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A comparison study of Weibull, normal and Boulevard distributions for wireless mesh networks considering different router replacement methods by a hybrid intelligent simulation system

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第8期14卷 10181-10194页

作者： Barolli, Admir Bylykbashi, Kevin Qafzezi, Ermioni Sakamoto, Shinji Barolli, Leonard Aleksander Moisiu Univ Durres Dept Informat Technol L1 Rruga Currila 2001 Durres Albania Fukuoka Inst Technol Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan Fukuoka Inst Technol Grad Sch Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan Kanazawa Inst Technol Dept Informat & Comp Sci 7-1 Ohgigaoka Nonoichi Ishikawa 9218501 Japan

The number of connected devices is increasing exponentially, calling for more and more dynamic networks that need little maintenance and are able to optimize end users experience in the process. Wireless mesh networks (WMNs) are viewed as a solution to keep end users satisfied with reliable connectivity while minimizing the maintenance and upfront costs. However, designing a robust WMN at low cost requires the use of the least possible mesh routers but still interconnected and able to offer full coverage. Therefore, the placement of mesh routers over the area of interest is a problem that entails thorough planning. To deal with this problem, we have previously implemented a simulation system that combines particle swarm optimization (PSO) and distributed genetic algorithm (DGA), in a hybrid intelligent system called WMN-PSODGA. In this work, we implement three distributions of mesh clients: Weibull, normal and Boulevard, and evaluate the performance of the network for different router replacement methods. The router replacement methods considered are constriction method, random inertia weight method, linearly decreasing inertia weight method (LDIWM), linearly decreasing Vmax method and rational decrement of Vmax method. By comparing all the simulated scenarios, we conclude that the best scenario in terms of both coverage and load balancing is normal distribution with LDIWM as a router replacement method.

关键词： WMN intelligent algorithm PSO DGA Hybrid system Connectivity Coverage Load balancing

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An intelligent hybrid model for power flow optimization in the cloud-IOT electrical distribution network

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 13109-13118页

作者： Zhang, Yantian Wang, Haoxiang Xie, Yanzhao Wuhan Power Supply Design Inst Wuhan Hubei Peoples R China Cornell Univ Ithaca NY USA Huazhong Univ Sci & Technol Sch Software Engn Wuhan Hubei Peoples R China

Power quality is an essential parameter in delivery of electrical power through the distributions networks in real time. Power transmission quality is affected by several factors which include frequently occurring voltage variations in the form of swells and sags, harmonic distortions, voltage and noise spikes. They severely degrade the quality of power delivered if unattended and directly have a consequence on the energy consumption which in turn increases the system cost and efficiency. The proposed research paper proposes and investigates an intelligent hybrid implementation of electrical network driven by a five layer ANFIS module driven by a set of FIS rule base. The proposed model has been tested after an elaborate mathematical modeling to arrive at the design electrical equations which are exported on to the ANFIS for training and validation. The proposed technique has been compared against existing ANN and Fuzzy algorithms and superior performance with respect to stability, settling time, reduction in noise have been observed in the experimentation. Faster convergence towards optimal power flow is achieved using the ANFIS model.

关键词： Power quality intelligent algorithm ANFIS Transformation matrix

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A stable control method for planar robot with underactuated constraints via motion planning and intelligent optimization

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MEASUREMENT & CONTROL 2023年第9-10期56卷 1826-1834页

作者： Huang, Zixin Li, Xinpeng Wei, Ziang Wan, Xiao Wang, Lejun Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Complex Syst & Bion Control Chongqing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Complex Syst & Bion Control Chongqing 400065 Peoples R China

For the planar robot with underactuated constraints, a stable control method is presented on the foundation of motion planning method and intelligent optimization, which includes two stages. (1) Designing the controllers to control the actuated manipulators to given target states. (2) Planning the motion trajectory combined with the underactuated constraints between all links, using the intelligent algorithm to find the adaptable trajectory parameters, and tracking such planned trajectories to control full manipulators to the given states simultaneously. At last, multigroup simulations demonstrate the validity of the proposed method.

关键词： Planar underactuated robot motion planning stable control intelligent algorithm

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Summary of intelligent algorithms in Planning & Scheduling of Earth Observation Satellite

Summary of Intelligent Algorithms in Planning & Scheduling o...

