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At present, the research of soft crawling robot pays more attention to the material manufacturing, but neglects the robot modeling. The high degree of freedom of the soft crawling robot makes it more difficult to establish its motion model and analyzes its motion performance. The centimeter-level wireless driven soft crawling robot has great advantages and application scenarios in narrow space exploration. Therefore, this paper proposed a soft robot driven by magnetism to crawling inchworm. By studying the crawling behavior of inchworm and the characteristics of flexible materials driven by magnetism, the structure of the soft robot was designed and the motion model of inchworm was established. The motion model is analyzed and simulated, the structure size of the robot is optimized, and the effectiveness of the model is verified by experiments. The robot's crawling motion is realized by coupling the structure of the robot's torso and legs with the flexible magnetic film. Driven by the alternating magnetic field, the maximum motion speed of the robot is 28.24 mm/s. At the same time, the robot can also move in narrow space such as pipes, which can satisfy the centimeter-level space detection and ensure the high efficiency, providing a new idea for narrow space detection. [ABSTRACT FROM AUTHOR]

Wind speed prediction is a complex task in the field of wind energy resource forecasting. For prediction, highly nonlinear temporal features are required for wind speed time series, which are highly variable data sources. In this paper, a two-stage fuzzy nonlinear fusion model is proposed for the utmost short-term wind speed prediction problem of 5 and 15 min ahead. First, empirical mode decomposition decomposes the wind speed time series, and the resulting intrinsic mode functions (IMFs) are employed as features in the later modeling study. The first stage of modeling follows. Each IMF feature is fed into one of the three sub-models of the T-S fuzzy model based on triangle, fuzzy C-mean clustering, and Gaussian, yielding three prediction outputs. The second stage is then modeled, which takes advantage of the IT2-based nonlinear aggregation mechanism to overcome the inherent flaws of single methods and linear combinations. Finally, two real cases from wind farms in Colorado, USA, are analyzed to demonstrate the validity of the TFG-IT2 model. The prediction effect of various approaches was measured using three assessment indicators and a statistical test. The simulation results reveal that the TFG-IT2 model outperforms the other seven models in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper describes the development and validation of a rapid internal defect detection method for multilayer composite components. Coplanar array capacitive imaging is based on electrical capacitance tomography, in which all electrodes are arranged in a single plane. The coplanar array capacitive sensor system is based on the capacitive edge effect and reconstructs the dielectric distribution in the sensitive area by measuring the capacitance of the sensor. A 4 × 3 array of coplanar electrode sensors is established and used to image the defects in the inner layers of multilayer composite components. Using a 3D model of the sensor and the sensitivity field, the variation pattern of the sensitivity field is analyzed. By placing different objects into the sensitivity area of the system, changes in the dielectric constant can be observed. Multilayer composite components with void defects are placed in the measurement area for defect detection. The dielectric distribution is visualized by reconstruction algorithms from the capacitance data and sensitivity field data. The results show that the imaging system based on a coplanar array capacitive sensor can reproduce the location of defects and realize the nondestructive testing of complex multilayer composite components. [ABSTRACT FROM AUTHOR]

With the continuous development of sensor and computer technology, human-computer interaction technology is also improving. Gesture recognition has become a research hotspot in human-computer interaction, sign language recognition, rehabilitation training, and sports medicine. This paper proposed a method of hand gestures recognition which extracts the time domain and frequency domain features from surface electromyography (sEMG) by using an improved multi-channels convolutional neural network (IMC-CNN). The 10 most commonly used hand gestures are recognized by using the spectral features of sEMG signals which is the input of the IMC-CNN model. Firstly, the third-order Butterworth low-pass filter and high-pass filter are used to denoise the sEMG signal. Secondly, effective sEMG signal segment from denoised signal is applied. Thirdly, the spectrogram features of different channels' sEMG signals are merged into a comprehensive improved spectrogram feature which is used as the input of IMC-CNN to classify the hand gestures. Finally, the recognition accuracy of IMC-CNN model, three single channel CNN of IMC-CNN model, SVM, LDA, LCNN and EMGNET are compared. The experiment was carried out on the same dataset and the same computer. The experimental results showed that the recognition accuracy, sensitivity and accuracy of the proposed model reached 97.5%, 97.25% and 96.25% respectively. The proposed method not only has high average recognition accuracy on MYO collected dataset, but also has high average recognition accuracy on NinaPro DB5 dataset. Overall, the proposed model has more advantages in accuracy and efficiency than that of the comparison models. [ABSTRACT FROM AUTHOR]

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Structural defects are inevitably produced by residual stress in the 3D printing process, which reduce the structural-functional properties. For a typical 3D printing lattice structure, a new sliding window approach to intelligently identify defects, based on the difference principle, in the inspection process was proposed. Industrial computed tomography (CT) was used for image information acquisition. The grayscale standard deviation in the local range of the CT slice is calculated to characterize the grayscale variation of the defect location. On this basis, a sliding window method is proposed for traversing CT slices. Three different sizes of window and the corresponding calculation methods were established, and then the appropriate window was selected through comparison. After differential processing is performed on the traversed image to extract the defect feature, automatic defect recognition rules are created. The experimental results show that the recognition rate of the proposed method is 98.5% for typical internal defects of metal 3D lattice structures and the validity of the method is verified. Compared with a manual marking method, this method can effectively improve the efficiency of defect detection. [ABSTRACT FROM AUTHOR]

