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Identifying characteristics of pipejacking parameters to assess geological conditions using optimisation algorithm-based support vector machines

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2020年 106卷

作者： Cheng, Wen-Chieh Bai, Xue-Dong Sheil, Brian B. Li, Ge Wang, Fei Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Peoples R China Shaanxi Key Lab Geotech & Underground Space Engn Xian 710055 Peoples R China Univ Oxford Dept Engn Sci Oxford England

Detecting sudden changes in geological conditions (e.g., karst cavern and fault zone) during tunnelling is a complex task. These changes can cause shield machines to jam or even induce geo-hazards such as water ingress and surface subsidence. Tunnelling parameters that relate closely to the surrounding geology have proliferated in recent years and present a substantial opportunity for the application of data-driven artificial intelligent (AI) techniques that can infer patterns from data without reference to known, or labelled, outcomes. This study explores the potential for support vector machines (SVM) to identify changes in soil type during tunnelling towards reducing the possibility of jamming and geo-hazard development. All tunnelling data were pre-processed to convert time series data into feature-based sub-series. A selection of the most popular parameter optimisation algorithms was explored to improve the accuracy of the AI predictions. Their relative merits were evaluated through comparisons with a recent pipejacking case history undertaken in gravel and clayey gravel soils. The results highlight an exciting potential for the use of optimisation algorithm-based SVMs to identify changes in soil conditions during pipejacking.

关键词： Pipejacking Machine learning optimisation algorithm Soft ground

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Smart load scheduling strategy utilising optimal charging of electric vehicles in power grids based on an optimisation algorithm

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IET SMART GRID 2020年第6期3卷 914-923页

作者： Lu, Maxim Abedinia, Oveis Bagheri, Mehdi Ghadimi, Noradin Shafie-khah, Miadreza Catalao, Joao P. S. Nazarbayev Univ Sch Engn & Digital Sci Dept Elect & Comp Engn Nur Sultan 010000 Kazakhstan Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users' anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles' owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs' batteries during the off-peak hours and drawing it from the EVs' batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs' charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

关键词： battery powered vehicles optimisation power grids electric vehicle charging power generation scheduling smart load scheduling strategy utilising optimal charging electric vehicles power grid optimisation algorithm charging stations smart charging model grid stability peak-demand hours EV charging intelligent algorithm standard models optimisation methods charge EV battery

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Finite-time $\mathcal {H}_{\infty }$H∞ filtering for Ito stochastic Markovian jump systems with distributed time-varying delays based on optimisation algorithm

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IET CONTROL THEORY AND APPLICATIONS 2019年第5期13卷 702-710页

作者： Wu, Zhengtian Jiang, Baoping Kao, Yonggui Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China Suzhou Key Lab Big Data & Informat Serv Suzhou Peoples R China Politecn Milan Dept Mech Engn Milan Italy Harbin Inst Technol Weihai Dept Math Weihai Peoples R China

This study is concerned with the problem of finite-time H-infinity filter design for a class of Ito stochastic systems with Markovian switching and distributed time-varying delays. Firstly, a partially mode-dependent filter is designed to accommodate to unreliable network transmission. The attention is focused on deriving sufficient conditions for the filtering error system to ensure the finite-time boundedness and to satisfy a prescribed H-infinity disturbance attenuation. Then based on stochastic functional theory, the existence of H-infinity filter is presented by solving existing linear matrix inequalities optimisation problems. Furthermore, the result is extended to the case where the mode information is completely transmitted. Finally, a numerical example is provided to show the effectiveness of the proposed results.

