Geospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions. We explore the Earth’s geomagnetic field, a globally distributed and naturally occurring resource, as a reliable alternative for navigation. Since vehicles can only observe the geomagnetic field along their traversed paths, they must rely on incomplete information to infer the navigation strategy; therefore, we formulate the navigation problem as a partially observed Markov decision process (POMDP). To address this POMDP, we employ proximal policy optimization with long short-term memory (PPO-LSTM), a deep reinforcement learning framework that captures temporal dependencies and mitigates the effects of noise. Using real-world geomagnetic data from the international geomagnetic reference field (IGRF) model, we validate our approach through experiments under noisy conditions. The results demonstrate that PPO-LSTM outperforms baseline algorithms, achieving smoother trajectories and higher heading accuracy. This framework effectively handles the uncertainty and partial observability inherent in geomagnetic navigation, enabling robust policies that adapt to complex gradients and offering a robust solution for geospatial navigation.

Distributed laser measurement systems, widely used in high-end equipment such as airplanes, ships, and other manufacturing fields, face challenges in large spatial measurements due to laser plane obstructions and weak intersections. This paper introduces a novel omnidirectional sensor with enhanced adaptability to complex environments and improved measurement accuracy. Initially, an integrated omnidirectional measurement model is established, followed by the analysis of the optical path of the front-end detector, and the design of a signal-conditioning circuit for the photoelectric conversion of the front-end laser signal, Subsequently, a circuit testing platform is established to validate the detection functionality, and the corresponding results indicate that the symmetry of the output waveform is under 10 ns, the response time is under 100 ns, and the maximum detection distance is 22 m. Further, experimental results demonstrate the superiority of omnidirectional sensors over planar ones in complex environments, successfully receiving 360° laser signals. The positional accuracy of the common point to be measured on the top of the omnidirectional sensor is confirmed to exceed 0.05 mm, and the accuracy of the angle of attitude exceeds 0.04°. Using the laser tracker, the measurement accuracy of the system is verified to be better than 0.3 mm. When rotating in the horizontal and pitch directions, the measurement accuracy is better than 0.35 mm and 0.47 mm, respectively, fulfilling the sub-millimeter precision requirement and expanding the application scope of distributed laser measurement systems.

To address the insufficient representation of fault characteristics in rotating machinery vibration signals, the sensitivity of conventional Transformers to variations in input response amplitudes, and the limited ability of fixed sequence embedding to preserve continuous temporal information, a rotating machinery fault diagnosis method based on Amplitude-Calibrated Sequence Embedding (ACSE) and a Response-Normalized Transformer (RNformer) is proposed. First, ACSE is designed to construct local temporal feature representations through continuous convolutional mapping, while an amplitude response estimation and adaptive amplitude calibration mechanism is employed to dynamically recalibrate the response intensity at different temporal positions. Rather than simply rescaling the signal amplitude range, amplitude calibration adaptively strengthens the feature contribution of regions associated with fault-induced impacts according to the vibration response intensity, thereby highlighting fault-sensitive information while suppressing the influence of noncritical amplitude fluctuations. In this way, continuous temporal characteristics are preserved while fault-relevant information is enhanced. Second, RNformer is constructed by incorporating a response normalization mechanism into the Transformer encoder to mitigate the interference of abnormal amplitude responses with global feature modeling, thereby improving the stability and robustness of feature representations under complex operating conditions. Finally, a lightweight channel attention mechanism is introduced to further enhance critical fault features and perform fault classification. Experiments were conducted on the Paderborn University bearing dataset and the University of Connecticut gear dataset. The proposed method achieved average diagnostic accuracies of 99.36% and 99.28%, respectively, outperforming the best-performing baseline methods by 1.82 and 1.56 percentage points. These results demonstrated the effectiveness of the proposed method for fault diagnosis.

Type-reduction is an important part of the interval type-2 fuzzy logic system (IT2 FLS), which is also the main factor that influences the effectiveness of IT2 FLS. The enhanced opposite direction searching (EODS) algorithm is currently the fastest form of the Karnik–Mendel algorithm, but it requires a large number of iterations. In this article, an improvement of the enhanced opposite direction searching (IEODS) algorithm is presented to obtain the type reduced set more efficiently. The IEODS algorithm improves the iterative process of the original EODS algorithm through the width of the upper and lower member functions of the IT2 FS. In this way, the IEODS algorithm uses a single-sided iteration process. It has a better initial point to greatly reduce the number of iterations. Quantitative analysis and simulation based experiments show that the IEODS algorithm can save more than 90% of the iterations required in the EODS algorithm. It makes the IEODS algorithm more suitable for practical applications, especially to various development platforms with an explanatory programming language as a tool, greatly expanding the application range of the IT2 FLS.

To address the limited extraction of local fault features, ineffective fusion of multi-scale fault information and interference from redundant noise in vibration-based fault diagnosis of rolling bearings and gears under complex operating conditions, a multi-scale gated feature refinement Vision Transformer (MGFR-ViT), is proposed. First, one-dimensional vibration signals are reconstructed as two-dimensional vibration matrices, making them compatible with patch embedding and Vision Transformer-based feature modeling. A locally enhanced Vision Transformer module is then developed by incorporating a local enhancement mechanism into the standard Vision Transformer architecture, thereby improving the extraction of locally fault-sensitive features while preserving global dependency modeling. Furthermore, a multi-scale gated feature refinement module is introduced to adaptively enhance fault-relevant information and suppress redundant features and noise through parallel multi-scale convolutions with different receptive fields, channel interaction, and gated weighting. Finally, global average pooling and a fully connected classifier are employed for fault classification. Experiments conducted on bearing and gear datasets demonstrated that MGFR-ViT achieved superior diagnostic performance and feature separability compared with several representative fault diagnosis models. Ablation studies further validated the effectiveness and complementarity of the proposed modules. These results indicate that MGFR-ViT provides an effective feature-learning framework for vibration-based fault diagnosis of rotating machinery.