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Deploying UAV-based detection of bridge structural deterioration with pilgrimage walk optimization-lite for computer vision

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Chou, Jui-Sheng Liu, Chi-Yun Guo, Pin-Jun Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei Taiwan

Bridges are crucial components of national infrastructure, requiring rigorous maintenance and inspections to ensure their safety and functionality. Inspections are incredibly challenging and costly when accessing elevated bridge decks and substructures, particularly over riverbeds. To address these challenges, this study introduces a novel system integrating advanced computer vision-based deep learning, metaheuristic optimization, and Unmanned Aerial Vehicle (UAV) technology to revolutionize bridge inspections. This system uses UAVs to capture high-resolution images, which are then processed by the You Only Look Once (YOLO) models for instance segmentation. The YOLOv7 model, fine-tuned with the Pilgrimage Walk optimization (PWO)-Lite algorithm, achieved the highest accuracy, recording a 65.6 % mAP50 on the testing set. The PWOLite algorithm enhanced the YOLOv7's performance, increasing mean average precision by 4.1 % compared to YOLOv7 with augmented images and 13.9 % compared to YOLOv7 using original photos. These improvements significantly boost the model's generalization capability. This integration facilitates precise, automated deterioration quantification and supports accurate, efficient maintenance cost estimation. Explicitly designed for detecting underside deterioration in composite bridges, this system provides bridge management authorities and construction firms with a comprehensive tool. Our findings indicate improvements in predictive maintenance for bridges, offering actionable insights that aid informed decision-making in bridge inspections and maintenance planning. This advancement enhances the sustainability and safety of bridge infrastructure, representing a substantial step forward in infrastructure management and UAV applications.

关键词： Bridge deck and substructure Bridge inspection Unmanned aerial vehicle Computer vision-based deep learning Instance segmentation metaheuristic optimization algorithm Engineering information integration Maintenance cost estimation system

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Direct Model-Reference Adaptive Control for Wheelchair Simulator Control via a Haptic Interface 15th

Direct Model-Reference Adaptive Control for Wheelchair Simul...

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15th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design and Evaluation of Human-Machine Systems (HMS)

作者： Ghezala, Amel Ait Sentouh, Chouki Pudlo, Philippe Univ Polytech Hauts de France CNRS UMR 8201 LAMIH Valenciennes France

A wheelchair locomotion simulator (WCS) is an innovative solution to assess the biomechanical cost of wheelchairs (WC) accessibility in a controlled and safe virtual environment. In this context, this paper presents a haptic feedback control architecture based on a direct model reference adaptive control (MRAC) with intelligent tuning of its adaptation gains. The control objective is to follow the reference model velocity while producing the force feedback during the push phase, in order to faithfully recreate the dynamic behavior of the WC in a virtual environment. To accomplish this, a wheelchair ergometer model with friction is used to provide realistic navigation in the virtual environment (VE), by detecting and driving the wheelchair wheels. A two-wheeled vehicle model including the rolling resistance aspect is used to describe the wheelchair dynamic behavior. Since the controller adaptation gains are operated on the tracking error between the reference model and the simulator output, the WC model is also used as a reference model to specify the desired dynamics of the adaptive control system. For an optimal solution, an intelligent metaheuristic algorithm Elephant Herding optimization (EHO) is employed to optimize the controller gain adaptation parameter to keep the tracking error as small as possible. Finally, the simulation results obtained show the effectiveness of the proposed control strategy. Copyright (C) 2022 The Authors.

关键词： Direct MRAC system People with motor disabilities Wheelchair Simulator metaheuristic optimization algorithm Haptic interface Parameter estimation

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Multi-objective group learning algorithm with a multi-objective real-world engineering problem

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Rahman, Chnoor M. Mohammed, Hardi M. Abdul, Zrar Khalid Charmo Univ Coll Sci Comp Dept Sulaymaniyah Iraq

The concern of multi-objective optimization is to deal with optimization problems with more than one objective that should be optimized at the same time. In this paper, a new multi-objective optimization algorithm called multi-objective group learning algorithm is examined. The algorithm is based on the effect of group leaders on the group members and the effect that the group members have on each other. The algorithm is evaluated against the five Zitzler, Deb and Thiele (ZDT) benchmark functions, two standard functions of the DTLZ benchmarks, and a real-world engineering problem. To quantitatively examine the ability of the algorithm four metrics are utilized. Additionally, to statistically confirm the results, standard deviation and the average metrics are used. Moreover, the Wilcoxon rank sum test is used to examine the importance of the results statistically. Finally, the results of the algorithm are evaluated against multi-objective ant lion optimizer, multi-objective evolutionary algorithm based on decomposition, Pareto envelop-based selection algorithm-II, and multi-object moth swarm algorithm. The results of the benchmark functions showed that the proposed work produced better results in most of the metrics for three out of five ZDT benchmarks, and for the rest of the benchmarks it produced better results in at least one of the metrics compared to the participated algorithms. Moreover, the result of the real world engineering problem proved the ability of the proposed algorithm in producing acceptable and better Pareto front in almost all the metrics. For the proposed algorithm, the result of the generational distance for the speed reducer design problem is 0.8002, whereas it is 7.9375710, and 61.98134 for the multi-objective evolutionary algorithm based on decomposition, multi-objective ant lion optimizer, respectively. Additionally, the produced result for the spacing metric by the proposed algorithm was smaller than the result of the other algorithms for

