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Simplified dynamic characteristic analysis method for parallel manipulators with flexure hinges

NONLINEAR DYNAMICS 2024年第8期112卷 6037-6059页

作者： Lin, Junwei Han, Hasiaoqier Yu, Peng Li, Peiyi Xu, Zhengbang Wu, Qingwen Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

To acquire precise high-resolution cryo-electron tomography data from transmission electron microscopy, a parallel manipulator (PM) with flexure hinges is often used to support and manipulate the samples. The precision and stability of the PM's motion are intricately associated with its dynamic characteristics. Thus, it is essential to predict and enhance the PM's dynamic characteristics during the initial design phase. To achieve this goal, a simplified dynamic characteristic analysis method (SDCAM) for PMs is developed. This method is based on the mass-spring model and accounts for only the mass of the mobile platform and the stiffness of the legs. A typical 6-PSS PM is employed as a case study to evaluate the proposed method. Analysis of the associated stiffness, mass, and frequency matrices allows us to derive the natural frequencies and modal shapes of the PM. The validity of the proposed SDCAM is confirmed through comparisons with the finite element method and the substructure synthesis method. Finally, using the average and population standard deviation of the first six-order natural frequencies of the PM as evaluation criteria, a hybrid optimization algorithm based on the multi-island genetic algorithm and sequential quadratic programming is adopted to optimize the configuration parameters and improve the PM's dynamic characteristics. The impact of load on dynamic characteristics and optimization is explored based on practical application scenarios of the PM. We find that the SDCAM can be used to estimate the natural frequencies of PMs with flexure hinges, and the hybrid optimization algorithm in conjunction with the SDCAM effectively enhances the dynamic characteristics of PMs, thus providing important guidance for early-stage design.

关键词： Parallel manipulator Flexure hinge Simplified analysis method Dynamic characteristics hybrid optimization algorithm

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A novel hybrid prediction model for PM2.5 concentration based on decomposition ensemble and error correction

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第15期30卷 44893-44913页

作者： Yang, Hong Zhao, Junlin Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

PM2.5 concentration is an important index to measure the degree of air pollution. It is necessary to establish an accurate PM2.5 concentration prediction system for urban air monitoring and control. Due to the nonlinear characteristics of PM2.5 concentration, it is difficult to predict it directly. Therefore, a novel hybrid model for PM2.5 concentration based on improved variational mode decomposition (IVMD), outlier-robust extreme learning machine (ORELM) optimized by hybrid cuckoo search (CS), and chimp optimization algorithm (ChOA), error correction (EC) is proposed named IVMD-ChOACS-ORELM-EC. First of all, an improved VMD based on energy loss coefficient, named IVMD, is proposed. IVMD decomposes the original data to obtain K IMF components. Then, a hybrid optimization algorithm based on ChOA improved by CS is proposed, named ChOACS. The hybrid optimization algorithm is used to optimize ORELM. On this basis, the prediction model ChOACS-ORELM is proposed, and the K IMF components are predicted by ChOACS-ORELM. Finally, the EC model based on decomposition ensemble is established to further improve the prediction accuracy. The PM2.5 concentration data collected at hourly intervals in Beijing, Shanghai, Shenyang, and Qingdao in China are used as experimental data. The experimental results show that the correlation coefficients between the prediction results and the actual values of the four cities are 0.9999, and the prediction performance of the proposed model is better than that of all comparison models.

关键词： PM2 5 concentration prediction Improved variational mode decomposition hybrid optimization algorithm Outlier-robust extreme learning machine Error correction

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A novel convolutional neural network framework based solar irradiance prediction method

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 114卷 105411-000页

作者： Dong, Na Chang, Jian-Fang Wu, Ai-Guo Gao, Zhong-Ke Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