关键词： filtering theory Markov processes discrete time systems control system synthesis Lyapunov methods time-varying systems optimisation linear matrix inequalities stochastic systems delays stochastic functional theory solving existing linear matrix inequalities optimisation problems Ito stochastic Markovian jump systems distributed time-varying delays optimisation algorithm $H filter design Ito stochastic systems Markovian switching partially mode-dependent filter unreliable network transmission filtering error system finite-time boundedness

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Accuracy improvement of positioning data in greenhouse for agricultural machinery via optimisation algorithm

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JOURNAL OF ENGINEERING-JOE 2019年第15期2019卷 547-551页

作者： Geng, Jing Wang, Xiang Zhu, Saihua Lin, Xiangze Nanjing Agr Univ Coll Engn Dept Elect Engn Nanjing Jiangsu Peoples R China

Although several indoor positioning systems for greenhouse have been developed in automated agricultural machinery monitoring, the data measured by positioning systems still has large errors. In order to solve the problem, in this study, improved weighted-centroid algorithm and maximum likelihood estimation (MLE) algorithm were developed to optimise positioning data of agricultural machinery in the greenhouse, which is measured by ultra-wide band-based indoor positioning system according to different agricultural machinery working states. Compared with real coordinate data, when agricultural machinery stays still, the average static error of the data was 66.4 mm with the improved weighted-centroid algorithm;when the machinery is in dynamic state, the average dynamic error of the data was 61 mm and the probability that error <60 mm was 0.848 according to the MLE. Hence, the optimisation algorithms presented in this study improve the positioning accuracy significantly and the position of agricultural machinery is able to be estimated better compared with the raw data.

关键词： maximum likelihood estimation indoor communication agricultural machinery ultra wideband communication greenhouse automated agricultural machinery monitoring improved weighted-centroid algorithm maximum likelihood estimation algorithm ultra-wide band-based indoor positioning system average dynamic error optimisation algorithm raw data indoor positioning systems size 66 4 mm size 61 0 mm size 60 0 mm

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optimisation algorithm for decision trees and the prediction of horizon displacement of landslides monitoring

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1698-1703页

作者： Zhao, Jiubin Liu, Yuanxue Hu, Ming Army Logist Univ PLA Chongqing Key Lab Geomech & Geoenvironm Protect Chongqing Peoples R China

In the era of big data, the data scale of landslide monitoring could reach above terabyte level, the traditional database and data mining technology could no longer meet the requirements of intelligent monitoring and early warning. To obtain early warning information with high reliability and real time by applying big data theory, mechanisms, models and methods as well as machine learning methods are the inevitable trends in the future. This study aimed to realise a real time and precise mid-long prediction of landslide displacement, proposed two distributed landslide displacement prediction models: DLDP-GBTs (distributed landslide displacement prediction with Gradient Boosted Trees algorithm) and DLDP-RF (distributed landslide displacement prediction with Random algorithm);the cross-validation method was also adopted to evaluate and adjust parameters to reduce the root mean squared error of the model predicted results. In addition, this study proposed the rapid selection of features by using XGboost model in distributed situations can improve the Model training efficiency under distributed condition. By comparing different regression algorithms models, it was found that the DLDP-GBTs model based on the gradient optimisation decision tree was better than the other two models in terms of accuracy and real-time performance, which meets the requirements under the big data background.

关键词： Geophysics computing distributed landslide displacement prediction model Big Data Other DBMS optimisation algorithm learning (artificial intelligence) optimisation techniques big data theory gradient optimisation decision tree regression algorithms models landslide monitoring machine learning methods DLDP-GBT model DLDP-with-gradient boosted trees algorithm regression analysis optimisation geophysics computing intelligent monitoring Knowledge engineering techniques Combinatorial mathematics early warning information root mean squared error reduction gradient methods geomorphology DLDP-with-random algorithm DLDP-RF model horizon displacement prediction Interpolation and function approximation (numerical analysis) model training efficiency XGboost model cross-validation method mid-long landslide displacement prediction data scale decision trees Other topics in statistics

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Product quality improvement method in manufacturing process based on kernel optimisation algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2017年第19期55卷 5597-5608页

作者： Wei, Zhe Feng, Yixiong Hong, Zhaoxi Qu, Rongxia Tan, Jianrong Northeastern Univ Sch Mech Engn & Automat State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Zhejiang Peoples R China

