this paper focusses on distributed formation control, a task in which the desired formation shape is controlled by maintaining relative distances between robots. this task is based on the acyclic directed triangular f...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
this paper focusses on distributed formation control, a task in which the desired formation shape is controlled by maintaining relative distances between robots. this task is based on the acyclic directed triangular formation with distance-based control. the novelty proposed in this work is the possibility of varying distances that need to be tracked in the formation control task. the focus is on the case where some distances are not constant values but a function dependent on the leader’s velocity. An example application, serving as motivation for the analysis conducted, is the vehicles moving in urban traffic, which should maintain a distance between preceding vehicles proportional to their speed for safety reasons.
Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. the video data streamed from the endoscope provides rich information to support context-awareness for next-gene...
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ISBN:
(纸本)9783031164491;9783031164484
Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. the video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. this is currently a bottleneck for the topic, as available public dataset is still extremely limited in the field of CAI. In this paper, we present and release the first integrated dataset (named AutoLaparo) with multiple image-based perception tasks to facilitate learning-based automation in hysterectomy surgery. Our AutoLaparo dataset is developed based on full-length videos of entire hysterectomy procedures. Specifically, three different yet highly correlated tasks are formulated in the dataset, including surgical workflow recognition, laparoscope motion prediction, and instrument and key anatomy segmentation. In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. the dataset is available at https://***.
Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of contin...
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ISBN:
(纸本)9781665408288
Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of continuum robots for control have been promising, although real data hungry-which may cause potential damage to robots and be time consuming-and getting poorer performance when trained with simulation data only. this paper presents a model-based learning framework for continuum robot closed-loop control that, by combining simulation and real data, shows to require only 100 real data to outperform a real-data-only controller trained using up to 10000 points. the introduced data-efficient framework withthree control policies has utilized a Gaussian process regression (GPR) and a recurrent neural network (RNN). Control policy A uses a GPR model and a RNN trained in simulation to optimize control outputs for simulated targets;control policy B retrains the RNN in policy A with data generated from the GPR model to adapt to real robot physics;control policy C utilizes policy A and B to form a hybrid policy. Using a continuum robot with soft spines, we show that our approach provides an efficient framework to bridge the sim-to-real gap in model-based learning for continuum robots.
the authors attempted to build a structure of efficiency indicators for managing railway station assets. the study aims to answer the following research question: how to model the structure of performance indicators f...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
the authors attempted to build a structure of efficiency indicators for managing railway station assets. the study aims to answer the following research question: how to model the structure of performance indicators for railway station management? this study focuses on the station and the assets of the Premium station. the article proposes a general model for calculating Key Performance Indicators (KPIs), which, when supplied with data, will enable a unified comparative assessment of station asset management at various railway stations.
Effective maintenance is a hard but very important task allowing keeping assets in operation. In particular this is something very relevant for critical infrastructure that are railways and railway stations. European ...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
Effective maintenance is a hard but very important task allowing keeping assets in operation. In particular this is something very relevant for critical infrastructure that are railways and railway stations. European wide analyses show, that there is no harmonization in data standards nor there are good solutions allowing to assist station operators in maintenance management. In this paper we focus on handling available data streams, integrating them with a data lake based centralisation and using it to provide a decision support system. Such system, based on dashboards created with Business Intelligence software, can provide significant help in managing of assets. As an example we provide appropriate routing mechanisms allowing to use the available maintenance personnel with more efficiency.
the aim of this paper is to check whenever usage of sequence based neural networks for predicting compressed air demand can be useful in screw compressor room supervisory control systems. Industrial enterprises freque...
the aim of this paper is to check whenever usage of sequence based neural networks for predicting compressed air demand can be useful in screw compressor room supervisory control systems. Industrial enterprises frequently employ compressed air systems to generate the compressed air needed for daily operations. Data was gathered from three different compressor rooms with different air demand characteristics and configuration over the period of one month. then data was prepared, analyzed, trained and tested followed by simulation tests which determined usefulness of trained networks. Since nowadays high energy prices force energy saving build of the screw compressor itself the purpose of this text was to check if there is any room for optimization in less modern and also modern applications.
One of the most critical challenges in Robotic Eye Surgery (RES) is the applied force of the surgical instrument of the robot as it penetrates the human eye. Safe surgery requires accurate control of this force. In a ...
One of the most critical challenges in Robotic Eye Surgery (RES) is the applied force of the surgical instrument of the robot as it penetrates the human eye. Safe surgery requires accurate control of this force. In a teleoperated eye surgical system, there is likely to be a time delay that can affect the system control. this paper focuses on designing a predefined-time Sliding Mode Control (SMC) method to control a teleoperated robotic eye surgical system under an unknown time delay of the communication channel. the Lyapunov theory is used to prove the system stability. For the master and slave parts, manipulator robots are considered for designing and testing the controller. MATLAB software is used to simulate the controller. the simulation results show the robustness of the controller against the time delay of the communication channel.
Energy storage systems (BESS) have been proposed to mitigate photovoltaics (PV) and wind generation variability, reducing the need to operate traditional spinning reserves and offering auxiliary grid services. BESS se...
Energy storage systems (BESS) have been proposed to mitigate photovoltaics (PV) and wind generation variability, reducing the need to operate traditional spinning reserves and offering auxiliary grid services. BESS selection necessary to mitigate PV and wind generation variability is directly related to the worst daily short-time PV and wind generation variability. this paper proposes a practical estimation of BESS nominal parameters required to mitigate prospective power generation variability associated with high PV and wind generation penetration in Electrical Power Systems (EPS) under actual conditions, which includes significant loads impact through scenario analysis.
this paper presents the development of a model of a corvette-type ship’s magnetic signature using an artificial neural network (ANN). the capabilities of ANNs to learn complex relationships between the vessel’s char...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
this paper presents the development of a model of a corvette-type ship’s magnetic signature using an artificial neural network (ANN). the capabilities of ANNs to learn complex relationships between the vessel’s characteristics and the magnetic field at different depths are proposed as an alternative to a multi-dipole model. A training dataset, consisting of signatures prepared in finite element method (FEM) environment Simulia Opera was constructed. A feedforward neural network was developed through a comparative analysis of different activation functions available in MATLAB’s Deep Learning Toolbox and the grid search method. Verification was performed using the leave-one-out cross-validation method (LOOCV). the model proved to be highly effective in predicting the magnetic signature for the northward direction in any measurement depth, with prospects to expand it to estimate other directions.
this article presents the problem of designing a nonlinear observer for an active magnetic suspension system. the design process of the nonlinear Luenberger observer (also known as the Kazantzis-Kravaris-Luenberger ob...
this article presents the problem of designing a nonlinear observer for an active magnetic suspension system. the design process of the nonlinear Luenberger observer (also known as the Kazantzis-Kravaris-Luenberger observer) is discussed. Particular attention was paid to the main nonlinearity of the system - the electromagnetic force, which was modeled applying the function describing the change in inductance as a function of the distance of the levitating object from the electromagnet surface. theoretical analyses were confirmed by the results of experimental studies in which the task of moving the sphere between the given positions using current control was carried out. Control tasks were conducted in the real-time regime on an embedded platform. the measured signals and estimated velocity were analyzed in the context of future implementations in control applications.
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