Whale optimization algorithm-based point cloud data processing method for sewer pipeline inspection

Liu, Wenli; Shao, Yixiao; Chen, Ke; Li, Chen; Luo, Hanbin

doi:10.1016/j.autcon.2022.104423

Cited by 22 publications

(4 citation statements)

References 44 publications

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“…Liu et al proposed a point cloud data processing method using whale optimization algorithm to enhance the assessment of internal blockage and deterioration in sewage pipelines. The applicability of this method was verified in actual sewage systems, and the results showed that it can accurately and effectively reconstruct the three-dimensional model of the sewer [8].…”

Section: Related Workmentioning

confidence: 94%

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

Zhao,

Liu,

2024

Journal of Applied Mathematics

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With the development of industrial demand, precise identification of system models is currently required in the field of industrial control, which limits the whale search algorithm. In response to the fact that whale optimization algorithms are prone to falling into local optima and the identification of important Hammerstein models ignores the issue of noise outliers in actual industrial environments, this study improves the whale algorithm and constructs a Hammerstein model identification strategy for nonlinear systems under heavy-tailed noise using the improved whale algorithm. Results showed that it had a lower rank average and an average success rate of 95.65%. It found the global optimum when the number of iterations reached around 150 and had faster convergence speed and accuracy. In identifying Hammerstein model under heavy-tailed noise, the average prediction recognition accuracy of the improved whale algorithm was 92.38%, the determination coefficient was 0.89, the percentage fitting error was 0.03, and the system error was 0.02. This research achievement has certain value in the field of industrial control and can serve as a technical reference.

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Section: Related Workmentioning

confidence: 94%

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

Zhao,

Liu,

2024

Journal of Applied Mathematics

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“…The results demonstrated that the hybrid RF, SVR, and WOA models performed better than other models. Liu et al (2022) presented WOA‐based point cloud data (WOAPCD) for inspection of sewage systems. This model was validated with actual sewer datasets and reconstructed the 3D model accurately.…”

Section: Introductionmentioning

confidence: 99%

Comparison of improved relevance vector machines for streamflow predictions

Adnan,

Mostafa,

Dai

et al. 2023

Journal of Forecasting

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This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Various lagged values of hydroclimatic data (e.g., precipitation, temperature, and streamflow) are used as inputs to the models. The relevance vector machine tuned with dwarf mongoose optimization algorithm improved the efficiency of single method in monthly streamflow prediction. It is found that the integrating metaheuristic algorithms into single relevance vector machine improves the prediction efficiency, and among the input combinations, the lagged streamflow data are found to be the most effective variable on current streamflow whereas precipitation has the least effect.

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“…The three operators of the whale optimization algorithm are the foraging behaviors of humpback whales using bubble nets, encircling prey, and searching for prey [56]. In 2022, Wenil Liu et al proposed whale optimization-based point cloud data for inspecting sewer pipelines [57]. Using a whale optimization algorithm, 2022 Guanghui Zhao presented a multi-stage charging technique that is health conscious.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Analysis of a Nonlinear Magnetic Levitation System Using PID Controller Optimized With COOT Algorithm

Maheedhar,

Deepa

2023

IEEE Access

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Recent growth in magnetic levitation can be attributed to its ability to minimize friction and disturbance in industries, transportation, aerospace, biomedicine, and magnetic bearings. Due to the magnetic levitation system's nonlinear and unstable nature, control engineers found it exceedingly challenging to design a stabilizing controller. The magnetic levitation system is abbreviated as a maglev system. Using the integral square error criterion, a newly developed metaheuristic algorithm named the COOT algorithm is used to optimize the PID controller parameters. The performance of the proposed algorithm is evaluated using simulation and hardware with several kinds of reference trajectories and compared to the performance of other algorithms, such as the genetic algorithm and the whale optimization algorithm. Based on simulation and hardware results, it was determined that the proposed algorithm performed well with less settling time, rise time, and integral square error.

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Whale optimization algorithm-based point cloud data processing method for sewer pipeline inspection

Cited by 22 publications

References 44 publications

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory

Comparison of improved relevance vector machines for streamflow predictions

Design and Performance Analysis of a Nonlinear Magnetic Levitation System Using PID Controller Optimized With COOT Algorithm

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