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As the market subject of China's economic development, private enterprises play an important role in fighting against pollution and solving ecological and environmental problems. It is especially important to evaluate the green development progress of Chinese private enterprises in the epidemic era. This paper conducts a questionnaire survey on 10,623 private enterprises in 31 provinces of China, and based on 6223 industrial survey results, it focuses on the production and operation status of private enterprises in terms of pollution reduction performance, energy saving, and carbon reduction intensity in order to construct a green development progress index. The spatial Moran index test and the spatial Durbin model are used to analyze the regional correlations and influencing factors of green development progress in China. The results show that the green development of enterprises with a main business income of more than 100 million CNY and key areas such as Fenwei Plain have improved significantly in 2021, especially with the increase in a private enterprise scale, the carbon reduction regime, the pollution abatement regime, and the pollution control manner, and because the investment, profitability, and pollution discharge of private enterprises is more significant. The indexes of the provinces in the southeast coastal area and the northeastern region of China are the highest and lowest, respectively, in terms of pollution, which is demonstrated by the spatial aggregation effect on the surrounding areas by Moran local index analysis. The urbanization level and government financial support for environmental protection are just two of the negative factors regarding this issue, while the economic development level and industrial structure are positive factors that have a spatial spillover effect. [ABSTRACT FROM AUTHOR]

Aiming at solving the issue of blurred images and difficult recognition of digital meters encountered by inspection robots in the inspection process, this paper proposes a deep-learning-based method for blurred image restoration and LED digital identification. Firstly, fast Fourier transform (FFT) is used to perform blur detection on the acquired images. Then, the blurred images are recovered using spatial-attention-improved adversarial neural networks. Finally, the digital meter region is extracted using the polygon-YOLOv5 model and corrected via perspective transformation. The digits in the image are extracted using the YOLOv5s model, and then recognized by the CRNN for digit recognition. It is experimentally verified that the improved adversarial neural network in this paper achieves 26.562 in the PSNR metric and 0.861 in the SSIM metric. The missing rate of the digital meter reading method proposed in the paper is only 1% and the accuracy rate is 98%. The method proposed in this paper effectively overcomes the image blurring problem caused by the detection robot during the detection process. This method solves the problems of inaccurate positioning and low digital recognition accuracy of LED digital meters in complex and changeable environments, and provides a new method for reading digital meters. [ABSTRACT FROM AUTHOR]

Conventional reconnaissance camera systems have been flown on manned aircraft, where the weight, size, and power requirements are not stringent. However, today, these parameters are important for unmanned aerial vehicles (UAVs). This article provides a solution to the design of airborne large aperture infrared optical systems, based on a monocentric lens that can meet the strict criteria of aerial reconnaissance UAVs for a wide field of view (FOV) and lightness of airborne electro-optical pod cameras. A monocentric lens has a curved image plane, consisting of an array of microsensors, which can provide an image with 368 megapixels over a 100° FOV. We obtained the initial structure of a five-glass (5GS) asymmetric monocentric lens with an air gap, using ray-tracing and global optimization algorithms. According to the design results, the ground sampling distance (GSD) of the system is 0.33 m at 3000 m altitude. The full-field modulation transfer function (MTF) value of the system is more than 0.4 at a Nyquist frequency of 70 lp/mm. We present a primary thermal control method, and the image quality was steady throughout the operating temperature range. This compactness and simple structure fulfill the needs of uncrewed airborne lenses. This work may facilitate the practical application of monocentric lens in UAVs. [ABSTRACT FROM AUTHOR]

Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time. [ABSTRACT FROM AUTHOR]

To solve the problem of inflexibility of offline hand–eye calibration in "eye-in-hand" modes, an online hand–eye calibration method based on the ChArUco board is proposed in this paper. Firstly, a hand–eye calibration model based on the ChArUco board is established, by analyzing the mathematical model of hand–eye calibration, and the image features of the ChArUco board. According to the advantages of the ChArUco board, with both the checkerboard and the ArUco marker, an online hand–eye calibration algorithm based on the ChArUco board is designed. Then, the online hand–eye calibration algorithm, based on the ChArUco board, is used to realize the dynamic adjustment of the hand–eye position relationship. Finally, the hand–eye calibration experiment is carried out to verify the accuracy of the hand–eye calibration based on the ChArUco board. The robustness and accuracy of the proposed method are verified by online hand–eye calibration experiments. The experimental results show that the accuracy of the online hand–eye calibration method proposed in this paper is between 0.4 mm and 0.6 mm, which is almost the same as the offline hand–eye calibration accuracy. The method in this paper utilizes the advantages of the ChArUco board to realize online hand–eye calibration, which improves the flexibility and robustness of hand–eye calibration. [ABSTRACT FROM AUTHOR]

For the task of obstacle detection in a complex traffic environment, this paper proposes a road-free space extraction and obstacle detection method based on stereo vision. The proposed method combines the advantages of the V-disparity image and the Stixel method. Firstly, the depth information and the V-disparity image are calculated according to the disparity image. Then, the free space on the road surface is calculated through the RANSAC algorithm and dynamic programming (DP) algorithm. Furthermore, a new V-disparity image and a new U-disparity image are calculated by the disparity image after removing the road surface information. Finally, the height and width of the obstacles on the road are extracted from the new V-disparity image and U-disparity image, respectively. The detection of obstacles is realized by the height and width information of obstacles. In order to verify the method, we adopted the object detection benchmarks and road detection benchmarks of the KITTI dataset for verification. In terms of the accuracy performance indicators quality, detection rate, detection accuracy, and effectiveness, the method in this paper reaches 0.820, 0.863, 0.941, and 0.900, respectively, and the time consumption is only 5.145 milliseconds. Compared with other obstacle detection methods, the detection accuracy and real-time performance in this paper are better. The experimental results show that the method has good robustness and real-time performance for obstacle detection in a complex traffic environment. [ABSTRACT FROM AUTHOR]

