•A novel vari-stiffness nonlinear isolator is designed, modeled, and tested.•The nonlinear stiffness can be adjusted and optimized with adjustable devices.•Repulsive force acts as a softening spring ...to increase the isolation bandwidth.•Experimental results validate the excellent nonlinear isolation performance.
Nonlinear isolators can decrease dynamic stiffness and thus enhance vibration isolation performance. This paper presents a novel passive vari-stiffness nonlinear isolator with the magnetic repulsive force and magnetic attractive force effects. The isolator consists of a passive mass-spring-damper element and a nonlinear magnetic coupling element. The nonlinear magnetic coupling element contains three moving ring permanent magnets and three fixed ring magnets. The fixed magnets assembled separately at the three adjustable devices that are uniformly distributed along the circumference. The expression of the nonlinear magnetic force is obtained according to the Amperian current model, and the corresponding equivalent nonlinear magnetic stiffness is derived and approximated. The governing equation of the proposed isolator is established and the transmissibility is analyzed with the harmonic balance method. The experiment was set up to verify both the theoretical model and simulations. Both the numerical and experimental results point out that the repulsive force serves as a softening spring to enhance the isolation performance at the higher frequency, while the attractive force serves as a hardening spring to improve the isolation performance at a relative lower frequency bandwidth.
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•Introduce image block technology to equally divide the overlapping leaf images and label the image sub-blocks.•Use genetic algorithm to optimize support vector machine, and compare and analyze the ...recognition performance of single texture feature or texture combination of different fusion strategies, and get the optimal feature fusion strategy.•An image block reconstruction method based on the comparison of the center point and eight-neighbor label value is proposed, and this is combined with the proportion of image blocks of two labels for comprehensive judgment.
For the problem of a low recognition rate and shape feature failure caused by overlapping seedlings and weeds during the development of an intelligent lettuce weeding robot, a method to identify seedling lettuce and weeds based on an image block and support vector machine (SVM) is proposed, which realizes their precise identification and boundary segmentation. The a* channel is used to grayscale the collected image. The Otsu and morphological methods are selected to extract all the green targets in the image. The connected component analysis method is applied to label the green targets with regions of interest (ROIs), and those with pixel areas larger than the area threshold are normalized to 256 × 256 pixels. The image blocking technique is introduced to separately aliquot the normalized ROI, with block sizes of 16 × 16, 32 × 32, and 64 × 64 pixels. On this basis, the image sub-blocks are manually labeled, block by block, to extract three texture features: histogram of oriented gradient (HOG), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). With the accuracy of fivefold cross-validation as the optimization objective, a genetic algorithm (GA) is used to optimize the SVM penalty and kernel parameters of 21 groups of research objects (one block size has three texture features, which are arbitrarily combined to form seven research objects, with a total of three block sizes). We compare the recognition performance of the SVM, RF, KNN, and GA-SVM classifiers in a single feature and a combination of fusion strategies through comparative analysis. When the block size is 32 × 32 pixels, the fusion of LBP and GLCM features under the GA-SVM classifier has the highest accuracy, and the optimal SVM model for the identification of lettuce and weeds in the seedling stage is obtained. For the misidentified image sub-blocks in optimization model recognition, an image block reconstruction method based on the comparison of the center point and eight-neighbor label value is proposed, and this is combined with the proportion of image blocks of two labels for comprehensive judgment. The center point label value is reconstructed to the improve recognition accuracy. Experimental results show that the average precision, recall, and F1 score of the proposed method are 0.9473, 0.9529, and 0.9498, respectively, and those of images without overlapping leaves can all reach 1, thus providing a theoretical basis for crop recognition and segmentation.
•We derive the equation of motion of a tumbler on an arc surface.•The natural frequency of the tumbler on arc can be as low as 2.7 Hz.•The motion of the tumbler can be chaotic and periodic under ...different parameters.•Low frequency characteristic is beneficial for broadband vibration isolation.
A tumbler oscillates around its geometric axis with a period under external disturbances. Due to the oscillation mechanism differs from traditional mass-spring-dampers, the dynamic response of tumblers is still a challenge. This work considers the dynamics behaviors of a tumbler on an arc surface. A tumbler prototype is designed and the corresponding equation of motion is derived according to the Lagrange's equation. Both the simulation and experimental efforts are performed to study the dynamics characteristics of the tumbler on an arc. The results demonstrate that the natural frequency of the tumbler can be 2.7 Hz, which is considerable to design low-frequency vibration devices in a new tunable perspective, such as vibration isolators or energy harvesters. The motion of the tumbler is periodic or quasi-periodic under a smaller excitation amplitude, and will be chaotic under a small damping. This work opens a window for the design and application of tumbler-inspired structures.
