基于改进粒子群算法的烧结配料预测模型

针对烧结配料系统中的非线性、复杂性和相关性,基于BP神经网络建立烧结配料的预测模型,并采用粒子群算法对预测模型参数进行优化。为了克服粒子群算法的局部收敛性,在迭代过程中,根据迭代次数对惯性权重进行动态非线性调整,从而提高算法的搜索能力。仿真结果表明,所提出的改进粒子群算法与传统的粒子群算法比较,收敛速度快、迭代次数少、具有较强的全局寻优能力。     

基于改进神经网络的农村电力系统短期负荷预测

为进一步提高农村电力系统短期负荷预测模型的性能,实现准确与快速预测农村电力系统短期负荷的目的,采用基于优化理论的Levenberg-Marquardt算法来改进传统的BP算法,并构造电力系统负荷预测模型。结果表明,基于L-M算法的神经网络预测模型具有较高的预测精度,在农村电力系统短期负荷预测方面具有较高的使用价值。     

Automated identification of sugar beet diseases using smartphones

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.     

基于模糊神经算法的区域地下水盐分动态预测

为探讨前馈型人工神经网络BP-ANN(back propagation artificial neural network)和模糊神经NF(neuro-fuzzy)2种神经网络算法在区域地下水盐分动态预测中的应用过程与效果,首先通过经典统计分析确定区域地下水盐分动态的主要驱动因子以及可用的模型输入因子组合,采用"试错法"确定神经网络模型的最优结构,进而开展地下水盐分中长期动态的有效模拟预测。结果表明,在长江河口寅阳和大兴地区以降水动态为单输入的NF(5-gbellmf-160)和以降水与内河水盐分动态为双输入的NF(4-gaussmf-100)为最优预测模型。研究表明神经网络模型对地下水盐分动态的预测精度优于常规线性模型,其中,NF、BP-ANN、线性模型在寅阳测点的预测相关系数分别为0.565、0.445、0.261,在大兴测点的预测相关系数分别为0.886、0.784、0.543。与BP-ANN、线性模型相比,基于模糊神经算法的NF模型具有更好的误差纠错和仿真能力,在寅阳和大兴测点的预测误差分别降低了30%以上和50%以上。相关研究结果在区域水盐动态科学预警研究领域有较好地应用前景。     

自然环境下重叠果实图像识别算法与试验

针对非结构化自然环境中光照变化和对象重叠特征等外界因素给图像处理带来的难题,提出了一种自然环境下重叠果实的图像识别与边界分割的组合优化算法。该组合优化算法首先对原始图像进行噪声滤波处理,然后利用Sobel算子以及改进算子的最大类方差法(OTSU)来辨识重叠果实目标;接着采用K-means算法对重叠目标的像素进行聚类得到单个目标位置,再结合边缘检测结果的连通域分析及区域生长获得单个目标边界的大致区域;最后利用基于限制区域的分水岭算法,得到目标的精确边界。为了验证所提算法的有效性和适应性,进行了试验研究。试验结果表明:所提出的组合优化算法不仅可以在自然环境下从重叠物体图像背景中识别出重叠目标,而且还可以从重叠目标中分割出单个目标的精确边界。     

基于DTN算法的采摘机器人控制系统可行性研究

为进一步提高我国采摘机器人的智能采摘水平,以优化系统数据采集与传送效能参数、确保控制系统运行稳定性为目标,针对采摘机器人的控制系统进行设计研究.利用DTN算法体系与DTN网络层级数据处理算法,实现网络信息拥堵控制,建立DTN算法模型,设计采摘机器人控制系统整体架构并展开控制试验.试验结果表明:该采摘机器人控制系统应用D...     

QRS complexes detection using quadratic b-spline wavelet transform algorithm

Aiming at the low accuracy and low adaptability of wave detection, a QRS complexes detection algorithm is proposed based on quadratic b-spline wavelet, while combined with binary search algorithm and arc approximating curve algorithm. The signal is decomposed with quadratic b-spline wavelet through Mallat algorithm and the R wave is detected by adjusting the threshold with binary search and modulus maximumizing. The T wave and P wave are detected by using arc approximating curve algorithm based on the least square. This algorithm is certified with the ECG signals from MIT-BIH database and is demonstrated that the algorithm enhanced the adaptability of R wave detection and improved the accuracy of T wave and P wave detection. The simulation experiment shows that the improved algorithm can effectively improve the automatic detection capabilities of ECG signals.     

用于可靠性评测的优化检测方法

在最小方差检测策略基础上,提出了用于可靠性估计的自适应检测算法.通过比较可知,自适应检测算法对可靠性估计的方差值小于操作框架均匀分布的随机检测方法和操作框架指定分布的一般检测方法的值.结果表明了自适应检测算法是有效的.     

离心泵动—静子流动耦合数值算法研究

针对离心泵动-静子流动耦合问题,采用有限体积方法(FVM)离散不可压旋转流动方程组。研究对流、扩散及源项时、空计算格式。对动子采用非惯性参考系求解相对运动方程组,对静子采用惯性参考系求解绝对运动方程组,动-静界面通过Neuman条件保持通量守恒。将动子出口相对速度转为绝对速度,作为静子进口来流条件,实现动-静子流场的耦合求解。采用模式切换实现算法。为验证算法,对Ubaldi实验用的ERCOFTAC离心叶轮及扩压器耦合流场进行数值研究,获得定常及非定常数值计算结果,并与文献进行比较,结果表明数值预测与实验在数量和分布趋势上均较贴近,验证本算法具有准确预测动-静子流场能力,可用于离心泵流动预测与研究。     

可调球面四杆机构函数优化综合方法

提出一种用于实现多函数的可调球面四杆机构优化综合方法。通过改变连架杆杆长实现可调球面四杆机构不同函数综合要求。在研究球面四杆机构输入角与输出角函数关系的基础上,建立了可调球面四杆机构函数优化综合数学模型。给出可调球面四杆机构多函数优化综合的目标函数,使函数生成机构的最大结构误差达到最小;给出函数优化综合的约束条件,保证机构存在曲柄并具有良好的传动性能。采用免疫遗传算法对可调球面四杆机构进行函数优化综合,相应的算例证明了方法的有效性。