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2010 IEEE International Conference on intelligent Computing and intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Hongfei WANG Xuzhi LI Yurong LIU Bing ZHOU Academy of opto-electronics Chinese academy of sciences Beijing China

Mission planning system is the core part of Earth observation satellites ground management, and it is a complex NP hard problem. This paper summarized the advantages and disadvantages of various mathematical models of the planning system today. And papers also analyzed the adaptability and Optimization result of intelligent algorithms in many different situations. Finally, the article gives the current EOS satellite mission planning system's latest developments and trends in the future.

关键词： Earth Observation Satellite Planning & Scheduling Planning model intelligent algorithm

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Prediction of the sulfur solubility in pure H2S and sour gas by intelligent models

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JOURNAL OF MOLECULAR LIQUIDS 2020年 299卷 112242-112242页

作者： Bian, Xiao-Qiang Song, Yi-Lun Mwamukonda, Martin Kelvin Fu, Yu Southwest Petr Univ Petr Engn Sch Chengdu 610500 Peoples R China

In order to reduce time and enhance accuracy, four intelligent models named Grey Wolf Optimizer based Least Squares Support Vector Machine (GWO-LSSVM), Grey Wolf Optimizer based Radial Basis Function (GWO-RBF), Genetic algorithm based Adaptive Network Fuzzy Inference System (GA-ANFIS), and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System (PSO-ANFIS) were applied to predict sulfur solubility in pure H2S and sour gas. According to Pearson correlation analysis, the content of H2S, critical temperature, temperature, gas density, and pressure were selected as input variables and sulfur solubility was selected as an output variable in sour gas. The contradistinction among the four models reveals GWO-LSSVM behaves the best performance with the minimum average absolute relative deviation (AARD = 3.5029%), and the maximum determination coefficient (R-2 = 0.9976) in all 239 data. But according to the minimum root mean squared error (RMSE), PSO-ANFIS performs best in pure H2S and sour gas among the four models. The leverage method was used to search outlier data for sulfur solubility, indicating that there are only 5 anomalous data points of all 239 data for the best GWO-LSSVM model. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Sulfur solubility intelligent algorithm GWO-LSSVM GWO-RBF GA-ANFIS PSO-ANFIS

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A novel intelligent method for slab front-end bending control in hot rolling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第9-10期126卷 4199-4212页

作者： Song, Lebao Xu, Dong Wang, Chengyun He, Hainan Wang, Xiaochen Li, Hui Yu, Haijun Yang, Quan Univ Sci & Technol Beijing Natl Engn Technol Res Ctr Flat Rolling Equipment Beijing 100083 Peoples R China SAIC GM Wuling Automobile Co Ltd Liuzhou 545000 Guangxi Peoples R China Hunan Lianyuan Iron & Steel Co Ltd 2250 Hot Strip Mill Loudi 417000 Hunan Peoples R China

A novel intelligent control strategy based on machine learning (ML) and an optimal feed-forward control method are proposed to realize the high-precision prediction and precise control of slab front-end bending in hot rolling. By analyzing the mechanism of slab front-end bending, the factors influencing hot rolling slab front-end bending are analyzed by a simulation model, and the actuator efficiency model of slab front-end bending is established. Through exploring the genetic law of slab front-end bending, an optimization design for slab front-end bending control that combines bar feed-forward with pass feed-forward is presented. By comparing different ML methods, the XGBoost model is selected to establish a prediction model and is combined the proposed optimal control strategy to realize slab front-end bending control. The proposed control strategy has been successfully applied to industrial sites and achieved satisfactory results, which the proportion of the serious slab turn-up phenomenon has been reduced by 11.12%, and the proportion of bending values within 50 mm has increased by 18.56%. The product quality is significantly improved, and automatic control of slab front-end bending for hot rolling is realized.

关键词： Front-end bending Asymmetric rolling Optimization control Data-driven intelligent algorithm

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An intelligent response surface method for analyzing slope reliability based on Gaussian process regression

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INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS 2019年第15期43卷 2431-2448页

作者： Zhu, Bin Pei, Huafu Yang, Qing Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China

Problems in geotechnical engineering inevitably involve many uncertainties in the analysis. Reliability methods are important for evaluating slope stability and can take the uncertainties into consideration. In this paper, a novel intelligent response surface method is proposed in which a machine learning algorithm, namely Gaussian process regression, is used to approximate the high-dimensional and highly nonlinear response hypersurface. An iterative algorithm is also proposed for updating the response surface dynamically by adding the new training point nearest to the limit state surface to the initial training database at each step. The proposed Gaussian process response surface method is used to analyze three different case studies to assess its validity and efficiency. Direct Monte Carlo simulation is also carried out in each case to serve as the benchmark. Comparing with other methods confirms the accuracy and efficiency of the novel intelligent response surface method, which requires fewer performance function calls and avoids the need to normalize the correlative non-normal variables.

