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Model predictive control of hemodynamics during intravenous general anesthesia

Model predictive control of hemodynamics during intravenous ...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Hamed Farbakhsh Erhan Yumuk Ghada Ben Othman Robin De Keyser Dana Copot Isabela Birs Clara M. Ionescu Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 Ghent University Zwijnaarde Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Department of Automation Technical University of Cluj-Napoca Memorandumului no. 28 Cluj-Napoca Romania

In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a difficult task while monitoring high-risk patients. Cardiac output optimization has been found to enhance the result of high-risk patients in terms of hospital stay, mortality rate, post-operative problems, etc. The application of standard control approaches is restricted because the mean arterial pressure response of a patient using vasoactive medicines is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input. In order to circumvent implementation challenges, this work develops an approximation technique that describes the system using a higher-order model. Predictive control is therefore used to comprehend the practical application of higher-order hemodynamic systems. The effectiveness of this strategy is demonstrated by the simulations and outcomes that are given.

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Scheduled Sampling Training Framework for ANN-Based PID control

Scheduled Sampling Training Framework for ANN-Based PID Cont...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Pau Comas Jose Lopez Vicario Antoni Morell Ramon Vilanova Wireless Information Networking (WIN) Research Group Escola d'Enginyeria Universitat Autònoma de Barcelona Bellaterra Spain Automation and Advanced Control Systems (ASAC) Research Group Escola d'Enginyeria Universitat Autònoma de Barcelona Bellaterra Spain

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Proportional-Integral-Derivative (PID) controllers are extensively used in industrial control applications due to their simplicity and effectiveness in various control tasks. In recent years, there has been a growing emphasis on the integration of Artificial Neural Networks (ANNs) with control theory. This integration aims to harness the versatility of ANNs to facilitate controller design in dynamic environments. Additionally, it opens the possibility of transfer learning for these models, allowing knowledge gained from one control scenario to be applied to others' thereby enhancing adaptability and efficiency in control applications. In this work-in-progress paper, we propose a training framework that addresses the key challenges of modeling PIDs as ANNs, specifically the discrepancy between training and inference behaviors, known as exposure bias, commonly encountered in text summarization models. To tackle this, we integrate scheduled sampling, a technique devised for text sequence generation tasks, with an online control simulation environment.

关键词： Training Adaptation models PI control Transfer learning Text summarization Process control Artificial neural networks control theory PD control Manufacturing automation

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Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interaction

Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interactio...

引用

Chinese control Conference (CCC)

作者： Xin-Heng Li Zhen-Tao Liu School of Automation China University of Geosciences (Wuhan) Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Recently, the emotional robot has basic functions of perceiving and expressing emotions, but it still hard to communicate naturally between humans and robots. One major reason is that communication atmosphere is seldom considered in Human-robot Interaction (HRI). We propose Multi-modal (i.e., music background, speech, and semantics) Based Fuzzy Atmosfield (FA), which can not only realize the dynamic adjustment of FA but also dynamically regulate human emotions. In the experiment, a Pepper robot is used and one hundred volunteers are invited for HRI, and soothing piano pieces are used as background music. Questionnaires were filled by the volunteers after the experiments, from which the results show that 90% of the volunteers felt the dynamic changes in the communication atmosphere and 77% of the volunteers felt significant emotional regulation, which demonstrates the effectiveness of our method.

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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第42届中国控制会议

作者： Mengting Ao Changhe Li Shengxiang Yang 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 School of Computer Science and Informatics De Montfort University

Aiming at the prediction of truck travel time in open pit mines,we established a prediction model based on long short-term memory(LSTM).This model fully accounts for 11 factors,including the nature of trucks,weather,road conditions,and driver's behaviors,as well as the influence of neighbor road segments in the route on the current predicted road *** experiment shows that the error of the LSTM prediction model is significantly reduced compared with S VR and BP *** addition,the maximum absolute mean error under different conditions is less than 12 seconds.

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Pixel-based Hole Quality Evaluation in Robot Drilling Manufacturing Process

Pixel-based Hole Quality Evaluation in Robot Drilling Manufa...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Chaoyue Niu Bin Chen Erica Smith Robert Bramley Pete Crawforth Mahdi Mahfouf Visakan Kadirkamanathan Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Advanced Manufacturing Research Centre with Boeing University of Sheffield Rotherham U.K.

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Aircraft assembly entails drilling numerous holes, often in multi-material stacks, then joining parts with fasteners fitted through the holes. There are stringent quality requirements on the holes, and assessment of hole quality is crucial to ensuring the integrity of the joints. Carbon fibre reinforced polymer (CFRP) is a commonly used material in aircraft structures due to its desirable properties. However, it is susceptible to defects not associated with metals, such as delamination and uncut fibres. While there have been multiple metrics for assessment of these defects proposed in literature, relatively few attempts to consolidate them have been seen. Furthermore, common measurement methods used for assessing these defects (e.g. 3D-microscopy) are well established, but can be time-consuming; manual interrogation of raw inspection data can also be highly subjective. To address these challenges, this paper proposes a set of combined metrics along with an automatic image processing framework for fast assessment of delamination and uncut fibre defects. The combined metric for the delamination region is aggregated from across six factors and uncut fibre uniform metrics from across five factors. The image processing framework receives grayscale images as inputs, taken from an optical coordinate measuring machine, and outputs the combined metrics along with eleven separate metrics. Experimental results, using a preexisting dataset from twenty-four holes on two workpieces from a real robotic drilling operation, are given to demonstrate the effectiveness of the proposed combined metrics and the corresponding image processing framework.

