Foreign Object Debris Detection and Automatic Elimination for Autonomous Electric Vehicles Wireless Charging Application

Power pad designing, misalignment reduction, safety, automation, living object detection (LOD), and foreign object debris (FOD) detection are the key challenges in the commercialization of the high voltage wireless charging of Electric Vehicles (EV). The interruption from unwanted and sensitive foreign objects such as metal objects and living objects over the charging pads is an immense challenge for the static wireless charging of EV. In this manuscript, the problem of interference due to foreign objects and living objects has been analyzed, and an innovative laser- and sensor-based FOD detection method is proposed and verified by developing a prototype setup. Modeling and analysis of the effects of foreign objects have been performed using Finite Element Analysis (FEA) in Ansys Maxwell® environment. The analysis compares the consequence of the presence of foreign objects on the wireless charging power pad. The proposed method utilizes laser light and sensor for the detection and two-dimensional signal processing for the elimination of FOD. The proposed method is compatible with all types of static wireless charging systems without interrupting the power transfer and power circuit. The proposed system has been analyzed and compared with the various available FOD detection techniques. The feasibility of the proposed system has been assessed with the help of an on the bench hardware prototype implementation in the lab environment.

Automated Program-Semantic Defect Repair and False-Positive Elimination without Side Effects

The alarms of the program-semantic defect-detection report based on static analysis include defects and false positives. The repair of defects and the elimination of false positives are time-consuming and laborious, and new defects may be introduced in the process. To solve these problems, the safe constraints interval of related variables and methods are proposed for the semantic defects in the program, and proposes a functionally equivalent no-side-effect program-semantic defect repair and false-positive elimination strategy based on the test-equivalence theory. This paper realizes the automatic repair of the typical semantic defects of Java programs and the automatic elimination of false positives by adding safe constraint patches. After the repair, the program functions are equivalent and the status of each program point is within the safety range, so that the functions before and after the defect repair are consistent, and the functions and semantics before and after the false positives are eliminated. We have evaluated our approach by repairing 5 projects; our results show that the repair strategy does not require manual confirmation of alarms, automated repair of the program effectively, shortened the repair time greatly, and ensured the correctness of the program after the repair.

Semi-automatic LiDAR point cloud denoising using a connected-component labelling method

The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape. Laser scanning technology (LiDAR - Light Detection and Ranging) enables such approach producing dense 3-D point clouds of millions of points, which, however, contain noise. Therefore, we developed a new approach allowing for a semi-automatic elimination of data noise resulting from motion of objects within the scanned scene such as persons. We used a connected-component labelling method to filter out the noise points from terrestrial laser scanning point clouds. Our approach was based on a step-by-step object classification with a proper parameterisation. In the first step, all points located close to the predicted terrain were selected. In the second step, the points representing the terrain and floor were classified using the surface filter tool implemented in the RiScan Pro software by RIEGL. The rest of points were classified using point cloud clustering via the connected-component labelling method implemented in the CloudCompare software. In the final step, the operator manually decides whether the point cluster represents the noise. The method was applied to the Cathedral of Saint Elizabeth, a sacral object located in the historical centre of the city of Košice in Slovakia during normal operating hours. We managed to capture approximately 80% of the data noise in total. The method provides a better flexibility in surveying overcrowded city locations using the laser scanning technology.

Automating the Refactoring Process

To decrease software maintenance cost, software development companies use static source code analysis techniques. Static analysis tools are capable of finding potential bugs, anti-patterns, coding rule violations, and they can also enforce coding style standards. Although there are several available static analyzers to choose from, they only support issue detection. The elimination of the issues is still performed manually by developers. Here, we propose a process that supports the automatic elimination of coding issues in Java. We introduce a tool that uses a third-party static analyzer as input and enables developers to automatically fix the detected issues for them. Our tool uses a special technique, called reverse AST-search, to locate source code elements in a syntax tree, just based on location information. Our tool was evaluated and tested in a two-year project with six software development companies where thousands of code smells were identified and fixed in five systems that have altogether over five million lines of code.