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Background: Crop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests. Methods: Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images. An attention module is added to its decoder to focus on the edge of the insect pest image. Results: The experiments on the crop insect pest image IP102 dataset are implemented, and achieved the detection accuracy of 92.16% and IoU of 91.2%, which is 3.3% and 1.5% higher than that of MSR-RCNN, respectively. Conclusion: The results indicate that the proposed method is effective as a new insect pest detection method. The dilated Inception can improve the accuracy of the model, and the attention module can reduce the noise generated by upsampling and accelerate model convergence. It can be concluded that the proposed method can be applied to practical crop insect pest monitoring system. [ABSTRACT FROM AUTHOR]

Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field. [ABSTRACT FROM AUTHOR]

Addressable gated field emitter arrays (FEAs) have important applications in vacuum microelectronic devices. To fabricate high-performance device, a comprehensive understanding on the field emission characteristics of gated FEAs is necessary, which requires a quantitative analysis method. In this work, a general model based on Fowler-Nordheim (FN) theory has been established to fulfill this blank. It is found that nonlinear FN plot with positive and negative slopes could occur in gated FEAs, which its turning point is related to the proportion of anode and gate field. This provides a way for obtaining the field strengths applied by the anode and gate structures. Besides, the transconductance of the gated FEAs increases exponentially with the total surface field, which indicates that both the anode and gate field need to be increased for achieving a high transconductance device. As a demonstration, the addressable gated ZnO nanowire FEAs using multimicrosize pattern with radius as small as $2.5 ~\mu \text{m}$ have been designed and fabricated. The relationship between the transconductance and anode voltage can be well fitted by using the model, which deduces the proportion of anode and gate field in the device to be about 3:1. [ABSTRACT FROM AUTHOR]

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA. [ABSTRACT FROM AUTHOR]

Plant species recognition using leaves is an important and challenging research topic, because the plant leaves are various and irregular and they have very large within-class difference and between-class similarity. Considering that leaves have different discriminant performance and contribution to plant recognition task, based on maximum neighborhood margin discriminant projection (MNMDP), we propose a global–local maximum margin discriminant projection (GLMMDP) algorithm for plant recognition. GLMMDP utilizes the local and class information and the global structure of the data to model the intra-class and inter-class neighborhood scatters and a global scatter, obtaining the projection matrix by minimizing the local intra-class scatter and meanwhile maximizing both the local inter-class scatter and the global between-class scatter. Compared with MNMDP, GLMMDP not only can detect the true intrinsic manifold structure of the data, but also can enhance the pattern discrimination between different classes by incorporating the global between-class scatter into MNMDP. The global between-class scatter fully indicates the difference and similarity between classes. The experimental results on the ICL (Intelligent Computing Laboratory) leaf datasets and Leafsnap leaf image datasets demonstrate the effectiveness of the proposed plant recognition method. The recognition accuracy is more than 95% on the ICL datasets and more than 90% on Leafsnap datasets. [ABSTRACT FROM AUTHOR]

Purpose: To solve the problem that the traditional received signal strength indicator real-time location method does not test the attenuation characteristics of the electromagnetic wave transmission in the location area, which cannot guarantee the accuracy of the location, resulting in a large location error. Design/methodology/approach: At present, the compressed sensing (CS) reconstruction algorithm can be roughly divided into the following two categories (Zhouzhou and Fubao, 2014; Lagunas et al., 2016): one is the greedy iterative algorithm proposed for combinatorial optimization problems, which includes matching pursuit algorithm (MP), positive cross matching tracking algorithm (OMP), greedy matching tracking algorithm, segmented orthogonal matching tracking algorithm (StOMP) and so on. The second kind is the convex optimization algorithm, which also called the optimization approximation method. The common method is the basic tracking algorithm, which uses the norm instead of the norm to solve the optimization problem. In this paper, based on the piecewise orthogonal MP algorithm, the improved StOMP reconstruction algorithm is obtained. Findings: In this paper, the MP algorithm (OMP), the StOMP and the improved StOMP algorithm are used as simulation reconstruction algorithms to achieve the comparison of location performance. It can be seen that the estimated position of the target is very close to the original position of the target. It is concluded that the CS grid-based target stepwise location method in underground tunnel can accurately locate the target in such specific region. Originality/value: In this paper, the offline fingerprint database in offline phase of location method is established and the measurement of the electromagnetic noise distribution in different localization areas is considered. Furthermore, the offline phase shares the work of the location process, which greatly reduces the algorithm complexity of the online phase location process and the power consumption of the reference node, meanwhile is easy to implement under the same conditions, as well as conforms to the location environment. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) via coupled magnetic resonance is a promising technology to be applied in many fields. In general, there will be a radial gap in practical application, and some special application environments need to limit the radius of the coils. Therefore, in this paper, considering the comprehensive analysis of the radial gap and the radius of the coils, the concept of the ratio of radial gap to the radius of the coils was proposed. Based on the circuit theory, the formula between the ratio and transmission efficiency of WPT was deduced, and the effects of the ratio on the transmission efficiency were studied respectively at different axial distances. Simulation experiments were carried out and the results not only demonstrate the influence law of the ratio on transmission efficiency, but also validate the value of the ratio which the axial distance has the greatest effect on the transmission efficiency. Besides, the results shows the relationship between the effect of the ratio on the efficiency and the effect of the axial distance on the efficiency. The experimental results show that the theoretical analysis is correct, thus providing an useful theoretical reference for the design and further research on the wireless power transfer system in complicated environment. [ABSTRACT FROM AUTHOR]