Department of Mathematics Applications and Methods for Artificial Intelligence, Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland.
Institute of Energy and Fuel Processing Technology, 41-803 Zabrze, Poland.
Sensors (Basel). 2022 Sep 26;22(19):7297. doi: 10.3390/s22197297.
The paper presents research on a specific approach to the issue of computed tomography with an incomplete data set. The case of incomplete information is quite common, for example when examining objects of large size or difficult to access. Algorithms devoted to this type of problems can be used to detect anomalies in coal seams that pose a threat to the life of miners. The most dangerous example of such an anomaly may be a compressed gas tank, which expands rapidly during exploitation, at the same time ejecting rock fragments, which are a real threat to the working crew. The approach presented in the paper is an improvement of the previous idea, in which the detected objects were represented by sequences of points. These points represent rectangles, which were characterized by sequences of their parameters. This time, instead of sequences in the representation, there are sets of objects, which allow for the elimination of duplicates. As a result, the reconstruction is faster. The algorithm presented in the paper solves the inverse problem of finding the minimum of the objective function. Heuristic algorithms are suitable for solving this type of tasks. The following heuristic algorithms are described, tested and compared: Aquila Optimizer (AQ), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Dynamic Butterfly Optimization Algorithm (DBOA). The research showed that the best algorithm for this type of problem turned out to be DBOA.
本文提出了一种针对具有不完整数据集的计算机断层扫描问题的特定方法的研究。在不完整信息的情况下，这种情况是很常见的，例如在检查大型物体或难以接近的物体时。专门针对这类问题的算法可用于检测对矿工生命构成威胁的煤层异常。这种异常的最危险的例子可能是压缩气体罐，它在开采过程中迅速膨胀，同时喷出岩石碎片，这对工作人员构成了真正的威胁。本文提出的方法是对以前的想法的改进，其中检测到的对象由点序列表示。这些点表示矩形，其特征是其参数的序列。这一次，代替表示中的序列，有对象的集合，这允许消除重复项。结果，重建速度更快。本文提出的算法解决了寻找目标函数最小值的逆问题。启发式算法适用于解决这类任务。描述、测试和比较了以下启发式算法：Aquila Optimizer (AQ)、Firefly Algorithm (FA)、Whale Optimization Algorithm (WOA)、Butterfly Optimization Algorithm (BOA) 和 Dynamic Butterfly Optimization Algorithm (DBOA)。研究表明，对于这类问题，最佳算法是 DBOA。