关键词： optimization Multi-objective optimization Evolutionary algorithm Group learning algorithm metaheuristic optimization algorithm Multi-objective group learning algorithm MOGLA

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A new evolutionary algorithm: Learner performance based behavior algorithm

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EGYPTIAN INFORMATICS JOURNAL 2021年第2期22卷 213-223页

作者： Rahman, Chnoor M. Rashid, Tarik A. Charmo Univ Coll Med & Appl Sci Appl Comp Dept Sulaimany Iraq Sulaimany Polytech Univ Tech Coll Informat Sulaimany Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization;exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner's studying behavior at university to improve the level of their study, respectively. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.

关键词： Evolutionary algorithms Genetic algorithm LPB Learner performance based behavior algorithm optimization metaheuristic optimization algorithm

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Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Thieu, Nguyen Van Nguyen, Ngoc Hung Sherif, Mohsen El-Shafie, Ahmed Ahmed, Ali Najah PHENIKAA Univ Fac Comp Sci Ha Dong 12116 Hanoi Vietnam Artificial Intelligence Independent Res Grp Hanoi Vietnam United Arab Emirates Univ Coll Engn Civil & Environm Engn Dept POB 15551 Al Ain U Arab Emirates Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirate Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates Sunway Univ Sch Engn & Technol Dept Engn 5 Jalan Univ Bandar Sunway 47500 Selangor Darul Malaysia

Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge-Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson's correlation index (R) of 0.9374, and a Nash-Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

关键词： River streamflow Forecasting model Extreme learning machine metaheuristic optimization algorithm Nature-inspired algorithms Nile river

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Application of Particle Swarm optimization to a Hybrid H∞/Sliding Mode Controller Design for the Triple Inverted Pendulum System

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algorithmS 2024年第10期17卷 427页

作者： Shafeek, Yamama A. Ali, Hazem I. Univ Technol Iraq Control & Syst Engn Dept Baghdad Iraq

The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro-mechanical devices that are constructed in the semblance of humans and have the ability to sense their environment and take actions accordingly. The control of humanoids is broken down to the following: sensing and perception, path planning, decision making, joint driving, stability and balance. In order to establish and develop control strategies for joint driving, stability and balance, the triple inverted pendulum is used as a benchmark. As the presence of uncertainty is inevitable in this system, the need to develop a robust controller arises. The robustness is often achieved at the expense of performance. Hence, the controller design has to be optimized based on the resultant control system's performance and the required torque. Particle Swarm optimization (PSO) is an excellent algorithm in finding global optima, and it can be of great help in automatic tuning of the controller design. This paper presents a hybrid H infinity/sliding mode controller optimized by the PSO algorithm to control the triple inverted pendulum system. The developed control system is tested by applying it to the nominal, perturbed by parameter variation, perturbed by external disturbance, and perturbed by measurement noise system. The average error in all cases is 0.053 deg and the steady controller effort range is from 0.13 to 0.621 N.m with respect to amplitude. The system's robustness is provided by the hybrid H-infinity/sliding mode controller and the system's performance and efficiency enhancement are provided by optimization.

关键词： metaheuristic optimization algorithm particle swarm optimization engineering design H-infinity sliding mode control triple inverted pendulum robust control uncertainty torque

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Dynamic Hunting Leadership optimization: algorithm and applications

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 69卷

作者： Ahmadi, Bahman Giraldo, Juan S. Hoogsteen, Gerwin Univ Twente Dept Elect Engn Math & Comp Sci Enschede Netherlands

The Dynamic Hunting Leadership (DHL) algorithm is an innovative heuristic technique that draws inspiration from nature to find almost optimal solutions for various optimization problems. It consists of four variants, each highlighting distinct leadership strategies to guide the hunting process. The development of the algorithm was based on the realization that effective leadership during the hunting process can significantly improve its efficacy. The concept behind these methods is to dynamically modify the number of leaders, which can enhance the algorithm's performance. The stability of DHL variants in exploring the unknown area of the search space and exploitation phases is compared, and the advantages of exploration or exploration ability for the different variants of DHL are discussed. Moreover, the results are compared with more than twenty well-known algorithms. The efficacy of the proposed algorithms in discovering near-optimal solutions is tested across several real-world applications, and the outcomes demonstrate that the DHL algorithm outperforms other competing algorithms. The distinctiveness of the DHL algorithm is its ability to identify the global minimum on various benchmark problems and its superior performance in enhancing the objective value for the welded beam design problem and tension-compression spring problem, surpassing the values achieved by other algorithms. The algorithms' performance is also tested on an optimal allocation problem of distributed generation (DG) and energy storage system (ESS) for balanced electrical distribution systems. The results show that all four variants of DHL obtained the global minimum for the problem. For the optimal control strategy problem for voltage regulators in three-phase unbalanced power systems, the DHL algorithm improved the objective value by 35.7% compared to the best results found by other algorithms. Based on the analysis and comparison of the best objective values and convergence behavior for all

