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Accurate and comprehensive extraction of water bodies facilitates the study of watershed changes and water quality monitoring and also provides a reference for ecological evolution. When extracting tributaries and small water bodies based on remote sensing images, there are problems such as uncertain threshold selection, more edge voids, and extraction difficulties. The improved water body extraction method (Combination Normalized Difference Water Index), fuses Digital Elevation Model and remote sensing images. The river network is extracted from the Digital Elevation Model and the remote sensing image is normalized. After fusion, threshold comparison is performed, and the threshold of the river network location is used as the basis for connecting the broken water bodies, it is compared and verified with the normalized thresholds to achieve accurate identification and complete extraction of water bodies. Taking the water bodies in Xinyang City as an example, the overall accuracy of the confusion matrix was the highest, with an overall accuracy of 93.0% compared with the traditional index method. Combined with water bodies in Zhengzhou City for model validation, it has high applicability. Whether the water body is large or small, the extraction results are complete, with no voids or broken, and the accuracy is high. [ABSTRACT FROM AUTHOR]

In the big data era, traditional data mining technology cannot meet the requirements of massive data processing with the background of intelligent manufacturing. Aiming at insufficient computing power and low efficiency in mining process, this paper proposes a improved K-means clustering algorithm based on the concept of distributed clustering in cloud computing environment. The improved algorithm (T.K-means) is combined with MapReduce computing framework of Hadoop platform to realize parallel computing, so as to perform processing tasks of massive data. In order to verify the practical performance of T.K-means algorithm, taking machining data of milling Ti-6Al-4V alloy as the mining object. The mapping relationship among cutting parameters, surface roughness, and material removal rate is mined, and the optimized value for cutting parameters is obtained. The results show that T.K-means algorithm can be used to mine the optimal cutting parameters, so that the best surface roughness can be obtained in milling Ti-6Al-4V titanium alloy. [ABSTRACT FROM AUTHOR]

Generally, off-line methods are used for surface roughness prediction of titanium alloy milling. However, studies show that these methods have poor prediction accuracy. In order to resolve this shortcoming, a prediction method based on Cloudera's Distribution including Apache Hadoop (CDH) platform is proposed in the present study. In this regard, data analysis and process platform are designed based on the CDH, which can upload, calculate, and store data in real time. Then this platform is combined with the Harris hawk optimization (HHO) algorithm and pattern search strategy, and an improved Harris hawk optimization optimization (IHHO) method is proposed accordingly. Then this method is applied to optimize the support vector machine (SVM) algorithm and predict the surface roughness in the CDH platform. The obtained results show that the prediction accuracy of IHHO method reaches 95%, which is higher than the conventional methods of SVM, BAT-SVM, gray wolf optimizer (GWO-SVM), and whale optimization algorithm (WOA-SVM). [ABSTRACT FROM AUTHOR]