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

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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Calibration and measurement method of PMD based on the optimization algorithm of swarm intelligent body

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Applied Optics 2025年第9期64卷 2102-2111页

作者： Xu, Xingnan Yuan, Zihang Zhu, Jiaxin Zhou, Hong Sun, Wenqing Key Laboratory of Intelligent Optoelectronic Devices and Chips Jiangsu Higher Education Institutions School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Advanced Technology Research Institute Taihu Photon Center School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Suzhou Huadian Electric Technology Service Co. Ltd. Suzhou215000 China

Traditional phase measuring deflectometry (PMD) systems encounter challenges related to the stability of calibration parameter optimization, often resulting in insufficient reconstruction accuracy and low computational efficiency. In response to this technical challenge, this paper proposes an optimization method for the two-step calibration of the PMD system based on a swarm-intelligent body optimization algorithm, aiming to significantly improve the reconstruction accuracy and processing efficiency of the system. First, a traditional calibration method is used for the initial calibration of the measurement system. Subsequently, the dung beetle optimization (DBO) algorithm—to our knowledge, a novel swarm intelligence optimization algorithm known for its rapid convergence and global search capabilities—is introduced to construct a multi-objective optimization model with the three-dimensional system reconstruction error as the objective function. By applying the DBO to solve this model, a set of optimal calibration parameters is obtained. Experimental results demonstrate that the DBO algorithm achieves significant improvements in both reconstruction accuracy and stability compared to traditional calibration methods, thereby verifying its effectiveness and practicality in PMD systems. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Swarm intelligence

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Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm optimization and Bayesian Optimizer

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 871-892页

作者： Shengdong Cheng Juncheng Gao Hongning Qi State Key Laboratory of Eco-Hydraulics in Northwest Arid Region Xi’an University of TechnologyXi’an710048China School of Resources and Safety Engineering Central South UniversityChangsha410083China

Driven piles are used in many geological environments as a practical and convenient structural ***,the determination of the drivability of piles is actually of great importance in complex geotechnical *** methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological ***,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian optimization(BO)and Particle Swarm optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison *** order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model ***,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R^(2) values than other implemented techniques,specifically 0.966,0.884,and *** addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)*** can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.

关键词： Random forest regression model pile drivability Bayesian optimization particle swarm optimization

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MHDO-MAGE based Performance optimization Strategy for Steam Generator Level Control System ofNuclear Power Plant

MHDO-MAGE based Performance Optimization Strategy for Steam ...

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2024 China Automation Congress, CAC 2024

作者： Shi, Jinguang Kong, Xiangsong Li, Xiaoyu Yang, Zean Chen, Weiming School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

The steam generator level control system is one of the critical control systems in a nuclear power plant, and its performance is vital for the safe and stable operation of the plant. The control parameters are key factors that affect the operational performance of the steam generator level control system. Therefore, during the commissioning of the nuclear power plant, it is usually necessary to adjust the parameters through the trial-and-error method. This tuning process mainly relies on the experience of the commissioning personnel and may potentially compromise plant safety due to human errors. Considering that the parameter tuning process of control systems can be regarded as a special type of batch processes, this paper proposes to apply an efficient data-driven optimization method, multi-source hybrid data-driven optimization method based on model accuracy gradient estimation (MHDO-MAGE), to the problem of parameter tuning for the steam generator control system. This method effectively combines the advantages of traditional MBO (Model-Based optimization) and MFO (Model-Free optimization) methods, while efficiently utilizing prior data information related to the optimization problem. This method was implemented on a widely adopted steam generator control system model specifically designed for steam generator simulations. The simulation results clearly indicate that the MHDO-MAGE method significantly enhances the efficiency of parameter tuning for the steam generator level control system, ultimately leading to a reduction in the costs associated with nuclear power plant commissioning. © 2024 IEEE.