关键词： failure probability Gaussian process regression intelligent algorithm Monte Carlo simulation response surface method slope reliability analysis

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Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization

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KNOWLEDGE AND INFORMATION SYSTEMS 2020年第11期62卷 4407-4444页

作者： Das, Amit Kumar Nikum, Ankit Kumar Krishnan, Siva Vignesh Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 W Bengal India SVNIT Dept Mech Engn Surat 395007 India

Non-traditional optimization tools have proved their potential in solving various types of optimization problems. These problems deal with either single objective or multiple/many objectives. Bonobo Optimizer (BO) is an intelligent and adaptive metaheuristic optimization algorithm inspired from the social behavior and reproductive strategies of bonobos. There is no study in the literature to extend this BO to solve multi-objective optimization problems. This paper presents a Multi-objective Bonobo Optimizer (MOBO) to solve different optimization problems. Three different versions of MOBO are proposed in this paper, each using a different method, such as non-dominated sorting with adaptation of grid approach;a ranking scheme for sorting of population with crowding distance approach;decomposition technique, wherein the solutions are obtained by dividing a multi-objective problem into a number of single-objective problems. The performances of all three different versions of the proposed MOBO had been tested on a set of thirty diversified benchmark test functions, and the results were compared with that of four other well-known multi-objective optimization techniques available in the literature. The obtained results showed that the first two versions of the proposed algorithms either outperformed or performed competitively in terms of convergence and diversity compared to the others. However, the third version of the proposed techniques was found to have the poor performance.

关键词： Multi-objective optimization Bonobo Optimizer intelligent algorithm Multi-objective Bonobo Optimizer Multi-criteria optimization

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Planning of manufacturing networks using an intelligent probabilistic approach for mass customised products

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第9-12期74卷 1747-1758页

作者： Doukas, M. Psarommatis, F. Mourtzis, D. Univ Patras Dept Mech Engn & Aeronaut Lab Mfg Syst & Automat Patras Greece

Manufacturers around the globe are presented with the evident need to successfully capture and efficiently satisfy the increasing demand towards highly customised products. The trend for higher levels of customisation increases the operational costs, affects delivery times and worsens the environmental footprint of production. Moreover, the feasible alternative manufacturing network configurations increase together with the exploding product variety and the large pool of cooperating suppliers. The proposed research work describes an intelligent method that utilises three adjustable control parameters and can be used for the identification of efficient globalised manufacturing network configurations capable of carrying out the production of mass customised products. The decision support system presented allows the generation of alternative manufacturing network configurations and their evaluation, through a set of multiple conflicting user-defined criteria of cost, time, quality and environmental impact. The suggested approach, which is implemented into a web-based software tool, is investigated through a probabilistic analysis for guiding the decision-maker when selecting the values of the adjustable control parameters, in order to obtain high-quality manufacturing network designs. The applicability of the method is validated through a real-life pilot case, using data acquired from an automotive manufacturer.

关键词： Manufacturing network design Mass customisation Decision-making intelligent algorithm Decentralised manufacturing

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Modeling and controlling a semi-active nonlinear single-stage vibration isolator using intelligent inverse model of an MR damper

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2020年第9期34卷 3525-3532页

作者： Mousavi, Seiyed Hamid Tech & Vocat Univ TVU Tehran Branch Fac Enghelab Dept Mech Engn Tehran Iran

Semi-active systems with variable stiffness and damping have demonstrated excellent performance. The aim of this study is to investigate the new configuration of a semi-active single-stage nonlinear vibration isolation system with a Magneto-Rheological (MR) damper to reduce the magnitude of force transmissibility over the both resonant and non-resonant regions. The magnitude of force transmissibility is widely used to performance measurement for the isolation system. In current study, to achieve this reduction, two horizontal springs and one MR damper were added to the isolator. Theoretical analysis reveals that the nonlinear system with MR damper can produce ideal vibration isolation. However, due to the nonlinear characteristics behavior of the MR damper, conventional control algorithms to reach the desired are cumbersome. To address this issue, an artificial intelligent strategy using wavelet network and fuzzy logic controller is considered to be constructed to copy the inverse dynamics of the MR damper and the nonlinear isolator. Accordingly, simulation results demonstrated that the intelligent algorithm has acceptable performance.

关键词： Force transmissibility intelligent algorithm MR damper Nonlinear vibration isolator

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