As an important part of solar power system, photovoltaic grid-connected system and solar thermal system, solar irradiance has the inherent characteristics of variability and uncertainty. Existing data analysis methods are difficult to demonstrate better generalization. Hence, resource planners must be adaptable to accommodate these uncertainties while conducting planning. To improve the accuracy of solar energy prediction and efficiently organize the utilization of solar energy, a novel convolutional neural networks framework has been constructed. Firstly, we have established a convolutional neural network framework for solar prediction based on meteorological data from surrounding sites and different sampling times. Secondly, the chaotic GA/PSO1 hybrid algorithm is applied to optimize the hyper parameters of the novel framework, which alleviates the imperfect performance caused by improper hyper parameters. At the meantime, the hybrid algorithm can reduce the manpower and resources of manual parameter adjustment. Excitability of the novel framework has been verified by benchmark tests. In the solar irradiance prediction studies, the annual average Mean Absolute Error of the proposed method is reduced by 0.1463 MJ.m(-2) compared with single CNN2 framework. The annual average Mean Absolute Error of the proposed method is reduced by 49.47%, 47.6%, 20.34%, respectively, compared with ANN(3), K-means-RBF4 and GBRT.(5) The superiority has been fully illustrated through all the simulation test results. Therefore, the proposed method provides a basis for accurate estimation of solar power, which can promote further development of the whole power system.

关键词： Solar irradiance prediction Convolutional neural network hybrid optimization algorithm Hyperparameter Renewable energy

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A hybrid algorithm based on the proposed square strategy and NSGA-II for ship pipe route design

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OCEAN ENGINEERING 2024年 305卷

作者： Yan, Wei-qiang Yang, Ming-jun Lin, Yan Dalian Univ Technol Sch Naval Architecture & Ocean Engn Dalian 116024 Peoples R China

Ship pipe route design (SPRD) is a difficult and time-consuming job due to the complexity of the routing space and the large number of pipes. This paper introduces a hybrid optimization algorithm called SQNSGA-II based on the proposed Square strategy and NSGA-II to efficiently solve SPRD problems in complex situations. The details of the hybrid algorithm are introduced, including the principle of the Square strategy, genetic operators and pipe repair operators et al. Additionally, a new method called Intercept Search strategy is proposed for optimizing branch pipe design in complex scenarios. Finally, we conducted three cases of simulation experiments to demonstrate that the proposed algorithm and strategy can efficiently and accurately optimize the design of pipes, highlighting their pipe routing capabilities in complex engineering projects.

关键词： Ship pipe route design hybrid optimization algorithm NSGA-II Branch pipe routing

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Black Hole and Selective Forwarding Attack Detection and Prevention in IoT in Health Care Sector: hybrid meta-heuristic-based shortest path routing

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2021年第2期13卷 133-156页

作者： Srinivas, T. Aditya Sai Manivannan, S. S. Vellore Inst Technol SCOPE Vellore Tamil Nadu India Vellore Inst Technol SITE Vellore Tamil Nadu India

In the current health care scenario, security is the major concern in IoT-WSN with more devices or nodes. Attack or anomaly detection in the IoT infrastructure is increasing distress in the field of medical IoT. With the enormous usage of IoT infrastructure in every province, threats and attacks in these infrastructures are also mounting commensurately. This paper intends to develop a security mechanism to detect and prevent the black hole and selective forwarding attack from medical IoT-WSN. The proposed secure strategy is developed in five stages: First is selecting the cluster heads, second is generating k-routing paths, third is security against black hole attack, fourth is security against the selective forwarding attack, and the last is optimal shortest route path selection. Initially, a topology is developed for finding the cluster heads and discovering the best route. In the next phase, the black hole attacks are detected and prevented by the bait process. For detecting the selective forwarding attacks, the packet validation is done by checking the transmitted packet and the received packet. For promoting the packet security, Elliptic Curve Cryptography (ECC)-based hashing function is deployed. As the main contribution of this paper, optimal shortest route path is determined by the proposed hybrid algorithm with the integration of Deer Hunting optimization algorithm (DHOA), and DragonFly algorithm (DA) termed Dragonfly-based DHOA (D-DHOA) by concerting the parameters like trust, distance, delay or latency and packet loss ratio in the objective model. Hence, the entire phases will be very active in detecting and preventing the two fundamental attacks like a black hole and selective forwarding from IoT-WSN in the health care sector.

