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Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and mfo algorithm 20

Teaching Control Theory using Mobile Robot Obstacle Followin...

20th Latin American Robotics Symposium (LARS) / 15th Brazilian Symposium on Robotics (SBR) / 14th Workshop on Robotics in Education (WRE)

作者： Pastrana, M. A. Bautista, J. Mendoza-Penaloza, Jose Oliveira, L. H. Mendes, D. A. Munoz, Daniel M. Univ Brasilia Dept Mech Engn Mechatron Grad Program Brasilia DF Brazil Univ Brasilia Fac Gama Elect Engn Undergrad Program Brasilia DF Brazil Univ Amazonia Fac Engn Syst Undergrad Program Florencia Caqueta Colombia

ISBN: (纸本)9798350315387

The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (mfo) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.

关键词： Teach Control systems PID controller mfo algorithm mobile robots

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Forecasting ionospheric TEC using least squares support vector machine and moth-flame optimization methods in China

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ADVANCES IN SPACE RESEARCH 2024年第3期74卷 1482-1496页

作者： Tang, Jun Liu, Chang Fan, Cihang Kunming Univ Sci & Technol Fac Land Resources Engn Kunming Peoples R China East China Jiaotong Univ Sch Transportat Engn Nanchang Peoples R China

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth -Flame Optimization (mfo) algorithm is proposed. The parameters of the LSSVM model are optimized by the mfo algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the mfo_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the mfo_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short -Term Memory (LSTM) model shows that the mfo_LSSVM model has better performance than the single LSTM model. In conclusion, the mfo_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models. (c) 2024 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Ionosphere Forecast Total electron content LSSVM mfo algorithm

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An improved LSSVM discrimination model based on factor analysis and moth flame optimization algorithm for identifying water inrush sources across multiple aquifers in mines

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ENVIRONMENTAL EARTH SCIENCES 2024年第14期83卷 424-424页

作者： Bi, Yaoshan Shen, Shuhao Wu, Jiwen Huainan Normal Univ Sch Mech & Elect Engn Huainan 232001 Peoples R China Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China

To accurately and swiftly identifying the source of water inrush in mines, a discrimination model based on factor analysis (FA) and the moth flame optimization (mfo) algorithm coupled with least squares support vector machine (LSSVM) is proposed. Drawing from the hydrogeological conditions of the Yuanyi Mine in the Huaibei Mining Area, water samples from three aquifers were collected, and ten hydrochemical indicators were selected for the purpose of identifying the water inrush sources. Firstly, FA was performed on these ten indicators to extract five new components that can comprehensively reflect most of the information of all indicators, eliminating redundant information between the original indicators. Then, the extracted new components was used as inputs to the LSSVM model. Finally, the mfo algorithm was used to automatically optimize the two pivotal parameters of the penalty factor C and the kernel function parameter g of LSSVM, and a discrimination model based on FA-mfo-LSSVM was established. Furthermore, a comparative analysis of the discrimination performance of the FA-mfo-LSSVM model against five other models was carried out. The results unequivocally indicate that the FA-mfo-LSSVM model has high discrimination accuracy for both training and testing samples, and compared to the other five models, this model exhibits stronger discriminative performance.

关键词： mfo algorithm LSSVM model Factor analysis Water inrush Water source discrimination Coal mine

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An Efficient Approach to Reduce Energy Consumption in a Fog Computing Environment Using a Moth Flame Optimization algorithm

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SN Computer Science 2024年第6期5卷 1-24页

作者： Asgarnezhad, Razieh Department of Computer Engineering Aghigh Institute of Higher Education

After decades of growth in the computer computing field, cyber-physical systems (CPS), a combination of physical and tangible hardware and virtual and supernatural tools and concepts, distributed. Today, fog and cloud computing are the most complex cyber-physical systems available. Theretofore, the cloud data centers developed to provide the resources needed by users, home, and industrial businesses. Cloud computing has provided the possibility of providing services near the occurrence of requests with processing in proximity (PP). However, edge or fog computing will supply a possible solution to improve the quality of service delivery compared to using cloud computing. Applications in cyber-physical fog systems utilize different services provided by diverse resources in fog colonies based on criteria and restriction rules. Since internet of things (IoT) applications executed in real-time and sensitive to time, the problem of delay in providing service to application requests in cloud computing distributed resources is very challenging. Fog computing supply an ideal platform for CPSs with fully geographically distributed features. At first, by placing services on resources are located in the edge layer, the cloud decrease the volume of requests sent to the cloud. Moreover, the response and the average delay time be solved in the proposed method. As a result, it makes the problem of placing services in cloud computing more complicated than in other areas like cloud computing and standard distributed systems. In addition, to balance the load and ensure the quality of services, requested services in the fog system can be freely processed by any of the resources (nodes) available in the fog computing. According to the characteristics of the geographic distribution of fog nodes, the complexity of placing services to provide services to reduce energy consumption will be very high. In this study, we offer a solution based on the meta-heuristic algorithm of moth flame optim

