It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations a...
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Chlorophyll-a (Chl-a) is an important parameter in water bodies. Due to the complexity of optics in water bodies, it is difficult to accurately predict Chl-a concentrations in water bodies by current traditional metho...
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Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study ai...
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Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems in
Continual Few-shot Relation Extraction (CFRE) aims to continually learn new relations from limited labeled data while preserving knowledge about previously learned relations. Facing the inherent issue of catastrophic ...
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Accurate extraction of crop row is very important for automation of agricultural *** rows are required for accurate machine guidance in agricultural production such as fertilization,plant protection,weeding and *** th...
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Accurate extraction of crop row is very important for automation of agricultural *** rows are required for accurate machine guidance in agricultural production such as fertilization,plant protection,weeding and *** this study,an efficient crop row detection algorithm called Crop-BiSeNet V2 was proposed,which combined BiSeNet V2 with a spatial convolutional neural *** proposed Crop-BiSeNet V2 detected crop rows in color images without the use of threshold and other pre-information such as number of rows.A data set had 2697 maize crop images was constructed in challenging field trial conditions such as variable light,shadows,presence of weeds,and irregular crop *** proposed system was experimentally determined to overcome the interference of different complex *** it can be applied to crop rows of different numbers,straight lines and *** analyses were performed to check the robustness of the *** this algorithm with the Fully Convolutional Networks(FCN)algorithm,it exhibited superior performance and saved 84.85 *** accuracy rate reached 0.9811,and the detection speed reached 65.54 ms/*** Crop-BiSeNet V2 algorithm proposed in this study show strong generalization performance for seedling crop row *** provides high-reliability technical support for crop row detection research and assists in the study of intelligent field operation machinery navigation.
Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of upd...
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Fe-Mn-C-Al alloys have been recognized as promising materials for certain low-temperature applications due to their exceptional mechanical properties and ***,their limited low-temperature toughness restricts their lar...
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Fe-Mn-C-Al alloys have been recognized as promising materials for certain low-temperature applications due to their exceptional mechanical properties and ***,their limited low-temperature toughness restricts their large-scale applications in specific *** influence of trace amounts of rare earth cerium(Ce)on the low-temperature toughness of Fe-18Mn-0.6C-1.8Al alloys was *** addition of Ce effectively alters the inclu-sions in the alloy,transforming large-sized irregular inclusions into fine ellipsoidal rare earth *** leads to a significant reduction in both the proportion and average size of the inclusions,resulting in their effective dispersion throughout the matrix and improved cryogenic *** presence of Ce-containing inclusions within the matrix reduces stress concentration,thereby inhibiting microcrack formation and improving impact absorption ***-cally,the addition of rare earth Ce alters the fracture behavior of the material at room temperature and low temperature,changing from brittle cleavage fracture to a more ductile failure *** impact toughness of the Fe-Mn-C-Al alloy is significantly improved by the addition of 0.0048 wt.%Ce,particularly at-196℃where the impact toughness reaches 103.6 J/cm^(2),representing an impressive improvement of 87.3%.
Script Event Prediction (SEP) aims to forecast the next event in a sequence from a list of candidates. Traditional methods often use pre-trained language models to model event associations but struggle with semantic a...
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Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis method...
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Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective *** a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision *** the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased ***,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant ***,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic *** address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease *** addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other *** extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant *** addition,our pre-trained models will be open-sourced at https://***/and Zenodo platform https://***/10.5281/zenodo.7856293.
In the acoustic localization of mass projectiles, due to the large number of mass projectiles and short firing time, the traditional algorithm has some problems, such as waveform superposition and the order of explosi...
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