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Latest articles for Measurement Science and Technology
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Current signal-based power frequency filtering and Clark transform for ship propulsion motor fault diagnosis
To address the challenges of weak fault feature extraction and accumulated multi-source interference for ship propulsion motors under unsteady operating conditions and harsh environments, a current signal-based method based on power frequency filtering and Clark transform method is proposed. This method first uses a designed power frequency filtering technique to effectively suppress power frequency and its harmonic interference, enhancing the recognizability of fault characteristics. Subsequently, by combining the Clark transform and wavelet transform with signal dimensionality reduction characteristics, the fault features of the current signal were accurately extracted; on this basis, a SE ResNet18 network model with integrated attention mechanism is constructed, and its powerful ability to capture two-dimensional image features is utilized to ultimately establish an end-to-end fault diagnosis framework. Through two case experiments and multiple method comparisons, the results show that the diagnostic accuracy of the proposed method can reach 99.58%, −100%, which is significantly superior to existing approaches. This fully demonstrates the superiority of the method in fault diagnosis for ship propulsion motors.
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Multi-AUV cooperative localization system without bottom tracking velocity assistance
Multi-autonomous underwater vehicle (multi-AUV) collaborative systems have become critical platforms for underwater topographic mapping, yet suffer from compromised mapping accuracy due to acoustic interference between the multibeam echosounder and the Doppler velocity log (DVL), which impedes DVL-aided inertial navigation system velocity error correction during navigation. In this paper, we propose a multi-AUV collaborative system for bathymetric mapping, in which only the master AUV carries a DVL, and the slave AUVs estimate their own positions by a proposed graph-based moving single beacon localization algorithm, which fuses inertial navigation information (without DVL assistance) and range measurements between master and slave AUVs to accurately construct seafloor topographic maps. Notably, an assistant beacon method enhances positioning validity, and an optimal formation configuration minimizing positioning error is established through horizontal dilution of precision analysis. Comprehensive simulation experiments validate the system’s positioning performance.
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YKD-SLAM: a visual SLAM system in dynamic environments based on object detection and region segmentation
Simultaneous localization and map building (SLAM) is crucial in autonomous robot navigation. However, existing SLAM systems generally assume a static environment, which makes it difficult to cope with the interference caused by moving objects in dynamic scenes, affecting the system’s localization accuracy and robustness. To address this challenge, this paper proposes YKD-SLAM, a visual SLAM system for indoor dynamic environments, which is based on the ORB-SLAM2 framework and incorporates YOLOv8 target detection, RCF-KMeans (Region-ConstrainedFastK-Means), and epipolar geometric constraints to realize the accurate rejection of dynamic feature points and improve the localization performance in dynamic environments. YKD-SLAM first uses YOLOv8 to detect dynamic objects in the scene, generates a detection frame, optimizes the depth map through open operations, and performs multi-region segmentation of the region within the detection frame by combining RCF-KMeans. Subsequently, through the dynamic feature point rejection strategy based on epipolar geometric constraints, different regions in the detection frame are discriminated into dynamic and static regions, and the feature points in the dynamic region are rejected to improve the localization accuracy and robustness of the system in dynamic environments. The experimental results show that YKD-SLAM performs well in several dynamic scenes in the TUMRGB-D dataset. Compared with ORB-SLAM2, its ATE is reduced by 98.37%; compared with DynaSLAM, the system operation efficiency is improved by 95.35%. In addition, practical experiments conducted in indoor dynamic scenes further validate its potential in real applications.
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LCTA-Net: a deep fusion model for multi-scenario vessel trajectory prediction
Vessel trajectory prediction tasks currently face several challenges, including the diversity of behavioral patterns, interference from anomalous data, and limited generalization capabilities of existing models. These challenges are particularly pronounced when dealing with multi-pattern trajectory characteristics, where achieving both high prediction accuracy and robust cross-scenario adaptability remains difficult. To address these issues, this paper proposes a high-precision vessel trajectory prediction framework. Firstly, the local outlier factor algorithm is applied to eliminate anomalies from raw automatic identification system data, thereby improving data quality. The Douglas–Peucker simplification and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) trajectory clustering algorithm are then used to construct datasets characterized by different turning behaviors. Subsequently, a prediction model called local-causal temporal attention network (LCTA-Net) is developed, based on a pruned Transformer encoder. LCTA-Net incorporates a temporal residual encoding module and a causal feature-augmented attention mechanism. These components jointly improve the model’s capacity to model temporal dependencies within trajectory sequences. In addition, a fixed-window local neighborhood search strategy is introduced to improve the spatial continuity and physical feasibility of the predicted trajectories. Finally, experimental results on two constructed trajectory datasets demonstrate that the proposed method significantly outperforms several state-of-the-art models across multiple error metrics, confirming its superior prediction accuracy and strong cross-scenario generalization capability.
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Fiber-optic bolometer with low detection limit fabricated using thermal release tape and precise laser heating
Fiber-optic bolometers (FOBs) based on a fiber-tipped silicon Fabry–Perot (FP) interferometric temperature sensor and a gold disk absorber have been shown to be an attractive alternative to conventional resistive bolometers for plasma radiation measurement in fusion devices. Either a high-finesse FP or a low-finesse FP can be used, each with trade-offs between noise performance and fabrication complexity. In this paper, we present an FOB design that overcomes these limitations by combining a low-finesse long silicon FP cavity with a large gold disk absorber to achieve enhanced sensitivity and noise performance without increasing the fabrication complexity and the time constant. We also demonstrated a fabrication method for the sensor head facilitated by thermal release tape and precise laser heating. Our FOB demonstrates a temperature resolution of 0.08 mK, a cooling time constant of 230 ms, and a noise equivalent power density of 0.015 W m−2. This represents an eightfold improvement over previous high-finesse FOBs and 26-fold improvement over previous low-finesse FOBs with similar demodulation bandwidths and similar cooling time constants.