关键词： Fog computing IoT mfo algorithm Node Physical cyber Service placement

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Improved off-grid wind/photovoltaic/hybrid energy storage system based on new framework of Moth-Flame optimization algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第5期46卷 6711-6729页

作者： Abbassi, Abdelkader Ben Mehrez, Rached Abbassi, Rabeh Saidi, Salem Albdran, Saleh Jemli, Mohamed Univ Kairouan Inst Appl Sci & Technol Kasserine ISSATKas POB 471 Kasserine 1200 Tunisia Univ Carthage Not Engn Sch Carthage Res Lab Smart Elect & ICT Tunis Tunisia Univ Hail Coll Engn Dept Elect Engn Hail Saudi Arabia Univ Carthage Natl Inst Adv Technol Borj Cedria Dept Elect Engn Tunis Tunisia Natl Higher Engn Sch Tunis ENSIT Engn Lab Ind Syst & Renewable Energies LISIER Tunis Tunisia

This paper suggests a new sizing optimization method of an off-grid renewable energy system. To perform an accurate analysis of the distribution of the exchanged energy with all storage elements, the discrete Fourier transform tool has been used. Besides, different frequency strains have been achieved in accordance with the dynamic of each storage device. Thus, the problem formulation targets the minimization of the Total Cost of Electricity (TCE) for the purposes of improving the reliability, and increasing the performance and the exploitation of the renewable energies for electrification fulfillment. The Loss of Power Supply Probability (LPSP) is also considered to improve the reliability rate of the selected configuration. Considering all storage dynamics to achieve the energy management strategy, a modified Moth-Flame Optimization (mfo) algorithm is proposed to address this problem. Based on economic evaluation and analysis, an optimal configuration of the system has been established. Referring to a high level of economic feasibility, the obtained results affirm that this architecture is able to deal successfully with the current sizing problem through its flexibility to achieve the lowest cost which is decreased by around 0.7 e(04)$ compared with the start of the search for the desired solution.

关键词： economic evaluation green renewable energy LPSP mfo algorithm total cost of electricity

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A Strategy-based Optimization algorithm to Design Codes for DNA Data Storage System 21st

A Strategy-based Optimization Algorithm to Design Codes for ...

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21st International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Rasool, Abdur Qu, Qiang Jiang, Qingshan Wang, Yang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab High Performance Data Min Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Beijing 100049 Peoples R China

ISBN: (纸本)9783030953881;9783030953874

The exponential increase of big data volumes demands a large capacity and high-density storage. Deoxyribonucleic acid (DNA) has recently emerged as a new research trend for data storage in various studies due to its high capacity and durability, where primers and address sequences played a vital role. However, it is a critical biocomputing task to design DNA strands without errors. In the DNA synthesis and sequencing process, each nucleotide is repeated, which is prone to errors during the hybridization reactions. It decreases the lower bounds of DNA coding sets which causes the data storage stability. This study proposes a metaheuristic algorithm to improve the lower bounds of DNA data storage. The proposed algorithm is inspired by a moth-flame optimizer (mfo), which has exploration and exploitation capability in one dimension, and it is enhanced by opposition-based learning (OBL) strategy with three-dimension search space for the optimal solution;hereafter, it is mfoL algorithm. This algorithm is programmed to construct the DNA storage codes by reducing the error rates of DNA coding sets with GC-content, Hamming distance, and No-runlength constraints. In experiments, 13 benchmark functions and Wilcoxon rank-sum test are implemented, and performances are compared with the original mfo and three other algorithms. The generated DNA codewords by mfoL are compared with a state-of-the-art Altruistic algorithm and KMVO algorithm. The proposed algorithm improved 30% DNA coding rates with shorter sequences, reducing errors during DNA synthesis and sequencing.

关键词： DNA data storage Biocomputing DNA coding sets Opposition-based learning mfo algorithm

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Research on Grid-Tied Control Strategy of Distributed Photovoltaic-Energy Storage System Based on DVR 5

Research on Grid-Tied Control Strategy of Distributed Photov...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Zhang, Jie Zeng, Guohui Huang, Bo ShangHai Univ Engn Sci Sch Elect & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728152813

In order to improve the utilization rate and power quality of distributed new energy power generation technology and, to solve the voltage fluctuation problem in the operation of the distributed photovoltaic storage and grid-connected system. This paper proposes a control strategy based on DVR (dynamic voltage restorer) for operation of distributed photovoltaic storage and grid-connected. The remaining photovoltaic output energy is stored in energy storage via dual active bridge to reduce the waste of photovoltaic power. To compensate the output voltage fluctuation of photovoltaic grid-connected inverter, the DVR was connected to the energy storage. And PI controller parameters of the DVR are optimized by Moth- Flame Optimization (mfo) algorithm, realize the recovery of output voltage fluctuation of the photovoltaic grid-connected inverter. The advantages of the proposed control strategy are demonstrated using simulations, and the results show that the proposed strategy can ensure the quality of PV output voltage in the photovoltaic storage and grid-connected system.

关键词： voltage sag DVR mfo algorithm photovoltaic storage

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