关键词： Internet of Things Wireless Sensor Network health care sector block hole attack selective forwarding attack shortest route path selection hybrid optimization algorithm

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Design for maintenance: new algorithmic approach

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2021年第1期27卷 129-143页

作者： Adjoul, Oussama Benfriha, Khaled Aoussat, Ameziane HESAM Univ Arts & Metiers Inst Technol Dept Prod Design & Innovat Lab Paris France

Purpose This paper proposes a new simultaneous optimization model of the industrial systems design and maintenance. This model aims to help the designer in searching for technical solutions and the product architecture by integrating the maintenance issues from the design stage. The goal is to reduce the life-cycle cost (LCC) of the studied system. Design/methodology/approach Literature indicates that the different approaches used in the design for maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and the maintainability of a multicomponent system as well as the modeling of the dynamic maintenance. This article proposes to go further in the optimization of the product, by simultaneously characterizing the design, in terms of reliability and maintainability, as well as the dynamic planning of the maintenance operations. This combinatorial characterization is performed by a two-level hybrid algorithm based on the genetic algorithms. Findings The proposed tool offers, depending on the life-cycle expectation, the desired availability, the desired business model (sales or rental), simulations in terms of the LCCs, and so an optimal product architecture. Research limitations/implications In this article, the term "design" is limited to reliability properties, possible redundancies, component accessibility (maintainability), and levels of monitoring information. Originality/value This work is distinguished by the use of a hybrid optimization algorithm (two-level computation) using genetic algorithms. The first level is to identify an optimal design configuration that takes into account the LCC criterion. The second level consists in proposing a dynamic and optimal maintenance plan based on the maintenance-free operating period (MFOP) concept that takes into account certain criteria, such as replacement costs or the reliability of the system.

关键词： Reliability Maintainability Simultaneous optimization Dynamic maintenance Design for maintenance (DFM) Life-cycle cost (LCC) hybrid optimization algorithm Multicomponent industrial systems

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Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2015年第1期21卷 1-19页

作者： Yang, Yimin Lin, Xiaofeng Miao, Zhiqiang Yuan, Xiaofang Wang, Yaonan Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

In this paper, we propose a novel nonlinear predictive control strategy based on an extreme learning machine to address the path-tracking control problem of wheeled mobile robots in the presence external disturbances. The hybrid chaotic optimization algorithm (HCOA), which can avoid being trapped in local minima and improve convergence in dealing with the large space and high-dimension optimization problems, is used to perform real-time nonlinear minimization of the cost function of a mobile robot to enhance the control accuracy. The proposed improved bidirectional extreme learning machine is employed to model the mobile robot plant and estimate future plant output. The experimental results of tracking the automation mobile robot indicate that the proposed controller can provide more accuracy and faster tracking performance than traditional neural network predictive controllers.

关键词： Bidirectional Extreme Learning Machine Model Predictive Control hybrid optimization algorithm Neural Network

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Prediction of Rice Yield Based on Multi-Source Data and hybrid LSSVM algorithms in China

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INTERNATIONAL JOURNAL OF PLANT PRODUCTION 2023年第4期17卷 693-713页

作者： Zhao, Long Qing, Shunhao Wang, Fei Wang, Hui Ma, Hao Shi, Yi Cui, Ningbo Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Henan Prov Tobacco Co Zhumadian Co Zhumadian 463000 Henan Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Sichuan Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Sichuan Peoples R China

