Nuclear safety has always been a focal point in the field of nuclear applications. Mobile robots carrying specific sensors for nuclear-radiation detection have become an alternative to manual detection. This work presents an autonomous α/β radiation mapping framework, using a mobile robot carrying a light detection and ranging (LiDAR) and a nuclear-radiation-detection sensor. The method employs simultaneous localization and mapping (SLAM) techniques and radiation-detection sensors. Cartographer is adopted as a demonstration example to map the unknown environment. Radiation data are obtained through the radiation detection sensor and projected onto the environment map after coordinate alignment. The color-coded radiation map is shown on the environment map according to the dose rate. The simulation and real-environment experiments in a robot-operating system (ROS) validate the effectiveness of the proposed method in different radiation scenarios for both indoor and outdoor environments.

An uncollimated scintillator array encodes source direction and distance in both its spatial response and total count rate. We modeled an isotropic 11.6 keV point source and a 12 × 12 yttrium orthosilicate array in Geant4 11.3.2, using a scintillation yield of 15,300 photons MeV⁻¹. A spatial–global dual-branch convolutional neural network, Full, combined normalized response patterns with six global descriptors for joint localization conditional on known emitted exposure. The response library comprised 272 training and 96 validation positions, with a 72-position multi-distance evaluation grid and 18 fixed-distance diagnostic positions. On the multi-distance grid, Full achieved three-seed mean directional and distance MAEs of 10.4° and 26.0 mm. At 300 randomly sampled positions and 4 million primaries, the corresponding means were 15.6° and 52.6 mm. An input-matched multilayer perceptron gave 16.7°/55.2 mm at 4 million primaries. Same-coordinate exposure controls and independent Monte Carlo repetitions characterized statistical sensitivity. A forward-response inverse estimator performed better in a far-field, near-axis subset. The results support combining spatial and rate information for localization under the simulated conditions and identify exposure and viewing geometry as major limits on accuracy.

In this paper, we propose an ensemble approach for the intelligent fault diagnosis of machinery, which consists of six feature selection methods and classifiers. In the proposed approach, six filters, based on distinct metrics, are utilized. Each filter is combined with an improved sparse representation classifier (ISRC) to form a base model, in which the ISRC is an improved version of a sparse representation classifier and has the advantages of high classification accuracy and being less time consuming than the unimproved version. For each base model, the filter selects a feature subset that is used to train and test the ISRC, where the two hyper-parameters involved in the filter and ISRC are optimized by the binary particle swarm optimization algorithm. The outputs of six base models are aggregated through the cumulative reconstruction residual (CRR), where the CRR is devised to replace the commonly used voting strategy. The effectiveness of the proposed method is verified on six mechanical datasets involving information about bearings and gears. In particular, we conduct a detailed comparison between CRR and voting and carry out an intensive exploration into the question of why CRR is superior to voting in the ensemble model.

Direction and distance affect both the magnitude and spatial distribution of counts in a compact scintillator array, which makes their simultaneous estimation difficult. We modeled radiation transport and detector response in Geant4 11.3.2 for a 12 × 12 yttrium orthosilicate (YSO) array irradiated by an isotropic 11.6 keV photon point source. The analysis assumes a known emitted exposure. A dual-branch convolutional neural network combined the normalized 12 × 12 response with six global descriptors—the total photon rate, two centroid coordinates, two spatial spreads, and covariance—to estimate a front-hemisphere unit direction vector and the source-to-detector distance. The dataset contained 272 training, 96 validation, and 72 in-dependent test positions. Across three initializations, the full model achieved a polar-angle MAE of 5.16 ± 0.42°, circular azimuth MAE of 13.56 ± 0.85°, three-dimensional directional MAE of 9.68 ± 0.84°, distance MAE of 26.33 ± 1.43 mm, and MAPE of 6.29 ± 0.26%. Shape-only and Global-only ablations showed that the spatial pattern and global statistics contributed different information, and the full model outperformed Random Forest, Extra Trees, and template/k-nearest-neighbor baselines on the same test set. The dominant failure mode was azimuth estimation for far-field sources close to the array normal. These results are lim-ited to the simulated test positions and do not establish real-detector performance or reliable full-angle localization at 1 m.

The NaI(TI) detector is highly favored in gamma radiation detection and widely applied in fields such as environmental radiation monitoring, nuclear medicine, and laboratory gamma-ray spectroscopy. Its detection performance determines the results of quantitative gamma-ray detection, making it a crucial indicator in detector design and development. This study employs the Monte Carlo method and utilizes TopMC 1.0 software to establish a NaI(TI) detector model. First, the effects of crystal size, ray energy, cladding thickness, and distance on the detector’s detection efficiency were investigated. Subsequently, the energy resolution and peak-to-total ratio of the detector were simulated and calculated, with comparisons made to experimental values. The results indicate that all three detection efficiencies of the NaI(TI) detector are positively correlated with crystal size and exhibit an initial increase followed by a decrease with rising gamma-ray energy. Both the absolute detection efficiency and full-energy peak detection efficiency first increase and then decrease with increasing cladding thickness, while showing a negative correlation with detection distance. The intrinsic detection efficiency is almost unaffected by cladding thickness and initially rises before declining with increasing detection distance. The simulated values of energy resolution closely match experimental values, improving with higher gamma-ray energy. The deviation between simulated and experimental values for different source peak-to-total ratios remains within 6.25%, verifying the model’s reliability and the accuracy of simulation data. These findings provide valuable references and guidance for optimizing detection performance, conducting source-free efficiency calibration, and structural design of NaI(TI) detectors.