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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.

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.

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.

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.

The paper aims to apply an innovative inversion method to the problem of imaging (location, direction and size) of concrete rebars by means of eddy current measurements.

An accurate numerical model of the probe‐rebar interaction, including eddy currents and skin effect, is considered. The inverse problem is approached with a very efficient inversion procedure previously introduced in a different context.

A critical analysis of the issues to be considered for the quantitative imaging of rebars is given, and the possibility of relevant simplifications in the numerical model outlined, allowing the development of an accurate and computationally efficient method.

The proposed formulation is applied for the first time to the problem of rebars imaging. Experimental tests have been carried out to validate the numerical model and its underlying hypothesis.

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

This study aims to solve the problems of ambiguous localization, large calculation, poor real-time and limited applicability of bolt thread defect detection.

First, the acquired ultrasound image is used to acquire the larger area of the image, which is set as the compliant threaded area. Second, based on the determined coordinates of the center point in each selected region, the set of coordinates on the left and right sides of the bolts is acquired by DBSCAN method with parameters eps and MinPts, which is determined by data set dimension D and the k-distance curve. Finally, the defect detection boundary line fitting is completed using the acquired coordinate set, and the relationship between the distance from each detection point to the curve and d, which is obtained from the measurement of the standard bolt sample with known thread defect, is used to locate the bolt thread defect simultaneously.

In this paper, the bolt thread defect detection method with ultrasonic image is proposed; meanwhile, the ultrasonic image acquisition system is designed to complete the real-time localization of bolt thread defects.

The detection results show that the method can effectively detect bolt thread defects and locate the bolt thread defect location with wide applicability, small calculation and good real-time performance.

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

This paper aims to present an automated system that measures the volume of excavated soil in earthmoving operations. It focuses primarily on presenting the use of the radio frequency identification – real time location technology and image analysis techniques in isolation of stockpiles from their noisy backgrounds, extraction of their geometrical attributes and determination of their location.

An extensive literature review of image analysis techniques is performed to identify the ones most applicable in developing the proposed automated system. A set of techniques are selected and experimented to evaluate their effectiveness in the proposed application. A review of the state‐of‐the‐art distance measurement technologies is also conducted to identify the most suitable system to be used in the proposed system.

A set of image analysis techniques that positively contribute to the accuracy of the proposed system is identified. Geometrical attributes of stockpiles are automatically extracted for volume measurement purposes. The use of an affordable and reliable automated distance measurement tool is also suggested.

Although the proposed methodology is believed to be applicable to most configurations of stockpiles, it was tested on conical ones only.

The current practice of measuring the volume of excavated soil is time consuming and costly. Furthermore, it does not facilitate monitoring of excavation activities on close time intervals. The proposed system overcomes the problems associated with the current practice and provides an automated strategy that can be easily used by field personnel and/or home office management staff to closely monitor the progress of their earthmoving operations without physical human intervention.

The purpose of this paper is to propose a new video prediction-based methodology to solve the manufactural occlusion problem, which causes the loss of input images and uncertain controller parameters for the robot visual servo control.

This paper has put forward a method that can simultaneously generate images and controller parameter increments. Then, this paper also introduced target segmentation and designed a new comprehensive loss. Finally, this paper combines offline training to generate images and online training to generate controller parameter increments.

The data set experiments to prove that this method is better than the other four methods, and it can better restore the occluded situation of the human body in six manufactural scenarios. The simulation experiment proves that it can simultaneously generate image and controller parameter variations to improve the position accuracy of tracking under occlusions in manufacture.

The proposed method can effectively solve the occlusion problem in visual servo control.

Trans-oral robotic surgery (TORS) is increasingly employed in obstructive sleep apnoea (OSA) management. Objective outcomes are generally assessed through polysomnography. Pre-operative magnetic resonance imaging (MRI) can be a useful adjunct in objective upper airway assessment, in particular the tongue base, providing useful information for surgical planning and outcome assessment, though care must be taken in patient positioning during surgery. The purpose of this paper is to identify pitfalls in this process and suggest a protocol for pre-operative MRI scanning in OSA.

This study is a four-patient prospective case-series and literature review. Outcome measures include pre- and post-operative volumetric changes in the pharynx as measured on MRI and apnoea–hypopnea indices (AHI), with cure being OSA resolution or a 50 per cent reduction in AHI.

All patients achieved AHI reduction and/or OSA cure following TORS, despite a decrease in pharyngeal volume measurements at the tongue base level. This study and others lacked standardisation in the MRI scanning protocol, which resulted in an inability to effectively compare pre- and post-operative scans. Pitfalls were related to variation in head/tongue position, soft-tissue marker usage and assessed area boundary limits.

TORS appears to be effective in OSA management. A new protocol for patient positioning and anatomical landmarks is suggested.

The findings could provide directly comparable data between scans and may allow correlation between tongue base volumetric changes and AHI through subsequent and historical study meta-analysis.

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.

To improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.

The authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.

The ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.