Accurate prediction of rice yield is essential for national food security and the development of the national economy. Currently, owing to the influence of data sources and model parameters, it is difficult to obtain simple and highly accurate models for rice yield prediction. In this study, nine typical rice ecological observation stations in China were selected to build a rice yield prediction model integrating multi-source data based on the least squares support vector machine (LSSVM) model. To improve the accuracy of the rice yield prediction model, the genetic optimization algorithm (GA), particle swarm optimization algorithm (PSO), and grey wolf optimization algorithm (GWO) were selected to optimize the parameters of the least squares support vector machine model. The correlation significances of yield with different influencing factors followed the order: total solar radiation (Ra) > number of spikes (NS) > plant height (H) > average pressure (P) > maximum temperature (Tmax) > relative humidity (RH) > precipitation (Pre) > average surface temperature (Ts) > minimum temperature (Tmin) > sunshine hours (n) > accumulated temperature (Ta), and it was highly significant with meteorological data (P = 63.1%) and significant with phenotypic data (P = 36.9%). With an increasing number of influencing input factors, the model accuracy tended to increase and then decrease when prediction model was constructed. The results showed that in the input models with different variables, the prediction accuracy was the highest when the input was Ra, NS, H, P, Tmax, RH, Pre, Ts, Tmin, and n (R-2 = 0.712-0.841, RMSE = 1.139-1.458 ton/ha, MAE = 0.814-1.085 ton/ha, and NSE = 0.702-0.831). With the reintroduction of input variables, the accuracy of the rice prediction model could not be significantly improved. Compared with stand-alone LSSVM models, hybrid optimization algorithms can significantly improve the accuracy of the LSSVM model prediction results. The results of the GA, GWO,

关键词： Rice yield LSSVM hybrid optimization algorithm Meteorological data Phenotypical data

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Positioning system design of closed-loop magnetically driven laser steering manipulator for endoscopic microsurgery

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Biomimetic Intelligence & Robotics 2023年第4期3卷 40-46页

作者： Qi Zhang Han Xu Li Zhu Li Liu School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China Shenzhen Key Laboratory of Robotic Perception and Intelligence Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China

This study presents a closed-loop magnetically driven laser steering manipulator positioning system for endoscopic microsurgery.A multimagnetic field strength sensor circuit is embedded in a 16−mm diameter analog laser steering *** magnetic field distribution of a cylindrical permanent magnet in three-dimensional space is first modeled using an integral model to overcome the large error induced by the magnetic dipole model when the sensor and permanent magnet are *** integral in the model is then decomposed using the Gauss–Legendre quadrature to improve the computational efficiency of the ***,five commonly used global search optimization methods are ***,the algorithm with the fastest computational rate among these five algorithms,the tree-seed algorithm,is fused with the Levenberg–Marquardt algorithm,which performs well in local search,to obtain a hybrid optimization ***,it is demonstrated through static and dynamic experiments that the system based on the hybrid algorithm can obtain satisfactory computational errors while maintaining a high computational rate.

关键词： Magnetic localization Minimally invasive surgery hybrid optimization algorithm Robot-assisted surgery

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Modified electromagnetism-like algorithm and its application to slope stability analysis

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Journal of Central South University 2011年第6期18卷 2100-2107页

作者：张科曹平 School of Resources and Safety Engineering Central South University

In the view of the disadvantages of complex method (CM) and electromagnetism-like algorithm (EM), complex electromagnetism-like hybrid algorithm (CEM) was proposed by embedding complex method into electromagnetism-like algorithm as local optimization algorithm. CEM was adopted to search the minimum safety factor in slope stability analysis and the results show that CEM holds advantages over EM and CM. It combines the merits of two and is more stable and efficient. For further improvement, two CEM hybrid algorithms based on predatory search (PS) strategies were proposed, both of which consist of modified algorithms and the search area of which is dynamically adjusted by changing restriction. The CEM-PS1 adopts theoretical framework of original predatory search strategy. The CEM-PS2 employs the idea of area-restricted search learned from predatory search strategy, but the algorithm structure is simpler. Both the CEM-PS1 and CEM-PS2 have been demonstrated more effective and efficient than the others. As for complex method which locates in hybrid algorithm, the optimization can be achieved at a convergence precision of 1×10-3, which is recommended to use.

关键词： slope stability hybrid optimization algorithm complex method electromagnetism-like algorithm predatory searchstrategy

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