposed algorithm effectively resolves the magnetic field coupling issue without affecting overall accuracy.

Affordance is a property of object with respect to the observer, which is related to the attributes of the object. In the present study, we examined whether affordance elicitation is primarily based on the conceptual attributes or instance attributes of the object. To distinguish the role of the two types of attributes in elicitation of affordance, we manipulated the size of a pan in virtual reality (Experiment 1). The critical condition is the giant pan, which should elicit manipulability affordance if affordance is concept-based and it should not elicit manipulability affordance if affordance is instance-based. The results support the former assumption, i.e., the elicitation of affordance is concept-based. To confirm the conclusion, we created a water-handled pan in virtual reality and examined its manipulability affordance (Experiment 2). The water-handled pan looks similar to a normal pan, but its handle is composed of flowing water which, in concept, cannot be grasped. Consistent with the concept-based conclusion, the water-handled pan did not elicit manipulability affordance. The present findings provided convergent evidence that ordinary people rely primarily on conceptual attributes of the object to elicit manipulability affordance.

Rail transit tunnels span long distances, are large-scale structures and pass through complicated geological conditions; thus, the risk of uneven settlement cannot be ignored. To address this issue, a method for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed in this study. First, with the distributed optical fiber sensing technology, a method for determining the intervals of strain measurement points with strong spatial correlations is proposed based on a support vector machine. Second, combined with the statistical analysis of the influence range of the uneven settlement of a tunnel, an algorithm for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual tunnel data.

Pile foundations of long-span bridges are often deeply buried in soil, and their structural condition is difficult to accurately diagnose by conventional methods. To address this issue, a method for diagnosing the structural condition of bridge pile foundations based on the spatial correlation of high-density strain measurement points is proposed. The strain data of the high-density measurement points of a bridge pile foundation are obtained by using distributed optical fiber sensing technology based on Brillouin scattering, and then an algorithm for diagnosing the structural condition of the pile foundation based on geographically weighted regression analysis is presented. On this basis, aiming at the scour of the pile foundation of long-span bridges, an algorithm for estimating the scour depth of the pile foundation based on sliding plane clustering is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual bridge data.

The progressive collapse resistance of the terminal building of Zhongchuan Airport in Lanzhou, China was studied, which is a long-span curved spatial grid structure with main trusses. Firstly, the finite element model was built using MSC. Marc software adopting the fiber model based on material. Secondly, an improved method of zoned concept judgment and sensitivity analysis was proposed to determine the key components. Thirdly, the initial failure components were removed individually based on the alternate load path method (AP method). The responses of remaining structure were calculated using nonlinear dynamic analysis method. Lastly, the influences brought by the cross-sectional sizes of grid members were investigated through conducting parametric analysis. According to the results, the proposed selection method can avoid omitting the key components. The structural responses are significant when removing the concrete filled steel tubular (CFST) column SC14 directly supporting the front middle part of the roof, with a maximum vertical displacement of 10 m at cantilever end, which should be focused on for this kind of large-span spatial structure. In addition, the tensile strength and cross-sectional area of the upper and lower chords should be increased by 20% to enhance the progressive collapse resistance of the structure, and the changes of axial compression ratios of the columns supporting the roof have little effects.

During the construction process, it is difficult to ensure the structural safety of shallow buried tunnel with the ultra-small clear distance since the tunnel is prone to instability and the surrounding rock and soil are in an adverse stress condition. To address this issue, a hybrid construction method is proposed to enhance tunnel stability and reinforce the surrounding rocks and soil. First, aiming at an actual tunnel, numerical analysis are provided to compare the effectiveness of different construction methods such as the bench method, advanced reinforcement method, and grouting reinforcement method. Second, the performance of the combination of advanced reinforcement and grouting reinforcement are discussed, and, on the basis of this discussion, the hybrid construction method, combining the advanced small pipes reinforcement, middle rock wall reinforcement, and grouting reinforcement, is proposed. And the characteristics of proposed method is compared with the traditional CRD construction method. The results reflect that using the hybrid construction method can enhance the stability of the tunnel and its effect is similar to that of the CRD method. Finally, the effectiveness of proposed hybrid construction method is verified by using the measured data obtained during the construction of an actual tunnel with the ultra-small clear distance. The results shown that the proposed method can enhance the stability of the tunnel and improve the bearing capacity of the surrounding rock and soil.

Simultaneous Machine Translation (SiMT) requires high-quality translations under strict real-time constraints, which traditional encoder-decoder policies with only READ/WRITE actions cannot fully address. We extend the action space of SiMT with four adaptive actions: SENTENCE_CUT, DROP, PARTIAL_SUMMARIZATION and PRONOMINALIZATION, which enable real-time restructuring, omission, and simplification while preserving semantic fidelity. We implement these actions in a decoder-only large language model (LLM) framework and construct training references through action-aware prompting. To evaluate both quality and latency, we further develop a latency-aware TTS pipeline that maps textual outputs to speech with realistic timing. Experiments on the ACL60/60 English-Chinese and English-German benchmarks show that our framework consistently improves semantic metrics (e.g., COMET-KIWI) and achieves lower delay (measured by Average Lagging) compared to reference translations and salami-based baselines. Notably, combining DROP and SENTENCE_CUT yields the best overall balance between fluency and latency. These results demonstrate that enriching the action space of LLM-based SiMT provides a promising direction for bridging the gap between human and machine interpretation.