一种改进的单纯形算法实现及其应用

针对资源最优配置问题,在对单纯形算法描述的基础上,提出了一种改进的单纯形算法,介绍了其实现方法,并用实例验证了其有效性。     

Estimation of soil organic matter in the Ogan-Kuqa River Oasis,Northwest China,based on visible and near-infrared spectroscopy and machine learning

Visible and near-infrared (vis-NIR) spectroscopy technique allows for fast and efficient determination of soil organic matter (SOM). However, a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information. Therefore, this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM. The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017. Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test. The successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths. Finally, partial least squares regression (PLSR) and random forest (RF) models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content. The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features (i.e., 1400.0, 1900.0, and 2200.0 nm), and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0-510.0 nm. Both models can achieve a more satisfactory prediction of the SOM content, and the RF model had better accuracy than the PLSR model. The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination (R²) of 0.78 and the residual prediction deviation (RPD) of 2.38. The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content. Therefore, combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.     

基于模拟退火算法的供水泵站优化调度

采用模拟退火算法用于供水系统的多泵组合和变频调节的复杂的优化计算，并给出了优化计算的编码、译码和能量函数表达式。计算结果表明，模拟退火算法非常适用于既有离散量又有连续量的供水系统的非线性优化中。     

基于遗传算法的制造质量控制多目标的优化

根据零件加工过程的加工和测量能力,给出了制造质量控制多目标的优化数学模型.针对遗传算法的特点,提出了基于遗传算法的制造质量控制多目标的优化方法.算例计算结果表明,遗传算法在制造质量控制多目标的优化方面具有良好的应用前景.     

小波-智能优化支持向量机模型及其对农业可持续发展的评价

农业可持续发展评价是世界性农业研究的热点问题,也是中国新时代乡村振兴和农业现代化发展的重大课题。改革开放促进中国农业经济获得快速发展,但农业发展与农业资源及生态环境的矛盾严重。党的十八大以来,农业可持续发展被提到更加突出的位置。研究农业可持续发展评价具有重要意义。该文研究国内外农业可持续评价的相关文献,在小波变换、支持向量机、遗传算法、蚁群算法基础上,提出小波-智能优化支持向量机相结合评价方法,参考已有评价指标体系,结合中国的农业发展现实状况,建立农业可持续发展评价备选指标集。运用Spss软件对150个初选指标进行显著性和相关性分析,确立包含62个指标的评价指标体系。运用一维离散平稳小波分析,数据消噪处理,遗传算法(genetic algorithm,GA)、蚁群算法(ant colony optimization,ACO),优化支持向量机参数(support vector machine,SVM),得出较好的惩罚参数、核函数、不敏感系数,再对支持向量机训练,该方法提高了训练准确度。对中国31个省(市)农业可持续发展进行小波-遗传算法优化支持向量机(GA-SVM)、小波-蚁群算法优化支...     

Multi-step ahead soil temperature forecasting at different depths based on meteorological data: Integrating resampling algorithms and machine learning models

Direct soil temperature (ST) measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning (ML) tools is helpful.In this study,ML approaches,including KStar,instance-based K-nearest learning (IBK),and locally weighted learning (LWL),coupled with resampling algorithms of bagging (BA) and dagging (DA)(BA-IBK,BA-KStar,BA-LWL,DA-IBK,DA-KStar,and DA-LWL) were developed and tested for multi-step ahead (3,6,and 9 d ahead) ST forecasting.In addition,a linear regress...     

基于参数循环算法的HMCVT燃油经济性多参数优化

为了使有液压机械无级变速器的中小型拖拉机获得更高的燃油经济性,使得拖拉机在任意工况下都能工作在整机经济性最佳的工作点上,该研究结合一种行星比连续可变的液压机械无级变速传动系统(hydro-mechanical continuously variable transmission,HMCVT),提出以发动机燃油消耗率g_e与HMCVT传动效率η_b之比为拖拉机整机经济性目标函数,在考虑发动机转速、转矩和HMCVT变速传动系统中变量泵排量比调节的基础上,加入HMCVT变速传动系统中牵引式行星排的行星比这一辅助变量,对发动机工作点和HMCVT工作点进行多参数调节。然后采用参数循环算法对多参数调节中的控制参数进行优化,得到任意负载和目标车速下的最佳发动机转速、转矩和最佳HMCVT变量泵排量比和牵引式行星排的行星比。随后分析了不同作业车速下牵引式行星排的行星比对拖拉机整机经济性的影响,得到低、中、高3种车速工况下的最佳行星比及排量比,最后从HMCVT的传动效率和变速比两个方面验证多参数调控效果。结果表明,具有排量比和行星比双参数可调的液压机械无级变速传动系统能满足中小型拖拉机的犁耕作业,与国标相比牵引式行星排行星比的加入使拖拉机的燃油消耗率降低29.86%;行星比越大,拖拉机整机比油耗越低。本文提出的多参数控制策略能够保证拖拉机在宽速比和高效率区域下工作,且易于实时调控,研究结果可为推动无级变速拖拉机变速器开发应用和进一步改善拖拉机动力性与燃油经济性提供理论参考。     