In order to improve industrial production efficiency, a hand–eye system based on 3D vision is proposed and the proposed system is applied to the assembly task of workpieces. First, a hand–eye calibration optimization algorithm based on data filtering is proposed in this paper. This method ensures the accuracy required for hand–eye calibration by filtering out part of the improper data. Furthermore, the improved U-net is adopted for image segmentation and SAC-IA coarse registration ICP fine registration method is adopted for point cloud registration. This method ensures that the 6D pose estimation of the object is more accurate. Through the hand–eye calibration method based on data filtering, the average error of hand–eye calibration is reduced by 0.42 mm to 0.08 mm. Compared with other models, the improved U-net proposed in this paper has higher accuracy for depth image segmentation, and the Acc coefficient and Dice coefficient achieve 0.961 and 0.876, respectively. The average translation error, average rotation error and average time-consuming of the object recognition and pose estimation methods proposed in this paper are 1.19 mm, 1.27°, and 7.5 s, respectively. The experimental results show that the proposed system in this paper can complete high-precision assembly tasks. [ABSTRACT FROM AUTHOR]

Herein we design a fiber sensor able to simultaneously measure the temperature and the pressure under harsh conditions, such as strong electromagnetic interference and high pressure. It is built on the basis of the fiber-optic Fabry–Perot (F–P) interference and the temperature sensitive mechanism of fluorescent materials. Both halogen lamps and light-emitting diodes (LED) are employed as the excitation light source. The reflected light from the sensor contains the low coherent information of interference cavity and the fluorescent lifetime. This information is independent due to the separate optical path and the different demodulation device. It delivers the messages of pressure and temperature, respectively. It is demonstrated that the sensor achieved pressure measurement at the range of 120–400 KPa at room temperature with a sensitivity of 1.5 nm/KPa. Moreover, the linearity of pressure against the cavity length variation was over 99.9%. In the meantime, a temperature measurement in the range of 25–80 °C, with a sensitivity of 0.0048 ms/°C, was obtained. These experimental results evince that this kind of sensor has a simple configuration, low-cost, and easy fabrication. As such, it can be particularly applied to many fields. [ABSTRACT FROM AUTHOR]

Herein we design a fiber sensor able to simultaneously measure the temperature and the pressure under harsh conditions, such as strong electromagnetic interference and high pressure. It is built on the basis of the fiber-optic Fabry⁻Perot (F⁻P) interference and the temperature sensitive mechanism of fluorescent materials. Both halogen lamps and light-emitting diodes (LED) are employed as the excitation light source. The reflected light from the sensor contains the low coherent information of interference cavity and the fluorescent lifetime. This information is independent due to the separate optical path and the different demodulation device. It delivers the messages of pressure and temperature, respectively. It is demonstrated that the sensor achieved pressure measurement at the range of 120⁻400 KPa at room temperature with a sensitivity of 1.5 nm/KPa. Moreover, the linearity of pressure against the cavity length variation was over 99.9%. In the meantime, a temperature measurement in the range of 25⁻80 °C, with a sensitivity of 0.0048 ms/°C, was obtained. These experimental results evince that this kind of sensor has a simple configuration, low-cost, and easy fabrication. As such, it can be particularly applied to many fields.

Laser-induced-plasma-assisted ablation (LIPAA) is a promising micro-machining method that can fabricate microstructure on hard and transparent double-polished single crystal sapphire (SCS). While ablating, a nanosecond pulse 1064 nm wavelength laser beam travels through the SCS substrate and bombards the copper target lined up behind the substrate, which excites the ablating plasma. When laser fluence rises and is above the machining threshold of copper but below that of SCS, the kinetic energy of the copper plasma generated from the bombardment is mainly determined by the laser fluence, the repetition rate, and the substrate-to-target distance. With a lower repetition rate, SCS becomes metallized and gains conductivity. When micro-machining SCS with a pulsed laser are controlled by properly controlling laser machining parameters, such as laser fluence, repetition rate, and substrate-to-target distance, LIPAA can ablate certain line widths and depths of the microstructure as well as the resistance of SCS. On the contrary, conductivity resistance of metalized sapphire depends on laser parameters and distance in addition to lower repetition rate.

Surface-enhanced Raman scattering (SERS) has been extensively investigated as an effective approach for trace species detection. Silver nanostructures are high-sensitivity SERS substrates in common use, but their poor chemical stability impedes practical applications. Herein, a stable and sensitive SERS substrate based on the hybrid structures of graphene/silver film/laser-textured Si (G/Ag/LTSi) was developed, and a simple, rapid, and low-cost fabrication approach was explored. Abundant nanoparticles were directly created and deposited on the Si surface via laser ablation. These aggregated nanoparticles functioned as hotspots after a 30 nm Ag film coating. A monolayer graphene was transferred to the Ag film surface to prevent the Ag from oxidation. The SERS behavior was investigated by detecting R6G and 4-MBT molecules. The experimental results indicate that the maximum enhancement factor achieved by the G/Ag/LTSi substrate is over 10⁷ and less than 23% SERS signals lost when the substrate was exposed to ambient conditions for 50 days. The covering graphene layer played crucial roles in both the Raman signals enhancement and the Ag nanostructure protection. The stable and sensitive SERS performance of G/Ag/LTSi substrate evince that the present strategy is a useful and convenient route to fabricate large-area graphene-silver plasmonic hybrids for SERS applications., Competing Interests: The authors declare no conflict of interest.