•A lightweight deep learning model for tea bud recognition based on Yolov5 is proposed.•It greatly reduces the amount of calculation and parameters of the model, and improves the recognition ...accuracy.•It provides theoretical research and technical support for intelligent picking of tea buds in actual scenes.
Tea bud detection technology is highly significant in realizing the automation and intelligence of tea bud picking. However, there are still some challenges with tea bud detection technology. For example, the problems of low detection accuracy, heavy computing, and large detection model size make the technology inconducive to the deployment of mobile terminals. Thus, a lightweight tea bud detection model based on the Yolov5 model was proposed in this study. Improvements were made in the following aspects: the Ghost_conv module was introduced to replace the original convolution, considerably reducing the computing and model size; the bottleneck attention module (BAM) was added to the backbone network to suppress invalid information and improve the model detection accuracy; the weighted feature fusion was used in the neck network to efficiently fuse the low-level and high-level features, helping the network to extract effective information for recognition and improve detection accuracy; and CIoU was used as the bounding box loss function to accelerate regression prediction and improve the positioning accuracy of the bounding box of the model. A test was conducted on the collected dataset to verify whether the modified model improved the detection performance of tea buds. The results showed that compared with that of the original Yolov5 model, the mean average precision of the modified model increased by 9.66%, and the floating-point operations and params reduced by 52.402 G and 22.71 M, respectively. An ablation study proved that the proposed method improves the detection performance of the Yolov5 model for tea buds. Compared with other detection algorithm models, the superiority of the proposed algorithm in tea bud detection can be seen. The proposed improved Yolov5 can effectively detect tea buds, which provides theoretical research and technical support for intelligent picking of tea buds in actual scenes.
•An algorithm was developed for image correction of skewness plug trays.•An algorithm was developed to identify the seedling quality in each cell.•The evaluation accuracy was compared in the angle ...correction situation.
The acquisition of quality information for plug seedlings is the foundation for automatic seedling transplanting. Machine vision technology is an extensively used method for this task. In the visual inspection of plug seedlings, the skewness of the images of dense-cell seedlings and the accuracy of target extraction affect the quality evaluation of plug seedlings. This study proposed a skewness correction algorithm on the basis of Canny operator and Hough transform for the images of a plug tray to improve the visual inspection method for transplanting equipment. Watershed algorithm was used to segment overlapping leaves, and gravity center method was applied to distinguish transboundary leaves. The leaf area and number of seedling leaves in the images of plug trays were extracted and used for quality evaluation. Two-week-old seedlings of Salvia splendens in 200-cell plug trays in a greenhouse were the objects of this study. Industrial cameras were used to capture images of the plug trays. These images underwent grayscale conversion in 2b–g–r channel, median filtering, Canny contour detection, and Hough transform to complete contour detection and skewness correction. The corrected angle deviation of the trays was less than 0.85°. Pre-treatment with grayscale conversion in 2g–r–b channel, binarization, and watershed algorithms were adopted to extract the target of the seedlings in the images. The image of the plug tray was divided into 200 small square areas in accordance with the cell area. The gravity center of the seedling leaves in the cell was calculated to locate the seedling. The number of the leaves and the area of seedlings in each cell were obtained and used as criteria to discriminate the quality of each seedling. The skewness angle of the plug tray during the actual delivery of plug seedlings was within the range of ±3°. Four sets of plug trays with skewness of ±1°, ±2°, and ±3° were obtained. Subsequently, the quality of seedlings in the four sets of plug trays was identified. The evaluation accuracy in the tray skewness and the angle correction situations were determined. Results showed that the average accuracy of seedling evaluation is 98% after angle correction. In contrast to the uncorrected images, the corrected ones can increase by 1.1–9.4 percentage points, thereby improving the evaluation accuracy for automatic seedling transplanting.
Owing to the ultra-low frequency vibration isolation performance without compromising static stiffness, high-static-low-dynamic-stiffness (HSLDS) vibration isolators (VIs) have advantages over linear ...vibration isolators. This paper presents a lever-type HSLDS vibration isolator (L-HSLDS-VI) by employing negative resistance electromagnetic shunt damping (EMSD) together with eddy current damping to eliminate the jump phenomenon and thus to improve the stability of L-HSLDS-VIs. The lever inerter system can amplify the mass effect to broaden the isolation band. The theoretical model of L-HSLDS-VIs with EMSD (L-HSLDS-VI-EMSD) was established. The effects of negative resistance and lever ratio on the vibration isolation performance of L-HSLDS-VI-EMSD were investigated analytically and experimentally. The L-HSLDS-VI can provide significant nonlinear stiffness, which can realize the quasi-zero stiffness (QZS), and thus broaden the isolation band. EMSD produces a considerable damping effect to enhance the vibration mitigation performance of L-HSLDS-VI. The combination of the EMSD and nonlinear ECD damping is an efficient approach to improve the vibration isolation performance to overcome the jump phenomenon. This paper utilizes the lever effect to amplify damping effects, which could provide a guideline to modify the performance of HSLDS-VIs.
