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Abstract In this paper, the accurate free vibration characteristics of thin-walled box beam are discussed and the kinematic model is established by using fine shear deformation theory. The model adopts the in-plane and out-of-plane displacement fields including extension, torsion, warping and distortion, as well as the transverse shear due to bending and warping due to torsion. One-dimensional high-order beam theory is applied to the dynamic solution of thin-walled box beam, and the analytical solution of high free vibration modes of multi-deformation coupled modes under different boundary conditions is derived. The results show that warping, distortion and shear deformation play an important role in the free vibration characteristics of thin-walled box beam, and the validity of the model is verified. In addition, finite element software (ANSYS) is used for finite element simulation. The application of vibration mode in the structure design of thin-walled box beam is summarized, especially in the case of higher natural frequency. The calculation method is in good agreement with the finite element results. [ABSTRACT FROM AUTHOR]

Face recognition technology has been widely used in the field of artificial intelligence. The technology needs to be carried out normally under the appropriate light, however, there is not ideal light, even poor-lighted for the face recognition device, and with the head in deflect angle. The poor-lighted under various head poses will influence the face recognition significantly. To address the issue, we present a novel and practical architecture based on deep fully convolutional neural network and generative adversarial networks for illumination normalization of facial images. The proposed method is termed as illumination normalization generative adversarial network. Compared with previous methods based on deep learning, our approach does not require identity and illumination label as input. We preserve identities of faces by an elaborately-designed generator together with content loss. Moreover, the framework of our scheme is simpler than previous methods based on deep learning. It can address the illumination of frontal and non-frontal face. In order to fairly evaluate the proposed method against state-of-the-art models, the peak signal to noise ratio is employed to estimate the performance of illumination normalization algorithm. Experimental results show that the proposed method achieves favorable normalization results against previous models under various head poses and illumination challenges. [ABSTRACT FROM AUTHOR]

Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%. [ABSTRACT FROM AUTHOR]

The evaluation metrics of the spatial robustness evaluation (SRE) and the temporal robustness evaluation (TRE) are listed in the graphical abstract. In the long term tracking, the deformation of target is considered to be difficult and challenge. Our proposed algorithm is better than other algorithms. [Display omitted] Automatic target detection and tracking systems are used extensively in complex scenes. In long-term tracking, some visual attributes of objects are changing, such as illumination, size, profile, and so on. To address the issue, it is particularly important to describe the essential properties of the objects in tracking. An enhanced kernelized correlation filter tracking strategy fused multiple features with location prediction is proposed. To make the object appearance models more accuracy and robustness, based on the original histogram of oriented gradient features, we integrate the hue, saturation, value, and grayscale information to construct a new descriptor to represent the target appearance. Moreover, location prediction and bi-linear interpolation are employed to obtain the more accurate target position. Experiments show that the proposed strategy can obtain superior or competitive performance in challenging benchmark data sets. In practice, the algorithm is applied to track shuttle bus targets in the airport apron. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult. Design/methodology/approach: In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition. Findings: The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition. Originality/value: A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal. [ABSTRACT FROM AUTHOR]

Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O (1) can be achieved regardless of image resolution theoretically. [ABSTRACT FROM AUTHOR]

As a core step of obstacle avoidance and path planning, dynamic obstacle detection is critical for autonomous driving. This study aimed to propose a dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving. First, a drivable area of an unmanned vehicle was detected using U–V disparity images. Then, obstacles in the drivable area were detected using U–V disparity images and the geometric relationship between obstacle size and its disparity. Finally, the motion likelihood of each obstacle was estimated by compensating the camera ego-motion. The innovation of the proposed method was that the searching range of the moving obstacles was greatly narrowed by detecting the obstacles in the drivable area, which greatly improved not only the moving obstacle detection efficiency but also the detection accuracy. Datasets from the KITTI benchmark and our self-acquired campus scene data were chosen as testing samples. The experimental results showed that our method could achieve high detection precision, low missed detection rate and less time consumption. [ABSTRACT FROM AUTHOR]