关键词： Nuclear power plants

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Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system

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Concurrent Engineering Research and Applications 2024年第1-4期32卷 3-19页

作者： Naceur, Ferdaws Ben Toumi, Sana Ben Salah, Chokri Mahjoub, Mohamed Ali Tlija, Mehdi National Engineering School of Sousse LATIS - Laboratory of Advanced Technology and Intelligent Systems University of Sousse Sousse Tunisia University of Brest Brest France University of Monastir Monastir Tunisia ISSAT of Sousse University of Sousse Sousse Tunisia Department of Industrial Engineering College of Engineering King Saud University Riyadh Saudi Arabia

This paper describes a decentralised smart grid system containing renewable energies, storage systems and distributed generation with human control and intervention. The importance of each element and the interaction between them leads to think about a decision-making strategy. In fact, the integration of a Photovoltaic Panel (PVP) is used due to its availability and its participation in the carbon emissions reduction. Also, a battery is required to fill a power gap or absorb extra generated energy. Moreover, an optimal sizing is needed to get an efficient system with minimum cost. Also, an energy management strategy (EMS) is essential to ensure the power resources scheduling in order to keep a continuous equilibrium supply-demand of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. In the first part, the proposed smart grid optimal sizing is determined under real weather data (solar radiation) of the city of Sousse, Tunisia, using the hybrid optimization of Multiple Energy Resources (HOMER) software technique. This approach is chosen thanks to its simplicity, effectiveness, and high precision compared to traditional techniques. In this paper, several configurations (Grid, (Grid-battery), (Grid-PVP), (Grid-PVP-battery)) are studied. The obtained results prove that the (Grid-PVP-battery) system configuration is the most efficient and economical solution. In the second part, a robust energy management strategy (EMS) is proposed for two smart grid configurations (grid-battery, grid-PVP-battery). This strategy is based on Fuzzy Logic Control (FLC) thanks to its non-linear modelling and its ability to make decisions relating to energy management. The primary goal of the suggested (EMS) is to ensure the energy resources scheduling in order to keep a continuous equilibrium among the production and consumption of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. As input data, (F

关键词： MATLAB

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optimization of Carbon Emissions in Asphalt Pavement Construction 10

Optimization of Carbon Emissions in Asphalt Pavement Constru...

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10th International Conference on optimization and Applications, ICOA 2024

作者： Benmamoun, Zoubida Elkhechafi, Mariam Abdo, Ahmad Abu Jebbor, Ikhlef Liwa College of technology Faculty of Engineering Abu Dhabi United Arab Emirates Iscae Larem Laboratory Groupe Casablanca Morocco Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9798350387353

This paper investigates the optimization of carbon emissions in asphalt pavement construction by deep Q-learning. The study developed a model to identify optimal hybrid systems that are environmentally and cost-effective. The optimized system resulted in a 5% reduction in CO2 emissions compared to the classical methods, significant improvements in energy consumption, water use and hazardous waste generation in Besides, the inclusion of plastic waste in asphalt mix not only increased the sustainability but also saved costs by 0.8%. The results demonstrate the effectiveness of deep Q-learning to overcome challenging optimization challenges in pavement construction, to promote environmental and economic benefits. Despite the computational demands of the model, the findings highlighted the potential of advanced Deep learning techniques to increase product sustainability and efficiency. The integration of deep Q-learning provides a new approach to construction optimization, significantly reducing carbon emissions and operating costs while promoting sustainable development. © 2024 IEEE.

关键词： Zero-carbon

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Landslide Prediction methods: A Comparative Overview of Traditional and AI-Based Decision-Making Approaches

Landslide Prediction Methods: A Comparative Overview of Trad...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ajraoui, Abdelrhani Chakiri, Said Mansouri, Hammou Benhaddou, Mohamed Laassilia, Oussama Saouabe, Abdelkrim IbnTofail University Laboratory of Geosciences of Natural Resources Faculty of Sciences Kénitra Morocco Casablanca20110 Morocco Salé Morocco Chouaib Doukkali University El Jadida Morocco IbnTofail University Computer Science Research Laboratory Faculty of Sciences Kénitra Morocco

ISBN: (纸本)9798350369106

Landslides pose significant global hazards, and traditional methods for risk assessment, such as expert systems and geotechnical models, are resource-intensive and limited by human subjectivity. With the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL) methods have become prominent in landslide detection, offering advanced capabilities for modelling complex geotechnical data. Despite initial reliance on visual interpretation of remote sensing data, AI-driven methods have shown potential to improve accuracy and objectivity. ML models have been used to detect landslides based on pixel data and geological features, while DL models, especially CNNs like U-net and ResNet, have gradually replaced ML. However, DL methods are still in their early stages and face challenges due to the complexity of landslide characteristics across regions. © 2024 IEEE.