•A lever-type HSLDS vibration isolator (L-HSLDS-VI) is modeled and analyzed.•Electromagnetic shunt damping (EMSD) with negative resistance and eddy current damping are applied to eliminate the jump phenomenon of L-HSLDS-VIs.•The mass effect can be amplified by the lever inerter system to broaden the isolation band.•The lever effect can enlarge the relative motion between the coil and permanent magnet to enhance the damping effect.
Near infrared (NIR) spectroscopy is a non-destructive detection technology involving NIR spectral data acquisition and chemometric treatment. The use of an NIR spectrometer is evidently crucial in ...this regard; however, traditional benchtop NIR spectrometers considerably limit usage scenarios. Accordingly, the miniaturization of spectrometers with high level performance has become a research trend. Various commercial products have been developed, and new techniques have been applied to produce more portable NIR spectrometers. This paper reviews the main types of commercial portable NIR spectrometers and summarizes as well as compares their specifications. Moreover, new techniques for promoting miniaturization and the prospects for future development are introduced.
Accurate identification of healthy plug seedlings is vital to intelligent greenhouse transplanting. Based on machine vision, this study presents a method to improve the accuracy and efficiency of ...plug seedlings' health identification, which is achieved by using image pre-processing, multilayer perceptron neural network image segmentation and connectivity domain centre coordinate calculation. To reduce the effect of invalid data on plug seedling image processing, the projection method is used to extract the image of the target region in the seedling tray where the plug seedling is situated. In addition, a multilayer perceptron neural network algorithm is utilised to segment the image of the target region into the leaves of the plug seedling and the background substrate. To solve the problem of overlapping seedling leaves, the centre coordinates of the connectivity domain are precisely calculated and matched with the corresponding holes. The health of the plug seedlings is then assessed using an area threshold. Finally, a greenhouse transplanting robot was developed and experiments were conducted to affirm the effectiveness of this approach. The experiments show that under different light intensities, the average accuracy of health identification for plug seedlings is over 96.90%. Moreover, the average transplanting success rate is 95.86% and the average number of transplanted plug seedlings per hour is 2117.65, indicating that the proposed method can accurately and quickly identify healthy plug seedlings and perform transplanting tasks, which provides guidance for intelligent transplanting in greenhouses.
•MLP classifier is used to improve the accuracy of image segmentation.•Calculating the central coordinates of connected domains to enhance health discrimination.•The effectiveness of the method was verified by transplanting seedlings in greenhouses.
Soft grippers have attracted increasing attention due to safer and more adaptable human–machine and environment-machine interactions. However, it has always been a challenge for soft grippers to work ...under different sizes, shapes, and postures. This paper presents a four-finger soft gripper with two gripping sizes and four gripping modes. The fiber-reinforced bending actuator is used to mimic the finger of the soft gripper. A theoretical model is established to predict the relationship between the bending angle and pressure of the actuator. The single finger bending experiment is carried out to verify the theoretical model. The capability of variable gripping size of the soft gripper has been proofed, which has increased the gripping size range from 84 to 141 mm. The static gripping test and dynamic palletizing test have been performed. The results show that by varying the gripping modes, objects with various sizes, shapes, and postures can be steadily gripped. This study offers a promising solution for the design of multifunctional soft grippers.
•A synthesis method for eliminating isomorphism identification based on graph similarity is proposed.•Similar edges are further divided into two types, i.e., CESE and IESE.•The internal relations ...between similar edges and isomorphism are revealed.•Kinematic chains with up to seven loops and three DOFs are exposed.•All the synthesis process can be realized automatically by the computer.
A novel method for eliminating isomorphism identification is proposed to improve synthesis efficiency and synthesize planar nonfractionated kinematic chain (KCs) automatically. This method is based on the vertex insertion of contracted graphs. First, similar edges of contracted graphs are divided into groups, in which the similar edges are found and their characteristic matrices are calculated. The edge types are divided based on whether or not isomerism occurs after vertices are inserted. Then, the vertices are inserted into contracted graphs according to the edge condition. In this process, all isomerism caused by the location and number of inserted vertices is reserved, and the property change of similar edges is checked. Lastly, the rigid subchains of remaining isomerism are distinguished. Contracted graphs with four independent loops and some of their inserted vertices are presented in appendix. A complete set of nonfractionated KCs with up to seven independent loops and three degrees of freedom is also provided. The veracity and efficiency of the method are confirmed by conducting a comparative analysis between this synthesis and other literature results.
