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Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction

Autonomous intelligent systems 2024年第1期4卷 368-378页

作者： Qiufeng Yu Hui Zhao Li Teng Li Li Ansar Yasar Stephane Galland Control Science and Engineering College of Electronics and Information EngineeringTongji University4800 Cao’an HighwayShanghai201804China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804China Shanghai Urban Operation(Group)Co. Ltd.Shanghai200023China Shanghai Institute of Intelligent Science and Technology Shanghai201804China Transportation Research Institute Hasselt UniversityHasselt3500Belgium UTBM CIAD UMR 7533Belfort90010France

With the aim to enhance prediction accuracy for nonlinear time series,this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation(SNA)function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to *** Strategy was implemented in a two-state reconstruction process byfirst inputting the time series data to the model ***,the time data passes through the reservoirs and is activated by the SNA activation function,the new state for the reservoirs is *** state is input to the next layer,and the concatenate states module *** of states are selected from the activated multi-layer reservoirs and input into the state reconstruction *** input states are transformed through the state reconstruction module andfinally saved to the concatenate state *** evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.

关键词： Echo state networks Time series prediction Reconstruction model Self-normalizing activation function Reservoir computing

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Machine Learning for Analyzing Concrete Cover Separation in Externally Bonded FRP RC Beams

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Procedia Structural Integrity 2024年 64卷 1524-1531页

作者： Manish Prasad Cristina Barris Mehdi Aghabagloo Beatriz López Ricardo Perera Marta Baena Analysis and Advanced Materials for Structural Design AMADE University of Girona Girona 17003 Spain Control Engineering and Intelligent Systems EXIT University of Girona Girona 17003 Spain Department of Mechanical Engineering Technical University of Madrid 28006 Madrid Spain

Strengthening of concrete structures with Externally Bonded (EB) Fiber Reinforced Polymer (FRP) is one the most cost effective, efficient, and sustainable rehabilitation techniques. However, usually the ultimate tensile strength of the FRP cannot be achieved due to the premature debonding of the FRP or delamination of the concrete cover. In this last case, known as concrete cover separation (CCS), high stress concentration at the free end of FRP laminate, in combination with the shear stress at that section, cause the initiation of a shear crack that, if not controlled by the shear reinforcement, propagates in concrete just below internal reinforcement. Various analytical and empirical models have been developed to predict this phenomenon but none of them could give a failure load with reasonable accuracy. On the other hand, Machine Learning (ML) has been proved very effective in predicting behaviors that are difficult to quantify using mechanics. This paper explores ML algorithms to predict the failure load of flexural elements that undergo CCS. To this end, a database has been collected that comprises 140 four-point bending tests on beams reinforced with EB CFRP that failed by CCS. K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBOOST) algorithms have been applied and then ranked based on their R-squared score. R-squared scores obtained from ML are the highest compared to the analytical models described in this paper. Moreover, the mean values and standard deviation of the ratio of experimental failure load and that of calculated by the ML model are the lowest compared to the analytical models. The predictions from the ML models are found more aligned with the experimental results than the analytical models. KNN is chosen as the best ML algorithm for the prediction of failure load with an R-squared score of 0.97.

关键词： Fiber Reinforced Polymer Concrete cover separation Machine Learning

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Design of An intelligent control System for Underground Directional Drilling Trajectory in Coal Mines 41

Design of An Intelligent Control System for Underground Dire...

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第41届中国控制会议

作者： Wangnian Li Min Wu Ke Yao Quanxin Li Chengda Lu Sheng Du School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Directional drilling trajectory control technology is one of the key technologies to realize automation and intelligence of underground tunnel drilling in coal *** construction of directional drilling in underground coal mines mainly relies on manual observation of measurement-while-drilling parameters to control the drilling trajectory,which is highly dependent on manual *** paper designs an intelligent control system of underground directional drilling trajectory for coal *** the idea of hierarchical design and modular design,this system collects and displays geometric parameters,geological parameters,and engineering parameters of the directional drilling ***,an intelligent decision-making platform for directional drilling trajectory prediction and control is developed,laying a foundation for the research of directional drilling trajectory prediction and intelligent control for coal mines.

关键词： Trajectory prediction trajectory control geometric parameters geological parameters engineering parameters

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Design of an intelligent Monitoring System for Operating States in a Coal Mine Drilling Process 41

Design of an Intelligent Monitoring System for Operating Sta...

