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The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system.

A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets.

The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance.

Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

The purpose of this study is to explore the impact of environmental pollution and industrial structure upgrading on environmental pollution in different stages based on the temporal and spatial heterogeneity of economic development level and industrial structure upgrading level in eastern, central and western regions of China and discuss whether there is adjustment effect and threshold effect in the process of economic growth affecting environmental pollution, and finally realizes sustainable economic development.

Based on panel data from 30 provincial-level administrative regions of China (excluding Tibet and Hong Kong, Macao and Taiwan) from 2000 to 2019, this paper uses the environmental Kuznets curve, regulating effect model and panel threshold model to analyze the impact of economic growth and industrial structure upgrading on environmental pollution.

The results present that the uneven distribution of natural resources leads to different levels of economic development and industrial structure upgrading in eastern and western regions, and its impact on environmental pollution is also different. Economic growth and industrial structure upgrading have a positive effect on environmental pollution, and the relationship between economic growth and environmental pollution is inverted U-shaped. At present, the eastern, central and western regions of China are at the right end of the inverted U-shaped relationship. In general, industrial structure upgrading in eastern, central and western regions has a significant inhibitory effect on environmental pollution. Industrial structure upgrading has a negative moderating effect on the relationship between economic growth and environmental pollution, and the regulating effect is most significant in the central region, followed by the eastern region, and not significant in the western region. The results of panel threshold model show that the industrial structure upgrading can slow down the positive impact of economic growth on environmental pollution and strengthen the negative moderating effect of industrial structure upgrading on economic growth and environmental pollution.

The innovation of this study is to bring economic growth, industrial structure upgrading and environmental pollution into a unified analytical framework, analyze the impact of economic development and industrial structure upgrading levels in different periods on environmental pollution, and select industrial structure upgrading as the moderating variable and threshold variable. It provides a thought for the influence mechanism of different levels of industrial structure upgrading on economic growth and environmental pollution. Based on the panel data in China, this study emphasizes the concept of sustainable development, adheres to green development and proposes relevant policies to improve environmental pollution. And this paper proposes relevant policies to improve environmental pollution from the perspective of transforming economic growth mode and optimizing industrial structure in China, which also has reference significance for developing countries to realize sustainable economic development.

The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.

This study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.

The experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.

This study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.