Coplanar array capacitive imaging represents a non-destructive testing technology. To address the challenge of reduced utilisation of the high-sensitivity region caused by the ‘soft field’ effect characterised by severe nonlinear and inhomogeneous distribution of coplanar array capacitance sensitivity, resulting in difficulties in solving the problem of medium distribution, the proposed approach involves coplanar array capacitance sensor rotational scanning technology. This paper establishes both a physical rotation model and a sensitive field rotation model for a coplanar array capacitance sensor, demonstrating the inability to resolve the low-sensitivity region for medium distribution and illustrating how rotary scanning detection can enhance the utilisation rate of the high-sensitivity region. In conjunction with improved regional energy contrast, the images acquired under various rotation angles are fused, and experimental verification is conducted. The experimental results underscore that rotational scanning detection techniques can increase the utilisation rate of the high-sensitivity region within the coplanar array capacitance sensor by 74.69%. This enhancement effectively captures essential information within the reconstructed image and successfully addresses the challenge of solving difficulties in dielectric distribution. The research outcomes provide valuable insights for advancing non-destructive testing technologies employing coplanar array capacitance sensors. [ABSTRACT FROM AUTHOR]

A coplanar electrode array sensor is established for the imaging of composite-material adhesive-layer defect detection. The sensor is based on the capacitive edge effect, which leads to capacitance data being considerably weak and susceptible to environmental noise. The inverse problem of coplanar array electrical capacitance tomography (C-ECT) is ill-conditioning, in which a small error of capacitance data can seriously affect the quality of reconstructed images. In order to achieve a stable image reconstruction process, a redundancy analysis method for capacitance data is proposed. The proposed method is based on contribution rate and anti-interference capability. According to the redundancy analysis, the capacitance data are divided into valid and invalid data. When the image is reconstructed by valid data, the sensitivity matrix needs to be changed accordingly. In order to evaluate the effectiveness of the sensitivity map, singular value decomposition (SVD) is used. Finally, the two-dimensional (2D) and three-dimensional (3D) images are reconstructed by the Tikhonov regularization method. Through comparison of the reconstructed images of raw capacitance data, the stability of the image reconstruction process can be improved, and the quality of reconstructed images is not degraded. As a result, much invalid data are not collected, and the data acquisition time can also be reduced. [ABSTRACT FROM AUTHOR]

During the electromagnetic launching (EML) process, rail vibrations occur due to the electromagnetic force generated by an acute current. These vibrations result in a reduced launch velocity and decreased launch efficiency. To measure the vibration amplitude of a rail accurately and to analyze the internal relationships between current strength, rail material and vibrations, a small transient vibration in a strong electromagnetic field was studied in this paper. Related experiments were also performed on the test system. Fiber-optic displacement sensors were used to collect vibration data at key locations along the rail. Using virtual instrument technology, a software platform was designed that displayed the vibration waveform and analyzed the time, frequency domain and other parameters. We collected data on the vibration response and the modal parameters of the rail and determined the natural frequencies of the electromagnetic launch system by performing a modal analysis of its vibrations and dynamic responses that combined experimental and theoretical methods. Methods of optimizing the structure’s vibration and dynamic response and of assessing the damage to it could be studied further on the basis of the results described in this paper. [ABSTRACT FROM AUTHOR]

Objective . The challenge for motor imagery (MI) in brain-computer interface (BCI) systems is finding a reliable classification model that has high classification accuracy and excellent robustness. Currently, one of the main problems leading to degraded classification performance is the inaccuracy caused by nonstationarities and low signal-to-noise ratio in electroencephalogram (EEG) signals. Approach . This study proposes a novel attention-based 3D densely connected cross-stage-partial network (DCSPNet) model to achieve efficient EEG-based MI classification. This is an end-to-end classification model framework based on the convolutional neural network (CNN) architecture. In this framework, to fully utilize the complementary features in each dimension, the optimal features are extracted adaptively from the EEG signals through the spatial-spectral-temporal (SST) attention mechanism. The 3D DCSPNet is introduced to reduce the gradient loss by segmenting the extracted feature maps to strengthen the network learning capability. Additionally, the design of the densely connected structure increases the robustness of the network. Main results . The performance of the proposed method was evaluated using the BCI competition IV 2a and the high gamma dataset, achieving an average accuracy of 84.45% and 97.88%, respectively. Our method outperformed most state-of-the-art classification algorithms, demonstrating its effectiveness and strong generalization ability. Significance. The experimental results show that our method is promising for improving the performance of MI-BCI. As a general framework based on time-series classification, it can be applied to BCI-related fields., (© 2022 IOP Publishing Ltd.)

A coplanar electrode array sensor is established for the imaging of composite-material adhesive-layer defect detection. The sensor is based on the capacitive edge effect, which leads to capacitance data being considerably weak and susceptible to environmental noise. The inverse problem of coplanar array electrical capacitance tomography (C-ECT) is ill-conditioning, in which a small error of capacitance data can seriously affect the quality of reconstructed images. In order to achieve a stable image reconstruction process, a redundancy analysis method for capacitance data is proposed. The proposed method is based on contribution rate and anti-interference capability. According to the redundancy analysis, the capacitance data are divided into valid and invalid data. When the image is reconstructed by valid data, the sensitivity matrix needs to be changed accordingly. In order to evaluate the effectiveness of the sensitivity map, singular value decomposition (SVD) is used. Finally, the two-dimensional (2D) and three-dimensional (3D) images are reconstructed by the Tikhonov regularization method. Through comparison of the reconstructed images of raw capacitance data, the stability of the image reconstruction process can be improved, and the quality of reconstructed images is not degraded. As a result, much invalid data are not collected, and the data acquisition time can also be reduced., Competing Interests: The authors declare no conflict of interest.

This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied for data processing of measured capacitance values. Then, the improved PSO algorithm is proposed and applied for image reconstruction. Finally, some experiments are provided to verify the effectiveness of the proposed method in defect detection for adhesive layer of thermal insulation materials. The performance comparisons demonstrate that the proposed method has higher precision by comparing with traditional ECT algorithms., Competing Interests: The authors declare no conflict of interest.