Complete Model-Based Equivalence Class Testing for the ETCS Ceiling Speed Monitor

AbstractFeature discretization is an important preprocessing technology for massive data in industrial control. It improves the efficiency of edge-cloud computing by transforming continuous features into discrete ones, so as to meet the requirements of high-quality cloud services. Compared with other discretization methods, the discretization based on rough set has achieved good results in many applications because it can make full use of the known knowledge base without any prior information. However, the equivalence class of rough set is an ordinary set, which is difficult to describe the fuzzy components in the data, and the accuracy is low in some complex data types in big data environment. Therefore, we propose a rough fuzzy model based discretization algorithm (RFMD). Firstly, we use fuzzy c-means clustering to get the membership of each sample to each category. Then, we fuzzify the equivalence class of rough set by the obtained membership, and establish the fitness function of genetic algorithm based on rough fuzzy model to select the optimal discrete breakpoints on the continuous features. Finally, we compare the proposed method with the discretization algorithm based on rough set, the discretization algorithm based on information entropy, and the discretization algorithm based on chi-square test on remote sensing datasets. The experimental results verify the effectiveness of our method.

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Supporting Domain Experts by using Model-Based Equivalence Class Partitioning for Efficient Test Data Generation

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) ◽

10.1109/etfa.2019.8869145 ◽

2019 ◽

Cited By ~ 2

Author(s):

Kristof Meixner ◽

Dietmar Winkler ◽

Stefan Biffl

Keyword(s):

Equivalence Class ◽

Test Data ◽

Test Data Generation ◽

Data Generation ◽

Domain Experts ◽

Model Based ◽

Efficient Test

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Generic Language for Partial Model Extraction from an IFC Model Based on Selection Set

Applied Sciences ◽

10.3390/app10061968 ◽

2020 ◽

Vol 10 (6) ◽

pp. 1968 ◽

Cited By ~ 2

Author(s):

Xueyuan Deng ◽

Huahui Lai ◽

Jiayi Xu ◽

Yunfan Zhao

Keyword(s):

Data Sharing ◽

Xml Schema ◽

Redundant Information ◽

Complete Model ◽

Building Projects ◽

Industry Foundation Classes ◽

Partial Model ◽

Model Based ◽

Practical Project ◽

Generic Language

During data sharing and exchange of building projects, the particular business task generally requires a part of the complete model. This paper adopted XML schema to develop a generic language to extract the partial model from an Industry Foundation Classes (IFC) model based on the proposed Selection Set (called PMESS). In this method, the Selection Set was used to integrate users’ requirements, which could be mapped into IFC data. To ensure the validity of the generated partial IFC models in syntax and semantics, seven rules—including three basic rules for a valid IFC file, three extraction rules based on the Selection Set, and a processing rule for redundant information—were defined. Through defining PMESS-based configuration files, the required data can be extracted and formed as a partial IFC model. Compared with the existing methods, the proposed PMESS method can flexibly extract the user-defined required information. In addition, these PMESS-based configuration files can be stored as templates and reused in other tasks, which prevents duplicated work for defining extraction requirements. Finally, a practical project was used to illustrate the utility of the proposed method.

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