Quality data in manufacture process has the features of mixed type, uneven distribution, dimension curse and data coupling. To apply the massive manufacturing quality data effectively to the quality analysis of the manufacture enterprise, the data pre-processing algorithm based on equivalence relation is employed to select the characteristic of hybrid data and preprocess data. KML-SVM (Optimised kernel-based hybrid manifold learning and support vector machines algorithm) is proposed. KML is adopted to solve the problems of manufacturing process quality data dimension curse. SVM is adopted to classify and predict low-dimensional embedded data, as well as to optimise support vector machine kernel function so that the classification accuracy can be maximised. The actual manufacturing process data of AVIC Shenyang Liming Aero-Engine Group Corporation Ltd is demonstrated to simulate and verify the proposed algorithm.

关键词： manufacturing process quality kernel function hybrid manifold learning support vector machines optimisation algorithm

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Optimal multi-variable flatness control for a cold rolling mill based on a box-constraint optimisation algorithm

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IRONMAKING & STEELMAKING 2016年第6期43卷 426-433页

作者： Wang, Pengfei Qiao, Dongmiao Zhang, Dianhua Sun, Jie Liu, Hongmin Yanshan Univ Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Peoples R China Yanshan Univ Liren Coll Qinhuangdao 066004 Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang 110004 Peoples R China

As the flatness control system is a multi-variable control system, the key issue for high-precision flatness is the determination of the optimal adjustments of flatness actuators. In order to determine these the first step is the establishment of a multi-variable control model in the flatness control process, by which the actual flatness control problem has been reduced to a non-linear optimisation problem with box-constraints. Around this optimisation problem, characteristics and applicability of current optimisation algorithms were analysed. Based on the coordinate descent method, a new multi-variable optimisation algorithm with global convergence was proposed. In the algorithm, the problem was transformed into a series of univariate optimisations which could be solved by sequentially searching along coordinate directions. In order to make the objective function decrease rapidly, and ensure that each iteration are carried out within the feasible region, the accelerating step method is adopted to determine the iterative step size. A series of feasible points have been obtained through a series of iterations, which make the objective function decrease gradually until the optimal solution has been obtained. Finally, the algorithm has been tested by numerical experiments with production data of actual flatness control process, and applied to the 1450 mm tandem cold mill. Numerical experiments and application show that the proposed algorithm not only can satisfy the requirements of response speed, but also have a good accuracy, which can provide a reference for the realisation of high-precision flatness control processes of cold rolled strip.

关键词： optimisation algorithm Multi-variable control Flatness control Cold rolling

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Multi-objective optimisation of buoyancy energy storage technology using transit search algorithm

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RENEWABLE ENERGY 2024年 225卷

作者： Ng, Jian Yew Tan, Wen-Shan Chan, Ping Yi Elias, Azimin Monash Univ Malaysia Sch Engn & Adv Engn Platform Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Monash Univ Malaysia Ctr Net Zero Technol Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Tenaga Nas Berhad Res 1 Kawasan Inst PenyelidikanJalan Ayer Hitam Kajang 43000 Selangor Malaysia

Implementing energy storage solutions is crucial to address the intermittency challenges of marine renewable energy. Buoyancy energy storage technology (BEST) holds potential, but its development remains in its infancy. Additionally, optimisation has not been implemented to improve the design. Therefore, a transit search (TS)based optimisation model is developed to optimise both the small-scale (volume of tank less than 500 m 3 ) and large-scale (volume of tank less than 30,000 m 3 ) fabric BEST to maximise the power density and energy density. The optimisation results obtained through the transit search (TS) algorithm surpass those from the gray wolf (GW), whale, and artificial bee colony (ABC) algorithms in terms of consistency. The optimised large-scale fabric BEST design demonstrates improvements, with a 56% increase in power density from 345 W/m 3 to 539 W/m 3 and a 58% increase in energy density from 201 Wh/m 3 to 318 Wh/m 3 compared to the pre-optimised design from the literature. Moreover, linear relationships between the tank ' s submerged volume-to-water volume ratio and the extra weight limit were investigated through sensitivity analysis. The optimised fabric BEST shows competitive energy density when compared to other emerging energy storage technologies. Lithium-sulphur has higher energy density, but fabric BEST offers better safety and ease of installation.