As a core step of obstacle avoidance and path planning, dynamic obstacle detection is critical for autonomous driving. This study aimed to propose a dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving. First, a drivable area of an unmanned vehicle was detected using U–V disparity images. Then, obstacles in the drivable area were detected using U–V disparity images and the geometric relationship between obstacle size and its disparity. Finally, the motion likelihood of each obstacle was estimated by compensating the camera ego-motion. The innovation of the proposed method was that the searching range of the moving obstacles was greatly narrowed by detecting the obstacles in the drivable area, which greatly improved not only the moving obstacle detection efficiency but also the detection accuracy. Datasets from the KITTI benchmark and our self-acquired campus scene data were chosen as testing samples. The experimental results showed that our method could achieve high detection precision, low missed detection rate and less time consumption. [ABSTRACT FROM AUTHOR]

Multiple problems such as target assignment timeliness and path energy optimization make it challenging for USVs traversing targets. This paper proposes a novel multiple targets traversing path planning algorithm for USVs, which includes Event-Triggered bundled (ET-Bundled) genetic target assignment method and dynamic elliptical guidance region (DEGR) path planning method. In terms of solving above problems, comparative simulation studies with the state-of-the-art methods demonstrate that ET-Bundled can not only ensures that the traversing time and path length do not exceed the constraint range, but also reduces the average CPU time of membrane structure by 60%, and DEGR can optimize the path energy consumption of EGR path by 34%. [ABSTRACT FROM AUTHOR]

Previous studies have shown that contrast-enhanced ultrasound (CEUS) can be used quantitatively to analyze microcirculation blood perfusion in hepatocellular carcinoma patients. However, limited data have described the application of CEUS in hepatic microcirculation after liver ischemic-reperfusion injury (IRI). The purpose of this study was to explore the use of CEUS quantitatively to assess liver microcirculation after liver IRI. We randomly sorted 45 New Zealand rabbits into 3 groups (15 in each). Group A was a control group in which the rabbits underwent laparotomy alone. In groups B and C, hepatic blood was blocked for 30 min. Simultaneously, rabbits in group C underwent left lateral lobe resection. After 30 min of ischemia, CEUS was conducted after 0 h, 1 h, 6 h and 24 h of reperfusion in the 3 groups. Time-intensity curves (TICs) for CEUS were constructed and quantitative parameters (maximum intensity [IMAX], rise time [RT], time to peak [TTP] and mean transit time [mTT]) were obtained. In addition, serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were computed to estimate liver function before the operation and at 0 h, 1 h, 6 h and 24 h after reperfusion, respectively. Pathologic changes in the liver after reperfusion were also observed. Simultaneously, the correlations between serum transaminase and a variety of quantitative analysis parameters were analyzed. In groups B and C, the IMAX value decreased; whereas RT, TTP, mTT and serum ALT and AST levels increased significantly in comparison with those in group A after 0 h and 1 h of reperfusion. The pathology revealed that erythrocytes were destroyed and microcirculation was disturbed. Then, at 6 h of reperfusion, the IMAX continued to decrease. Additionally, the levels of RT, TTP, mTT and serum ALT and AST increased in comparison with those at 1 h of reperfusion. The pathologic analysis revealed inflammatory cell aggregation and leukocyte infiltration. After 24 h of reperfusion, the IMAX was reduced in comparison with that of the 6-h group. The levels of RT, TTP, mTT and serum ALT and serum AST were increased in comparison with that of the 6-h group. These findings were in accordance with the pathologic analysis. In addition, serum transaminase had a negative correlation with IMAX (p < 0.001) and a positive correlation with RT, TTP and mTT (all p < 0.001). So, in conclusion, the quantitative analysis of CEUS can be used to assess hepatic microcirculation after liver IRI. [ABSTRACT FROM AUTHOR]

A new three-dimensional metric reconstruction method for straight homogeneous generalized cylinders with elliptical cross-section (SHGC-E) from a single image is addressed. By using an elliptical truncated cone shape, the elliptical cross-section is computed by back-projection. The conditions under which the imaged limb can be considered an imaged meridian under perspective projection are discussed and proved. Furthermore, the imaged meridian of SHGC-E is identified and recovered by some of the proposed special properties of SHGC-E. [ABSTRACT FROM AUTHOR]