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第41届中国控制会议

作者： Xiao Yang Luefeng Chen Sheng Du Chengda Lu Wangnian Li Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development trend of intelligent drilling in coal mines,an intelligent monitoring system for operating states in a drilling process is developed to solve the problems of incomplete display of drilling parameters and ineffective *** system realizes the remote monitoring of drilling *** mainly adopts the Fuzzy C-Means clustering algorithm to cluster the drilling *** it combines expert empirical knowledge to classify coal seams into soft,hard and alternating soft and hard coal *** boundary values are calculated according to the boundary ***,three drilling operating states,rated,conservative and over-excited,are judged to provide guidance for the drilling process control.

关键词： Drilling process intelligent monitoring Fuzzy C-Means clustering Operating states

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Coxa Coordination System-Based Hierarchical control Framework for Bipedal Wheel-Leg Robot with Sprawling Mechanism

Coxa Coordination System-Based Hierarchical Control Framewor...

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IEEE Conference on systems, Process & control (ICSPC)

作者： Zulkifli Mansor Addie Irawan Robotics Intelligent Systems & Control Engineering (RiSC) research group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

The paper presents a Coxa Coordination System-Based Hierarchical control Framework (CCS-HCF) for a Bipedal Wheel-Leg Robot with a Sprawling Mechanism (BWLS), aimed at enhancing the flexibility navigation in rugged environments. The proposed method leverages the Coxa-Based Coordinate System (CCS) for optimizing leg motion and trajectory, facilitating stable and efficient locomotion across challenging terrains. The proposed method’s effectiveness is verified through various simulation works, highlighting the importance of specific parameter settings in the proposed CCSHCF, such as a Coxa angle for sprawling and mammalian postures, along with a swing height position. These configurations are crucial for maintaining stability and balance during operation. The findings demonstrate the bipedal wheel-legged robot such as BWLS system’s with the proposed CC-SHCF ready to be integrated by various stability and robust control layers for stable walking and skiing.

关键词： Legged locomotion Robust control Meters Navigation Robot kinematics Heuristic algorithms control systems Stability analysis Trajectory Optimization

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Modeling and Analysis of Bipedal Wheel-legged Robot with Sprawling Legs Mechanism

Modeling and Analysis of Bipedal Wheel-legged Robot with Spr...

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International Conference on Mechatronics (ICOM)

作者： Zulkifli Mansor Addie Irawan Nor Maniha Abdul Ghani Azrul Azim Abdullah Hashim Robotics Intelligent Systems & Control Engineering (RiSC) research group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

This paper presents the modeling and analysis of the novel Bipedal Wheel-Legged Robot with Sprawling Legs Mechanism (BWLS) utilizing multi body dynamics approaches and the proposed Coxa-Based Coordinate System (CCS) in kinematics modeling. The primary focus lies in transitioning the conceptual model of the BWLS into the Simscape Multibody (SM) environment. The proposed CCS is subjected to verification through the implementation of a Hierarchical control Framework (HCF) utilizing a simple stand-up input trajectory. Through the analysis, initial angular positions and stable range angular joints are determined, revising previous determinations based on hardware structure. Furthermore, employing the Forward Kinematics (FK) model, the stable boundary is identified through spatial leg motion plots. The reliability of the proposed CCS kinematics model is affirmed, rendering the model ready for further in-depth studies and applications.

关键词： Legged locomotion Analytical models Mechatronics Robot kinematics Dynamics Kinematics Rendering (computer graphics)

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium 8

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

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8th International Conference on Instrumentation, control, and Automation, ICA 2023

作者： Fathurrahman, Muhammad Farhan Catteeuw, Arne Gautama, Sidharta Aguirre, Angel L. Joelianto, Endra Sutarto, Herman Y. Ghent University Engineering and Product Design Department of Industrial Systems Ghent Belgium Department of Intelligent System Bandung Indonesia FlandersMake@UGent-Corelab ISyE Lommel Belgium Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-Temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-Temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-Temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-Temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant. © 2023 IEEE.

关键词： Long short-term memory

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Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition 16

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Gridchin, Aleksandr V. Koleda, Aleksei N. Barbin, Evgenii S. Nesterenko, Tamara G. Kolchuzhin, Vladimir A. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

ISBN: (纸本)9798350330885

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer's dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters. © 2023 IEEE.

关键词： Capacitance

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Towards Instance Segmentation-Based Litter Collection with Multi-Rotor Aerial Vehicle

Towards Instance Segmentation-Based Litter Collection with M...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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