关键词： Measurement Optical fibers Drilling Coordinate measuring machines Robot kinematics Optical variables measurement Gray-scale Optical imaging Delamination Aircraft

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A Novel Spindle Speed and Angle Estimation Strategy in Multi-material Robotic Drilling

A Novel Spindle Speed and Angle Estimation Strategy in Multi...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Bin Chen Chaoyue Niu Erica Smith Rob Bramley Pete Crawforth Mahdi Mahfouf Visakan Kadirkamanathan Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Advanced Manufacturing Research Centre with Boeing University of Sheffield Rotherham U.K.

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Aircraft assembly involves drilling vast numbers of holes, with stringent quality requirements, in multi-material stacks. To achieve high quality functional holes, one of the process parameters that is controlled carefully is the spindle speed. Variations in spindle speed from the programmed value can be indicative of the quality of the manufactured part and thus can be useful for process monitoring. Estimation of the tool angle has potential benefits in monitoring the spatial localisation of hole defects. Rotational speed estimation from encoded type detectors is an established methodology but the literature on parameter-free angle estimation is sparse. To address these challenges, this paper proposes a set of novel strategies for speed and angle estimation. The strategy includes the use of a fibre optic sensor with a novel encoding structure to facilitate angle estimation, and the analysis of the sensor signal using a staged signal processing framework. Experimental results from a real robotic drilling operation of a multi-material stack are given to demonstrate the effectiveness of the estimation strategy.

关键词： Drilling Process monitoring Velocity control Estimation Signal processing Robot sensing systems Sensors Timing Transient analysis Surface treatment

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Multi view 3D reconstruction method for weak texture objects based on "eye-in-hand" model

Multi view 3D reconstruction method for weak texture objects...

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2021 China automation Congress, CAC 2021

作者： Gong, Xuan Chen, Xin Jian, Xu He, Wenpeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781665426473

Size measurement and defect detection of ceramics are required in the production process of ceramic products. Therefore, it is necessary to perform high-precision and fast three-dimensional reconstruction of such targets with smooth surfaces and weak textures. However, the reconstruction of a large number of point clouds from multiple perspectives is time-consuming due to the matching of adjacent feature points. Based on this, a fast point cloud registration strategy based on the "eye-in-hand" model is proposed, which can obtain RGB-D images at multi-view around weak-textured objects with RGB-D camera for rapid point cloud registration and reconstruction. Meanwhile, a 3D point cloud segmentation method based on voxel-grid and euclidean distance clustering is applied to divide the scene point and obtain an accurate 3D model. The experimental results indicate that the multi view 3D reconstruction speed of this method is faster and the accuracy of objects point cloud is within 2 mm. Hence, this method can be well applied in reconstructing the 3D point clouds model for ceramic products. © 2021 IEEE

关键词： Textures

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Conual Information Fusion for Small Object Detection 40

Conual Information Fusion for Small Object Detection

引用

40th Chinese control Conference, CCC 2021

作者： Chen, Jun Chen, Xiaoqiang Luo, Linbo Wang, Ganbei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9789881563804

In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on conual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network model. The experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow conual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images. © 2021 Technical Committee on control Theory, Chinese Association of automation.

关键词： Remote sensing

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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Accurate TCP Position and Orientation Trajectory Generation in 6DOF Robotic Manipulators and CNC Machine Tools using FIR Filtering and Haversine Synchronisation 56

Accurate TCP Position and Orientation Trajectory Generation ...

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56th CIRP International Conference on Manufacturing systems, CIRP CMS 2023

作者： Ward, Rob Sencer, Burak Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Advanced Manufacturing Research Centre University of Sheffield RotherhamS60 5TZ United Kingdom Mechanical Engineering Department Oregon State University CorvallisOR United States

The use of robotics in manufacturing is rapidly growing. One promising application in particular is the use of robots as machining platforms. Robotic machining offers many advantages such as large operating envelopes, flexibility in operations and low installation and running costs. Robotic machining platforms much like computer numerically controlled (CNC) machine tools are commanded by numerical controllers (NCs) using the well established G-code part programming language (ISO-6983). Toolpaths are defined in the work piece coordinate system (WCS) as a series of discrete tool centre point (TCP) positions and tool orientations. The role of the NC is to generate smooth feed drive or robot joint commands such that the tool or end effector travels along the commanded toolpath whilst satisfying TCP position and orientation tolerances and robot/machine tool kinematic constraints. Interpolating tool orientations directly in the WCS requires complex real-time spherical interpolation computations. Overcoming this challenge, this paper introduces a C4 continuous tool path trajectory generation method for 6DOF motion. The tool centre point position is smoothed in the WCS, however, the tool orientation is smoothed directly in the rotary coordinate system without the requirement for spherical interpolation. The interpolated tool orientation trajectory follows the path of the great circle arc using the modified Haversine method eliminating non-linear interpolation errors present in linear interpolation of rotary axis positions (machine coordinate system interpolation). The proposed method is validated by simulation of a 6DOF robotic manipulator. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing systems 2023.

关键词： Interpolation Motion control Pose control Robotic Machining Trajectory Generation

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