关键词： Contrastive Learning

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Unreliability Tracing of Power systems for Identifying the Most Critical Risk Factors Considering Mixed Uncertainties in Wind Power Output

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Protection and Control of Modern Power systems 2024年第5期9卷 96-111页

作者： Siying Zhao Changzheng Shao Jinfeng Ding Bo Hu Kaigui Xie Xueying Yu Zihan Zhu Hubei Electric Power Planning Design and Research Institute Co.Ltd.Wuhan 430040China State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqing 400044China State Grid Chengdu Electric Power Supply Company Chengdu 610000China School of Electrical Engineering Si-chuan UniversityChengdu 610065China

For conventional power systems,the forced outage of components is the major cause of load *** tracing is utilized to allocate the total system load-shedding risk among individual components in accordance with their different ***,critical components are identified and pertinent measures can be taken to improve system *** integration of wind power introduces additional risk factors into power systems,causing previous unreliability tracing methods to become *** this paper,a novel unreliability tracing method is proposed that considers both aleatory and epistemic uncertainties in wind power output and their impacts on power system load-shedding ***,modelling methods for wind power output considering aleatory and epistemic uncertainties and component outages are ***,a variance-based index is proposed to measure the contributions of individual risk factors to the system load-shedding ***,a novel unreliability tracing framework is developed to identify the critical factors that affect power system *** studies verify the ability of the proposed method to accurately allocate load-shedding risk to individual risk factors,thus providing decision support for reliability enhancement.

关键词： Unreliability tracing epistemic uncer-tainty wind power multisource heterogeneous risk factors

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Machine learning for membrane bioreactor research:principles,methods,applications,and a tutorial

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Frontiers of Environmental Science & Engineering 2025年第3期19卷 119-144页

作者： Yizhe Lai Kang Xiao Yifan He Xian Liu Jihua Tan Wenchao Xue Aiqian Zhang Xia Huang Beijing Yanshan Earth Critical Zone National Research Station College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China Key Laboratory of Earth System Numerical Modeling and Application University of Chinese Academy of SciencesBeijing 101408China Environmental Chemistry Faculty College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Department of Energy Environmentand Climate ChangeSchool of EnvironmentResourcesand DevelopmentAsian Institute of TechnologyP.O.Box 4Klong LuangPathumthani 12120Thailand State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor(MBR)technologies for wastewater *** accurate prediction of the membrane filtration process is of great importance for identifying and controlling *** learning methods address the limitations of traditional statistical approaches,such as low accuracy,poor generalization ability,and slow convergence,particularly in predicting complex filtration and fouling processes within the realm of big *** article provides an in-depth exposition of machine learning *** study then reviews advances in MBRs that utilize machine learning methods,including artificial neural networks(ANN),support vector machines(SVM),decision trees,and ensemble *** on current literature,this study summarizes and compares the model input and output characteristics(including foulant characteristics,solution environments,filtration conditions,operating conditions,and time factors),as well as the selection of models and optimization *** modeling procedures of SVM,random forest(RF),back propagation neural network(BPNN),long short-term memory(LSTM),and genetic algorithm-back propagation(GA-BP)methods are elucidated through a tutorial *** simulation results demonstrated that all five methods yielded accurate predictions with R2>***,the existing challenges in the implementation of machine learning models in MBRs were *** is notable that integration of deep learning,automated machine learning(AutoML)and explainable artificial intelligence(XAI)may facilitate the deployment of models in practical engineering *** insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

关键词： Membrane bioreactor Machine learning Pollutant removal Membrane fouling Model prediction

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A Summary of Rotational Speed Measurement Techniques for Bearing and Rotor systems—Part I

A Summary of Rotational Speed Measurement Techniques for Bea...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Ren, Zhijun Lin, Tantao Zhu, Yongsheng Hong, Jun Feng, Ke Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong University Xi’an710049 China School of Engineering The University of British Columbia KelownaV1V1V7 Canada

ISBN: (纸本)9783031494208

Rotational speed measurement is a fundamental technology used in various industries to monitor and control the speed of rotating equipment. Accurate speed measurement is crucial for optimizing performance, ensuring safety, and monitoring equipment health. This summary explores different techniques used for measuring rotational speed, which are categorized into analog, digital, and hybrid analog-digital methods. Analog methods convert rotation into proportional signals in other media, while digital methods convert rotation into a series of electrical pulses with the number of pulses proportional to the speed. hybrid analog-digital methods convert rotation into proportional signals and calculate the frequency of the signal to obtain speed. This summary provides guidance for the design, optimization, and application of rotational speed measurement solutions in the industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Speed regulators

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