基于自适应注意力机制的复杂场景下牛脸检测算法

牛面部检测与识别是牛场智能化养殖的关键,但由于牧场养殖环境的复杂性,牛脸检测会受到模糊、逆光和遮挡3种常见环境因素的严重干扰。针对此问题,该研究提出一种复杂场景下基于自适应注意力机制的牛脸检测算法,该算法首先针对3种干扰因素分别设计了评价指标,并将3种不同类型的评价指标通过模糊隶属度函数进行归一化,并确定自适应权重系数,真实反映目标所处场景的复杂性;之后,基于YOLOV7-tiny在主干特征提取网络引入一种新型注意力机制CDAA(composite dual-branch adaptive attention),设计通道和空间注意力并行结构,并融合自适应权重系数,有效加强相应注意力分支的权重,提高网络在复杂场景下的特征提取能力,解决复杂场景下网络检测精度差的问题;最后,将图像场景评价指标引入损失函数,对大尺度网格损失函数的权重进行自适应调整,使网络在训练过程中更专注于数量较多的小型目标,从而提升网络整体的检测精度。为检测算法的有效性和实时性,在特定数据集上进行消融试验,并与多种经典检测算法进行对比,并移植至Jetson Xavier NX平台测试。测试结果表明,该算法检测精度达到89.58%,相较于原YOLOV7-tiny网络,牛脸检测精度提高了7.34个百分点。检测速度达到62帧/s,在检测速度几乎不损失的条件下,检测效果优于原网络与对比网络。 研究结果可为复杂场景下的牛脸高效检测提供参考。     

澜沧江流域卫星产品降尺度与融合方法

卫星降水产品具有覆盖范围广、更适用于无资料区域的优势,但分辨率较低、精度不足,为获取高时空分辨率、高精度的降水数据,需对卫星产品进行降尺度,并与地面观测数据融合,以提高数据质量。以澜沧江流域为例,在综合考虑地形、地理和植被等要素的基础上,建立地理加权回归（geographic weighted regression,GWR）模型对热带降雨测量卫星（tropical rainfall measurement mission,TRMM）和基于人工神经网络的遥感降水估计-气候数据记录（precipitation estimation from remotely sensed information using artificial neural networks-climate data record,PERSIANN-CDR）产品进行空间降尺度,再采用集合卡尔曼滤波算法,将地面气象站点经反距离加权插值法（inverse distance weighted,IDW）插值后的数据作为融合算法观测值,对降尺度后的TRMM、PERSIANN-CDR数据进行融合,以进一步提高降水数据精度。结果表明：...     

A Hybrid Artificial Intelligence Model for Aeneolamia varia (Hemiptera: Cercopidae) Populations in Sugarcane Crops

Sugarcane spittlebugs are considered important pests in sugarcane crops ranging from the southeastern United States to northern Argentina. To evaluate the effects of climate variables on adult populations of Aeneolamia varia (Fabricius) (Hemiptera: Cercopidae), a 3-yr monitoring study was carried out in sugarcane fields at week-long intervals during the rainy season (May to November 2005–2007). The resulting data were analyzed using the univariate Forest-Genetic method. The best predictive model explained 75.8% variability in physiological damage threshold. It predicted that the main climatic factors influencing the adult population would be, in order of importance, evaporation; evapotranspiration by 0.5; evapotranspiration, cloudiness at 2:00 p.m.; average sunshine and relative humidity at 8:00 a.m. The optimization of the predictive model established that the lower and upper limits of the climatic variables produced a threshold in the population development rate of 184 to 267 adult insects under the agroecological conditions of the study area. These results provide a new perspective on decision-making in the preventive management of A. varia adults in sugarcane crops.