关键词： Buoyancy energy storage optimisation algorithm Transit search

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Data-driven optimisation of process parameters for reducing developed surface area ratio in laser powder bed fusion

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第7-8期136卷 3821-3831页

作者： Qin, Yuchu Shi, Peizhi Lou, Shan See, Tian Long Jamal, Mikdam Zeng, Wenhan Blunt, Liam Scott, Paul J. Jiang, Xiangqian Univ Huddersfield EPSRC Future Adv Metrol Hub Sustainable Mfg Huddersfield HD1 3DH England Univ Leeds Leeds Univ Ctr Decis Res Business Sch Leeds LS2 9JT England Mfg Technol Ctr MTC Ansty Pk Coventry CV7 9JU England

Optimising process parameters is a key category of approaches to improve the surface quality of laser powder bed fusion parts. So far, many optimisation methods have been presented, which provide effective ideas and approaches for improving surface quality. However, these methods all focus on the improvement of Ra\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_a$$\end{document}, Sa\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_a$$\end{document}, Rsk\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{sk}$$\end{document}, or R Delta q\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{\Delta q}$$\end{document}. These parameters are sufficient for most applications. They are however not suitable for applications where functional performance is linked with surface area. To quantify the quality of surfaces for these applications, the developed surface area ratio (Sdr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_{dr}$$\end{document}) is a more appropriate parameter as it can be used to quantitatively express the exposed functional surface area. In this paper, a data-driven

关键词： Process parameter optimisation Surface quality Surface development ratio Laser powder bed fusion Machine learning optimisation algorithm

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Kinematic analysis and optimisation of a gantry spraying robot for ship blocks

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ALEXANDRIA ENGINEERING JOURNAL 2025年 116卷 385-396页

作者： Xu, Jiangmin Liu, Jinhao Jing, Xuwen Zhou, Honggen Hu, Xiaocai Ji, Jianbo Han, Zhenchun Jiangsu Univ Sci & Technol Marine Equipment & Technol Inst Zhenjiang Peoples R China Jiangsu Univ Sci & Technol Mech Engn Coll Zhenjiang Peoples R China Jiangsu Univ Sci & Technol Zhenjiang Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China CSD Water Serv CO Ltd Beijing Peoples R China

Nowadays, using existing automated coating equipment to meet the coating requirements of complex ship blocks both domestically and internationally has become challenging. As a result, this paper aims to propose a nine-axis gantry spraying robot system specifically designed for ship blocks. However, the presence of multiple redundant axes in the nine-axis gantry robot adds complexity to the inverse kinematics solution, making coating path planning difficult. To address this issue, the paper presents an inverse kinematics solution method for the nineaxis robot. It suggests keeping the X-axis and Y-axis of the crown block unchanged, while allowing the Z-axis to remain unchanged after completing the up and down movement. This transformation effectively simplifies the inverse kinematics problem of the nine-axis gantry robot into that of a six-axis robotic arm. Through simulation experiments in MATLAB and specific experiments on the Staubli RX160L robotic arm, the accuracy of the inverse kinematics analysis of the robot was confirmed. The differential evolution algorithm and the adaptive differential evolution algorithm were both utilized to solve the optimal inverse solution. The results indicated that the former achieved the optimal inverse solution with greater accuracy, whereas the latter was able to reach a better inverse solution more quickly. Data Availability: The authors confirm that the data supporting the findings of this study are available within the article.

关键词： Spraying robot Kinematics Kinematic inverse solution Ship blocks optimisation algorithm

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