关键词： metaheuristic optimization algorithm Nature-inspired computing optimization Power system planning Swarm intelligence

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Systematic Development of a Multi-Objective Design optimization Process Based on a Surrogate-Assisted Evolutionary algorithm for Electric Machine Applications

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ENERGIES 2023年第1期16卷 392页

作者： Choi, Mingyu Choi, Gilsu Bramerdorfer, Gerd Marth, Edmund Inha Univ Dept Elect Engn Incheon 22212 South Korea Johannes Kepler Univ Linz Inst Elect Drives & Power Elect A-4040 Linz Austria

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method.

关键词： electric machine design multi-objective design optimization interior permanent magnet synchronous machine (IPMSM) surrogate model (SM) metaheuristic optimization algorithm

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Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks

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INTERNATIONAL JOURNAL OF COAL GEOLOGY 2023年第1期275卷

作者： Nguyen, Hoang Bui, Xuan-Nam Topal, Erkan Hanoi Univ Min & Geol Dept Surface Min Min Fac 18 Vien StrDuc Thang Ward Hanoi 100000 Vietnam Hanoi Univ Min & Geol Duc Thang Ward Innovat Sustainable & Responsible Min ISRM Res Grp 18 Vien Str Hanoi 100000 Vietnam Curtin Univ Western Australian Sch Mines WASM Min Engn Met Engn Dept Perth WA Australia

The objective of this paper is to present a method for predicting blast-induced ground vibration in open-pit mines that is based on the use of self-organizing neural networks (SONIA) and metaheuristic algorithms. In order to improve the accuracy of the SONIA model, several metaheuristic algorithms were employed, including the Manta Ray Foraging optimization (MRFO), Hunger Games Search (HGS), Aquila optimization (AO), and Naked Mole Rat algorithm (NMRA). Additionally, the k-fold cross-validation technique was used to identify the best parameters for the algorithms, which were then used to retrain the models for predicting blast-induced ground vibration. The effectiveness of the proposed method was evaluated using a case study of an open-pit coal mine in Vietnam, which had 288 blasting events. The study found that SONIA was a suitable neural network for predicting blast-induced ground vibration due to its self-organizing structure, even with a small dataset containing complex relationships. However, the SONIA model could be further optimized using the metaheuristic algorithms to improve its accuracy. The study found that the MRFO-SONIA model was the most reliable and accurate, with the lowest error (MAE = 0.379, RMSE = 0.453, MAPE = 0.08) and the highest reliability (R2 = 0.896). Meanwhile, the HGS-SONIA, AO-SONIA, and NMRA-SONIA models provided lower performance, with MAE values of 0.455, 0.500, and 0.492, RMSE values of 0.552, 0.603, and 0.580, MAPE values of 0.100, 0.112, and 0.111, and R2 values of 0.845, 0.815, and 0.829, respectively. The results of this study demonstrated the potential of using metaheuristic-based SONIA models to enhance predictions of blast-induced ground vibration in open-pit mines. This approach could prove useful in other operations in open-pit mines where there is a need to predict vibrations or other disturbances/negative effects resulting from specific mining activities.

关键词： Peak particle velocity Blast-induced ground vibration Self-organizing neural network metaheuristic optimization algorithm Open-pit coal mine Applied soft computing

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Chaotic Mapping Lion optimization algorithm-Based Node Localization Approach for Wireless Sensor Networks

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SENSORS 2023年第21期23卷 8699-8699页

作者： Motwakel, Abdelwahed Hashim, Aisha Hassan Abdalla Alamro, Hayam Alqahtani, Hamed Alotaibi, Faiz Abdullah Sayed, Ahmed Int Islamic Univ Malaysia Dept Elect & Comp Engn Kuala Lumpur 53100 Malaysia Prince Sattam Bint Abdulaziz Univ Coll Business Adm Hawtat Bani Tamim Dept Management Informat Syst Al Kharj 16278 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Comp Sci Ctr Artificial Intelligence Dept Informat SystUnit Cybersecur Abha 61421 Saudi Arabia King Saud Univ Coll Arts Dept Informat Sci POB 28095 Riyadh 11437 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion optimization algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

关键词： anchor nodes metaheuristic optimization algorithm node localization tent chaotic mapping wireless sensor networks

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