Evaluation of Serviceability of Canal Lining Based on AHP–Simple Correlation Function Method–Cloud Model: A Case Study in Henan Province, China

In the process of sustainable development within modern agriculture, in order to ensure that agricultural production has adequate water resources, canal lining (CL) is often used to transport water in order to reduce water seepage, thus promoting the sustainable utilization of water resources. However, due to the influence of the terrain, environment, human factors and other factors, the CL often suffers a certain degree of damage. Therefore, it is necessary to evaluate the serviceability of the CL, so to realize the sustainable use of the CL strategy. Aiming at the weight assignment of CL evaluation indices that are subjective and not combined with actual index data, a weight calculation method based on the Analytic Hierarchy Process (AHP)–simple correlation function (SCF) method was proposed, and game theory was used to achieve combination weighting. For the evaluation indices with the characteristics of fuzziness and randomness, the cloud model (CM) was used to comprehensively consider these characteristics in order to realize the evaluation. Finally, a method to measure serviceability of CL based on AHP–SCF–CM was proposed. Taking a CL project in China as an example, this method was used to evaluate the serviceability of the CL. The evaluation result showed that the serviceability of the CL was poor, and the qualitative evaluation result was consistent with the actual damage condition of the project; meanwhile, a comparative study was performed in combination with the AHP–Entropy Weight (EW)–unascertained measurement theory (UMT). The quantitative evaluation results of the two methods displayed the same grade of serviceability, which verifies that the method proposed in this paper is more reasonable, objective and feasible from both qualitative and quantitative perspectives. Furthermore, the evaluation results lay the foundation for subsequent maintenance and fault prevention of the canal.

User-assisted Object Detection by Segment Based Similarity Measures in Mobile Laser Scanner Data

This paper describes a method that aims to find all instances of a certain object in Mobile Laser Scanner (MLS) data. In a userassisted approach, a sample segment of an object is selected, and all similar objects are to be found. By selecting samples from multiple classes, a classification can be performed. Key assumption in this approach is that a one-to-one relationship exists between segments and objects. In this paper the focus is twofold: (1) to explain how to get proper segments, and (2) to describe how to find similar objects. Point attributes that help separating neighbouring objects are presented. These point attributes are used in an attributed connected component algorithm where segments are grown, based on proximity and attribute values. Per component, a feature vector is proposed that consists of two parts. The first is a height histogram, containing information on the height distribution of points within a component. The second contains size and shape information, based on the components’ bounding box. A simple correlation function is used to find similarities between samples, as selected by a user, and other components. Our approach is tested on a MLS dataset, containing over 300 objects in 13 classes. Detection accuracies heavily depend on the success of the segmentation, and the number of selected samples in combination with the variety of object types in the scene.