Pest detection in agricultural cropfields is the most challenging task,so an effective pest detection technique is required to detect insects automatically.Image processing techniques are widely preferred in agricultur...Pest detection in agricultural cropfields is the most challenging task,so an effective pest detection technique is required to detect insects automatically.Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection,improved crop man-agement and productivity.On the other hand,developing the automatic pest mon-itoring system dramatically reduces the workforce and errors.Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy.Therefore,a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitor-ing and detection.The four-step image processing technique begins with image pre-processing,removing the insect image’s noise and sunlight illumination by utilizing an adaptive medianfilter.The insects’size and shape are identified using the Expectation Maximization Algorithm(EMA)based clustering technique,which involves not only clustering the data but also uncovering the correlations by visualizing the global shape of an image.Speeded up robust feature(SURF)method is employed to select the best possible image features.Eventually,the image with best features is classified by introducing a hybrid FF-GWO-CNN algorithm,which combines the benefits of Firefly(FF),Grey Wolf Optimization(GWO)and Convolutional Neural Network(CNN)classification algorithm for enhancing the classification accuracy.The entire work is executed in MATLAB simulation software.The test result reveals that the suggested technique has deliv-ered optimal performance with high accuracy of 97.5%,precision of 94%,recall of 92%and F-score value of 92%.展开更多
压缩感知(compressed sensing,CS)技术在采样中完成对数据的压缩,相比传统Nyquist采样方法有效降低采样信号数据量,克服采样端压缩复杂度高,对硬件需求大的缺点。该文通过理论证明指出电网信号基波–谐波稀疏度特性,并基于此特性提出一...压缩感知(compressed sensing,CS)技术在采样中完成对数据的压缩,相比传统Nyquist采样方法有效降低采样信号数据量,克服采样端压缩复杂度高,对硬件需求大的缺点。该文通过理论证明指出电网信号基波–谐波稀疏度特性,并基于此特性提出一种新型基波滤除谱投影梯度算法(SPGFF)。通过西门子Benchmark 0.4 k V电网通用模型实验,结果表明SPG-FF算法比现有方法有效提升了谐波检测精度和信号重构精度,对谐波和间谐波的检测误差分别小于6.8×10-5和6.2×10-3,重构信号的信噪比高于89 d B。展开更多
为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模...为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模型,将定位问题转化为寻优求解问题。然后,利用改进象群优化算法和OPTICS(ordering points to identify the clustering structure)聚类算法对第1次寻优结果中多端行波信息差异矩阵的各元素进行聚类分析,找出存在时间同步误差的坏数据。最后,在考虑正常数据的情况下进行第2次寻优,实现故障的精确定位。仿真结果表明,该方法在不需预设行波波速的情况下,能够实现较准确的故障定位,且具有较强时间误差鲁棒性。展开更多
基金supported by“Catalyzed and supported by Tamilnadu State Council for Science and Technology,Dept.of Higher Education,Government of Tamilnadu.”。
文摘Pest detection in agricultural cropfields is the most challenging task,so an effective pest detection technique is required to detect insects automatically.Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection,improved crop man-agement and productivity.On the other hand,developing the automatic pest mon-itoring system dramatically reduces the workforce and errors.Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy.Therefore,a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitor-ing and detection.The four-step image processing technique begins with image pre-processing,removing the insect image’s noise and sunlight illumination by utilizing an adaptive medianfilter.The insects’size and shape are identified using the Expectation Maximization Algorithm(EMA)based clustering technique,which involves not only clustering the data but also uncovering the correlations by visualizing the global shape of an image.Speeded up robust feature(SURF)method is employed to select the best possible image features.Eventually,the image with best features is classified by introducing a hybrid FF-GWO-CNN algorithm,which combines the benefits of Firefly(FF),Grey Wolf Optimization(GWO)and Convolutional Neural Network(CNN)classification algorithm for enhancing the classification accuracy.The entire work is executed in MATLAB simulation software.The test result reveals that the suggested technique has deliv-ered optimal performance with high accuracy of 97.5%,precision of 94%,recall of 92%and F-score value of 92%.
文摘压缩感知(compressed sensing,CS)技术在采样中完成对数据的压缩,相比传统Nyquist采样方法有效降低采样信号数据量,克服采样端压缩复杂度高,对硬件需求大的缺点。该文通过理论证明指出电网信号基波–谐波稀疏度特性,并基于此特性提出一种新型基波滤除谱投影梯度算法(SPGFF)。通过西门子Benchmark 0.4 k V电网通用模型实验,结果表明SPG-FF算法比现有方法有效提升了谐波检测精度和信号重构精度,对谐波和间谐波的检测误差分别小于6.8×10-5和6.2×10-3,重构信号的信噪比高于89 d B。
文摘为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模型,将定位问题转化为寻优求解问题。然后,利用改进象群优化算法和OPTICS(ordering points to identify the clustering structure)聚类算法对第1次寻优结果中多端行波信息差异矩阵的各元素进行聚类分析,找出存在时间同步误差的坏数据。最后,在考虑正常数据的情况下进行第2次寻优,实现故障的精确定位。仿真结果表明,该方法在不需预设行波波速的情况下,能够实现较准确的故障定位,且具有较强时间误差鲁棒性。
